Greetings Robert, Could you please share with us your professional journey and how you came to your current role as Chief Innovator of Monjur?

Thank you for having me. My professional journey has been a combination of law and technology. I started my career as an intellectual property attorney, primarily dealing with software licensing and IT transactions and disputes.  During this time, I noticed inefficiencies in the way we managed legal processes, particularly in customer contracting solutions. This sparked my interest in legal tech. I pursued further studies in AI and machine learning, and eventually transitioned into roles that allowed me to blend my legal expertise with technological innovation. We founded Monjur to redefine legal services.  I am responsible for overseeing our innovation strategy, and today, as Chief Innovator, I work on developing and implementing cutting-edge AI solutions that enhance our legal services.

How has Monjur adopted AI for streamlined case research and analysis, and what impact has it had on your operations?

Monjur has implemented AI in various facets of our legal operations. For case research and analysis, we’ve integrated natural language processing (NLP) models that rapidly sift through vast legal databases to identify relevant case law, statutes, and legal precedents. This has significantly reduced the time our legal professionals spend on research while ensuring that they receive comprehensive and accurate information. The impact has been tremendous, allowing us to provide quicker and more informed legal opinions to our clients. Moreover, AI has improved the accuracy of our legal analyses by flagging critical nuances and trends that might otherwise be overlooked.

Integrating technology for secure document management and transactions is crucial in today’s digital landscape. Can you elaborate on Monjur’s approach to this and any challenges you’ve encountered?

At Monjur, we prioritize secure document management and transactions by leveraging encrypted cloud platforms. Our document management system utilizes multi-factor authentication and end-to-end encryption to protect client data. However, implementing these technologies hasn’t been without challenges. Ensuring compliance with varying data privacy regulations across jurisdictions required us to customize our systems extensively. Additionally, onboarding clients to these new systems involved change management and extensive training to address their concerns regarding security and usability.

Leveraging cloud platforms for remote collaboration and accessibility is increasingly common. How has Monjur implemented these platforms, and what benefits have you observed in terms of team collaboration and accessibility to documents and resources?

Monjur has adopted a multi-cloud approach to ensure seamless remote collaboration and accessibility. We’ve integrated platforms like Microsoft, GuideCX and Filevine to provide our teams with secure access to documents, resources, and collaboration tools from anywhere in the world. These platforms facilitate real-time document sharing, and project management, significantly improving team collaboration. We’ve also implemented granular access controls to ensure data security while maintaining accessibility. The benefits include improved productivity, as our teams can now collaborate efficiently across time zones and locations, and a reduced need for physical office space, resulting in cost savings.

In what ways is Monjur preparing for the future and further technological advancements? Can you share any upcoming projects or initiatives in this regard?

At Monjur, we’re constantly exploring emerging technologies to stay ahead. We continue to training our Lawbie document analyzer and are moving toward our goal of being able to provide real-time updates to our clients legal documents.  

As the Chief Innovator, what personal strategies do you employ to stay abreast of the latest technological trends and advancements in your field?

To stay current, I dedicate time each week to reading industry reports, academic papers, and blogs focused on AI, machine learning, and legal tech. I also attend webinars, conferences, and roundtable discussions with fellow innovators and ch leaders. Being part of several professional networks, provides me with valuable insights into emerging trends. Additionally, I engage in continuous learning through online courses and certifications in emerging technologies. Lastly, I maintain an open dialogue with our  team and regularly brainstorm with them to uncover new ideas and innovations.

What advice would you give to our readers who are looking to integrate similar technological solutions into their organizations?

My advice would be to start by identifying your organization’s pain points and evaluating how technology can address them. Engage your teams early in the process to ensure their buy-in and gather their insights. When selecting technology solutions, prioritize scalability and interoperability to future-proof your investments. Start small with pilot projects, measure their impact, and scale up based on results. It’s also crucial to foster a culture of continuous learning and innovation within your organization. Finally, don’t overlook the importance of data security and compliance, and ensure that your solutions align with industry standards and regulations.

With your experience in innovation and technology, what are some key factors organizations should consider when embarking on digital transformation journeys?

Embarking on a digital transformation journey requires a clear strategy and strong leadership. Here are some key factors to consider:

1. Vision and Objectives: Clearly define your vision and set measurable objectives that align with your overall business goals.
2. Change Management: Prepare for organizational change by fostering a culture that embraces innovation and training teams to adapt to new technologies.
3. Stakeholder Engagement: Involve all stakeholders, including clients, to ensure their needs and concerns are addressed.
4. Technology Selection: Choose technologies that offer scalability, interoperability, and align with your specific business requirements.
5. Security and Compliance: Implement robust security measures and ensure compliance with relevant data protection laws.
6. Continuous Improvement: Treat digital transformation as an ongoing process rather than a one-time project. Regularly assess the impact of implemented solutions and refine your strategy accordingly.

By considering these factors, organizations can navigate the complexities of digital transformation more effectively and reap the full benefits of their technological investments.

Robert Scott

Chief Innovator at Monjur

Robert Scott is Chief Innovator at Monjur.  He provides a cloud-enabled, AI-powered legal services platform allowing law firms to offer long-term recurring revenue services and unlock the potential of their legal templates and other firm IP. redefines legal services in managed services and cloud law. Recognized as Technology Lawyer of the Year, he has led strategic IT matters for major corporations,  in cloud transactions, data privacy, and cybersecurity. He has an AV Rating from Martindale Hubbell, is licensed in Texas, and actively contributes through the MSP Zone podcast and industry conferences. The Monjur platform was recently voted Best New Solution by ChannelPro SMB Forum. As a trusted advisor, Robert navigates the evolving technology law landscape, delivering insights and expertise.

The post AITech Interview with Robert Scott, Chief Innovator at Monjur first appeared on AI-Tech Park.

Piers, congratulations on your appointment as the new CEO of The ai Corporation. Can you share your vision for leading the organization into the fuel and mobility payments sector?

Our vision at The ai Corporation (ai) is to revolutionise the retail fuel and mobility sector with secure, efficient, and seamless payment solutions while leading the charge against transaction fraud. ai delivers unparalleled payment convenience and security to fuel retailers and mobility service providers, enhancing the customer journey and safeguarding financial transactions. 

We believe in fuelling progress by simplifying transactions and powering every journey with trust and efficiency. In an era where mobility is a fundamental aspect of life, we strive to safeguard each transaction against fraud, giving our customers the freedom to move forward confidently. We achieve that by blending innovative technology and strategic partnerships and relentlessly focusing on customer experience: 

Seamless Integration: We’ve developed an advanced payment system tailored for the fuel and mobility sector. By embracing technologies like EMV and RFID, we ensure contactless, swift, and smooth transactions that meet our customers’ needs. Our systems are designed to be intuitive, providing easy adoption and enhancing the customer journey at every touchpoint.

Unmatched Security: Our robust fraud detection framework is powered by cutting-edge AI, meticulously analysing transaction patterns to identify and combat fraud pre-emptively. We’re committed to providing retailers with the knowledge and tools to protect themselves and their customers, fostering an environment where security and vigilance are paramount.

With the increasing demand for sustainable fuels and EV charging, how do you plan to address potential fraud and fraudulent data collection methods in unmanned EV charging stations?

The emergence of new and the continued growth of existing sustainable fuels means our experts are constantly identifying potential risks and methods of exploitation proactively. The increase in unmanned sites is particularly challenging as we observe a steady rise in fraudulent activity that is not identifiable within payment data, such as false QR code fraud. In these circumstances, our close relationships with our fuel retail customers enable us to utilise additional data to identify at-risk areas and potential points of compromise to assist in the early mitigation of fraudulent activity.

Mobile wallets are on the rise in fleet management. How do you navigate the balance between convenience for users and the potential risks of fraud and exploitation associated with these payment methods?

When introducing any new payment instruments, it is critical to balance the convenience of the new service with the potential risk it presents. As with all fraud prevention strategies, a close relationship with our customers is vital in underpinning a robust fraud strategy that mitigates exposures, while retaining the benefits and convenience mobile wallets offer. Understanding the key advantages a fleet management application brings to the end user is vital for understanding potential exposure and subsequent exploitation. That information enables us to utilise one or multiple fraud detection methods at our disposal to mitigate potentially fraudulent activity whilst balancing convenience and flexibility.

The trend of Abuse of Genuine fraud is noticeable despite advancements in mobile wallet payments. How do your AI-driven scoring systems combat this complex fraud type in the industry?

Our teams identify Abuse of Genuine fraud by using enhanced behavioural profiling across extended periods and utilising sector-specific data in full to enable us to create a detailed and accurate profile for both payment instruments and vehicles. Industry-specific data, for example, from fleet odometers, is exceptionally valuable when you are developing a behavioural profile for a specific vehicle. Combined with other methods, this enables us to quickly identify areas of increased spending or a change of spending profile. That insight is vital when identifying Abuse of genuine fraud, as this type of fraud is often perpetrated for long periods of time and in very high volumes.

Opportunistic fraud and overclaiming by legitimate customers can inflate fraudulent values. How can businesses enhance confidence in point-of-compromise identification and distinguish genuine customer behavior from fraudulent activity?

The short answer is that businesses need to ensure that they are working with experts who understand fraud and understand the impact that false positives can have on a fraud strategy. Incorrectly identified fraudulent transactions affect bottom-line losses and can severely harm a business fraud strategy and AI Scoring Systems. 

As a result, we firmly hold that visualising precise trend profiles and pinpointing potential compromise points are as critical as receiving the initial fraud alert. By combining industry-specific data with payment and transaction information, we can often clearly identify deviations from legitimate activities through proper visualisation. This forensic approach enhances our ability to understand and act on fraudulent behaviour effectively.

With the move to open-loop payment capabilities, what measures need to be taken to address the increased fraud and security risks associated with this wider acceptance payment instrument?

Robust security measures are crucial as open-loop payments gain traction in the fleet and mobility sectors:

• Multi-factor authentication, including biometrics, verifies user identity. 
• Machine learning analyzes transactions for suspicious patterns. 
• Encryption and tokenization protect sensitive data. 
• Fraud management systems monitor transactions and notify users of suspicious activity. 
• User and employee education on fraud tactics strengthen security. 
• Collaboration between payment providers allows for sharing best practices and adhering to industry regulations like PCI DSS, creating a secure payment environment. 

These efforts balance security with convenience to ensure safe user experiences.

Innovation is key in the fuel and mobility sectors. How does your technology contribute to fraud prevention while engaging directly with end-users, encouraging community growth, and promoting interaction with brands?

ai’s advanced technology has been developed to shield the fuel and mobility sectors from fraud. Our machine learning detects suspicious transactions, fake accounts, and identity theft in real-time, protecting businesses and helping them stay ahead of evolving fraudster tactics.

In addition to providing our users with a comprehensive rules management platform, our sophisticated fraud management solutions deploy machine learning to optimise rules in production, recommend new rules, and identify underperforming ones to remove. 

We model data in real time to enable probabilistic scoring or transactions to assess the likelihood they are fraudulent, allowing authorisation decisions to be taken in real-time to prevent fraud. By leveraging advanced algorithms and machine learning, our clients can stay ahead of fraudsters.

Our technology also ensures data quality by distinguishing deliberate fraud from genuine mistakes. This empowers businesses to make accurate fraud decisions. Additionally, our collaboration across industries strengthens the fight against fraud through shared solutions and regulations.

Beyond security, our technology fosters positive brand-consumer relationships – enabling our users to provide personalized experiences, loyalty programs, and feedback mechanisms to build a strong community with their customers.

Technology protects against fraud, ensures data reliability, and facilitates meaningful interactions between brands and their communities. By embracing innovation, businesses can safeguard operations while promoting growth and trust.

As vehicles become payment mechanisms, what security considerations and fraud prevention strategies should businesses adopt, especially in the context of innovations like integrating payment choices into vehicles?

As vehicles evolve into payment mechanisms, retailers need to put in place robust security measures and fraud prevention strategies to ensure the safety of financial transactions. Some payment security measures to consider include:

• Encryption – Employ robust encryption protocols to protect sensitive data during transmission and prevent unauthorized access.
• Tokenisation – replacing actual payment card details with tokens – unique identifiers that are useless to fraudsters even if intercepted.
• Secure communication channels – ensuring secure communication between vehicles and payment gateways to prevent/deter unauthorised use.
• Authentication – implementing multi-factor authentication to verify users’ identities will prevent the unauthorized use of payment instruments.
• Secure Hardware – consider using tamper-resistant hardware for payment processing within vehicles.

In terms of fraud prevention strategies, key considerations should include:

• Fraud detection systems – leveraging advanced machine learning algorithms to identify suspicious patterns and activities.
• Know Your Customer (KYC) – Deploy rigorous KYC practices to help verify user identities and prevent fraudulent transactions and account abuse.
• Regulatory compliance – adhering to industry standards and regulations to maintain a secure payment environment is a must, including PCI DSS compliance.
• Customer education – education of end users around safe payment practices and potential risks is the front line for fraud prevention.
• Behavioural analysis – monitoring user behaviour to detect anomalies – which can be enhanced and automated by using machine learning detection models.
• Real-time alerts – setting up real-time alerts to end users for unusual transactions or activities.
• Geolocation verification – validating the location of the transaction against the vehicle’s actual position.
• Device fingerprinting – creating unique fingerprints for each device to detect suspicious behaviour.

Businesses must adopt a holistic, layered approach that combines robust security practices, fraud prevention strategies, and regulatory compliance adherence to safeguard financial transactions while integrating payment choices into vehicles.

Tokenization is being considered to fight fraud. How do you approach this technology, considering potential regulatory requirements, and what implications do you foresee for PSD3?

Tokenization combats payment fraud by replacing sensitive data with meaningless tokens during transactions. This protects actual card details and can also be applied to other sensitive data.

New European regulations (PSD3) emphasize security and user privacy, aligning well with tokenization’s benefits. PSD3 is expected to tighten security measures further and encourage anti-fraud technologies.

While tokenization enhances security, regulations like PSD3 may not definitively address liability for fraudulent token transactions. As tokenization becomes more widespread, clear guidelines for such cases will be essential.

There is no doubt that tokenization is a powerful tool against fraud, but balancing security, innovation, and user rights will be essential for any robust payment ecosystem to comply with PSD3.

How do you foresee intelligent fuel management and predictive vehicle maintenance playing a role in fraud prevention and operational efficiency within the fuel and mobility sectors?

Intelligent fuel management and vehicle maintenance powered by AI are revolutionizing transportation. Businesses can optimise fuel usage, predict maintenance needs, and prevent fraud by analysing vast amounts of data, ultimately that translates to reduced costs, improved efficiency, and a more sustainable future.

Here’s how:

• AI optimises routes: Real-time traffic data helps choose the most efficient paths, saving fuel and time.
• Predicting demand patterns: Businesses can anticipate needs and strategise fuel management across different transportation modes, streamlining inventory control.
• Enhanced supply chain resilience: AI forecasts disruptions, identifies inefficiencies, and tracks inventory for better preparedness.
• Proactive vehicle maintenance: Sensor data helps detect potential problems before they become major breakdowns, reducing downtime and repair costs.
• Preventing fuel theft: In-vehicle sensors monitor fuel levels and detect unauthorised access, ensuring fuel security.

Intelligent fuel management and predictive maintenance create a win-win situation for businesses and the environment.

Piers Horak

Chief Executive Officer at The ai Corporation 

Piers Horak is Chief Executive Officer of The ai Corporation (ai). Horak brings over 15 years of extensive expertise in enterprise retail payments, banking, and fraud prevention. Horak is responsible for building on ai’s track record of developing innovative technology that allows its clients and their customers to take control and grow profitably by managing omnichannel payments and stopping fraud.

The post AITech Interview with Piers Horak, Chief Executive Officer at The ai Corporation first appeared on AI-Tech Park.

Greetings George, Could you please share with us your professional journey and how you came to your current role as Chief Technology Officer at Upwave?

My professional journey has been a bit unconventional. I studied philosophy at Yale, not expecting to go into tech. I ended up working as an investment analyst, doing a lot of data analysis and model building on topics like how government stimulus impacts the economy.

Realizing finance wasn’t my passion, I started a music startup that ultimately didn’t pan out. Nearly 8 years ago, I joined Upwave as a software engineer focused on data. Over time, I became manager of our data team, director of engineering, VP of engineering, and now CTO overseeing all product and technology.

Given the urgency of embracing AI-driven strategies, how has Upwave integrated artificial intelligence into its operations to drive innovation and maintain a competitive edge?

AI is deeply integrated into everything we do at Upwave. We’ve long used what I call “predictive AI” – machine learning techniques that have existed for decades. From the beginning, we built ML and statistical analysis algorithms to optimize ad campaigns.

In the last year and a half, we’ve also embraced the newer “generative AI” exemplified by tools like ChatGPT. Over the past 9 months, we’ve leveraged generative AI to help customers get more value from the brand measurement we provide. Just yesterday, we launched the open beta of our AI Campaign Insights Reports, which use generative AI to synthesize and summarize campaign results into easy-to-understand language, charts, and actionable recommendations. We’re very excited about it.

Revolutionizing brand ROI through marketing measurement is a critical aspect of modern business. Could you provide insights into Upwave’s approach to this and the impact AI-driven marketing analytics have had on enhancing brand performance?

Modern brand campaigns are extremely complex, with vast amounts of money spent across dozens or even hundreds of channels. It’s simply too much for humans to track and optimize manually. That’s where AI shines – at digesting huge datasets to find mathematical optimizations and make concrete recommendations to improve cost-effectiveness and ROI.

Upwave takes a two-pronged AI approach: First, using predictive AI for high-quality measurement and clear analysis of ROI opportunities. Second, leveraging generative AI to communicate those opportunities to customers as clearly and actionably as possible.

We’ve found that customers who lean into this data-driven, AI-powered approach are seeing dramatic performance improvements and ROI increases. The combination of unique data, insightful analysis, and powerful communication enabled by AI is a game-changer.

As the CTO at Upwave, what are your insights into the future of brand analytics, and how do you foresee AI shaping the landscape in the coming years?

I’m a huge believer in the potential of generative AI. It’s easy to assume today’s capabilities are all these systems will ever have, but having worked in AI for over 15 years, I’ve seen firsthand how these tools continuously improve in compounding, accelerating ways. I’m very bullish.

Even if AI never advances further, it will still become widely deployed across brand advertising – analytics, media planning, creative, and so on. But these tools will only get more powerful over time, perhaps dramatically so.

I anticipate AI will substantially augment more and more day-to-day activities in advertising. Organizations that embrace AI, understand how to harness it, and adapt their workflows will become far more productive, efficient, and effective. Those resistant to change will be left behind.

The transformative potential of GenAI is fascinating. How do you anticipate AI advancements surpassing human capabilities and reshaping marketing content creation strategies at Upwave?

As I said, I expect these AI tools to improve dramatically over time, taking on more functions humans currently handle. But I see this more as AI augmenting rather than replacing humans.

Consider how a bulldozer is far more efficient than a human at clearing a construction site, yet you still have a human operating the bulldozer. One human with a bulldozer can now do the work of dozens or hundreds in far less time.

I expect a similar dynamic with AI – it will replace certain aspects of jobs humans do, but humans will still orchestrate the AI to achieve their goals, just far more efficiently. People will be able to produce more and better advertising, then use tools like Upwave to measure performance and continuously optimize in a virtuous cycle.

So while advertising will become much more automated, there will still be an important role for humans in guiding the process.

In your role as Chief Technology Officer, what personal strategies do you employ to stay informed about the latest advancements in AI and technology, ensuring Upwave remains at the forefront of innovation?

I’m rather obsessive about keeping up with AI given how transformative I believe it will be for both tech and advertising.

For me, this means firsthand experience – regularly using tools like ChatGPT, Claude, Anthropic, paying for premium versions, participating in hackathons to build hands-on AI projects. I even recently won the TED AI hackathon with a team.

Secondly, I stay closely involved with Upwave’s AI product development, frequently discussing capabilities and limitations with our engineers and PMs.

To track the cutting edge, for all its issues, I find Twitter an excellent source of direct info from top researchers. I also subscribe to several great newsletters that summarize key AI news.

It takes a multi-pronged approach as the AI field is moving extremely rapidly, but falling behind would be a huge risk.

What advice would you offer to our audience, particularly businesses seeking to integrate AI-driven strategies into their marketing and brand analytics efforts?

Take AI very seriously. It’s going to significantly impact nearly every business. Yes, there are dodgy “AI” products and snake oil salesmen out there, but that doesn’t negate the genuine value being created by real AI capabilities. And these tools are only going to get better over time.

Even if AI can’t perfectly solve your needs today, that may change by next month. Effectively applying AI still takes skill, and initial attempts may not pan out, but that doesn’t mean AI can’t provide huge value with the right approach.

I strongly recommend building real AI expertise, either in yourself or your organization. Understand both the potential of today’s tools and how that will evolve going forward. Your competitors certainly are. Allowing them to gain an AI advantage now risks them leaving you in the dust as that edge compounds.

So be discerning, but don’t dismiss AI. The risk of ignoring it is too great.

With your extensive experience in technology and marketing, what key considerations should companies keep in mind when implementing AI solutions for marketing purposes?

First, be thoughtful about applying AI to problems. Naively throwing AI at an issue without carefully considering the problem space and the model’s constraints can lead to reputational or even legal issues, as these systems can be erratic.

However, you also can’t overcorrect and entirely avoid AI just because it involves some risk. Throughout history, many valuable technologies have been dangerous when misused but extremely beneficial when wielded properly.

Within 5 years, I believe it will be nearly impossible to remain competitive in marketing without heavy AI usage. Building those capabilities now will be key to keeping pace.

Even with today’s AI tools, if applied effectively, there is substantial potential to unlock. Tools like Upwave can help you increase your ROI by 2-3 times, for instance. So there’s already a lot of value to capture from existing tools, and that will only grow.

Can you share a success story or milestone where Upwave has effectively utilized AI-driven strategies to enhance brand ROI or marketing performance?

Absolutely. While I can’t name names, Upwave offers Persuadability Scores which directly measure brand advertising’s impact, similar to tracking clicks or conversions for direct response.

We worked with a major financial services advertiser and DSP to feed in these AI-generated metrics, allowing the DSP to steer ads towards the highest-impact, best-ROI opportunities. The result was material performance improvements for campaigns using these Persuadability Scores. That’s a great example of predictive AI boosting marketing effectiveness.

On the generative AI side, our new AI Campaign Insights Reports provide easily digestible summaries and recommendations that enable customers to align internal stakeholders and optimize in-flight campaigns.

The substantial leverage of generative AI makes it far more time and cost efficient to perform the necessary analysis and communication to understand campaign performance and disseminate those insights to key decision-makers. We’re seeing strong customer uptake and satisfaction so far.

Finally, considering your expertise, what are your reflections on the future of AI in marketing, and any additional insights you’d like to share with our audience?

As I’ve touched on, AI capabilities are going to advance substantially, likely in ways that eventually unsettle people as AIs become able to handle much of the work humans currently do. In certain domains, AIs will simply outperform even the most skilled humans.

This means very significant changes are coming to marketing and advertising, whether we want them or not. These are global technological forces beyond any individual company’s control.

In this sense, an AI tsunami is approaching. We can either learn to surf that wave or get crushed by it, but we can’t stop it.

So it’s crucial for businesses of all kinds to very seriously consider how they’ll navigate the AI-transformed future and hopefully leverage AI as a competitive advantage. Because organizations that fail to appreciate the gravity of this shift are in for an extremely challenging decade ahead.

George London

Chief Technology Officer at Upwave

George is a seasoned technology leader who has spent his whole career helping companies use data to make better decisions. George started his career doing macroeconomic modeling and investment research at Bridgewater Associates (the world’s largest hedge fund), and then founded a startup that used data to help consumers explore and discover music.

As one of Upwave’s first engineering hires, George originally joined Upwave with the mission of building Upwave’s statistical capabilities from scratch. Since then he’s grown with the company to become Head of Data, then Vice President of Engineering, and now CTO. In his years at Upwave, George has both contributed to nearly every aspect of Upwave’s systems and product and has also hired, managed, and coached Upwave’s entire technical team.

George holds a BA in Philosophy from Yale University and lives in Oakland with his wife and labradoodle.

The post AITech Interview with George London, Chief Technology Officer at Upwave first appeared on AI-Tech Park.

Kiranbir, we’re delighted to have you at AITech Park, could you please share your professional journey with us, highlighting key milestones that led you to your current role as a Senior Staff Engineering Manager at Google?

I started as a software engineer at Garmin then Apple. As I grew my career at Apple, I wanted to help and lead my peers the way my mentors helped me. I also had an arrogant epiphany about how much more I could get done if I had a team of people just like me. That led to my first management role at Microsoft.

Initially, I found it challenging to balance my desire to have my team work my way with prioritizing their career growth. Eventually, I was responsible for a program where I had to design, develop, and ship an accessory for the Hololens in only six months. I was forced to delegate and let go of specific aspects and realized I was getting in the way of progress. 

My team was delivering amazing solutions I never would have thought of. I realized I didn’t need to build a team in my image. I had hired a talented team with unique skills. My job now was to empower them and get out of their way. This realization was eye-opening and humbled me.

I also realized the skills I used for engineering weren’t the same skills I needed to be an effective leader. So I started focusing on being a good manager. I learned from even more mistakes over the years and ultimately established three core values for every team I lead:

1. Trust your team and peers, and give them autonomy.
2. Provide equity in opportunity. Everyone deserves a chance to learn and grow.
3. Be humble.

Following my growth as a manager, Microsoft presented me with several challenges and opportunities to help struggling teams. These teams moved into my organization after facing cultural setbacks, program cancellations, or bad management. Through listening, building psychological safety, providing opportunities, identifying future leaders, and refusing egos, I helped turn them around. 

Helping teams become self-sufficient has defined my goals and career in senior management. That led to opportunities at Google where I could use those skills and my engineering experience.

In what ways have you personally navigated the intersection of diversity, equity, and inclusion (DEI) with technology throughout your career?

Personally, as a Sikh, I rarely see people who look like me in my city, let alone in my industry.  At times, I have felt alone. I’ve asked myself, what will colleagues think and see the first time we meet?

I’ve been aware of representing my community well, so nobody holds a bias against those who come after me. I feel the need to prove my community, not just myself, while feeling grateful for the Sikhs who broke barriers, so I didn’t have to be the first. When I started looking for internships, I considered changing my name. When I first worked on the Hololens, I couldn’t wear it over my turban.

These experiences led me to want to create a representative workplace that focuses on what you can do rather than what you look like or where you came from. A workplace that lets you be your authentic self. A workplace where you create products for everyone.

Given your experience, what personal strategies or approaches have you found effective in promoting diversity within tech teams and ensuring equitable outcomes?

One lesson I received early in my career in ensuring our recruiting pipeline was more representative was patience. One of my former general managers shared a statistic or a rule of halves:

• 32 applications submitted
• 16 resumes reviewed by the hiring manager
• 8 candidates interviewed over an initial phone screen
• 4 candidates in final onsite interviews
• 2 offers given
• 1 offer accepted

His point was that if you review applications in order, you will likely find a suitable candidate in the first thirty applications. To ensure you have a representative pipeline, you have to leave the role open to accept more applications, and you get to decide which applications to review first. 

Additionally, when creating job requisitions, prioritize what’s important for the company and not just the job. What are the skills and requirements in the long term? What skills are only necessary for the short term? I like to say, don’t just hire the best person for the job today, hire the best person for the team for the next five years. Try to screen in instead of screening out.

To ensure equitable outcomes, I point to my second leadership value, equity in opportunity. The reality of any team is that there might be limited high-visibility opportunities at any given time. For my teams, no matter how well someone delivered in the past, the next opportunity and challenge are given to someone else. Even if others might complete it faster, everyone deserves a chance to learn and grow. 

Moreover, we can focus on moving far, not just fast, when everyone grows. When this is practiced and rewarded, teams often find themselves being patient and supporting those currently leading efforts. While I don’t fault individuals who disagree, their growth isn’t more important than the team’s.

From your perspective, what advice would you offer to tech leaders and organizations looking to strengthen their DEI initiatives, particularly in the context of developing and deploying AI technologies?

My first advice for any DEI initiative is to be patient. You won’t see changes in one day, so you want to focus on seeing changes over time. That means not giving up early, with leaders providing their teams more time to recruit and interview rather than threatening position clawbacks if the vacancy isn’t filled.

Ultimately, AI models are only as good as the data they are trained on. Leaders need to think about the quality of the data. Do they have enough? Is there bias? Is there data that might help remove human biases? 

How do biased AI models perpetuate diversity disparities in hiring processes, and what role do diverse perspectives play in mitigating these biases in AI development?

Companies that already lack representation risk training their AI models on the skewed data of their current workforce. For example, among several outlets, Harvard Business Review has reported that women might only apply to a job if they have 100% of the required skills compared to men who apply when they meet just 60% of the skills. Suppose a company’s model was built on the skills and qualifications of their existing employees, some of which might not even be relevant to the role. In that case, it might discourage or screen out qualified candidates who don’t possess the same skillset.

Organizations should absolutely use data from current top performers but should be careful not to include irrelevant data. For example, how employees answer specific interview questions and perform actual work-related tasks is more relevant than their alma mater. They can fine-tune this model to give extra weight to data for underrepresented high performers in their organization. This change will open up the pipeline to a much broader population because the model looks at the skills that matter.

In your view, how can AI technologies be leveraged to enhance, rather than hinder, diversity and inclusion efforts within tech organizations?

Many organizations already have inherent familiarity biases. For example, they might prefer recruiting from the same universities or companies year after year. While it’s important to acknowledge that bias, it’s also important to remember that recruiting is challenging and competitive, and those avenues have likely consistently yielded candidates with less effort.

However, if organizations want to recruit better candidates, it makes sense to broaden their recruiting pool and leverage AI to make this more efficient. Traditionally, broadening the pool meant more effort in selecting a good candidate. But if you step back and focus on the skills that matter, you can develop various models to make recruiting easier. 

For example, biasing the model towards the traditional schools you recruit from doesn’t provide new value. However, if you collect data on successful employees and how they operate and solve problems, you could develop a model that helps interview candidates to determine their relevant skills. This doesn’t just help open doors to new candidates and create new pipelines, but strengthens the quality of recruiting from existing pipelines.

Then again, reinforcing the same skills could remove candidates with unique talent and out-of-the-box ideas that your organization doesn’t know it needs yet. The strategy above doesn’t necessarily promote diversity in thought.

As with any model, one must be careful to really know and understand what problem you’re solving and what success looks like, and that must be without bias.

In what specific ways do you believe AI can be utilized to identify and address systemic barriers to gender equality and diversity in tech careers?

When we know what data to collect and what data matters, we understand where we introduce bias, place less effort, and miss gaps. For example, the HBR study I shared that indicated women needed 100% of the skills to apply also debunked the idea that confidence was the deciding factor. Men and women cited confidence as the reason not to apply equally. The reality was that people needed to familiarize themselves with the hiring process and what skills were considered. So our understanding and biases come into play even when trying to remove bias!

An example I often use for AI is medical imaging. A radiologist regularly looks at MRIs. However, their ability to detect an anomaly could be affected by multiple factors. Are they distracted or tired? Are they in a rush? While AI models may have other issues, they aren’t susceptible to these factors. Moreover, continuous training of AI models means revisiting previous images and diagnoses to improve further because time isn’t a limitation. 

I share this example because humans make mistakes and form biases. Our judgment can be clouded on a specific day. If we focus on ensuring these models don’t inherit our biases, then we remove human judgment and error from the equation. This will ideally lead to hiring the mythical “best” candidate objectively and not subjectively.

As we conclude, what are your thoughts on the future of AI in relation to diversity and inclusion efforts within the tech sector? What key trends or developments do you foresee in the coming years?

I am optimistic that a broader population will have access to opportunities that focus on their skills and abilities versus their background and that there will be less bias when evaluating those skills. At the same time, I predict a bumpy road. 

Teams will need to reevaluate what’s important to perform the job and what’s helpful for the company, and that’s not always easy to do without bias. My hope is that in an economy of urgency, we are patient in how we approach improving representation and that we are willing to iterate rather than give up.

Kiranbir Sodhia

Senior Staff Engineering Manager at Google

Kiranbir Sodhia, a distinguished leader and engineer in Silicon Valley, California, has spent over 15 years at the cutting edge of AI, AR, gaming, mobile app, and semiconductor industries. His expertise extends beyond product innovation to transforming tech teams within top companies. At Microsoft, he revitalized two key organizations, consistently achieving top workgroup health scores from 2017 to 2022, and similarly turned around two teams at Google, where he also successfully mentored leaders for succession. Kiranbir ‘s leadership is characterized by a focus on fixing cultural issues, nurturing talent, and fostering strategic independence, with a mission to empower teams to operate independently and thrive. Kiranbir Sodhia: Transforming Tech Teams; Cultivating Leaders

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Joscha, would you mind sharing with us some insights into your professional journey and how you arrived at your current role as Head of Product at Connectly.ai?

My path to the tech industry and product management took a bit of an unconventional route. My introduction to product development started in the hair care sector, where I had the opportunity to dive deep into human needs and master the art of user-centric design. When I found myself looking for a more dynamic environment, I embarked on a nearly decade-long journey at Google.

I began in sales and gained invaluable insights into customer pain points and the intricacies of building relationships. This then laid the groundwork for my transition into a product role within the Ads organization at Google.

After my time at Google, I took a leap into the unknown and joined Connectly as the fourth employee—a decision fueled by the thrill of building something from the ground up.. Today, we have a global team of more than 50, we partner with category-defining customers, and we are pushing the boundaries of AI research and product development. I couldn’t be more excited about where we’re headed next.

How does RAG revolutionize customer interaction and business intelligence in sales, with a special emphasis on the critical aspects of accuracy and timeliness of information?

By combining a generative model with a retrieval system, Retrieval-Augmented Generation (RAG) enhances AI responses with accurate, current data. 

Large Language Models (LLMs) in a production environment are constrained by their static datasets, and often lack in accuracy and timeliness. However, RAG introduces a dynamic component that leverages real-time external databases. This ensures that every piece of information it provides or action it recommends is grounded in the latest available data.

As the Head of Product at Connectly.ai, how do you foresee integrating RAG technology into your product offerings to enhance customer experiences and sales effectiveness?

RAG is one part of a cohesive AI strategy. At Connectly we also found that we had to start training our own embeddings as well as models to help make our AI Sales Assistant efficient, fast and reliable.

Traditional AI models often encounter challenges with stale data sets and complex queries. How does RAG address these limitations, and what advantages does it bring to AI systems in terms of improving responsiveness and relevance of information?

Complex queries that would stump earlier AI models are now within reach with enhanced query resolution. By employing sophisticated retrieval systems to gather data from numerous sources, RAG can dissect and respond to multifaceted questions in a nuanced way that was previously unachieveable. 

Additionally, RAG has the capability to pull in and analyze data from diverse sources in real-time, which transforms it into a powerful tool for market analysis. This can then equip businesses and leaders with the agility to adapt to market shifts with insights derived from the most current data, offering a hard-to-match competitive edge.

Could you kindly elaborate on how Connectly.ai is leveraging RAG to enhance its AI sales assistants and provide more personalized and contextually relevant interactions for users?

Of course! RAG is one part of the AI sales assistant that we have built. Businesses share their product catalog with Connectly to inform our sales assistant. This product catalog can have many million products with different variants. The inventory and prices might change on a daily basis. In order to provide the end customer with real time and reliable data, we leverage RAG as part of our architecture.

In your esteemed experience, what key considerations or best practices should companies keep in mind when seeking to enhance their AI models with technologies like RAG to create better customer experiences?

I would recommend starting with a narrow use case first and learn from there. In our case we had to learn the hard way that, for example, offering a multi language product from the start came with many hurdles. Clothes sizing for example can be different from country to country. English makes up more than 40% of common crawl data, so language embeddings and foundational models will work better in English first.  

What personal strategies or approaches do you employ to stay informed about emerging technologies and industry trends, particularly in the realm of AI and customer interaction?

There is so much happening and the AI industry is moving at a crazy pace. I have gathered a list of people I follow on X to stay up to date with some of the latest trends and discussions. I’m also lucky to be living in San Francisco where you will overhear a conversation about AI just about anywhere you go. 

Drawing from your expertise, what valuable advice would you extend to our readers who are interested in implementing RAG or similar technologies to improve their own AI systems and customer interactions?

If you are incorporating AI into your business, I would always start with a design partner in mind who can provide you valuable feedback and insights and is willing to build with you. This can be an external stakeholder like a customer or an internal team. The external validation is extremely helpful and important to help solve actual problems and pain points. 

As we come to the end of our discussion, would you be open to sharing any final thoughts or insights regarding the future of RAG technology and its implications for sales and customer engagement?

There is a lot of interesting discussion around the future of memory in AI. If a sales assistant can remember and learn from all previous conversations it had with a customer, it will evolve into a true personal shopper. 

Finally, Joscha, could you provide us with some insight into what’s next for Connectly.ai and how RAG fits into your broader product roadmap for enhancing customer experiences?

We have a lot of exciting launches in the pipeline. We launched our sales assistant, Sofia AI, about 6 months ago and are already partnering with major global brands. One of the new features I am most excited about is our continued work on AI insights from the conversations customers are having with our sales assistant. These insights can be imported directly into a CRM and help our businesses truly understand their customers. Previously this would have only been possible by interviewing every member in the Sales staff.

Joscha Koepke

Head of Product at Connectly.ai

Joscha Koepke is Head of Product at Connectly. As part of the company’s founding team, he leads the product team in building and innovating its AI-powered conversational commerce platform, which enables businesses to operate the full flywheel – marketing, sales, transactions, customer experience – all within the customer’s thread of choice. Prior to Connectly, Joscha was a Global Product Lead for Google, leading the product & go-to-market strategy of emerging online-to-offline ad format products across Search, Display, YouTube, & Google Maps. 

The post AITech Interview with Joscha Koepke, Head of Product at Connectly.ai first appeared on AI-Tech Park.

Hello Hussein, can you share with us your professional journey and how you became involved in the field of AI and technology, leading to your role as co-founder of Momentable?

I’ve always been fascinated with technology and sci-fi. AI is one of those things that sticks in your mind, and you can’t help but think about it. 

I studied engineering and worked in tech, and even with all the advancements in technology we have been witnessing in the past two decades, AI was one of those things that we always thought would remain a sci-fi pipe dream for a long time. 

This is not because there was nothing happening. But those who work in tech know it takes a while for the evolution of these technologies, and advancements are usually several degrees of separation from the regular user. 

I’m always learning, reading, and building tech products, so AI was a field of study; however, implementing it was never accessible for early-stage products. 

The status of AI has forever changed. OpenAI’s ChatGPT launch has had a remarkable impact on the field of AI and tech in general. AI is now available for regular users. People like me working in tech can now use AI in everything they are doing, which will accelerate product development and will impact the kind of products we can build and deliver to customers.

In addressing concerns surrounding AI ethics, you mentioned the importance of regulatory measures, technological transparency, and societal readiness. Could you elaborate on how Momentable approaches these areas to mitigate potential ethical dilemmas?

With great power comes great responsibility. AI is a powerful technology, and it’s very easy for those wielding it to amplify the impact of the good and the bad in the work they do. 

While we, in the tech space, are doing our very best to build great products that deliver great value, we are not social scientists, psychologists, or public servants. So, we can’t be expected to regulate and supervise ourselves, nor can we evaluate the impact of these technologies and the products using them on the individual and on society. 

It’s great when companies have values, codes of ethics, missions, and visions; however, those are not enough. Just like we do not rely on drivers to drive safely, we have traffic laws, signs, lights, and we make sure people driving a car are licensed and trained. We need to do the same with technologies, which, I would argue, have a massive impact on shaping our future as a species more than anything we’ve ever had in our history. 

At Momentable, we are acutely aware of the impact of generative AI on our stakeholders, artists, cultural organizations, and art lovers. We engaged our stakeholders, ran several experiments where generative AI created artworks with input from artists, with their permission and consent. 

In addition to using AI to enhance customer experience on our platform, we are using the learnings to evolve our product and introduce Generative AI in a thoughtful way that adds value and advances the art and culture space.

How do you personally strategize and prioritize addressing the ethical implications of AI within Momentable’s projects and initiatives?

We start by listening to our stakeholders; artists, art lovers, clients, and team. From simple Slack messages, to meetings with artists who are friends of Momentable, to talking to the experts, and sharing YouTube videos of leading content creators in the art space. 

By taking in the input, feedback, concerns, and advice, we make sure we are thoughtful about the next steps we plan to take. In addition to the data and numbers we get from market reports, we use the qualitative input we gather to help us focus on where we can add significance. 

We understand the AI conversation is ongoing, and as the industry keeps moving at rapid speed, we must stay engaged, always learning, and maintain an open attitude.

As someone deeply involved in the AI industry, what advice would you offer to our readers who are concerned about the ethical use and bias in AI technology?

 Ethical use and bias are not new to tech; it’s further amplified in AI, particularly generative AI. Three core reasons lead to challenges in ethical use and bias in generative AI: 

1. Products are developed by the tech sector, which deals with many ethical challenges and major bias due to the lack of diversity. These challenges are amplified by keeping the technology and products developed closed, which. 
2. The data used to train and develop the AI models also has many issues with how it was sourced, used, and also carries within it implicit bias. These issues are amplified even further since many AI models keep their. 
3. The nature of generative AI severely exacerbates these issues and challenges. By producing content mimicking the training data using code developed by a sector dealing with ethical challenges and bias, generative AI is adding to the problem with every answer it provides. 

Your ability to influence or mitigate the ethical use and bias in AI depends on where you are in the systemic hierarchy of the tech ecosystem. As a product builder and customer, there is very little you can do to change things.

The sector requires regulatory and systemic intervention. But it can’t be done without engaging with the stakeholders and having them at the table.

This is not to say that as a consumer you do not have any power; you do. You can make your voice heard through social media, customer feedback, calling your representatives, and voting.

I encourage you to learn and gain some hands-on experience to develop your understanding and appreciation for the technology and how powerful it is.

In your view, what role do education and skill development play in preparing society for the impact of AI, particularly in addressing job displacement and socio-economic challenges?

As technology continues to evolve and take over more of our roles at work as we know it today, the transformation will have massive implications on our lifestyles, how we do things, and even how we define ourselves and the value we assign to our roles.

We need to stop thinking about education as a precursor to job placement. This limited view meant that education is always lagging behind the needs of the economy and helplessly lacking in addressing any of the needs of our society.

Education must focus on the future beyond the jobs of today or tomorrow. It must graduate innovators and value creators. Education must focus on graduating creatives skilled at solving the problems we face 50-100 years from now.

To create a better world, schools and universities must become open spaces for research and discovery, where art, technology, and culture collide and fuse to inspire new thought forms.

Could you share some examples of how Momentable ensures transparency in its AI technologies, particularly regarding decision-making processes and algorithms?

We do everything in collaboration and coordination with our key stakeholders. This gives us a baseline to measure against.

It’s easy to be influenced by what we read and watch and think it’s an accurate representation of the world. To avoid the pitfalls of building on the learnings and understanding within our own bubble, we always start by expanding our perspective. Put simply, we talk to people.

It’s slow, inefficient, and important. If we are going to use technology to impact people’s lives, we better speak to those people, learn from them, understand their perspective, and take into consideration what matters to them.

This approach led us to experimenting with AI without limitations at the very beginning. We shared our results with our community: our users, partners, artists, and our advisors.

We wrote about our process and shared it through workshops and webinars, and we took on all the feedback we could gather.

While the inclination at the beginning was to keep things close to the chest, this open and transparent approach helped us focus on the areas where AI can add the most value in our work.

In the case of Momentable, we use AI to help us deliver the best user experience and make it easier, faster, and better for our users to use Momentable and capitalize on the democratized access to the largest collection of great art in the world.

Considering the rapid advancements in AI, how do you navigate the balance between innovation and the need for regulatory control within Momentable’s operations?

Until a clear regulatory framework is developed and introduced, like most companies, we continue to operate within the regulatory frameworks for the tech sector and business in North America and Europe.

At Momentable, we are governed by our internal ethical code and guided by our strong sense of mission to bring the best visual experience to customers through innovative software, personalization, and immersive storytelling.

With our stakeholders being engaged and involved throughout the process, we make sure we create a space for creativity and innovation with boundaries that keep our work focused on adding value with minimal negative impact on our stakeholders.

What steps do you believe are necessary for governments and regulatory bodies to effectively oversee AI development and ensure alignment with ethical and safety standards?

Bring all the stakeholders, industry players, academia, builders, users, communities, regulators, and the public to the table to collaborate and constructively build for the benefit of all.

Form a steering board and create a framework for engagement so that adding value to all stakeholders is a main condition.

Be clear and transparent about the objectives and outcomes you are after.

Develop a roadmap with realistic short-term goals and objectives, in addition to highlighting the mid-term and long-term areas of focus.

Maintain connection with stakeholders through regular roundtable meetings. Share regularly, and invite input, feedback, and criticism.

Keep moving forward and getting things done.

From your perspective, what are the most pressing ethical dilemmas or challenges currently facing the AI industry, and how can businesses and individuals contribute to addressing them?

The most pressing ethical dilemmas or challenges currently facing the AI industry can be viewed from three perspectives: long-term, mid-term, and short-term.

Long-term: AI is going to play a significant role in shaping who we are as a species and how we live our lives. Just like there are generations today that do not know a world without smartphones and the internet, we will have generations who do not know a world before AI, and we will have a generational gap and challenges that arise from this gap. Older generations will feel left behind, while new generations will be heavily dependent on AI and AI-enabled devices. The energy consumption will be extreme, and errors caused by AI will have massive ramifications, especially since AI will be embedded in essential services, infrastructure, and defense. In many ways, some might say we will be at the mercy of AI, and even if AI doesn’t become aware or evil, mistakes AI makes are possibly disastrous.

Mid-term: AI will cause massive socio-economic shifts that require offering support and help to those individuals and businesses impacted until the transformation is complete. Changes to the education sector are inevitable, and the evolution of our economy will have positive and negative implications that must be observed and prepared for. Focusing on the energy sector, making sure equitable, democratized, and open access to AI tools and training is crucial. New incubators, accelerators, resources, and support services must be made available to help manage the shift and protect society and the economy from the negative implications. As more people become proficient in using AI tools, they will be able to build massive businesses that compete with existing businesses, and just like smaller teams were able to disrupt businesses with software, now individuals can disrupt businesses with a few tools. Not to mention the malicious use of these tools can lead to even more challenges and threats.

Short-term: The immediate priority lies in creating spaces for engagement, learning, and hands-on experience with AI. It’s crucial to create an environment where individuals and businesses can understand, interact with, and ethically utilize AI technologies. This involves opening dialogues, providing educational resources, and encouraging ethical AI use through policy advocacy and community involvement. Businesses can lead by example, ensuring their AI applications adhere to ethical standards and are transparent in their operations, and share their learnings and discoveries. By actively participating in these efforts, we can navigate the complex and ever-changing terrain brought forth with the advancements in AI.

Finally, what are your thoughts on the future of AI and its potential to positively impact society, and do you have any closing remarks or key insights you’d like to share with our audience?

The future of AI holds remarkable potential for bettering every part of our lives. This technological evolution will accelerate advancements and enable breakthroughs in healthcare, climate science, education, and the sustainability of our species.

This optimistic vision is dependent on democratizing access, sharing openly, and ensuring there is transparency in how AI models work.

In addition, we must have an unwavering commitment to ethical principles, inclusivity, and equitable access to AI technology, prioritizing creating and delivering value to ensure all technological advancement, including AI, is a catalyst for positive change.

I invite you, the reader, to think of yourself as an active participant in this future being shaped today. Do not be a spectator; instead, take part, engage with AI, learn, build, and innovate. 

Now more than ever, the barriers to entry are minimal, and you can make an impact with less time, money, and resources. Embrace your roles as a shaper of the future, and engage with the world being created in front of our eyes with your thoughts, words, and actions for the greater good.

Hussein Hallak

Co-founder of Momentable

Hussein Hallak is the Founder and CEO of Next Decentrum, the launchpad for the world’s most iconic NFT products.  Heavily experienced in the art and technology fields, his recent roles include General Manager of Launch, one of North America’s top tech hubs and startup incubators, where he helped over 6500+ founders and 500+ startups raise over $1 billion. In 2019, Hussein joined 3 tier logic as VP of Products & Strategy and worked with some of the world’s most valuable brands including Universal Studios, P&G, and Kimberly Clark.

Hussein writes and speaks about startups, blockchain, and NFTs, and advises several blockchain and tech startups including Ami Pro, Gigr, Mobile Art School, Fintrux, Majik Bus, Traction Health, Cloud Nine, and Peace Geeks.  He was recognized in 2019 as one of 30 Vancouver tech thought-leaders and influencers to follow and has been featured in Forbes, BBC, BetaKit, Entrepreneur, DailyHive, Notable, and CBC.  When not building products, he enjoys writing, reading, and engaging in meaningful conversations over good coffee, and his favorite pastimes include playing chess with his kids, binging on good drama and science fiction, drawing, and learning new guitar licks, sometimes all at the same time.

The post AITech Interview with Hussein Hallak, Co-founder of Momentable first appeared on AI-Tech Park.

Becky, please provide a brief overview of your role and expertise within Orium, particularly in assisting commerce and retail brands with their digital transformation journey?

I’m the VP Digital Programs at Orium, which means I’m the executive sponsor for all projects and programs that fall within this line of business. The duties of an executive sponsor on a project or program at Orium involve providing strategic guidance, oversight, and support throughout the project lifecycle, with specific internal and external duties. Here are some key responsibilities of an executive sponsor in this context:

• Program / customer alignment
• Leadership and support to clients, teams and internal stakeholders
• Strategic decision making
• Stakeholder management in programs
• Risk management in programs

What is the role of composable commerce and MACH architecture, and what is its significance in today’s digital transformation landscape?

When digital commerce first emerged, brands operated two separate sales streams: in-store and online. This isn’t the case anymore, and as the where, when and how of commerce experiences has evolved, retailers have started leveraging a MACH approach (Microservices-based, API-first, Cloud native, and Headless) to overcome the rigidity of older technology stacks and enable them to serve their customers better.

With the growth of the MACH ecosystem, brands are recognizing the value of a composable approach. Composable architectures mean every component is independent, and they’re brought together in a curated, best-for-me system. This means brands can choose each element of their digital services to best meet their specific business model needs. The realities of modern commerce require brands to be able to respond effectively and efficiently to changes in the marketplace and the ability to custom curate and seamlessly integrate solutions is a core part of how brands will grow and thrive in the future.

What challenges do you believe organizations face when considering a transition to MACH architecture, based on your experience working with various brands?

One of the biggest challenges brands face is understanding how to work within this new paradigm. Monolithic solutions can be overly rigid and limiting, but they do take a lot of the decision-making out of the equation. One of the areas I work most closely with our clients on is helping determine both the what and the how— where do they need new tech today to create or seize opportunities and how should they approach implementation to maximize success.

Accelerators are an extremely effective way for brands to take advantage of the interoperability of a composable architecture while streamlining a lot of the early decision-making and integration. Orium’s Composable Accelerators, for example, provide a pre-integrated framework to operate from, which enables brands to launch on a new system in as few as 6 weeks, without compromising the ability to select the vendors that make the most sense for their unique business needs.

Could you elaborate on the key insights from the “Get MACH Ready” report regarding the importance of understanding the motives behind transitioning to MACH?

Making a move to a new tech stack — and especially to a new approach to how you architect and manage your tech stack — requires complete organizational buy-in. As with any investment, it’s not to be taken lightly. It will change not just the technology, but the ways in which teams are structured and how your organization operates, what skills your team members need and how you think about and approach challenges. Because of that, it is imperative that everyone is bought into the initiative from the start. And to secure that buy-in, you need to be aligned on why this matters.

How will making a move to MACH improve the function of the organization? How will it help teams in their day to day work? What impact will it have on helping everyone meet the strategic goals of the company? Understanding what you’re aiming towards is crucial. It’s often referred to as the “North Star”— that future-state of org-level functionality that means you are able to achieve what you want, how you want, when you want it.

How essential is it for organizations to garner support from all impacted departments before involving the C-suite in the decision-making process, as outlined in the report?

Gaining universal buy-in, especially when people are entrenched in the status quo, can be really challenging. By digging into the challenges of each department and helping them understand how a move to a MACH-based composable architecture can positively affect their day-to-day work and help them achieve what they need to, you can start to build a groundswell of support. The C-Suite, especially the CEO and CFO, are going to be extremely motivated by results that can drive revenue or decrease costs. When you connect directly with impacted departments, you can present real data about what to expect from the improvements that come with MACH.

In your opinion, what are the critical components of building a compelling financial case for transitioning to MACH, and how does it contribute to the success of the overall strategy?

I talk a lot about the Total Cost of Ownership (TCO), because I think it’s one of the most critical parts to understand about the move to MACH. With an all-in-one monolith, people have always looked at cost as just the number on the contract. One year of this solution costs X amount of money, the end. But that has always been an overly simplistic view of cost. Understanding things like time to first value and ROI are important, but don’t overlook the impact of efficiency gains. Does your marketing team have the ability to adjust messaging without the support of developers with this new approach? How does that contribute to revenue? Are developers building new features instead of wasting days, weeks, and even months on maintenance and bug fixes with a legacy platform? How does that impact revenue? TCO doesn’t just look at the cost of the solutions, it also looks at the gains, because these things aren’t separate from one another. Even things like employee engagement should be examined— hiring and training new staff because employees are frustrated by a lack of growth opportunities or bad experiences with outdated software is an expensive way to justify keeping your legacy stack, and long term can have a terrible impact on your company culture. It’s all connected and the more you’re able to reinforce the holistic view of the financials, the better.

Could you discuss the significance of talent and change management in the context of transitioning to MACH, and how can organizations effectively address these aspects?

As I noted earlier, switching to a composable architecture isn’t just about the technology. Because technology doesn’t operate itself (at least, not yet…). Ultimately, there are people at every single level who will be working with and impacted by the adoption of this new approach. There will be new skills to be learned and old skills may no longer be relevant. Your team and organizational structures may need to shift. Operational routines, in particular, will change. These are challenging things for people! Change is challenging! But when handled thoughtfully, when planned for and communicated clearly at every stage, this kind of change can present incredible opportunities for growth. Communication is key— listen to the team’s concerns and do your best to address those issues head on. Don’t be afraid to be open and honest throughout the process. These are the people who are going to either embrace or reject your new approach. Why not make it as easy as possible from them to embrace it?

What are the potential pitfalls of implementing MACH architecture out of order, and how can businesses navigate these challenges based on the clear seven-step process outlined in the report?

In any transformation, there are going to be risks. Adopting a MACH approach is no different. Broadly speaking, there are three categories of risk:

• Lack of buy-in
• Lack of planning
• Lack of communication

Buy-in:
I’ve already talked about how important it is to get align around the North Star and garner buy-in before you even commit to making the move to MACH, but this isn’t something that’s done at the outset and then is done forever. Buy-in is an ongoing process. Ensuring you not only get, but maintain, support from the whole team is crucial. Even one or two powerful naysayers can tank a great program, so take the time to check-in regularly, gauge where people are at and how they’re feeling, and address concerns quickly before they become the freight train that has no brakes.

Planning:
It goes without saying, but I’ll say it anyway: you can’t stumble into success here. A transition to a new tech approach is extremely doable, but only if you’ve taken the time and care to do it well. A trusted systems integrator is invaluable in this, as they’ll be able to help you think through what you don’t already know, identify potential areas of concern and roadblocks with your specific circumstances, help you select the solutions that make the most sense for your needs, and guide you through the process of change management. We’ve seen it all before, we can help you, too!

Communication:
Keep, People. Informed. It sounds so simple, but it proves, time and again, to be one of the biggest stumbling blocks for brands. It’s not enough to talk to team members once at the start, or to just tell them what’s happening and not include them in the decision making process. You want and need diverse perspectives to ensure you know where your most pressing issues are up front, and then to know where things are going right, if things are going wrong, and how to fix them. Maintaining stakeholder support only happens with effective communications. Expectation setting, sharing of wins, timeline updates… all of this needs to happen on a set cadence so everyone knows where and when they’ll hear news and have the ability to ask questions. Don’t leave people in the dark.

Following the launch of a MACH implementation, what strategies do you recommend for organizations to monitor and optimize their performance, particularly in terms of metrics and analytics?

Each organization will care about and want to examine different metrics, depending on what they were investing in and focused on, and part of the aligning on a North Star and setting expectations early on process should include identifying key metrics you’ll measure to understand what success looks like. Maybe your experiences had terrible performance in the past and you were losing customers because of that— page load speeds are going to be a key metric to measure. Or maybe you replaced your checkout experience, and you identified average order size and checkout completion rates as the key metrics. The important thing is you examine what matters most and refine your approach if you’re not hitting your benchmarks. When you do, you can move on to focus on other aspects of the experience for improvement, but don’t stop monitoring those key first areas. You want to ensure that once you hit those targets, you keep hitting them and where and when possible, set new targets to work towards.

Lastly, how can organizations ensure they maximize the return on investment (ROI) of their MACH transition, and what ongoing strategies do you suggest for continuous improvement and adaptation?

There are two things I would suggest for getting the most out of your MACH architectures.

1. Monitor and optimize. Just because you improved page load times by 300% doesn’t mean you never need to think about it again. Monitor, refine, check in again. Composable is inherently capable of supporting a strong, cross-platform data infrastructure. Dig into it and find the areas of opportunity!
2. Leverage the experts. The advantage of a composable approach is you have access to support from the people who know search (or checkout, or front-end performance, or order management and inventory oversight… you get the picture) the best! Your SI and the vendors you work with will be able to help you not just use the basic functions of your implementation, but truly take advantage of all the bells and whistles these best of breed vendors have to offer.

The other thing to remember: the whole point of adopting a composable approach is that you get what your business needs, not whatever comes in the box. If something isn’t working for you, you can and should swap it out.

Becky Parisotto

VP, Commerce & Retail Platforms at Orium

ecky Parisotto is the Vice President, Digital Programs at Orium, bringing over 13 years of experience in eCommerce client services and program management to some of the biggest client engagements. With a focus on in-store technology, loyalty programs and customer data activation, Becky’s work supports the future of unified commerce. Orium is focused on large-scale digital composable commerce transformations for the retail space, bringing omni-channel technologies together. Key accounts that Becky works with are Harry Rosen and Princess Auto in Canada, and SiteOne Landscape, Shamrock Foods, in the USA.

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Vsu, as the SVP of Content Engineering for Avalara, can you provide insights into how your role involves managing Avalara’s tax and compliance content database and driving the company’s use of AI and machine learning?

I lead the research and engineering teams that maintain and grow Avalara’s compliance content database, which powers our compliance products that make tax compliance easy and fast for our customers. I also drive the vision and use of artificial intelligence (AI) and machine learning (ML) across Avalara both for internal use and use in our products.

Given your experience in overseeing large-scale engineering programs at Optum and Thomson Reuters, how has that background influenced your practical approach to implementing AI, and what perspectives do you find refreshing for the audience?

Experimenting with AI versus implementing AI at-scale are quite different. Implementing AI requires the same operational sophistication needed to run any large software engineering solutions — they need to be resilient and be able to deal with unexpected situations. Implementing AI at-scale also requires high levels of monitorability and traceability. In scale implementations, the AI models must be supported by a solid engineering infrastructure, architecture, and overall operations.

Can you elaborate on Avalara’s sales tax plugin for ChatGPT? What specific use case can ChatGPT users solve with the plugin? 

Avalara launched the first tax compliance plugin for ChatGPT that allows users to ask the platform to calculate and research general sales tax rates based on their location. We were also the first tax compliance software provider to work with OpenAI to use its protocol to build an integrated plugin for ChatGPT. 

Businesses and consumers who have ChatGPT Plus accounts can install the Avalara plugin from ChatGPT’s plugin store. Once the plugin is installed, users can ask ChatGPT for general sales tax rates on the sale of tangible personal property by location or to calculate general sales tax on a specific sale by location. ChatGPT chooses when to use the plugin based on the question and conversation. 

How does Avalara differentiate itself by applying AI and machine learning in a practical way that makes a significant business impact, as opposed to focusing on creating flashy new AI experiences?

At Avalara, we’re on a mission to make tax “less taxing, more relaxing” by automating the steps of the tax compliance process. We think about AI as a strategic tool to allow us to work better, smarter, and faster in pursuit of this mission. 

We’re looking for places across our business and throughout the customer journey where AI can provide value around efficiency, simplicity, and more. For example, we use AI-powered tax classification tools to quickly and efficiently classify products or services to aid in sales tax taxability determinations and cross-border tariff determinations.

How has the integration of AI technologies allowed Avalara to streamline its processes and deliver better results? Are there specific examples or use cases that highlight the tangible benefits?

One step of the sales tax compliance journey for many businesses it to manage exempt sales, also known as exemption certificate management. Exemption certificates contain a lot of different information depending on the supplier, buyer, transaction details, and more. At Avalara, we process forms and notices and leverage AI to automatically extract information, which helps our customers manage and maintain exemption certificates more easily and effectively.

How do you balance the need for innovation with the practical application of AI to ensure it aligns with Avalara’s business goals and objectives?

We conduct regular hackathons to encourage innovation and experimentation within our engineering teams — many of which lead to new innovations for Avalara. When we find promising ideas, we do an ROI analysis and assess alignment with our strategic priorities before we invest in any of these ideas.

Can you share any challenges or lessons learned in applying AI in a tax automation context, and how has Avalara addressed these challenges to achieve successful outcomes?

At Avalara, we’re making sure that being quality-focused is a priority across the business. We know that AI is a capability that we can leverage, but it’s not a cure-all. That’s why we’ve been intentional about developing and releasing policies around AI to ensure that we are all following responsible AI guidelines and using it safely and efficiently across the business.

What strategies have you employed to foster a culture of innovation and collaboration within the research and engineering teams at Avalara?

We employ numerous strategies to ensure that we are fostering a culture of innovation and collaboration within Avalara Engineering. One key example is our use of hackathons, which allows members from across engineering and the company at-large to participate in the hackathon, We also set aside two weeks each quarter designated as an “innovation sprint,” which is time that our teams can spend focused on new innovation, learning and forward thinking planning.

We also make a range of trainings available to allow our teams to continue learning new skills and best practices as they emerge within the industry at-large. 

In what ways do you see the landscape of tax automation evolving with the continued integration of AI and machine learning technologies? Are there emerging trends or developments that you find particularly noteworthy?

What we’re seeing right now is that more customers are starting to ask how AI will be used in compliance. I believe there is an expectation that gradually AI will help in assisting with the compliance journey. At a time where many people are leaving some compliance-related professions, we’re seeing a growing dearth in skill sets that create an opportunity for AI to help with many mundane manual tasks.

What areas do you see further opportunities for leveraging AI to enhance the company’s offerings and maintain a competitive edge in the tax automation software market?

Avalara was there at the beginning of the automation journey for tax. We have always been a pioneer in the tax space by being among the first to leverage the power of the best in technology, so AI will be no different. We are harnessing the power of AI to help us do what we do best already, in an effort to bring more scale and convenience to customers. We will continue to identify other areas within our products, customer experience, and internally where AI can help us deliver tax compliance automation to businesses with greater efficiency and speed at-scale.

Vsu Subramanian

SVP of Engineering & ML Services at Avalara

Vsu leads the research and engineering teams responsible for managing Avalara’s proprietary tax and compliance content database. He also drives the company’s use of AI and machine learning. He previously held senior leadership positions at Optum and Thomson Reuters overseeing large-scale engineering programs.

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Joel, how have the unique challenges faced by small and medium-sized enterprises influenced their adoption of AI in identity management and security practices?

So we commission a biannual small to medium-sized enterprise (SME) IT Trends Report that looks specifically at the state of SME IT. This most recent version shows how quickly AI has impacted identity management and highlights that SMEs are kind of ambivalent as they look at AI. IT admins are excited and aggressively preparing for it—but they also have significant concerns about AI’s impact. For example, nearly 80% say that AI will be a net positive for their organization, 20% believe their organizations are moving too slowly concerning AI initiatives, and 62% already have AI policies in place, which is pretty remarkable considering all that IT teams at SMEs have to manage. But SMEs are also pretty wary about AI in other areas. Nearly six in ten (62%) agree that AI is outpacing their organization’s ability to protect against threats and nearly half (45%) agree they’re worried about AI’s impact on their job. I think this ambivalence reflects the challenges of SMEs evaluating and adopting AI initiatives – with smaller teams and smaller budgets, SMEs don’t have the budget, training, and staff their enterprise counterparts have. But I think it’s not unique to SMEs. Until AI matures a little bit, I think that AI can feel more like a distraction.

Considering your background in identity, what critical considerations should SMEs prioritize to protect identity in an era dominated by AI advancements?

I think caution is probably the key consideration. A couple of suggestions for getting started:

Data security and privacy should be the foundation of any initiative. Put in place robust data protection measures to safeguard against breaches like encryption, secure access controls, and regular security audits. Also, make sure you’re adhering to existing data protection regulations like GDPR and keep abreast of impending regulations in case new controls need to be implemented to avoid penalties and legal issues.

When integrating AI solutions, make sure they’re from reputable sources and are secure by design. Conduct thorough risk assessments and evaluate their data handling practices and security measures. And for firms working more actively with AI, research and use legal and technical measures to protect your innovations, like patents or trademarks.

With AI, it’s even more important to use advanced identity and authentication management (IAM) solutions so that only authorized individuals have access to sensitive data. Multi-factor authentication (MFA), biometric verification, and role-based access controls can significantly reduce that risk. Continuous monitoring systems can help identify and thwart AI-related risks in real time, and having an incident response plan in place can help mitigate any security breaches. 

Lastly, but perhaps most importantly, make sure that the AI technologies are used ethically, respecting privacy rights and avoiding bias. Developing an ethical AI framework can guide your decision-making process. Train employees on the importance of data privacy, recognizing phishing attacks, and secure handling of information. And be prepared to regularly update (and communicate!) security practices given the evolving nature of AI threats.

AI introduces both promises and risks for identity management and overall security. How do you see organizations effectively navigating this balance in the age of AI, particularly in the context of small to medium-sized enterprises?

First off, integrating AI has to involve more than just buzzwords – and I’d say that we still need to wait until AI accuracy is better before SMEs undertake too many AI initiatives. But at the core, teams should take a step back and ask, “Where can AI make a difference in our operations?” Maybe it’s enhancing customer service, automating compliance processes, or beefing up security. Before going all in, it’s wise to test the waters with pilot projects to get a real feel of any potential downstream impacts without overcommitting resources.

Building a security-first culture—this is huge. It’s not just the IT team’s job to keep things secure; it’s everybody’s business. From the C-suite to the newest hire, SMEs should seek to create an environment where everyone is aware of the importance of security, understands the potential threats, and knows how to handle them. And yes, this includes understanding the role of AI in security, because AI can be both a shield and a sword.

AI for security is promising as it’s on another level when it comes to spotting threats, analyzing behavior, and monitoring systems in real time. It can catch things humans might miss, but again, it’s VITAL to ensure the AI tools themselves are built and used ethically. AI for compliance also shows a lot of promise. It can help SMEs stay on top of regulations like GDPR or CCPA to avoid fines but also to build trust and reputation. 

Because there are a lot of known unknowns around AI, industry groups can be a good source for information sharing and collaboration. There’s wisdom and a strength in numbers and a real benefit in shared knowledge. It’s about being strategic, inclusive, ethical, and always on your toes. It’s a journey, but with the right approach, the rewards can far outweigh the risks.

Given the challenges in identity management across devices, networks, and applications, what practical advice can you offer for organizations looking to leverage AI’s strengths while addressing its limitations, especially in the context of password systems and biometric technologies?

It’s a surprise to exactly no one that passwords are often the weakest security link. We’ve talked about ridding ourselves of passwords for decades, yet they live on. In fact, our recent report just found that 83% of organizations use passwords for at least some of their IT resources. So I think admins in SMEs know well that despite industry hype around full passwordless authentication, the best we can do for now is to have a system to manage them as securely as possible. In this area, AI offers a lot. Adaptive authentication—powered by AI—can significantly improve an org’s security posture. AI can analyze things like login behavior patterns, geo-location data, and even the type of device being used. So, if there’s a login attempt that deviates from the norm, AI can flag it and trigger additional verification steps or step-up authentication. Adding dynamic layers of security that adapt based on context is far more robust than static passwords.

Biometric technologies offer a unique, nearly unforgeable means of identification, whether through fingerprints, facial recognition, or even voice patterns. Integrating AI with biometrics makes them much more precise because AI algorithms can process complex biometric data quickly, improve the accuracy of identity verification processes, and reduce the chances of both false rejections and false acceptances. Behavioral biometrics can analyze typing patterns, mouse or keypad movements, and navigation patterns within an app for better security. AI systems can be trained to detect pattern deviations and flag potential security threats in real time. The technical challenge here is to balance sensitivity and specificity—minimizing false alarms while ensuring genuine threats are promptly identified.

A best practice with biometrics is to employ end-to-end encryption for biometric data, both at rest and in transit. Implement privacy-preserving techniques like template protection methods, which convert biometric data into a secure format that protects against data breaches and ensures that the original biometric data cannot be reconstructed.

AI and biometric technologies are constantly evolving, so it’s necessary to keep your systems updated with the latest patches and software updates. 

How has the concept of “identity” evolved in today’s IT environment with the influence of AI, and what aspects of identity management have remained unchanged?

Traditionally, identity in the workplace was very much tied to physical locations and specific devices. You had workstations, and identity was about logging into a central network from these fixed points. It was a simpler time when the perimeter of security was the office itself. You knew exactly where data lived, who had access, and how that access was granted and monitored.

Now it’s a whole different ballgame. This is actually at the core of what JumpCloud does. Our open directory platform was created to securely connect users to whatever resources they need, no matter where they are. In 2024, identity is significantly more fluid and device-centered. Post-pandemic, and with the rise of mobile technology, cloud computing, and now the integration of AI, identities are no longer tethered to a single location or device. SMEs need for employees to be able to access corporate resources from anywhere, at any time, using a combination of different devices and operating systems—Windows, macOS, Linux, iOS, Android. This shift necessitates a move from a traditional, perimeter-based security model to what’s often referred to as a zero-trust model, where every access transaction needs to have its own perimeter drawn around it. 

In this new landscape, AI can vastly improve identity management in terms of data capture and analysis for contextual approaches to identity verification. As I mentioned, AI can consider the time of access, the location, the device, and even the behavior of the user to make real-time decisions about the legitimacy of an access request. This level of granularity and adaptiveness in managing access wasn’t possible in the past.

However, some parts of identity management have stayed the same. The core principles of authentication, authorization, and accountability still apply. We’re still asking the fundamental questions: “Are you who you say you are?” (authentication), “What are you allowed to do?” (authorization), and “Can we account for your actions?” (accountability). What has changed is how we answer these questions. We’re in the process of moving from static passwords and fixed access controls to more dynamic, context-aware systems enabled by AI.

In terms of identity processes and applications, what is the current role of AI for organizations, and how do you anticipate this evolving over the next 12 months?

We’re still a long away from the Skynet-type AI future that we’ve all associated with AI since the Terminator. For SMEs, AI accelerates a shift away from traditional IT management to an approach that’s more predictive and data-centric. At the core of this shift is AI’s ability to sift through vast, disparate data sets, identifying patterns, predicting trends, and, from an identity management standpoint, its power is in preempting security breaches and fraudulent activities. It’s tricky though, because you have to balance promise and risk, like legitimate concerns about data governance and the protection of personally identifiable information (PII). Tapping AI’s capabilities needs to ensure that we’re not overstepping ethical boundaries or compromising on data privacy. Go slow, and be intentional.

Robust data management frameworks that comply with evolving regulatory standards can protect the integrity and privacy of sensitive information. But keep in mind that no matter the benefit of AI automating processes, there’s a critical need for human oversight. The reality is that AI, at least in its current form, is best utilized to augment human decision-making, not replace it. As AI systems grow more sophisticated, organizations will require workers with  specialized skills and competencies in areas like machine learning, data science, and AI ethics.

Over the next 12 months, I anticipate we’ll see organizations doubling down on these efforts to balance automation with ethical consideration and human judgment. SMEs will likely focus on designing and implementing workflows that blend AI-driven efficiencies with human insight but they’ll have to be realistic based on available budget, hours, and talent. And I think we’ll see an increase in the push towards upskilling existing personnel and recruiting specialized talent. 

For IT teams, I think AI will get them closer to eliminating tool sprawl and help centralize identity management, which is something we consistently hear that they want. 

When developing AI initiatives, what critical ethical considerations should organizations be aware of, and how do you envision governing these considerations in the near future?

As AI systems process vast amounts of data, organizations must ensure these operations align with stringent privacy standards and don’t compromise data integrity. Organizations should foster a culture of AI literacy to help teams set realistic and measurable goals, and ensure everyone in the organization understands both the potential and the limitations of AI technologies.

Organizations will need to develop more integrated and comprehensive governance policies around AI ethics that address:

How will AI impact our data governance and privacy policies? 

What are the societal impacts of our AI deployments? 

What components should an effective AI policy include, and who should be responsible for managing oversight to ensure ethical and secure AI practices?

Though AI is evolving rapidly, there are solid efforts from regulatory bodies to establish frameworks, working toward regulations for the entire industry. The White House’s National AI Research and Development Strategic Plan is one such example, and businesses can glean quite a bit from that. Internally, I’d say it’s a shared responsibility. CIOs and CTOs can manage the organization’s policy and ethical standards, Data Protection Officers (DPOs) can oversee compliance with privacy laws, and ethics committees or councils can offer multidisciplinary oversight. I think we’ll also see a move toward involving more external auditors who bring transparency and objectivity.

In the scenario of data collection and processing, how should companies approach these aspects in the context of AI, and what safeguards do you recommend to ensure privacy and security?

The Open Worldwide Application Security Project (OWASP) has a pretty exhaustive list and guidelines. For a guiding principle, I’d say be smart and be cautious. Only gather data you really need, tell people what you’re collecting, why you’re collecting it, and make sure they’re okay with it. 

Keeping data safe is non-negotiable. Security audits are important to catch any issues early. If something does go wrong, have a plan ready to fix things fast. It’s about being prepared, transparent, and responsible. By sticking to these principles, companies can navigate the complex world of AI with confidence.

Joel Rennich

VP of Product Strategy at JumpCloud 

Joel Rennich is the VP of Product Strategy at JumpCloud residing in the greater Minneapolis, MN area. He focuses primarily on the intersection of identity, users and the devices that they use. While Joel has spent most of his professional career focused on Apple products, at JumpCloud he leads a team focused on device identity across all vendors. Prior to JumpCloud Joel was a director at Jamf helping to make Jamf Connect and other authentication products. In 2018 Jamf acquired Joel’s startup, Orchard & Grove, which is where Joel developed the widely-used open source software NoMAD. Installed on over one million Macs across the globe, NoMAD allows macOS users to get all the benefits of Active Directory without having to be bound to them. Joel also developed other open source software at Orchard & Grove such as DEPNotify and NoMAD Login. Over the years Joel has been a frequent speaker at a number of conferences including WWDC, MacSysAdmin, MacADUK, Penn State MacAdmins Conference, Objective by the Sea, FIDO Authenticate and others in addition to user groups everywhere. Joel spent over a decade working at Apple in Enterprise Sales and started the website afp548.com which was the mainstay of Apple system administrator education during the early years of macOS X.

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Bernard, kindly brief us about Generative AI and its impact on various industries such as retail, healthcare, finance, education, manufacturing, marketing, entertainment, sports, coding, and more?

Generative AI (GenAI) is revolutionizing multiple sectors by enabling the creation of new, original content and insights. In retail, it’s personalizing shopping experiences; in healthcare, it’s accelerating drug discovery and patient care customization. Finance is seeing more accurate predictive models, while education benefits from tailored learning materials. Manufacturing, marketing, entertainment, sports, and coding are all experiencing unprecedented innovation and efficiency improvements, showcasing GenAI’s versatility and transformative potential.

Your latest book, “Generative AI in Practice,” is set to release soon. Could you share some key insights from the book, including how readers can implement GenAI, its differences from traditional AI, and the generative AI tools highlighted in the appendix?

In “Generative AI in Practice,” I explore how GenAI differs fundamentally from traditional AI by its ability to generate novel content and solutions. The book offers practical guidance on implementing GenAI, highlighting various tools and platforms in the appendix that can kickstart innovation in any organization. It’s designed to demystify GenAI and make it accessible to a broader audience.

With your extensive experience advising organizations like Amazon, Google, Microsoft, and others, what role do you see GenAI playing in transforming business strategies and performance?

It’s clear that Generative AI (GenAI) is poised to become a pivotal element in reshaping business strategies and boosting performance across industries. By leveraging GenAI, companies can gain a significant competitive advantage through the acceleration of innovation, the automation of complex and creative tasks, and the generation of actionable insights. This transformative technology enables businesses to refine their decision-making processes and enhance customer engagement in ways previously unimaginable. As we move forward, the integration of GenAI into core business operations will not only optimize efficiency but also open up new avenues for growth and value creation, marking a new era in the corporate landscape.

Why is Generative AI considered the most powerful technology humans have ever had access to, and what makes it stand out compared to other advancements in the tech industry?

Generative AI not only stands out as perhaps the most potent technology available today due to its capacity for creativity and innovation, surpassing prior tech advancements by enabling machines to understand, innovate, and create alongside humans, but it also offers a pathway to artificial general intelligence (AGI). This potential to achieve AGI, where machines could perform any intellectual task that a human can, marks a significant leap forward. It represents not just an evolution in specific capabilities, but a foundational shift towards creating systems that can learn, adapt, and potentially think with the breadth and depth of human intelligence. This aspect of generative AI not only differentiates it from other technological advancements but also underscores its transformative potential for the future of humanity.

GenAI brings forth unique risks and challenges. Can you discuss how businesses and individuals can navigate these challenges, especially in areas such as misinformation, disinformation, and deepfakes, particularly in an election year?

The unique risks and challenges presented by Generative AI, particularly in the realm of misinformation, disinformation, and the creation of deepfakes, demand a proactive and informed approach, especially during critical times such as election years. Businesses and individuals can navigate these challenges by adopting a commitment to ethical AI use, which includes the development and implementation of policies that emphasize accuracy and integrity. Additionally, investing in and utilizing advanced detection tools that can identify AI-generated misinformation or deepfakes is crucial. Equally important is the cultivation of GenAI literacy, ensuring that users can critically assess the information they encounter and understand its origins. This multi-pronged strategy is essential for safeguarding the informational ecosystem and maintaining public trust in digital content.

The impact of GenAI on the job market is a critical topic. What types of work do you anticipate being replaced or significantly altered by this groundbreaking technology, and how can individuals prepare for these changes?

The advent of Generative AI is set to significantly reshape the job market, introducing efficiencies that automate routine tasks, which could lead to the displacement of jobs in areas such as data entry, content creation, and customer service. Despite these disruptions, GenAI also promises the emergence of new job categories focused on AI supervision, ethical governance, and the creative industries, reflecting the technology’s dual impact on the workforce. To navigate this evolving landscape, individuals must prioritize lifelong learning and skill development, focusing on areas that AI is unlikely to replicate easily, such as creative problem-solving, emotional intelligence, and ethical decision-making. By adapting to the changes brought about by GenAI, workers can prepare for and thrive in the new job market dynamics it creates.

In your forthcoming book, you touch on how GenAI interacts with other transformative technologies. How do you foresee GenAI collaborating with gene editing, immersive internet, conventional AI, blockchain, quantum computing, etc., to create a world of hyper-innovation?

I explore the transformative potential of Generative AI (GenAI) as it intersects with groundbreaking technologies such as gene editing, the immersive internet, conventional AI, blockchain, and quantum computing, heralding a future of hyper-innovation. GenAI’s capability to produce novel content and solutions enhances gene editing for personalized medicine, enriches the immersive internet with dynamic virtual experiences, and augments conventional AI’s problem-solving abilities. In combination with blockchain, it promises more secure and efficient transaction systems, while its integration with quantum computing could revolutionize our approach to complex challenges, from material science to cryptography. This synergy across technologies suggests a paradigm shift towards a future where the acceleration of breakthroughs across fields from medicine to environmental science could vastly expand the horizons of human capability and knowledge.

Ethical concerns surrounding GenAI, including misinformation and deepfakes, are important considerations. What measures do you believe should be taken to address these concerns and ensure responsible use of Generative AI?

To effectively address the ethical concerns surrounding Generative AI, a multi-faceted approach is essential. This includes establishing transparency in AI development and deployment processes, adhering to rigorous ethical standards that are continuously updated to reflect emerging challenges, and actively engaging the public and stakeholders in discussions about AI’s societal impacts. Furthermore, the development of robust guidelines and regulatory frameworks for responsible AI use is critical, not only to mitigate risks like misinformation and deepfakes but also to foster trust and understanding among users. Such measures should aim to balance innovation with ethical considerations, ensuring GenAI serves the public good while minimizing potential harms.

Everyday activities are expected to be impacted by GenAI. Could you provide examples of how GenAI will influence tasks like searching for information, cooking, and travel in the near future?

Generative AI is poised to revolutionize everyday activities by enhancing efficiency and personalization. In the realm of information search, GenAI can provide more accurate and context-aware results, effectively understanding and anticipating user needs. For cooking, it could offer recipe customization based on dietary preferences, available ingredients, or desired cuisine, making meal planning simpler and more enjoyable. When it comes to travel, GenAI can tailor recommendations for destinations, accommodations, and activities to individual tastes and requirements, simplifying the planning process and enhancing the travel experience. These examples illustrate just a few ways GenAI will make everyday tasks more intuitive, enjoyable, and aligned with personal preferences.

In tracing the evolutionary blueprint of GenAI, from the 1950s to today, what key milestones and developments have played a significant role in shaping its current capabilities and applications?

The journey of Generative AI from its nascent stages in the 1950s to its current state has been marked by several pivotal milestones. The invention of neural networks laid the foundational architecture for AI to process information in a manner akin to the human brain. Subsequent advancements in machine learning algorithms have dramatically improved AI’s ability to learn from data, leading to more sophisticated and capable AI systems. The launch of platforms capable of generating human-like text and understanding natural language has significantly broadened GenAI’s applications, enabling it to write articles, compose music, develop code, and more. These key developments have not only advanced the capabilities of GenAI but also expanded its potential applications, setting the stage for its continued evolution and growing impact on society.

Bernard Marr

CEO and Founder of Bernard Marr & Co.

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity.

He is a multi-award-winning and internationally best-selling author of over 20 books, writes a regular column for Forbes and advises and works with many of the world’s best-known organisations.

He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world.

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