The rapid advancement of AI has revolutionized industries worldwide, transforming the way businesses operate. While some organizations are still catching up, AI is undeniably a game-changer, reshaping industries and redefining enterprise operations.

Estimates from Goldman Sachs suggest that AI has the potential to increase global GDP by approximately 7% (almost $7 trillion) over the next decade by enhancing labor productivity. Even with conservative predictions, AI is poised to drive significant progress in the global economy.

Perception Problems Around AI

The impact of AI on the workforce is both profound and complex. While there are many proven instances where AI integration has led to performance improvements and enhanced well-being for employees, concerns about job displacement still loom large. Reports citing AI-related job cuts have only bolstered that fear, however it’s imperative to remember the dual nature of technological innovation.  

While certain jobs may become redundant, new opportunities, particularly in AI and tech-related fields, are emerging. Gartner’s optimistic prediction suggests that AI could generate over half a billion jobs by 2033, emphasizing the need for a workforce skilled in AI technologies and applications.

It’s also crucial to consider how current roles might evolve to integrate AI tools alongside human workers. For instance, doctors could leverage advanced data analysis software to improve diagnostic accuracy, while IT professionals might utilize generative AI to swiftly and precisely obtain the scripts they need. In these scenarios, human involvement remains indispensable, but tasks can be completed more quickly and accurately.

Shifting Our Mindsets

For IT departments, traditionally at the forefront of technological innovation, the rise of AI signals a paradigm shift. AI is revolutionizing the IT industry by automating and optimizing workflows, increasing team output, and boosting cross-organizational efficiency. Rather than replacing the IT technician, AI has the potential to serve as the ultimate assistant by automating the manual, tedious tasks and enabling the technician to spend their time on high-value projects they wouldn’t otherwise have the time to tend to. This transition, however, necessitates not just adaptation to new tools, but also a fundamental shift in mindset towards embracing intelligent systems.

Central to this shift is the concept of closed-loop AI systems—an aspect of responsible AI—which ensures that any inputs to the system (such as data, sensitive information, etc.) are never used for outputs outside of the organization. In other words, any information given to the AI stays within the system, ensuring no information is compromised outside the organization, and the data is not used to train the AI or algorithm.

The Importance of Training and Development

Training and development also play a critical role in this AI-driven evolution. Recent data showed that 66% of American IT professionals agreed it’s harder for them to take days off than their colleagues who are not in the IT department, which has serious implications for burnout, employee retention, and overall satisfaction. This makes AI integration more important than ever before. But first, proper training is essential.

As IT professionals are beginning to leverage AI’s power, emphasis must be placed on cultivating skills in data analysis, algorithm development, and system optimization. Especially as organizations embrace closed-loop AI systems, considerations around data security, ethics, and workforce upskilling become imperative.

AI companions are becoming increasingly essential to ensure efficient IT operations. Luckily, innovative solutions are emerging with capabilities like ticket summaries, response generation, and even AI solutions based on device diagnostics and ticket history to help streamline daily tasks and empower IT professionals to focus on higher-value issues.

Integrating Closed-Loop Systems to Supercharge Your AI Integration

The evolution of AI technology and closed-loop systems is set to revolutionize enterprise operations. As businesses navigate this future, embracing these advancements responsibly will be crucial for staying competitive and efficient. AI’s ability to enhance decision-making, streamline processes, and drive innovation opens new avenues for growth and success.

By integrating closed-loop systems and prioritizing responsible AI, enterprises can create more responsive and adaptive environments, ensuring continuous improvement and agility. The future of enterprise technology is here, and those who adapt and leverage these powerful tools responsibly will undoubtedly lead the way in their industries.

Explore AITechPark for top AI, IoT, Cybersecurity advancements, And amplify your reach through guest posts and link collaboration.

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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.

Geoffrey, can you provide a brief overview of your role as the Vice President of Data Analytics at Alight and your expertise in AI and data analytics within the HR domain?

I look after Alight’s AI, personalization, and analytic capabilities from a product and data science perspective. This includes Alight’s chatbots, search engines, personalized nudges, and recommendations capabilities we provide to our clients and their employees as well as some of the AI-enabled automations we’re putting in-place to deliver high-quality ongoing service.  

We’re continuously enhancing our capabilities. For example, We recently unveiled Alight LumenAI, our next-generation AI engine to power Alight Worklife ®, Alight’s employee experience platform. 

We observed three consistent imperatives for creating leap-frog HR AI capabilities: 

1. Adoption of Generative AI (Gen-AI) 

2. Tying together AI capabilities with one unified view of an employee

3. The use of ever-growing internal and external datasets to improve model performance.  

That’s why we launched Alight LumenAI – to ensure we could continue bringing market-leading AI capabilities to our HR clients.

I’ve been passionate about AI and data-enabled SaaS products for a long time, regardless of sector, and that’s reflected in my prior roles building AI-powered experiences in cybersecurity at SecurityScorecard, finance at Bloomberg, or consumer goods at Arena AI.

I joined Alight because applying AI and data science to the Human Resources (HR) space is a chance to deploy AI “for good” – ensuring people are enrolling in the right benefits, preparing appropriately for retirement, having a seamless employee experience, and generally maximizing the wellbeing opportunities offered by their employers.

Right now is an especially exciting time to be at Alight: our clients are being pushed by their CEOs to demonstrate transformational AI strategies within HR and the AI capabilities Alight offers fit very well into these strategies and can deliver  transformational wins.

AI and employee experience:

How do you envision generative AI and AI-powered platforms shaping the future of the employee experience in the workplace?

There’s a baseline level of transformation happening everywhere, where most of the tools we use to do our work are getting generative AI upgrades. 

Taking a step back, we broadly see AI fitting into 5 categories within the HR domain – and these mirror the capabilities that we bring to market for our clients:

• AI Personalization: capabilities that drive a greater than 10% increase in targeted client HR outcomes through personalized, “next best action” content
• AI Assistance: capabilities with natural language/intent models to maximize digital engagement, supporting a 90% self-service rate
• AI Recommendation: capabilities providing automated decision support and choice optimization for benefits and care, saving employees on average $500 in premium expenses annually
• AI Insights: capabilities with data trend analysis for high-precision employer analytics to identify hotspots in the employee experience and HR processes
• AI Automation: capabilities that streamline repetitive workflows, such as document processing or at-scale call monitoring

Whereas in the past, an HR team might adopt a few of the above capabilities, we’re seeing that teams succeeding with AI transformation are adopting tech across all 5 of those categories.

Can you give examples of how AI-driven innovations have already improved the employee experience in organisations?

 We’ve been working with clients to deploy AI for years, even before the surge in generative AI interest that has taken place over the last year.  A great example we’ve seen is through our Interactive Virtual Assistant (IVA), or chatbot, that has helped employees answer their benefits questions in a personalized and self-service way and helps drive a 90%+ digital interaction rate (so folks aren’t needing to get in touch with a call center).  

When we launched IVA about 5 years ago, its initial performance was “ok” – but in the intervening years we’ve spent millions of dollars on teams tuning the algorithm based on the results of performance across 30M interactions with employees – so that now our IVA offers market-leading performance. 

It’s an important lesson to remember: AI systems often require ongoing maintenance and investment by professionals to achieve differentiated performance.  Having a “human in the loop” is incredibly important.

Our AI-powered IVA continues to see increased engagement from employees and was recently enhanced to also execute transactions – for example, by allowing employees to re-enroll in their health coverage plans during annual enrollment.  

We’re also excited to be piloting a GenAI-enhanced version of our IVA, powered by Alight LumenAI, that provides more holistic and helpful answers to questions where information was locked-up in complicated policy or benefits documents.  The results have been pretty spectacular – one of our clients when they used it for the first time said, “this is amazing, can we just roll this out now!”

Efficiency and productivity:

In what specific ways can AI enhance efficiency and productivity for both employees and employers in today’s evolving work environment?

In the HR vertical, efficiency is often about trying to reduce call, ticket and email volume for HR teams so that work shifts away from repetitive administrative employee needs and towards more consultative high-value activities.  

Anything AI can do to reduce the volume of administrative calls and tickets is immensely helpful.  AI can help HR teams diagnose, at scale, what is driving the high call and ticket volumes to shorten what are often very long process-improvement cycles…and it can also help create more effective interception-points to help individuals self-service their needs.  

For example, imagine an employee with an HR need logging into their internal HR portal, and then using an IVA chatbot to try to answer their question, and then using a voice-based Interactive Virtual Response (IVR) call-routing system when they call the call center. That’s three interception points where AI has an opportunity to help an employee self-service for a better, faster experience before they get to an agent.

Intelligent Document Processing is another great example of how we partner with clients to improve experience and reduce cost.  Many HR processes still depend on employees submitting documents (deposit checks, birth certifications, etc) and so when we deploy intelligent document processing we reduce the time it takes to process documents and provide feedback to users from 10+ minutes to near-instant.  Not only is this fast feedback loop a better experience for employees, it also tends to reduce calls to the call centers from employees asking about document status.

Personalization:

How can AI enable more personalised experiences for employees, and why is personalization important for overall employee satisfaction and engagement?

Personalization is a pretty broad term and can encompass many things. It can start as basic as knowing what benefits someone is eligible for and only showing them information about those and scale all the way to using AI to nudge or prompt employees according to a next-best action framework.  

Without a baseline of personalization in place, employees can quickly become disengaged by an experience that feels irrelevant to them. Once that baseline is there, you can start to play with personalization that drives outcomes. We partner often with clients on personalized communication campaigns that drive outcomes such as increased 401k contribution, HSA contribution, or increased utilization of specific programs like healthcare navigation.

For example, in March 2024, a pharmaceutical client selected Alight to help improve the financial wellbeing of their workforce through personalized messaging that encouraged the adoption of changed saving behaviors.  With only 75% of employees participating in a Health Savings Account (HSA) and a majority saving below the maximum allowed amount, the company aimed to encourage greater participation in retirement and health savings plans and ensure that employees were taking advantage of the company match to both the 401(k) and HSA.

With a focus on employees who had not yet maximized the value of tax-advantaged accounts, the company partnered with Alight to leverage personalized email and web messaging that would influence saving behaviors. This personalized messaging was made possible with adaptive, “Always On” AI technology that dynamically adjusted engagement strategies to drive up retirement and health savings contributions over time. Upon partnering with the client, Alight took strategic steps to ensure seamless integration and successful implementation of the AI-driven program. 

Key initiatives included:

• Assessment: The Alight team conducted a comprehensive needs assessment to understand the specific challenges and goals of the client in-depth.
• Data analysis: Extensive analysis of existing data, including employee participation rates, savings patterns and financial behaviors, were undertaken to inform the AI-driven personalization strategy.
• Integration planning: Alight collaborated closely with the client to develop an integration plan, identifying areas for personalized content implementation within existing communication channels.
• Customization framework: A tailored framework for content personalization was established that considered the unique characteristics of the client’s workforce and desired outcomes.
• Pilot programs: Small-scale pilot programs were initiated to test the effectiveness of the AI-driven approach, allowing for adjustments and refinements before full-scale implementation.
• Continuous monitoring: The Alight team implemented continuous monitoring and feedback mechanisms to track the success of the AI-driven program and ensure ongoing adaptability.

Post-implementation, Alight conducted thorough assessments of the system’s impact on both 401(k) and HSA participation, and success was substantiated by the substantial increase in employee contributions to both. Additionally, tax savings projections were delivered to show the true value of these funds.  Planning, testing and effective execution of the new AI-driven messaging system took less than six months.

As a result, the pharmaceutical company realized a substantial 17% increase in employees starting or increasing their 401(k) savings. Achieved a commendable 6% increase in employees starting or increasing contributions to the HSA. Notably improved the average 401(k) contribution rate, showcasing an impressive increase of 5.4%, and demonstrated tangible financial impact with an average increase of $1,750 in employee HSA contributions.

Measuring value:

What strategies can companies employ to effectively measure the value derived from their investments in employee experience and well-being initiatives, using data-driven insights?

Most importantly, companies need to know the outcome they’re trying to achieve upfront, and they need to be measuring that on an ongoing basis.  Once that’s in place, there are varying levels of sophistication clients can deploy to measure and attribute changes in the planned outcome to the interactions they are executing.  

The gold-standard for these is treatment vs. control groups, but even basic attribution can give a basic measure of success.  In many cases, if there is a specific action an employer is trying to drive, they can track who took that action after experiencing a personalized nudge, and attribute these to the personalized nudge. Examples of impact we’ve seen using this basic measurement methodology include:

• Nudges delivered over 6 months to direct employees to financial coaches resulted in a 7% increase in enrollment in HDHP health plans
• Nudges delivered over 6 months to encourage employees to contribute more to their HSA campaigns resulted in a 33% conversion rate from messaging to action, and the increase in HSA contributions yielded ~$1M in FICA tax savings for the employer 

Data utilisation:

Could you elaborate on how organisations can responsibly utilise employee data to enhance the employee experience while maintaining data privacy and security?

Sure – organizations need to think both about overall data security as well as ensuring appropriate use of data specific to each experience use-case.  In general, the less places you send and store your employee data, the better and the less opportunities there are for data breach or inappropriate use.  When it comes to appropriate use of data, using it to enhance the employee experience should be governed by standard data risk management and security review processes.

Alight’s clients include government entities and defense contractors, so we’ve already been operating in a very robust data and cybersecurity framework.  Last year we formalized our approach to AI risk and now assess all use-cases of AI technology against an 8-part risk framework that looks at things like data risk, bad output risk, bias risk, etc.

Challenges of implementing AI:

What are the common challenges that organisations face when implementing AI-powered solutions for employee experience, and how can they mitigate these challenges?

We like to use an “AI Intrapreneur” framework that lays out five important considerations for any new AI use case and recommend careful consideration –if you’re thoughtful about these five factors you will successfully launch an AI use-case:

• Pick the right areas – Focus on problems AI can solve now, not speculative future capabilities. Validate with small, low-risk pilots.
• Resource wisely – Build in-house for differentiated capabilities, use vendors for commoditized capabilities.
• Avoid high-risk AI uses – AI will make mistakes: don’t use AI where those mistakes have severe consequences.
• Keep humans in the loop – Humans must oversee AI systems. Design AI use cases for human oversight.
• Measure extensively – Rigorously measure performance, error rates, biases and business impact. Establish feedback loops.

We took the above approach in our current Gen-AI IVA pilot – testing with a small number of users at a small set of clients, building some of the technology ourselves so that we could be differentiated in the market, and being very thoughtful about how we keep humans in the loop to ensure accurate answers to employee’s HR-related questions.

Ethical considerations:

Are there ethical considerations organisations should be aware of when integrating AI into employee experience initiatives, and how can they ensure ethical AI practices?

The most important ethical consideration – which we touched on in the above – is understanding what the consequence is of a bad model output and its consequence on a person.

Leadership and management changes:

With the integration of AI, how do you foresee the role of leadership and management evolving in HR and employee experience, and what challenges might this transformation present?

The biggest shift is likely to be that whereas before managers might be managing the quality of output of their team, they will now spend an increasing amount of time managing the quality of an algorithm’s output.  No AI system is perfect, and they all require some amount of human oversight.

Final thoughts:

As AI technologies evolve rapidly, what advice would you offer HR and business leaders to stay informed and leverage the latest AI innovations effectively for employee experiences?

Read and absorb as much as possible and stay curious!  Don’t expect to stay fully up to date – even AI researchers are getting surprised these days by sudden developments in the field.

More generally, be aware of your organization’s overall risk appetite and be comfortable with it – some organizations want to be on the leading edge, others may want to take a more conservative approach – both are OK.

Geoffrey Peterson

Vice President of Data & Analytics at Alight

Geoffrey Peterson is the Vice President of Data and Analytics at Alight Solutions, a role he’s held since 2023. Before joining Alight, he was Global Head of Product Management and Data Governance at Bloomberg and a Senior Product Manager at Security Scorecard. Earlier in his career, he was a Business Analyst and Associate at McKinsey & Company before moving into management roles at South African Breweries Limited. Peterson earned a BA in Computer Science and Economics from Harvard University and an ME in Computer Science from Cornell University.

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IT companies and consulting firms are on a relentless quest to stay innovative in a rapidly evolving digital world. Industries worldwide are embracing the digital landscape, using AI to help transform their operations and adapt to new challenges.

Digital transformation integrates digital technologies into all operational areas, streamlining processes, enhancing customer interactions, fostering a forward-thinking work culture, and improving overall strategic planning. By embracing digital transformation, companies have the potential to save money while maximizing efficiency.

A Grim Reality: Economic Challenges and Layoffs

In response to economic challenges, including significant layoffs in the tech sector, companies must innovate and adapt swiftly. Digital transformation, especially through AI, provides a lifeline.

In 2023, 1,186 tech companies laid off 262,682 employees and this year alone, 168 tech companies have laid off 42,324 employees. Major consulting firms are also at risk. This is forcing them to stay ahead of the curve and innovate before it is too late.

Why Digital Transformation Matters

Digital transformation, especially when incorporating AI, can be a strategic solution for the challenges in the tech sphere. Imagine this: a mid-sized IT company experiencing fast-declining revenues and an increase in operational costs integrates AI into its workflow. AI acts as a catalyst to streamline processes and reduce manual errors while freeing up time for employees to focus on more strategic tasks. This results in increased productivity, efficiency and profitability. This is what companies need to stay ahead of competition.

By 2027, AI tools are expected to be used for digital transformation to cut process costs in half and reduce modernization expenses by 70%.

But despite its potential, digital transformation is much more difficult for companies to adopt than it seems. Only 35% of businesses have successfully adopted digital transformation efforts which highlights a pressing issue: many organizations are not fully prepared to embrace digital change and integration.

The Challenges: Skills Gap and Readiness

According to recent surveys, 54% of IT professionals believe their organizations lack the necessary IT skills or transformation expertise to properly adopt digital processes. Further, 56% of IT leaders do not feel prepared to handle digital transformation disruptions, and more than 70% of IT experts do not see their company’s IT attributes as supportive of rapid generative AI adoption.

Without a skilled workforce that’s well-versed in AI and other digital technologies, organizations will naturally struggle to keep up with the constant changes digital transformation requires.

The Benefits: Reduced Costs and Efficiency

While many IT companies don’t feel readily prepared for digital transformation, it doesn’t take away its benefits. AI-driven technology has the potential to cut company costs and improve efficiency. It does this by automating routine tasks, analyzing large volumes of data to inform better decision-making, and optimizing resource allocation. 69% of IT decision-makers believe that digital transformation can boost process efficiency. Additionally, 39% of organizations are expanding their utilization of AI – meaning more companies are recognizing the value of AI to help them reach their goals. Simply put, digital transformation through AI can ultimately help save a company’s future.

Strategies for Successful Digital Transformation

While adopting new technologies can seem daunting, companies who open their doors to the world of digital transformation will find new opportunities. To overcome the challenges and reap its benefits, organizations can consider adopting several key strategies:

1. Invest in Strong Data Systems: Reliable data systems are essential for supporting AI initiatives. Investing in scalable and flexible data solutions helps companies effectively manage and use data.
2. Build a Skilled Workforce: Companies have to invest in ongoing training and learning programs to equip their teams with the skills needed for AI and digital technologies.
3. Focus on Talent Retention: Keeping skilled employees on the team is extremely important. They are needed to train new hires effectively. Creating a supportive work environment helps with this.
4. Create Internal Task Force: Companies can also create task forces centered around AI where teams can work together to experiment and develop new ideas quickly. This approach encourages a flexible and creative work culture to help companies innovate faster and stay competitive.
5. Use AI to Increase Flexibility: Embracing AI can make organizations more adaptable and responsive, which is key to handling the complexities of digital transformation.

The Role of Digital Transformation Firms

Companies that want to make the most of AI and digital transformation can also consult digital transformation firms. These firms specialize in digital and tech transformations and act as catalysts for organizational change – offering expertise in AI services, data engineering, and cloud operations. Partnering with these specialists allows organizations to  accelerate their digital transformation journey and stay ahead of the competition.

Digital transformation is the future of business. By embracing it now, companies can turn challenges into growth opportunities and thrive in the evolving digital landscape. IT companies looking to protect and evolve their operations can rely on this approach to ultimately tackle economic challenges and layoffs. By investing in skill building, promoting innovation and planning accordingly, organizations can turn the challenges they face to opportunities of growth. While adopting digital transformation strategies may be difficult now, it is the future of business. Companies who embrace it can thrive in the evolving digital landscape.

About BlueCloud    

BlueCloud is not just another entity in the cloud computing space; it stands as a trailblazer in the digital transformation revolution. Positioned as architects of the future, BlueCloud leads the way for enterprises seeking to thrive in the digital age with its bold vision and unwavering commitment to innovation. The company’s comprehensive portfolio, encompassing avant-garde AI services, data engineering solutions, and transformative digital strategies, has propelled businesses into a new era, resulting in a staggering 185% year-over-year revenue growth and securing a valuation surpassing $100 million, thanks to its partnership with Hudson Hill Capital. By serving titans of various industries and forging collaborations with technology behemoths like Snowflake and ThoughtSpot, BlueCloud has demonstrated its prowess in navigating the intricacies of the digital domain. More than merely transforming businesses, BlueCloud is on a mission to reshape the digital landscape itself, one innovative cloud solution at a time. Visit www.blue.cloud.    

The post AI Answers Urgent Call for Digital Transformation 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.

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With data-driven decision-making now the best competitive advantage a company can have, business leaders will increasingly demand to get the information they need at a faster, more consumable clip. Because of this, we’ll continue to see calls for AI to become a business-consumer-friendly product rather than one that only technically savvy data scientists and engineers can wield. It’s this vision for the future that’s driving the five trends in AI-driven analysis that we see right now:

Trend #1:  Users demand an explainable approach to data analysis

As AI technology advances, understanding the processes behind its results can be challenging. This “black box” nature can lead to distrust and hinder AI adoption among non-technical business users. However, explainable AI (XAI) aims to democratize the use of AI tools and make it more accessible to business users. 

XAI generates explanations for its analysis and leverages conversational language, coupled with compelling visualizations, so non-data experts can easily interpret its meaning. XAI will be crucial in the future of AI-driven data analysis by bridging the gap between the complex nature of advanced models and the human need for clear, understandable, and trustworthy outcomes. 

Trend #2: Multimodal AI emerges

Multimodal AI is the ultimate tool for effective storytelling in today’s data-driven world. While Generative AI focuses on creating new content, Multimodal AI can be seen as an advanced extension of Generative AI with its ability to understand and tie together information coming from different media simultaneously. For example, a multimodal generative model could process text to create a story and enhance it with pertinent images and sounds.

As data sets become more complex and robust, it’s become difficult to comprehensively analyze that data using traditional methods. Multimodal AI gives analytics teams the ability to consume and analyze heterogeneous input so they can uncover critical information that leads to better strategic decision-making. 

Trend #3:  Enterprise AI gets personalized

Generative AI excels in creating tailored solutions that fit the unique needs of enterprises. This could be training a retail chatbot on region-specific cultural nuances to better serve customers in that area or developing an AI routine for handling sensitive tasks, such as managing confidential information.  Moreover, Generative AI can analyze your customer base to identify communities and trends, enabling targeted marketing strategies and specialized customer service programs. 

Trend #4: Data science investments will rise

Whether companies are looking to create their own personalized AI models in-house or purchase new technologies to help them scale automation, we’ll see a rise in data science investments. Tied to this is the role of data scientists becoming more focused on building and managing the implementation of these systems. 

As the need for AI becomes more ubiquitous, there will also be an increased demand for AI platforms that enable data scientists to build and deploy AI-powered applications in an environment familiar to them. These applications will facilitate critical decision-making. These apps must be designed to be easily deployed company-wide while also being actionable decision-making tools for non-technical business leaders. 

Trend #5: The business analyst role evolves 

As the data scientist’s role changes, business analysts will add more value to the enterprise data strategy and provide answers in the context of the corporate vision. The same AI apps that make data more accessible to business leaders will empower analysts to extract meaningful patterns from vast and disparate datasets, enabling them to predict market trends, customer behavior, and potential risks. 

By combining their business acumen and technical skills with AI, business analysts will be at the forefront of transforming how organizations translate data into actionable, strategic plans. 

Always trending: AI ethics and safety

Across all AI-driven analytics trends, it is crucial to emphasize AI safety and ethical practices as fundamental aspects in all areas of the business. For instance, Ethical AI is essential to help ensure that AI technologies are beneficial, fair, and safe to use. That is because AI models can inadvertently perpetuate biases present in the training data. As AI becomes increasingly personalized, incorporating a wider variety of data inputs and innovations, it is crucial that responsible AI governance and training are implemented across all levels of the organization. When everyone understands both the advantages and limits of AI, the future truly becomes brighter for all. 

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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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Whether you’re choosing a restaurant or deciding where to live, data lets you make better decisions in your everyday life. If you want to buy a new TV, for example, you might spend hours looking up ratings, reading expert reviews, scouring blogs and social media, researching the warranties and return policies of different stores and brands, and learning about different types of technologies. Ultimately, the decision you make is a reflection of the data you have. And if you don’t have the data—or if your data is bad—you probably won’t make the best possible choice.

In the workplace, a lack of quality data can lead to disastrous results. The darker side of AI is filled with bias, hallucinations, and untrustworthy results—often driven by poor-quality data.

The reality is that data fuels AI, so if we want to improve AI, we need to start with data. AI doesn’t have emotion. It takes whatever data you feed it and uses it to provide results. One recent Enterprise Strategy Group research report noted, “Data is food for AI, and what’s true for humans is also true for AI: You are what you eat. Or, in this case, the better the data, the better the AI.”

But AI doesn’t know if its models are fed good or bad data— which is why it’s crucial to focus on improving the data quality to get the best results from AI for IT use cases.

Quality is the leading challenge identified by business stakeholders

When asked about the obstacles their organization has faced while implementing AI, 31% of business stakeholders involved with AI infrastructure purchases had a clear #1 answer: the lack of quality data. In fact, data quality ranked as a higher concern than costs, data privacy, and other challenges.

Why does data quality matter so much? Consider OpenAI’s GPT 4, which scored in the 92nd percentile and above on three medical exams, which failed two of the three tests. GPT 4 is trained on larger and more recent datasets, which makes a substantial difference.

An AI fueled by poor-quality data isn’t accurate or trustworthy. Garbage in, garbage out, as the saying goes. And if you can’t trust your AI, how can you expect your IT team to use it to complement and simplify their efforts?

The many downsides of using poor-quality data to train IT-related AI models

As you dig deeper into the trust issue, it’s important to understand that many employees are inherently wary of AI, as with any new technology. In this case, however, the reluctance is often justified.

Anyone who spends five minutes playing around with a generative AI tool (and asking it to explain its answers) will likely see that hallucinations and bias in AI are commonplace. This is one reason why the top challenges of implementing AI include difficulty validating results and employee hesitancy to trust recommendations.

While price isn’t typically the primary concern regarding data, there is still a significant price cost to training and fine-tuning AI on poor-quality data. The computational resources needed for modern AI aren’t cheap, as any CIO will tell you. If you’re using valuable server time to crunch low-quality data, you’re wasting your budget on building an untrustworthy AI. So starting with well-structured data is imperative.

Four facets of high-quality, trustworthy data for IT use cases

To understand why the quality of data matters, let’s look at AI in IT—an area that has value for nearly every industry. New AI models for IT can reduce the number of help tickets, dramatically lower the time needed to resolve problems and help you make better decisions by proactively highlighting potential issues before purchasing new software. In a field where a mistake can cost your organization millions of dollars at scale, a good AI solution is worth its weight in gold. But how do you ensure that it’s using good data?

The first thing to consider is the breadth of data. More data across more sources typically makes an AI more trustworthy, as long as you’re collecting good data. Think of it this way: a single restaurant review can offer a glimpse into its quality, but a restaurant with numerous reviews provides a more accurate assessment, allowing you to make a more informed decision. Was the one negative issue an outlier? Or is there a pattern that should be identified and evaluated?  Similarly, an AI trained for IT on 10,000 data points collected every 15 seconds from endpoints will be more useful than an AI trained on 800 data points every 15 minutes.

Next, focus on data depth. The amount of data a model has from IT endpoints can make a significant difference. In one instance, a company had 3,000 systems crash after a software patch didn’t play nice within the existing setup. The IT team quickly resolved the issue using a patented AI that identifies correlations between their system changes and device anomalies. This process was possible because the AI had been trained on their unique datasets, including historical data.

As AI trained for IT collects data, it’s crucial that the data is well-structured and as clean as possible. Most data sets will invariably have some noise—data that’s meaningless, irrelevant, or (in some cases) even corrupt, but training AI on high-quality, well-structured label makes all the difference. 

Finally, don’t forget about your people. AI is simply a tool. Change management and the impact of AI on humans are invaluable considerations when making decisions about introducing AI capabilities and use cases and (perhaps most important) evaluating if the AI you’re using for IT is delivering the most useful results for your organization. As AI continues to transform nearly every industry, think of data as the ingredients in the best AI recipe. If the ingredients are bland, the power and nuance of AI is lost. AI that’s fed robust, rich data, however, delivers on all the promises and opportunities of well-trained models. From there, the results will follow.

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Background:

Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

Code optimization in AI is all about maximizing results while minimizing inefficiencies and operational costs. It’s a key factor in driving the success and sustainability of AI initiatives in the dynamic and competitive landscape of modern businesses.

Code Optimization:

What are some common challenges and issues businesses face with code optimization when implementing AI solutions?

Businesses implementing AI solutions often encounter several challenges with code optimization, mainly due to the dynamic and complex nature of AI systems compared to traditional software optimization. Achieving optimal AI performance requires a delicate balance between code, model, and data, making the process intricate and multifaceted. This complexity is compounded by the need for continuous adaptation of AI systems, as they require constant updating to stay relevant and effective in changing environments.

A significant challenge is the scarcity of skilled performance engineers, who are both rare and expensive. In cities like London, costs can reach up to £500k per year, making expertise a luxury for many smaller companies.

Furthermore, the optimization process is time- and effort-intensive, particularly in large codebases. It involves an iterative cycle of fine-tuning and analysis, demanding considerable time even for experienced engineers. Large codebases amplify this challenge, requiring significant manpower and extended time frames for new teams to contribute effectively.

These challenges highlight the necessity for better tools to make code optimization more accessible and manageable for a wider range of businesses.

Could you share some examples of the tangible benefits businesses can achieve through effective code optimization in AI applications?

AI applications are subject to change along three axes: model, code, and data. At TurinTech, our evoML platform enables users to generate and optimize efficient ML code. Meanwhile, our GenAI-powered code optimization platform, Artemis AI, can optimize more generic application code. Together, these two products help businesses significantly enhance cost-efficiency in AI applications.

At the model level, different frameworks or libraries can be used to improve model efficiency without sacrificing accuracy. However, transitioning an ML model to a different format is complex and typically requires manual conversion by developers who are experts in these frameworks.

At TurinTech AI, we provide advanced functionalities for converting existing ML models into more efficient frameworks or libraries, resulting in substantial cost savings when deploying AI pipelines.

One of our competitive advantages is our ability to optimize both the model code and the application code. Inefficient code execution, which consumes excess memory, energy, and time, can be a hidden cost in deploying AI systems. Code optimization, often overlooked, is crucial for creating high-quality, efficient codebases. Our automated code optimization features can identify and optimize the most resource-intensive lines of code, thereby reducing the costs of executing AI applications.

Our research at TurinTech has shown that code optimization can improve the execution time of specific ML codebases by up to around 20%. When this optimized code was tested in an Azure-based cloud environment, we observed cost savings of about 30% per hour for the virtual machine size used. This highlights the significant impact of optimizing both the model and code levels in AI applications.

Are there any best practices or strategies that you recommend for businesses to improve their code optimization processes in AI development?

Code optimization leads to more efficient, greener, and cost-effective AI. Without proper optimization, AI can become expensive and challenging to scale.

Before embarking on code optimization, it’s crucial to align the process with your business objectives. This alignment involves translating your main goals into tangible performance metrics, such as reduced inference time and lower carbon emissions.

Empowering AI developers with advanced tools can automate and streamline the code optimization process, transforming what can be a lengthy and complex task into a more manageable one. This enables developers to focus on more innovative tasks.

In AI development, staying updated with AI technologies and trends is crucial, particularly by adopting a modular tech stack. This approach not only ensures efficient code optimization but also prepares AI systems for future technological advancements.

Finally, adopting eco-friendly optimization practices is more than a cost-saving measure; it’s a commitment to sustainability. Efficient code not only reduces operational costs but also lessens the environmental impact. By focusing on greener AI, businesses can contribute to a more sustainable future while reaping the benefits of efficient code.

Generative AI and Its Impact:

Generative AI has been a hot topic in the industry. Could you explain what generative AI is and how it’s affecting businesses and technology development?

Generative AI, a branch of artificial intelligence, excels in creating new content, such as text, images, code, video, and music, by learning from existing datasets and recognizing patterns.

Its swift adoption is ushering in a transformative era for businesses and technology development. McKinsey’s research underscores the significant economic potential of Generative AI, estimating it could contribute up to $4.4 trillion annually to the global economy, primarily through productivity enhancements.

This impact is particularly pronounced in sectors like banking, technology, retail, and healthcare. The high-tech and banking sectors, in particular, stand to benefit significantly. Generative AI is poised to accelerate software development, revolutionizing these industries with increased efficiency and innovative capabilities. We have observed strong interest from these two sectors in leveraging our code optimization technology to develop high-performance applications, reduce costs, and cut carbon emissions.

Are there any notable applications of generative AI that you find particularly promising or revolutionary for businesses?

Generative AI presents significant opportunities for businesses across various domains, notably in marketing, sales, software engineering, and research and development. According to McKinsey, these areas account for approximately 75% of generative AI’s total annual value.

One of the standout areas of generative AI application is in data-driven decision-making, particularly through the use of Large Language Models (LLMs). LLMs excel in analyzing a wide array of data sources and streamlining regulatory tasks via advanced document analysis. Their ability to process and extract insights from unstructured text data is particularly valuable. In the financial sector, for instance, LLMs enable companies to tap into previously underutilized data sources like news reports, social media content, and publications, opening new avenues for data analysis and insight generation.

The impact of generative AI is also profoundly felt in software engineering, a critical field across all industries. The potential for productivity improvements here is especially notable in sectors like finance and high-tech. An interesting trend in 2023 is the growing adoption of AI coding tools by traditionally conservative buyers in software, such as major banks including Citibank, JPMorgan Chase, and Goldman Sachs. This shift indicates a broader acceptance and integration of AI tools in areas where they can bring about substantial efficiency and innovation.

How can businesses harness the potential of generative AI while addressing potential ethical concerns and biases?

The principles of ethical practice and safety should be at the heart of implementing and using generative AI. Our core ethos is the belief that AI must be secure, reliable, and efficient. This means ensuring that our products, including evoML and Artemis AI, which utilize generative AI, are carefully crafted, maintained, and tested to confirm that they perform as intended.

There is a pressing need for AI systems to be free of bias, including biases present in the real world. Therefore, businesses must ensure their generative AI algorithms are optimized not only for performance but also for fairness and impartiality. Code optimization plays a crucial role in identifying and mitigating biases that might be inherent in the training data and reduces the likelihood of these biases being perpetuated in the AI’s outputs.

More broadly, businesses should adopt AI governance processes that include the continuous assessment of development methods and data and provide rigorous bias mitigation frameworks. They should scrutinize development decisions and document them in detail to ensure rigor and clarity in the decision-making process. This approach enables accountability and answerability.

Finally, this approach should be complemented by transparency and explainability. At TurinTech, for example, we ensure our decisions are transparent company-wide and also provide our users with the source code of the models developed using our platform. This empowers users and everyone involved to confidently use generative AI tools.

The Need for Sustainable AI:

Sustainable AI is becoming increasingly important. What are the environmental and ethical implications of AI development, and why is sustainability crucial in this context?

More than 1.3 million UK businesses are expected to use AI by 2040, and AI itself has a high carbon footprint. A University of Massachusetts Amherst study estimates that training a single Natural Language Processing (NLP) model can generate close to 300,000 kg of carbon emissions.

According to an MIT Technology Review article, this amount is “nearly five times the lifetime emissions of the average American car (and that includes the manufacture of the car itself).” With more companies deploying AI at scale, and in the context of the ongoing energy crisis, the energy efficiency and environmental impact of AI are becoming more crucial than ever before.

Some companies are starting to optimize their existing AI and code repositories using AI-powered code optimization techniques to address energy use and carbon emission concerns before deploying a machine learning model. However, most regional government policies have yet to significantly address the profound environmental impact of AI. Governments around the world need to emphasize the need for sustainable AI practices before it causes further harm to our environment.

Can you share some insights into how businesses can achieve sustainable AI development without compromising on performance and innovation?

Sustainable AI development, where businesses maintain high performance and innovation while minimizing environmental impact, presents a multifaceted challenge. To achieve this balance, businesses can adopt several strategies.

Firstly, AI efficiency is key. By optimizing AI algorithms and code, businesses can reduce the computational power and energy required for AI operations. This not only cuts down on energy consumption and associated carbon emissions but also ensures that AI systems remain high-performing and cost-effective.

In terms of data management, employing strategies like data minimization and efficient data processing can help reduce the environmental impact. By using only the data necessary for specific AI tasks, companies can lower their storage and processing requirements.

Lastly, collaboration and knowledge sharing in the field of sustainable AI can spur innovation and performance. Businesses can find novel ways to develop AI sustainably without compromising on performance or innovation by working together, sharing best practices, and learning from each other.

What are some best practices or frameworks that you recommend for businesses aiming to integrate sustainable AI practices into their strategies?

Creating and adopting energy-efficient AI models is particularly necessary for data centers. While this is often overlooked by data centers, using code optimization means that traditional, energy-intensive software and data processing tasks will consume significantly less power.

I would then recommend using frameworks such as a carbon footprint assessment to monitor current output and implement plans for reducing these levels. Finally, overseeing the lifecycle management of AI systems is crucial, from collecting data and creating models to scaling AI throughout the business.

Final Thoughts:

In your opinion, what key takeaways should business leaders keep in mind when considering the optimization of AI code and the future of AI in their organizations?

When considering the optimization of AI code and its future role in their organizations, business leaders should focus on several key aspects. Firstly, efficient and optimized AI code leads to better performance and effectiveness in AI systems, enhancing overall business operations and decision-making.

Cost-effectiveness is another crucial factor, as optimized code can significantly reduce the need for computational resources. This lowers operational costs, which becomes increasingly important as AI models grow in complexity and data requirements. Moreover, future-proofing an organization’s AI capabilities is essential in the rapidly evolving AI landscape, with code optimization ensuring that AI systems remain efficient and up-to-date.

With increasing regulatory scrutiny on AI practices, optimized code can help ensure compliance with evolving regulations, especially in meeting ESG (Environmental, Social, and Governance) compliance goals. It is a strategic imperative for business leaders, encompassing performance, cost, ethical practices, scalability, sustainability, future-readiness, and regulatory compliance.

As we conclude this interview, could you provide a glimpse into what excites you the most about the intersection of code optimization, AI, and sustainability in business and technology?

Definitely. I’m excited about sustainable innovation, particularly leveraging AI to optimize AI and code. This approach can really accelerate innovation with minimal environmental impact, tackling complex challenges sustainably. Generative AI, especially, can be resource-intensive, leading to a higher carbon footprint. Through code optimization, businesses can make their AI systems more energy-efficient.

Secondly, there’s the aspect of cost-efficient AI. Improved code efficiency and AI processes can lead to significant cost savings, encouraging wider adoption across diverse industries. Furthermore, optimized code runs more efficiently, resulting in faster processing times and more accurate results.

Do you have any final recommendations or advice for businesses looking to leverage AI optimally while remaining ethically and environmentally conscious?

I would say the key aspect to embody is continuous learning and adaptation. It’s vital to stay informed about the latest developments in AI and sustainability. Additionally, fostering a culture of continuous learning and adaptation helps integrate new ethical and environmental standards as they evolve.

Leslie Kanthan

Chief  Executive Officer and Founder at TurinTech AI

Dr Leslie Kanthan is CEO and co-founder of TurinTech, a leading AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle. Before TurinTech, Leslie worked for financial institutions and was frustrated by the manual machine learning developing process and manual code optimising process. He and the team therefore built an end-to-end optimisation platform – EvoML – for building and scaling AI.

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Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. Lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. In the same way, many analytical positions will become redundant as smart chatbots assume more daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. The fast-growing demand for data analysis will remain immune to economic pressures, and those kinds of job positions will be less susceptible to budget cuts.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

AI Marketing Trends Impact Vertical Industry Groups

In addition to affecting job roles, the AI revolution is expected to supercharge marketing functions across nearly every type of industry. Two obvious examples include the retail and healthcare sectors. The retail industry has been quick to integrate AI to deliver efficiencies and increase sales. One emerging innovation is to combine neural networks with a shopper and a product to create new retail marketing experiences. For example, starting in 2024, you can expect an AI assistant to showcase an item of clothing on a model with similar dimensions to see exactly how it will look in various poses. Most industry watchers believe that such immersive, highly personalized virtual experiences will be the future of retail.

AI is also creating a radical new reality for the healthcare industry. For instance, digital twins are becoming increasingly ubiquitous for researchers, physicians, and therapists. A digital twin is a virtual model that accurately replicates a physical object or system. In this way, users can simulate physical processes through digital twins to test various outcomes without involving actual products or people, which greatly reduces operational costs and risks to public safety. For example, AI-powered digital twins could usher in new ways of marketing healthcare services for an aging population, by allowing people to live independently for longer. Or such twins might be used for future drug development projects.

AI will also play a pivotal role in the early diagnosis of potential health issues. For example, full-body MRIs will tap into the ability of AI to identify, analyze, and predict data patterns to help diagnose diseases long before any symptoms are visible to the human eye. In addition, AI will take a more prominent role in assisting medical staff to understand and interpret findings and provide treatments and care recommendations. All of these AI benefits will help sales and marketing teams to craft new messages that can communicate such considerable advantages to consumers.

Artificial intelligence engines have already upended marketing practices based on their extraordinary capacity for data analysis and efficiency, and this growth trend is only expected to continue in the coming years. To keep up with these technical developments, marketing professionals should become more comfortable using the AI tools which are rapidly remaking the entire marketing landscape.

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