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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How will AI impact our data governance and privacy policies? 

What are the societal impacts of our AI deployments? 

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

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

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

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

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

Joel Rennich

VP of Product Strategy at JumpCloud 

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

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HWArobotics combines innovative robotics with proven engineering to create high-performance logistics automation. By leveraging swarm intelligence, multi-dimensional movements, and all-electric technologies in its shuttle vehicles, the company has once again raised the bar for what automated storage and retrieval systems can achieve.

Innovative Shuttle Systems on Display
Attendees at Modex will get a chance to see how the SLS Tote Shuttle Series and the FPSS1500A Four-Directional Pallet Shuttle System are enabling the smart warehouses of the future.

To accommodate different needs, the SLS Tote Series is offered in the SLS300, SLS400, and SLS600 versions. To maximize throughput, the SLS300 standard shuttles carry uniformly sized boxes and containers with ease. The SLS400 allows load widths to be dynamically adjusted to accommodate a range of sizes for adaptable storage. The SLS600 is advanced; it has shuttles that can travel diagonally and vertically in dynamic 3D environments for maximum versatility.

Furthermore, the FPSS1500A system from HWArobotics uses modern technology to manage swarms of vehicles that effectively move pallets in four directions. It combines swarm intelligence algorithms with high-precision electric actuators to deliver exceptional speed, accuracy, and dependability for heavy-duty product movement.

Visit AITechPark for cutting-edge Tech Trends around AI, ML, Cybersecurity, along with AITech News, and timely updates from industry professionals!

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Aurelien, could you elaborate on how your professional experiences and background have contributed to your current position as Product Manager of Esker?

Prior to my current position as Product Manager of Esker’s Customer Service solution suite, I managed Esker technical support teams both in France and the US. I wanted to use the customer knowledge I gathered while helping Esker customers and bring my contribution to providing better products that fully answer customer needs. That led me to becoming a Product Owner within Esker’s R&D department, following the Agile Scrum methodology. I then became a Product Manager for a predictive lead scoring startup where I developed the necessary skills to position and market a new product, aiming at helping marketing and sales professionals develop their businesses. 

I then came back to Esker as a Product Manager where I can combine my technical background with my many years of business and technology experience to deliver solutions that relieve Customer Service professionals from time-consuming tasks and enable them to develop new skills.

What is Esker’s overall vision and mission as a company? How does the organization strive to make an impact in the market or industry it serves?

Esker’s mission is to create a better business experience: businesses face uncertainty and need to build stronger relationships with their employees, as well as their customers and suppliers. We want to enable all stakeholders in the ecosystem to generate value together and never come at another’s expense. This is what we call the Positive-sum growth. 

With our AI-powered cloud platform, we want to make an impact by automating finance and customer service processes, ensuring team members are more productive and engaged and eventually strengthening the business ecosystems of our customers.

As a Product Manager in the Order Management domain, what are the key challenges you face in delivering a successful SaaS product? How do you address these challenges?

The first challenge that I face is actually not specific to the Order Management domain but rather generic to all product managers: how do you make sure that you identify the most important problems and pains for your users and how do you make sure that you address them and provide value. In a nutshell, you need to remain close to your users and keep this user-centricity when developing your solutions. But I’ll come back to this topic in the following answers.

Then, as our solution targets B2B companies and each company operates slightly differently, another challenge consists in identifying the common needs that can make our product better globally, and not only for a niche of customers. But at the same time, sometimes, we want to provide features that mostly make sense for a given industry (such as pharma, medical device, or building materials), because there is a pain that is not answered by the market and we cannot only rely on the customization capabilities of our consultants to bridge the functionality gap. So, finding the right balance between adding generic and target industry-specific ones is a challenge.

Finally, we actually do not have a lot of competition for our Order Management solution that automates the data entry of orders in the ERP. This is both a good and bad thing for us because we spend a lot of time educating the market, conducting research and for instance get limited help from IT Analysts who don’t know very well our specific market, and focus more on the Supply chain (what happens after the order is in the ERP), or the B2C customer service (while we focus on B2B customer service automation). This research, market education and positioning takes more time and effort for this solution than for others in our portfolio in the Invoice-to-cash or Source-to-pay markets that are much more established and defined.

Gathering customer feedback is crucial for product improvement. How do you ensure a continuous feedback loop with customers, and how do you prioritize and incorporate their suggestions into the product roadmap?

Effectively, user-centricity is key when improving products to ensure new developments answer market needs. Our goal is to do UX research and remain close to our users by talking to prospects or customers regularly and understanding the daily job, challenges or aspirations. To feed my thought process for product enhancements, I speak to a potential user at least once a week: this can be through presales calls with prospects, follow-up meeting with customers, virtual and physical user groups in our different countries, or analysts who speak themselves to a lot of potential customers. 

Sometimes, we put a specific emphasis on UX research and design to explore a specific area or improve specific features that could answer better customer needs. We are usually involving external partners for this research and design projects, who are specialized and can bring us their expertise when interviewing prospects and customers to gather their needs or testing ideas and prototypes.

Our prioritization process is based on the alignment of a given feature or set of features with the vision and strategy that we have defined for the solution, as well as the number of customers that could be interested, the value that we would bring them if we can partially or completely solve their pain and also the technical feasibility and solution that we can propose. For instance, we recently added the ability to process free-text orders and automatically extract key data such as the shipping address or product quantities from unstructured orders received in the body of an email. This is something we had to set aside for a while because we did not have an appropriate technology to answer this need. But thanks to AI Large Language Models (LLM) such as ChatGPT, we can now understand natural language and have been able to implement the automated capture of key data from those free-text orders. 

Can you elaborate on how Esker on Demand, What specific features or capabilities does the platform offer that contribute to this streamlined experience for your customers?

We aim at making it easy for our users (the Customer Service Representatives of B2B companies) to use our solutions, and typically easier than the ERP systems, so that they can be onboarded quickly, feel comfortable using it and can advocate for the deployment in other divisions or countries.

For instance, our solution features easy-to-use collaboration features allowing CSRs to have tracked conversations on any orders, both internally with coworkers in sales, supply chain or finance and also externally with customers and partners. 

Also, as our solution relies on a lot of AI technology, we put effort into providing users with transparency, allowing them to understand what technology is used and how the system works, which increases their trust in the solution and facilitates user adoption.

As a Product Manager, you need to make decisions about prioritizing features and improvements. What frameworks or methodologies do you use to prioritize product enhancements?

We use agile methodologies at Esker for the product developments and our R&D uses the Scrum method with 2-weeks sprints containing small functionality increments, defined via user stories and organized in a prioritized backlog. Actually, at Esker, this is rather handled by our Product Owners who are part of the R&D department, and work hand-in-hand with the Product Managers who are part of the Marketing department. This scrum backlog allows us to have short-term visibility, at least over 2 or 3 sprints (so 2 months), but usually not more than this.

Actually, for the mid-term roadmap and long-term vision, we use other tools: we have defined our vision and strategic axis for each solution and review those at least once a year, first between the Product people, and then with the whole Esker leadership. This vision and strategic axis help us define the 6 months to 1 year roadmap where we place the big epics (set of features that are all related) or goals that we might to achieve. For this roadmap, we are currently migrating from an internal tool developed in-house to a product management solution that will help us gather the customer or internal feedback, the feature requests, and then generate the roadmap, tying the items to the associated requests and strategy axis, and providing more visibility both internally and externally, for instance with the ability to easily share our 1-year roadmap to prospects under NDA, analysts or customers.

In the SaaS industry, competition can be fierce. How do you differentiate Esker’s Order-to-Cash solutions from competitors, and how do you maintain a competitive edge in the market?

As mentioned earlier, we actually only have a limited number of competitors for our customer service solutions and mostly for order management automation. We have a lot of competitors globally on the Order-to-cash cycle, but the vast majority focuses only on the Invoice-to-cash piece of this cycle, meaning what happens after the invoice is issued and how it is paid and how this payment is allocated. There is an Invoice-to-cash Gartner Magic Quadrant that analyzes this market, and we were actually named as a leader this year. 

Going back to how we maintain a competitive edge in our solution, we first differentiate with our use of AI technology embedded in the solution. We’ve actually been innovative on AI for a very long time as we started introducing machine learning data capture algorithms 15 years ago (we recently obtained a patent for this technology, but we actually submitted the patent request 12 years ago and it took a very long time to be granted). So, we’ve had an edge on the technology over competitors that have more basic solutions with standard algorithms that cannot automatically learn or handle a vast diversity of order layouts and want to keep this edge by continuing to invest. Also, the technology that we add to the solution always answers users’ needs and brings them value, we are not driven by the marketing power that AI currently has in the market.

The other differentiator is our will to automate from end-to-end and provide a system that integrates with the IT infrastructure already in place, also not forcing our customer’s customer to change something in their process. To provide an example of the end-to-end automation capabilities for our Order Management solution, one of its main capabilities is that it can quickly and accurately extract order data, but there is a lot more around managing orders, including in the exception cases: we allow CSRs to manage all orders, not matter the type (email, but also EDI or ecommerce), we provide collaboration capabilities to clarify internally and/or externally an order, we integrate with the ERP and CRM systems, and can also deliver order confirmations and ship notices back to customers, according to their preference (email, EDI, portal). Then, we propose other Order-to-Cash solutions that integrate seamlessly with our Order Management solution as they are built on the same platform and use the same technology. This suite of end-to-end solutions allows our customers to start by automating one process of the cycle but then automate other areas and improve the efficiency.

Finally, even though this is not directly related to our product but rather to our company, we provide exceptional customer support thanks to our global presence, by setting the right expectations in presales, providing reasonable implementation timelines and proactively supporting our customers post-go-live via their account manager and an online forum to learn best practices and connect with the broader user community.

Can you discuss any upcoming projects at Esker that you are particularly excited about?

We have recently launched our Customer Service solution suite to automate all tasks of B2B customer service departments: new orders, but also requests for quotations, change orders, return orders, claims and general inquiries. This is exciting for me because it really expands the scope of our historical Order Management solution that was mostly focusing on automating order data entry, and we start to have more customers using those new capabilities and seeing benefits for their businesses. 

Associated with this expansion of scope, the AI technology advances such as the ChatGPT and the Large Language Models in general are also very exciting as there is an alignment of powerful tech capabilities that answer important needs that we could not necessarily solve before. Those advances open a new field of possibilities and could help us position and market our solutions better.

Esker’s product portfolio might expand over time. How do you ensure that the Order Management solution aligns with the overall product strategy and contributes to the company’s long-term goals?

In the past 3 to 5 years, we have released multiple new solutions to complete the Cash Conversion Cycle, both on the supplier (Source-to-Pay) and customer (Order-to-Cash) sides. Within the Order-to-Cash suite, we created a Customer service suite by adding Customer Inquiry Management and Claims Management to the more mature Order Management solution. So, for the mid-term, my goal is to improve our existing solutions and ensure they provide more value rather than launching new solutions. This objective aligns with our global strategy that consists in automating customer service and finance tasks and relieving back office users from tedious tasks so that they can focus on more value added activities.

Can you share your strategy or proposed measures to streamline the process and ensure a more seamless and satisfactory customer experience?

For our Customer Service solution suite, our vision is to help B2B companies offer the best customer experience possible, with personalized interactions, easy ordering, fast shipping and simple returns. We then defined the 4 following strategic axis: 

• Answer quickly and appropriately customer inquiries, with AI assisting the CSRs and proposing them relevant responses.
• Improve the order and other other sales documents automation: such as the free-text unstructured orders, or the request for quotations (unstructured free-text emails or semi-structured PDF or Excel document)
• Facilitate the Customer Service collaboration internally with sales, supply chain or finance departments or externally with customers and partners.
• Increase the customer Self-service by providing more options on the portal such as the ability to create and view inquiries, view and accept quotes, place new orders and make down payment, track the order shipment or trigger returns.

Aurélien Coq

Product Manager at Esker

Aurélien Coq is Product Manager at Esker’s headquarters in Lyon, France, where he is responsible for Esker’s Customer Service solution suite. Using his many years of business and tech experience, Aurélien works to relieve customer service professionals from time-consuming tasks and enables them to develop new skills by automating Order-to-Cash processes. Prior to Aurélien’s current position, he managed Esker’s technical support teams in both France and the U.S. and also held various positions as Product Manager and Product Owner at Esker as well as at a predictive lead scoring startup based in Nantes, France. Aurélien earned an engineering degree in telecommunications from INSA Lyon.

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Table of contents

Introduction

1. 2024 AI-powered Marketing Strategies

1.1. Interactive Content

1.2. AI-generated Video Marketing

1.3. Influencer Marketing Platforms

2. Future Path of Marketing with AI

In Summary

Introduction

During the holiday season, consumer expectations evolve at a breakneck speed, the constant change in algorithms is reshaping the digital terrain, and data has become the new gold. It can be a challenging task for Chief Marketing Officers (CMOs) to handle the upcoming turbulence in the ever-evolving business landscape. So, amid this turbulence, a powerful tool that emerges as an ally is artificial intelligence (AI). 

AI has taken center stage to understand and aid the demands of marketing leaders and business owners to stay ahead of the curve and ensure a long-term successful business. However, it is essential to future-proof the marketing strategies before we head into the upcoming years.

This article will be a strategic guide to future-proofing your marketing strategies with the help of AI.

1. 2024 AI-powered Marketing Strategies

Marketing leaders believe that in 2024, the marketing landscape will likely continue evolving, with new trends and strategies emerging. Let’s take a glance at some AI-powered marketing strategies that CMOs might find valuable:

1.1. Interactive Content

Engaging content is one of the easiest ways to interact with users, as it tends to grab more attention and shares, which often keeps them hooked on your website. Examples of interactive content include polls, AR/VR experiences, quizzes, and shoppable posts.

1.2. AI-generated Video Marketing 

Video is one of the most dominant forms of marketing, as users are more interested in watching the product than hearing about it. AI-generated visuals in the form of short videos, live streams, and interactive videos capture the audience’s attention and convey the correct message.

1.3. Influencer Marketing Platforms 

Influencer marketing is steadily rising as marketers believe that it helps to upscale their brand through marketing campaigns by 67% in comparison to not having AI influencer marketing platforms. These AI influencer marketing platforms provide AI-driven analytics to users to get valuable insights into audience demographics, engagement rates, and ROI.

1.4.  Privacy-Centric Marketing

The foundation of contemporary marketing is ethically trained AI, which provides unmatched accuracy in customer-centric monetization and communication. Businesses need to give user data protection a priority in light of growing privacy concerns. It will be essential to employ tactics like transparent data practices and zero-party data collection, which is the collection of data directly from customers. 

2. Future Path of Marketing with AI

The path of AI-powered manufacturing begins with a strategic vision to identify the areas where AI can add more value; whether it is campaigning or content creation, investing in the right AI marketing tools and platforms can ensure alignment with your overall manufacturing goals. However, AI is not a magic wand; its power lies when humans and machines collaborate. So, CMOs must equip their teams with AI-related skills to ask the right questions and guide the technology’s application. This ensures that AI remains a tool and platform for amplifying human decision-making and not replacing it.

In Summary

In conclusion, marketing landscape agility and hyper-personalization with AI can transform and empower CMOs to navigate this dynamic terrain. As AI technologies continue to advance, it becomes crucial for marketers to adapt and future-proof their marketing strategies. By automating mundane tasks, predicting customer behavior, and personalizing messaging, AI has the power to propel your business beyond mere engagement, foster new and strong connections, and ignite brand passion.

Visit AITechPark for cutting-edge Tech Trends around AI, ML, Cybersecurity, along with AITech News, and timely updates from industry professionals!

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Access to real-time data and insights has become critical to decision-making processes and for delivering customised user experiences. Industry newcomers typically go to market as ‘real-time’ natives, while more established organisations are mostly at some point on the journey toward full and immediate data capability. Adding extra horsepower to this evolution is the growth of ‘mobile-first’ implementations, whose influence over consumer expectations remains formidable. 

Nonetheless, sole reliance on real-time data presents challenges, challenges that predominantly circle matters of interpretation and accuracy.

In this article, we explore why inaccurate real-time data and analytics transpire, explain the commonplace misinterpretation of both, and look at some of the tools that help businesses progress toward true real-time data competency. 

The Risks of Using Imperfect, Legacy, and Unauthorised Real-Time Data and Analytics

Businesses risk misdirecting or misleading their customers when they inadvertently utilise imperfect or legacy data to create content. Despite real-time capability typically boosting the speed and accessibility of enterprise data, mistakes that deliver inappropriate services can undermine customer relationships.

Elsewhere, organisations invite substantial risk by using data without proper authorisation. Customers will often question how a company knows so much about them when they are presented with content that’s obviously been put together using personal details they didn’t knowingly share. When such questions turn to suspicion, the likelihood of nurturing positive customer relationships shrinks.

Misinterpreting Data and the AI ‘Hallucination’ Effect 

Real-time data’s speed and accessibility are also impeded when full contexts are absent and can lead to organisations making hasty and incongruent decisions. Moreover, if the data is deficient from the start, misinterpretation of it becomes rife.

Today, the risks of flawed data and human oversight are exacerbated by a novel problem. Generative AI technology is known to ‘hallucinate’ when fed with incomplete datasets. At significant risk to the organisation, these large language models fill any gaps by inventing information. 

The Tools for Optimising Real-Time Data 

Though few question real-time data’s ability to increase the accessibility and speed of enterprise data, many have observed that it has promoted a transference from organised data warehouses to muddled data lakes.

Avoiding this transference requires a seamless combining of data sources with those applications that drive core operations and protect customer interactions. Certain auxiliary tools, such as iPaaS, API Management, Data Governance, and AI, are also essential in ensuring real-time data properly facilitates the constant influx of information.

Predictably, a trend is thus developing from a move away from simple data gathering to optimally harnessing existing resources. Yet, challenges remain. Analysing data, merging data silos, ensuring data is new and rich in quality, and embedding insights into live customer engagements and systematised business procedures remain significant hurdles.

However, even these hurdles can be overcome. By coupling data streams and governance tools to preserve data scope and integrity and by deploying workflow tools that offer filtering and context, accurate insights can be generated while the incidence of incorrect conclusions is slashed. Where real-time data analytics are relied upon, integration tools cut risk further by enabling efficient data exchanges across separate systems and ensuring data reaches its expected destinations.

Is Corporate Infrastructure Equipped for Real-Time Implementations?

The bedrock is there, but most corporate infrastructures are not yet equipped for real-time implementations. However, a path is being cleared by emerging advancements from the fusion of two domains within enterprise IT: the user-centric application, which operates in real-time, and the analytics domain, which is largely batch-processed.

The coming together of these two domains is powered by big data technology, which manages extensive data volumes at speed and scale. Reinforced by exponential advancements in AI that are entrenched in analytics but come to life within applications, the integration between these two domains appears set to tighten. 

Real-time data and analytics are evolving at great velocity. To keep up, organisations must identify and confront the inherent risks. By adopting data governance, workflow solutions, and integration approaches, the advantages of real-time data can be successfully accessed, and any inaccuracies, information breaks, and risks to customer confidence can be mitigated.

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The post The Risk of Relying on Real-Time Data and Analytics and How It Can Be Mitigated first appeared on AI-Tech Park.

Ulf, can you tell us about your background and how you became the CEO of ABBYY? How did you start, what were the most pertinent challenges, and how did ABBYY triumph?

• ABBYY has been special to me since the days when I worked at an investment firm earlier in my career. What attracted me to ABBYY was, and still is, making transformative changes to how customers can make their data work smarter and give them better business results. 
• My official journey with ABBYY began when I became a member of the ABBYY Group Board in 2002 then Chairman of the Board in 2015. I became CEO of ABBYY in 2017, so I have a long appreciation of the company and its values that I’m proud to now be leading. 
• Our biggest triumphs are when we make a difference to customers. We’re especially proud in how we helped many companies overcome significant challenges during and post pandemic. We helped them know where best to start automation initiatives and understand their business processes better, transition them to the cloud, accelerate their digitization efforts, and get them up and running quickly. Most notable included seeing our customers fund loans faster, review drug applications faster, and enable customers to conduct more self-service transactions from smartphones during a time when remote and social distancing forever changed how we work. From adapting to unstable markets to developing new solution strategies, the people at ABBYY have proved their resilience when it comes to embracing change and overcoming obstacles. I believe ABBYY’s customer-centric approach to teamwork and collaboration is a key component in our ability to work past adversity. 

Can you share your vision for ABBYY? What are your long-term goals and aspirations for the organization?

• We envision a world where organizations can put their information work more seamlessly, enabling businesses to provide customers with experiences that make their lives better, more productive, and fulfilling. We support this vision by drawing from over 30 years of experience as a trusted and innovative organization with a successful track record, and we use that confidence to transform businesses’ outcomes. We know we’re on the right track because we’ve been recognized by more than a dozen influential analyst firms as a leader in intelligent document processing (IDP) and process mining. Additionally, we have more than 10,000 customers worldwide, including many in the Fortune 500, who trust us to power their intelligent automation. No matter what document, format, language, or context we’re working with, we have the expertise to meet and exceed client expectations – and that remains as ABBYY’s ultimate goal. 

In your view, what do you perceive as the most noteworthy challenges confronting the IT services industry at present? 

• One of the biggest challenges – and opportunities – is the need to create impact and value for customers. KPIs like time to production, time to value and quality during production are more important than ever, meaning software vendors must become top quality service providers. The Software As A Service (SaaS) model also means customers can more easily switch providers if they are not seeing the expected value. An aspect to this is that we are moving from a vendor, buyer relationship to a partnership built on technology, service provision, and innovation. We strive to be a trusted partner to our customers in all questions around IDP and process understanding.
• Another major challenge is the currmaent economic and labor environment that has caused many decision makers to pause on major IT initiatives. Discrepancies in tech stacks between departments coupled with overzealous budgets during 2020-2021 left many organizations with technical debt, further causing CIOs to pause innovation until they have a clearer strategy on their roadmap. It’s prudent for innovation leaders to understand what their organization does or does not need, the technologies to adopt, and what will advance their business goals. It’s very similar to where enterprises were in 2020. We’re here to help them navigate through the hype of technology, especially as it relates to AI, and cut through the noise to know how to optimize operational efficiency, enhance workforce productivity and improve customer experiences.  

According to you, which three areas of an organization hold the most untapped potential for AI adoption and innovation, and how can it lead to a competitive advantage?

• There are three key areas to note: efficiency, experience, and revenue. 
• Operational efficiency through workflow optimization has significant potential. Organizations are always chasing efficiency, but many attempts to optimize or automate prove unsuccessful due to a lack of clear objectives, direction or visibility. Process intelligence – a combination of process and task mining technologies enhanced with AI – gives enterprises the visibility they need to make drastic improvements to their operations company-wide. For example, a company we worked with in financial services was able to determine it was incurring a staggering $17 million a year in costs due to not executing transactions from customers on time. Using process mining, our customer was able to gain visibility for its entire workflow end to end – which involved consolidating two million events and transactions a day across 40 different systems. This led to the implementation of a more uniform process to improve efficiency.
• Customer experience is another area where innovation and AI can improve retention, increase customer acquisition and generate more revenue. Onboarding, for example, is a frequent pain point for businesses that can be improved with intelligent automation. Arduous onboarding procedures that require repetitive information and excessive manual entry can result in abandonment and even damage brand reputation. Using intelligent document processing to streamline proof of identity documents will make onboarding a painless process for potential customers. A recent survey commissioned by ABBYY showed that 90% of organizations experience dropout during registration – which is a lot of potetial revenue untapped. However, respondents believe if the abandonment rate was reduced by just 50%, it would increase customer acquisition by 29% and increase revenue by 26%. Modifying the onboarding process with technology appears to dramatically improve overall customer experience by up to 43% and increase customer retention by over a third (37%), according to our survey. 
• Using AI and innovation for accelerating revenue is a strategic opportunity for organizations especially as it relates to accounts payable. AP is often laden with inefficiencies, but AI-driven solutions can prevent this. Automating AP processes can increase employee productivity by 400%, reduce invoice processing costs by 91%, and expedite processing times by 81%. Finance and IT leaders should be aware of their options when it comes to digitally transforming accounts payable. 
• If managers and leaders focus on improving these areas of their business, they’ll find that this standard of excellence will permeate throughout the rest of the organization. 

What unique value proposition does ABBYY offer in terms of AI and Innovation? How do you ensure your AI use is ethical and unbiased?

• ABBYY combines innovation and experience. We’ve been leveraging artificial intelligence in our solutions for decades, so AI is not just a fad to us – it’s at the center of what we do. The hype of generative AI has led to an upcropping of software vendors looking to capitalize on the train of excitement, but ABBYY is differentiated by our ability to transform data into intelligent actionable outcomes and serve our customers’ needs, enabling them to spend time on what matters most. In healthcare for example, patients often need a referral to see a specialist that requires several medical records documenting the patient journey. We’re able to streamline the business process, digitize information, and have pertinent data automatically ingested into electronic healthcare records so patients can receive specialist care much faster. Rather than chasing medical records and approvals, physician staff can spend more time with patients. 
• In regards to ethical use of AI, not only is our AI integration within our solutions intentional and purposeful, but it bears a commitment to privacy, confidentiality, equity, and social utility. We take compliance very seriously and ensure we’re in line with applicable laws and regulations. ABBYY also takes part in larger conversations concerning the future of AI, of its development, and of its use. 

Document processing has been around for decades, what makes it more relevant today? Would you consider it going through a new renaissance?

• At the center of every business, there will always be documents – whether they originate digitally or paper. “Intelligent document processing” twenty years ago meant sending a fax. Now, innovation has made it possible where consumers can snap pictures of documents and send it directly into a business’ mobile app or portal and knowledge workers can expedite work. Think about the REAL ID process at the Department of Motor Vehicles (DMV). You used to need to bring copies of a proof of address, auto insurance information, proof of identity documents like a passport, birth certificate and marriage certificate to the office and stand in line for hours. Now this entire process can be done with a smartphone via the DMV portal. IDP is the magic behind the scenes that transforms the data. We’re now seeing tech giants entering the IDP market, but we have decades of a head start in leveraging AI for documents. 

What’s the latest technology that you think can change our industry / How do you see the impact of latest technologies like ChatGPT for our industry?

• Generative AI is proving to create a massive shift in how we interact with technology. For all the different uses and functions it is being applied to, I truly believe that its most impactful use will be as a new interface between humans and technology. The advances that are now being made in the foundational models continue to accelerate, but there certainly are considerations to make as an enterprise. ABBYY has been leveraging the technologies found in these foundational models for many years, but always with compliance and ethics at the forefront of our development process.
• This rethinking of interfaces extends beyond just generative AI and into enterprise solutions and platforms as a whole. Enterprise workers want the mobile app experience – click, download, launch, and ready-to-use capabilities – for complex developer and business applications. Low/no-code solutions have changed that. We’ve led this effort with the industry’s first IDP solution that empowers “citizen developers” and business analysts to download AI skills to read and understand documents like a human. The democratization of AI with intuitive, easy to use interfaces are allowing organizations to work more efficiently. ABBYY has also experimented with implementing generative AI into low-code solutions to take the accessibility of IDP even further.

As the CEO, how do you inspire and motivate your team to achieve the organization’s goals? What are some significant goals you’ve achieved in the past year?

• The ABBYY culture very much centers on how we can transform our customers’ business and in turn, improve their customers’ lives. Sharing results and best practices among team members transcends to continuously sharing with existing, new, and future customers. Many of our customers are happy to participate with us in educating other innovation leaders on how using intelligent automation has impacted their organization. 
• ABBYY’s unique geographic and cultural distribution adds many different opportunities for motivating teams across the organization. Our headquarters are in Milpitas, California and we have offices in 14 other countries. We firmly believe that to enable our team to flourish, we must provide them with the flexibility to work in ways that suit them best. For this reason, we offer various flexible working arrangements, including remote and hybrid options, as well as flexible hours.
• Furthermore, we prioritize wellness and acknowledge what that means to different individuals, offering additional time off for volunteering and general well-being as a testament to this commitment. We like to spotlight how our team uses this time off and how they pursue their interests, both internally and on our Instagram page that focuses on life at ABBYY. The heart of our value is ultimately our culture of appreciation and recognition, and our commitment to embracing an uplifting environment where colleagues actively express support and gratitude for each other. 
• As for goals achieved, we’ve accomplished a great deal in the past year. ABBYY has undergone significant transitions; from an ISV focus to applications, to capture, and now to Intelligent Automation. Transforming from one market to another means we have had to rethink the product portfolio, how we create and scale solutions, the buyer persona – in some ways, we’ve had to rebuild our products altogether. This is no easy task, but we’ve come a long way. We’ve seen great acceptance and results from our low-code/no-code IDP platform Vantage and we know now that the product is reaching maturity. Our process intelligence solution Timeline has gained strong traction in a market that had previously been cornered by a large incumbent. This was all made possible through ABBYY’s strong emphasis on innovation and collaboration. We are building upon our 30 years of leadership in the market and our technology and AI advantage to continue our growth. But perhaps the biggest transition is to put the customer at the center of everything we do. This is an easy thing to say but a much more difficult thing to actually put in practice. We still have room for improvement – and will always look to improve further – but I am proud of the way all teams and business units at ABBYY are customer-centric in their work.

What are the 2-3 biggest lessons you’ve learned about leading a company over the past 2 years? What is the biggest piece of advice you would want to give to company leaders?

• For one, I’ve learned to act as quickly as possible. Acting quickly can often make life a little easier, in a variety of cases. If opportunities start to string you along or growth plans begin to seem out of reach, make proactive adjustments that will mitigate the impact before you’re entrenched in the consequences. Operate on the assumption that not everything will go according to plan – especially in an already shaky market that’s feeling the effects of global events.
• It’s also important to not be too hard on yourself. It’s tempting to reflect and dwell on what you could have done differently, but all things considered, you should be optimistic about where you are now. 

What’s next for ABBYY? Where do you see ABBYY three to five years from now? Are there any integration or expansion plans in the pipeline? 

• We’re planning on strong year-over-year growth over the next five years. Our initiatives focus on partnering closely with customers to ensure they achieve their business goals while also continuously reimagining how our solutions can perform better and be consumed faster and easier. 
• I also anticipate there will be significant consolidation in the market over the next three years. How this applies to ABBYY is yet to be determined, but we will grow organically through the tremendous growth opportunities ahead of us. Currently, ABBYY is successful in our independence and content to be driving our own strategy. 

Ulf Persson

Chief Executive Officer at ABBYY

Ulf Persson has been CEO of ABBYY since 2017. He first joined the company as a member of the ABBYY Group Board in 2002 and in March 2015, he became the Chairman of the Board. For over 25 years, Ulf has been involved as an investor and board member in 20 different growth companies, including jNetx (telecom software),  UCMS (HR & accounting BPO) and MyMoney (P2P car financing). Ulf is a graduate of Stockholm School of Economics and the Swedish Defense Language Institute and holds a B.Sc. in Economics.

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Cloud printing is a consumer-based service that allows users to print their documents from any device available on a network. Users can also use different devices, such as smartphones, laptops, desktops, tablets, etc. to print their documents. 

This is a secure and quick way of printing documents over the internet and allows users to print from anywhere. It is different from the traditional form of printing, in which the printer has to be connected to the device via a wired or Bluetooth-based medium.  

Typically the process of cloud printing involves a few steps. First, when the end user uses a print command on a document, the system creates a print job. Next, this print job is sent over to the cloud-based printing service, which redirects the request to the selected printer over the network. 

The printer then prints the document, which can be collected by the user. Just like with traditional printing, the user can see the status and content of the document while printing. They can also stop or cancel the print job if required. 

Advantages of Using Cloud Printing for Your Business  

Cloud printing is a seamless service that reduces the time and costs associated with traditional printing. This technique removes the need for printer drivers and specific connectors and allows printers to accept requests from different locations through third-party data centers. Let’s have a look at some other advantages of using cloud printing for your business. 

1. Saves Money 

As discussed above, cloud printing allows companies to reduce the costs associated with traditional direct-to-printer printing. Businesses don’t have to pay installation and maintenance costs for maintaining multiple printers and their peripherals, such as cartridges, toners, paper, etc. Cloud printing also reduces operating costs, such as the cost of electricity and drivers. Furthermore, companies don’t have to hire specialized technicians to maintain printers. 

Businesses can also save travel and transportation costs since they can directly print a document at the customer’s location.

2. Carbon Footprint 

Cloud printing is an amazing way to reduce your carbon emissions, as it reduces the waste generated by printers and allows users to manage their resources (cartridges and toners) efficiently. In addition, it also enables users to change document settings and fix them before printing. 

3. Printer Management 

Cloud printing allows users to efficiently manage different devices in a centralized way. It simplifies printing and enables users to utilize printing services without setting up extensive infrastructure. 

Cloud-printing setup is low-cost and easy to maintain. Through this, users can print their documents from anywhere. They can also set up custom document parameters and control project settings. 

It also enables users to monitor printer metrics (i.e., the average number of users per day, the average length of documents, the number of documents in a typical queue). IT admins can also use cloud printing to create a printer pull release. This is a secure form of printing in which users can submit multiple printing jobs to the printer. However, the printer doesn’t initiate print until the user uses an authentication pin/password to authorize the operation. 

4. Security 

Cloud printing is a highly secure process and lets businesses print documents securely. Cloud printing uses encryption for data transmission, which ensures sensitive data is protected from malicious hackers in transit. 

It also uses authentication to ensure that only authorized users access the printer. This guarantees information security and streamlines the workflow. 

Cloud printing also increases collaboration, as different people can work on a single document before printing. Furthermore, IT admins don’t have to manually update printer software and network settings for all devices on the network. Instead, all the printers on the cloud can be patched and updated at once. 

This is very different from traditional printing, where individual printers have to be maintained separately and team members have to physically share a device and queue their documents to print them. 

5. Accessibility 

Cloud printing allows users to print anything from anywhere, improving print accessibility and making it easy for remote and hybrid workers to print their documents. It also reduces business delays and decreases issues with device/printer downtime. 

What Teams Can Benefit From a Cloud-printing Strategy? 

Cloud printing is highly useful for teams that use different devices to connect to an organization’s network. Through cloud printing, users can use different devices, such as smartphones, smartwatches, tablets, etc., to print their documents. In addition, users don’t have to worry about installing platform or operating system-specific printer drivers to print documents. 

Today many companies across the world are implementing a BYOD policy to increase employee flexibility. Cloud printing greatly aids this effort by allowing employees to use their own devices to print documents. 

This also improves productivity, as employees don’t have to transfer files from personal computers to office computers for printing. This increases compatibility and reduces the manual effort required for document printing and distribution. Employees can print documents without worrying about data breaches and security issues. 

Some organizations have also started integrating cloud printing with document management tools and storage services such as OneDrive, Google Drive, SharePoint, etc. Implementing this strategy streamlines the printing process and optimizes workflow management by using a centralized platform for managing and printing documents. 

Even though the specifics depend on the device and printer model, most cloud-printing services are easy to set up. Generally, the users have to register their printers with the service and add the web/Android/iOS application to their device. Once done, the user can directly log in to the portal and print their document. 

If required, SharePoint admins can also automate this process, which means that users connected with the SharePoint cloud just have to select a PC client to print their documents. In addition, such portals allow users to access printer features such as configurations, queues, scanner layouts, etc. 

Are There Any Disadvantages to Implementing a Cloud-printing Strategy? 

Even though cloud printing has many benefits, it has a few disadvantages too. Customers who use cloud printing need a stable internet connection to print documents. This means that not having a reliable connection can cause users to lose access to their documents stored over the cloud.

Even though cloud-printing vendors create SLAs for infrastructure support and maintenance, printing can be disrupted due to technical issues and vendor outages. This can significantly affect business operations and cause delays. 

Moreover, since the customer doesn’t manage the cloud infrastructure, they have no control over the process. This means that they cannot fix issues and resume the printing process, even if they have the technical expertise. 

Cloud printing can also sometimes limit the printing options for a client by not allowing them to use certain types of printers or paper sizes. Cloud-connected printers can sometimes have compatibility issues with newer device sizes and require additional hardware/software patches to fix the issue. Problems such as these can impact productivity and halt a printing operation. 

Last, even though security is one of the major advantages of cloud printing, public clouds are not 100% secure. Malicious hackers can exploit system vulnerabilities and break their security measures, which will give them unchecked access to clients’ sensitive internal documents. 

What Are the Best Practices for Cloud Printing?  

There are many use cases of cloud printing for different industries and environments. However, there are a few best practices businesses can use. 

1. Device Management 

The client should ensure that all their printers are cloud-ready. They can also register new printers on the cloud if required. This can be done via cloud-printing services such as AirPrint and PrinterOn. Clients should also perform hardware maintenance and change service parts if required. 

Furthermore, clients should make sure that printing devices (smartphones, laptops, desktops, etc.) are properly configured for the cloud. This is important to ensure they are functional and ready to send print jobs to the printing service.  

2. Choosing a Cloud Vendor 

Before implementing a cloud-printing strategy, it is important to choose a cloud vendor who knows how to set up a cloud-printing service and configure it for a client’s infrastructure. The selected vendor should customize the strategy based on organizational requirements and budget. 

An optimized cloud-printing solution should also account for an offline printer scenario, in which job requests should be sent to a different printer in case the selected one is offline. Vendors should also be able to set up a company’s infrastructure in such a way that multiple team members can queue their print jobs onto a single device through group settings. 

Last, businesses should make sure that a cloud service is fully operationalized. Potential cloud providers should provide a comprehensive list of services and features to their clients. 

3. Print Limit

Some organizations like to limit their printing for environmental and sustainability reasons.

Such organizations can talk with their cloud vendor about limiting the number of printing operations their devices do in a single day. This option should allow admins to vet printing jobs before printing and monitor printer usage. 

Features like these will enable the organization to implement a company-wide print policy that focuses on reducing printing wastage. This will also help them track printer usage and manage printing costs. If required, clients can also set up daily or monthly printing quotes to discourage unnecessary printing.  

4. Security 

Failing to secure a cloud-printing operation can cause security issues and make a business susceptible to security threats. To prevent this, customers must use certain security practices when working on the cloud. 

Always use authentication methods such as 2FA, MFA, and biometrics to access the cloud. Clients can also set up role-based access control to restrict usage and enable only authorized profiles to access their network. 

Information in transit should be protected using inbuilt encryption algorithms and auditing. This ensures information safety and maintains data integrity. 

Businesses should also regularly update their networks and use comprehensive security measures such as firewalls, incident management and reporting software, and access management services to curb security flaws. Cloud-native security options such as failover and capacity management, backup, and response procedures should be added to a client’s infrastructure. 

The Future of Cloud Printing

Although cloud printing is a fairly new technology (it was first introduced in 2010 with the launch of Google Cloud Print), it has a wide potential to reach millions of small- and medium-scale businesses worldwide. Cloud printing is an amazing way to increase device interoperability, allowing users to print from different devices worldwide without missing out due to software and hardware issues. 

Cloud printing makes businesses more environmentally conscious by monitoring their printer usage and advising them on how to best manage and optimize their printing habits. It also enables organizations to use more eco-friendly products in their printing, such as eco-friendly cartridges, recycled papers, etc. 

Some organizations are also implementing AI algorithms to optimize print operations based on their previous usage. This allows them to analyze jobs and make recommendations for size, paper alignment frame, etc. It also enables them to remove common printing errors and improve printers’ reliability. 

AI can also make decisions regarding essential software upgrades and software patches to reduce system vulnerabilities. Furthermore, if a selected printer has a device breakdown, jobs can be automatically rerouted to other printers in the vicinity. 

As discussed above, cloud printing is a growing market with its own advantages and disadvantages. It is a unique method for streamlining print management. This is a rapidly evolving field, and the future will have many use cases for it.

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Clinical trials are undergoing digital transformation in every aspect. Over the past few years and further accelerated by the COVID-19 pandemic, Risk Based Quality Management (RBQM) – in which trial management can adjust and allocate monitoring resources based on the level of risk identified for a specific trial or site – has utilized this digital evolution to emerge as a more efficient way to facilitate trial delivery. RBQM strategies optimize data flows, increase oversight, enhance data quality, and improve subject safety. 

Vast amounts of data are collected through clinical studies. A lot of time and effort is spent sourcing this data from disparate systems, as well as standardizing and cleaning it for analytics, rather than generating the wealth of insights this data could provide if companies have the time to comb through it. However, the focus always needs to be on quality and efficiency. Advanced analytics tools using artificial intelligence (AI) and machine learning (ML) enable research teams to make sense of this information. To keep up with this digital transformation, sponsors need better data management technologies that utilize AI and ML to not only accelerate data collection but also streamline the review and decision-making process. 

It is critical to acquire the right data to deliver the right insights to drive the right actions.

The Right Data 

One of the biggest challenges in the realm of clinical trials is data spread across disparate systems with minimal interoperability. Point solutions hinder centralized access to data and restrict efficiency in decision-making. This makes it difficult to scale when organizations grow. Trial data captured in disparate systems prevent a cohesive view of the subject. To fully review the critical data at a subject level, monitoring teams must have access to a single data profile of the subject – sourced from several systems.

The ideal solution should be able to integrate disparate data sources such as EDC, CTMS, eCOA, labs, connected devices, IRT, and others in real-time. Once the data is curated, cleaned, harmonized, and housed quickly in a single repository for stakeholder use, it is easier to use custom artificial intelligence models and machine learning algorithms to connect the dots across different point solutions and make holistic assessments to identify data errors, outliers, and false entries. The longer it takes to generate the insights, the bigger the risk becomes. This in turn, enables more informed decision-making and reduces trial costs by enabling the team to focus on operational and strategic business processes. The learnings from the historical trials could be used to teach algorithms, which could be leveraged at a program or port.

The Right Insights 

RBQM relies on access to the “right data” to systematically generate the “right insights,” enabling oversight, risk mitigation and prevention, and issue resolution with respect to sites and subjects.

With improved access to data and technology frameworks, RBQM has gone through and continues to be on an evolution of its own.

Early generations of these tools and algorithms focused on single parameters to identify issues, which, out of context, often led to false positives. Advanced analytics have improved the user’s ability to apply tribal knowledge and contextual information to generate more accurate insights. 

For example, two sites – one with 1 subject enrolled and the other with 150 subjects enrolled – were historically flagged to be at similar risk for not reporting, or underreporting, adverse events irrespective of the number of subjects enrolled in each of the sites or the time for which the subjects have been exposed to the drug. Hence, Central Monitors (CMs) and Clinical Research Associates (CRAs) spent a lot of their time responding to triggers that may not have required much action. However, advanced analytics models can differentiate this relative risk between the two sites and indicate that the second site in this example is at higher risk. These normalization factors applied with trained and validated models in past studies have benefited the overall accuracy and effectiveness of trial oversight. CMs/CRAs now do not have to split their time evenly between every site looking for anomalies that often didn’t exist. Algorithms provide a list of potential outliers, which can be supplemented, validated, and prioritized by the stakeholders. Systemic observations with a particular issue can be used to reprioritize risks based on the study design.

Over the past few years, further enhancements have been made to these models by incorporating ML, which uses algorithms and decision support systems to learn from past searches, making the system ‘smarter’ with every review. This learning isn’t just limited to the current trial. These platforms are able to learn from historic clinical data sets, making them increasingly ‘knowledgeable’ with every application and resource. That translates to greater oversight, higher-quality data, and a safer trial environment. It also means sponsors can utilize clinical research staff more efficiently and focus on more value-added tasks, such as ensuring sites are operating within trial parameters and compliance measures.

The Right Actions 

By automating the data collection, cleaning, and review process, operational stakeholders are free to focus on more value-added tasks, and sponsors and sites get faster access to information. It also brings greater efficiency and job satisfaction.

AI algorithms, coupled with workflow automation, have resulted in more proactive risk management. The algorithms can help in reviewing the appropriate insights and flag risks automatically. These insights can then be used by the stakeholders to verify the risks and take the appropriate action, freeing monitors and site staff to focus on solutions, rather than reviewing all data and then deciding whether a risk warrants an action or not. 

Each issue receives the necessary level of scrutiny, resulting in the most optimal corrective and preventive actions that ensure the identified issue is mitigated and a similar issue does not repeat. The primary value drivers in terms of time savings are the prevention of repeated issues leading to a reduced time to clean, lock, and submit the data.

Shaping The Future

The overall success of an RBQM implementation depends on its ability to highlight high-priority areas that require immediate attention. The effective techniques of AI and ML are a sponsor’s best chance to rapidly achieve goals, employing advanced algorithms and statistical models for faster and more informed decision-making. With an increased demand for unsupervised learning, AI/ML has been leveraged for risk identification in multiple ways. 

A few use cases include:

• Holistic site risk assessment using composite risk indicators for identification and monitoring of high-risk sites
• Monitoring high-risk subjects through early signal detection of subject outliers for Labs and Vital Signs
• Predicting Protocol Deviations through historical trending allows the monitoring team to proactively implement mitigation actions
• Identification of duplicate or professional subjects with workflow alerting 

Technology as an Enabler

The technology landscape is continuously changing with the advent of mobile health (mHealth), wearable technologies, connected devices, and improved analytics. Accessibility to more patient data flowing from newer data sources could potentially eliminate the need for source verification. The need for CRAs to spend time onsite for data review is being replaced by centralized data review processes, as evidenced by the recent implementations of RBQM models. 

The key areas of focus for technology strategy could be:

• Advanced Analytics including implementation of machine learning and artificial intelligence
• Internet of Things platforms enabling the use of wearables technologies, connected devices, and smartphones
• Automation of workflows with suggested actions for quick issue escalation and management
• Prioritization of risks based on system-generated insights from historical and current trial information

Changing the Role of a CRA

The role of a traditional CRA has gone through a series of changes since the advent of RBQM. This shift in focus to the most critical risk identification means that the CRA has to manage compliance and relationships at the site. The role of the CRA would be geared more towards the safety of the patients rather than actions and mitigations that could be deployed remotely.

The paradigm shift in considering targeted monitoring that requires visits to the site based on risk data, instead of the traditional approach of reviewing all data on-site, can be attributed to improved ability in remote communication with site personnel through the different virtual channels. CRAs need to address complex issues virtually through a hybrid approach, which places a demand for a different set of communication skills from the CRAs. 

With access to data and analytics, the CRAs must be increasingly analytical with the interpretation of the data and insight to identify outlier trends. This entails the need for additional training, not only for the CRAs to embrace data and insights but also to prepare the stakeholders on the ground to be ready to use them. Critical thinking is crucial to the management of the site’s quality and maintaining patient safety.

Accelerating Innovation 

In summary, the current successes and enormous potential of using technology, AI, and ML to accelerate drug discovery while cutting costs and risks is a tremendous catalyst for innovation. By continuing to leverage data, digital intelligence, analytics, and domain expertise, there is a huge opportunity for the industry to fundamentally transform the clinical development landscape in ways that will greatly benefit trial participants, sites, and sponsors.

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Over the years, I have seen the IT industry witnessing a dynamic expansion.  

From the introduction of the Internet to the widening of the mobile industry and the introduction of technologies like Artificial Intelligence, Machine Learning, and high-speed network solutions like 5G, things have never been the same.  

Nevertheless, the initial focus of the development process and the technologies was only limited to converting every manual process to digital while enhancing the user touch with the technologies. However, I believe, the growing interactions of the users with the digital environment and the need for sustainable solutions have definitely become a reason for greater focus on quality and software testing.  

In other words, when working on a digital product, the quality assurance and software testing process allows for determining the fate of an application in terms of end-user experience, reliability, performance, security, etc. And therefore, the entire idea of software testing evolved tremendously, allowing the introduction of concepts like shift left testing and test automation.  

With that being said, it becomes necessary for testers, developers, business analysts, and other stakeholders to stay informed of how the future of software testing will likely change. And based on my personal experience and knowledge, here is how I see the future of software testing.  

Future Of Software Testing In 2023 & Beyond 

To underline, the future of software testing is more about existing and upcoming technologies and how they can be used to harness the maximum potential of QA processes. From refining the user experience to the integration of test automation with machine learning, IoT, and practices like Agile and DevOps, let us quickly jump on learning how they are likely to change the future of software testing.  

1. Machine Learning (ML) 

Right now, machine Learning is one of the most hyped concepts that are likely to change the quality assurance industry. With some remarkable changes made to the software and application development process, I can say, ML has all the potential to redefine the quality landscape.  

According to Statista, the machine learning market is expected to grow from around 22.6 billion U.S. dollars to nearly 126 billion U.S. dollars by 2025, which likely increases the adoption of machine learning technology in the IT sector.  

To underline, machine learning in software testing can be used to run code-specific tests with test suite optimization. Besides, Machine Learning could be used by testers to work on predictable test configurations and can run those tests automatically.  

Above all, integrating machine learning solutions into a test process could simplify the identification of high-risk application states, simplifying the ranking of the regression tests.  

2. IoT Testing 

Earlier, the vision of developers, tech enthusiasts, and users was limited to IoT devices when it came to the concept of IoT. However, with time, I see IoT as a developed market where every new product launched into the market is tuned to complement the IoT objectives.  

Moreover, the expansion of IoT signals a lot of advantages in the context of performance, security, and usability testing. From checking the device compatibility for different protocols and versions to data integrity assessment or tracking of connection delays, IoT offers all the scalability benefits. 

3. User Experience 

Another significant objective that comes with any software or application development project is the need for a pleasing user experience. From developers to testers as well as business stakeholders, UX testing allows the creation of products built to rule the market while aligning with the users’ needs.  

As for me, user experience makes an extremely important aspect of the software development lifecycle and comes as an inherent element for any app or software landing in the market. So, when working on a high-quality product, it is necessary that every element of the product must be identified and checked for the ultimate user experience while eliminating any elements that do not deliver the required effects.   

4. Test Automation 

Automation has become a buzzword for the tech audience of the future, and therefore everything designed in IT aims to include automation in the process. Though some organizations consider automation as a provision for increased benefits, I have always emphasized test automation as a tool that organizations need to yield quality and speed of development.  

In the past, I have personally gone through various opinions that stated automation interacts better when a high cost is associated. However, automation presently could allow organizations to yield high results at feasible costs. 

Besides, integrating automation into any process requires adjusting your test requirements as per the varying trends. Moreover, test automation could be leveraged in the quality assurance process to foster codeless automation, robotic process automation, IoT testing, complement agile practices, & more.  

5. Performance Engineering 

Another significant revelation that highlights the need for advanced software testing is performance testing. Also known as performance engineering, testers working on performance-oriented test scripts could work on improving aspects related to software and hardware.  

These include security, user experience, and configurations related to system functionalities. In short, performance engineering widens the scope of test initiatives allowing developers to meet customer expectations. Moreover, sticking to all these objectives is necessary to create high-performing products.  

6. Agile & DevOps 

With time, Agile & DevOps have become business essentials. From process improvement to streamlining the software development lifecycle, agile and DevOps have enabled businesses to yield tremendous value.  

While agile holds all the potential to adopt rapidly changing requirements, DevOps makes it easy for developers and testers to collaborate and deliver end products with improved output at greater speed. All in all, agile and DevOps are crucial to ensure both speed and quality when a team is developing technology while keeping a focus on quality assurance initiatives.  

To Sum Up… 

It would be nothing wrong to say that the role and contribution of software testers are going to be huge in the near future. However, the process might involve various twists and roles that will happen around the evolution of the software testing industry.  

Therefore, it would not be easy to predict what factors could reshape the industry with technologies like blockchain, cyber security, etc., that are dwelling to get bigger with each day. However, it can be confirmed that quality assurance and software testing will contribute a lot to the development of futuristic technologies, from user-oriented applications to enterprise solutions.  

More importantly, the future of software testing will likely be geared toward advanced testing practices like Agile, DevOps, DevSecOps, and QAOps, which will deliver all the potential to the development, operations, and testing activities. However, it can be predicted that the future of development or, say future of testing would need individuals who can showcase their value in both development and testing.

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1. Iftach, please give us a brief overview of your career trajectory so far.

I started my career in the Israeli army, where I served in an intelligence unit specializing in the field of electronic intelligence. After my service, I started working in the engineering field, which I’ve now been in for over 15 years. I got into app development and mobile engineering back before the app revolution took off, so I’ve been able to be at the forefront of the industry for a long time.

2. How has your role as the CTO and co-founder of ActiveFence impacted the growth of your organization?

At its core, I am responsible for leading the organization’s technology strategy and ensuring it has the necessary resources and infrastructure to support its development and growth. This includes:

Developing and implementing ActiveFence’s long-term technology strategy as well as prioritizing technology initiatives that align with the organization’s overall business goals and objectives.

Managing the development and deployment of new technologies and ensuring they are integrated effectively into the organization’s operations and processes.

Identifying and pursuing new growth opportunities that leverage the organization’s technical capabilities and expertise.

3. What are the core values upon which ActiveFence functions?

Our main focus is to ensure that platforms providing digital communities make these spaces as safe for users as possible. In essence, our work is centered around making the internet a more secure place. We enable Trust & Safety teams to eliminate blindspots on their platforms and protect their users from malicious content and behavior.

4. What was your inspiration behind venturing into the Trust and Safety and AI space? How has your entrepreneurial journey been so far?

While the world has built protection for cybersecurity, there was no solution for cyber safety – for the trust and safety of platforms and their users. This was a real problem that concerned all internet companies, regulatory bodies, and the media. The industry needed a concrete solution to make the internet a safer place through fast, smart technology, but it was clear to us that tech alone wasn’t the answer: machine learning needs a human touch. That’s why our AI is constantly learning and developing based on the intelligence and data collection that our analysts do every day. You can’t solve new problems with old solutions, which is why we were inspired to design new ones that would evolve with the times.

5. Tell us more about the full-stack solution that your company offers to Trust & Safety teams, worldwide.

ActiveFence is the leading tool stack for Trust & Safety teams worldwide. By employing our end-to-end solution, Trust & Safety teams of all sizes and across any industry are able to protect their platforms and users from malicious activity and online harm, regardless of the content format, language, or abuse area. With our platform in their toolkit, Trust & Safety teams can ensure that their content moderation operations are both efficient and effective, catching violations with a high level of accuracy, easing the workload of their workforce, and creating a safe, secure digital space for their users.

6. What sets ActiveFence apart from its competition?

ActiveFence is unique in its use of contextual AI. Everyone knows that machine learning is an incredible advent of technology, but it alone can’t get the job done. By informing our AI with continuously-updated intelligence, it learns more and more about not just the content it needs to detect but the context of that content as well. While some companies focus simply on bolstering their tech, we make sure ours is always learning, feeding it regularly with data from intelligence collected in more than 90 languages. In an ever-changing world, this is what helps us make sure our clients are always ahead of threat actors. This model is what has helped us protect more than 3 billion global users against the most complex threats on the internet.

7. What are your thoughts on facilitating partnerships and collaborations for your organization? What strategies do you think work best for growing a business?

For Trust & Safety companies to provide adequate responses to threats, collaboration is a necessity. ActiveFence advocates cooperation across the industry for the purpose of knowledge sharing and as a growth engine to enable scalability. We work with various partner organizations, including the Family Online Safety Institute, the National Center for Missing & Exploited Children, and the World Economic Forum, among others. We are proud to serve as a resource to these organizations, presenting them with intelligence findings that empower them to make informed decisions about policies and best practices in their work.

8. Tell us more about your leadership style. What, according to you, drives employees to reach their full potential?

Many factors can drive employees to reach their full potential, including a clear sense of purpose and direction, opportunities for growth and development, and a supportive and collaborative work environment. As a CTO and as a leader in general, I know I play a critical role in creating these conditions and helping my team members achieve their full potential.

I look at the big picture and understand how technology can be leveraged to support the organization’s overall business goals and objectives.

Ensuring a finger on the pulse of our internal innovations as well as external ones helps me to provide the best guidance and direction I can to the team when it comes to technical matters

9. With the continuous evolution of technology, How do you continue to stay one step ahead of bad actors online?

Threats are complex, and bad actors constantly deploy new tactics to further their operations while evading detection. Instead of tackling these threats as they pop up, ActiveFence advocates a proactive approach to platform safety, collecting off-platform intelligence about threat actors and their techniques and plans. Having a finger on the pulse means we can detect threats before they become a problem for platforms.

In practical terms, this information is not only shared with platforms but is used to teach our contextual AI to maintain our edge in the technological sphere. By having a breadth and depth of industry knowledge available, we can ensure that our solution is the most advanced on the market. In addition, the intelligence we collect is stored in a database where it’s given a risk score that companies can use to determine triaging and prioritization for threats that occur on-platform. By logging the intel that’s collected and determining the potential threats it presents with a statistical model, our solution also helps Trust & Safety teams work more efficiently. Decisions can be made confidently and at scale with a technology that’s constantly learning.

10. According to you, what trends will dominate the Trust and Safety industry in the coming years? What potential does AI hold in addressing/solving the most pressing issues?

T&S as currency / value-driver

The impact of the DSA and other new regulations coming into effect

The continued importance of Safety by Design in new platforms that pop up and changes to existing platforms

T&S Operating System

Generative AI as a disruptor to the industry and how Trust & Safety teams can ensure it’s a safe tech to have on platforms

Shift left

11. Please mention the awards and accolades received by ActiveFence so far.

AI Breakthrough Awards 2022 for our proactive detection technology.

12. Being a leader, how do you cope with the stress that comes with a position such as yours?

Since my partners and I founded ActiveFence, my role has changed as the company has grown and evolved. Throughout the years, I have developed my principles and surrounding environment to learn and adapt to the changing reality continuously.

Time Management: Time is my most precious resource. I’m a big fan of the Getting Things Done system by David Allen, which I have been practicing since 2015. The guiding principles in that book have allowed me to develop a system in which I can guarantee I’m efficient and effective in task management.

Clear boundaries: Work time is for work, but I make a conscious effort to ensure that any time I spend with my family is without distractions. Given that my work takes most of my day and night, every minute with my family is precious to me. My team: I’m lucky to be surrounded by an incredible team – my business partners, colleagues, and mentors. Israel has a fantastic tech ecosystem, and everywhere you look, there’s someone creating something new, and it’s an inspiring place to live and work. I’m constantly in the company of innovative people, and that’s played a part in helping me with my leadership and self-development.

Iftach Orr

CTO & Co-founder Active Fence

Iftach Orr is the CTO and co-founder at ActiveFence, a company that enables Trust & Safety teams to be proactive about keeping users safe from malicious online activities. Iftach has over 15 years of experience in the fields of large-scale systems, machine learning, and security. Previously, he worked as the VP of R&D at SimilarWeb and also served in an intelligence unit in the Israeli military.

The post AITech Interview with Iftach Orr, CTO & Co-founder ActiveFence first appeared on AI-Tech Park.