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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Can you tell us about your background and journey that led you to establish cyberconIQ?

I am both an academic and a consultant/entrepreneur who has been studying technology trends, information privacy and security issues and considering the impact of disinformation on society for many years.  In both my professional practice and personal experience, cybersecurity – and now AI which will rapidly transform this important issue even further – are technology problems with a human dimension that more technology alone cannot fix.  So we need to blend psychology and technology better together in order to address the human elements of cybersecurity risk with proven behavioral science methods instead of simply pretending that humans are programmable like machines – they are not.  Knowing something is not the same as doing something, so we founded cyberconIQ to create pathways to voluntary changes in user behavior that creates a security 1^st culture inside any organization more effectively than generic training that is unengaging and has proven to not have any meaningful impact on user behavior.

Dr. Norrie, could you please explain how cyberconIQ’s proprietary platform utilizes behavioral psychology to measure and manage personalized cybersecurity training and education programs?

By blending in proven elements of behavioral science including trait-based personality theory, understanding habituation and pattern interrupts as well as  the value of supporting humans as part of the solution instead of the problem, we EMPOWER  humans as your last line of organization defense against increasingly sophisticated attacks. Additionally, we can prove in side-by-side client studies that we can virtually eliminate phishing as a significant risk to your organization using this patented method.

Your research on third-party cyber attacks is fascinating. Could you share some key insights from your research and how modifying online behavior can effectively mitigate cyber risks associated with third-party interactions?

Social psychology helps us understand human behavior and different people respond differently to different social settings, stimuli and situations.  While crooks know this, they only know it because in a wide-scale attack only SOME humans among MANY will be vulnerable to any particular kind of attack.  On the surface, this may seem random.  But it is not.  Our research proves that different types of personalities respond differently to different kinds of online threats.  Now, in this case, I am not suggesting the content of the attack drives the vulnerability because it doesn’t.  Rather, it is the context of the threat architecture itself that matters – for instance, does it invoke authority or urgency as influencing factors.  Does it incorporate elements of persuasion derived from ego or fear for example?  While there are many factors in our model related to how we help users understand themselves, their profile – once established – helps us and them identify the most likely types of third party attacks that may make a person vulnerable and why – and then trains accordingly.  This method is sophisticated enough to take less time and is more effective than generically training everybody on every threat which, if someone is not particularly vulnerable to or which they can easily spot, why should they have to be trained on something they already know?  On the other side of that, if you train only against easy and frequent types of attacks you may miss a vector that, while rare, may be important for some of your users to be trained on – those who are most vulnerable to it – and not train everyone because that’s unproductive.

Often, there is a gap between the technology implemented by organizations and the potential for human error. How can individuals and organizations bridge this gap to create a more robust cybersecurity posture?

I opened with the premise that more technology cannot solve a problem that new technology originally created.  That is because for most technologies, there is still an operator who is a human.  And humans are not programmable just because they are told what they should do, does not mean that they will do it.  So how do you inspire individuals to think of themselves not as a weak link in the chain, but the strongest?  And then use that dedication to new security habits to improve your organizations overall security posture one human and one style at a time?  And it works. Very well.

Based on your expertise, what practical strategies can individuals adopt to enhance their personal cybersecurity, especially when handling sensitive information online or engaging with third-party platforms?

Of note, no organization is too small to be attacked.  And that is because almost any organization, of any scale, has something of value to steal; or has useful intellectual property that could be exploited if shared; or is a pathway to something that somebody else wants for some reason.  So, we need to have a natural skepticism about all things digital and online – questioning the sources of information; considering the risks of fraud and crime online; considering all requests as if they may have an ulterior motive, and certainly never feeling any need to do something quickly.  All of these are warning signs:  psychologists call this cognitive dissonance.  It is a nagging warning from our intuition or judgment that something may be amiss…in that instance, the hardest thing we must learn to do as humans is STOP.  This is the first letter of our proprietary and patented SAVE^TM method, and that first step is often the hardest to consistently provoke.  Once the user has stopped that first impulsive or instinctive response, we move on the assessing the situation using critical thinking skills, tips and techniques we provide to enhance their knowledge of security best practices.  The third step is to verify the source and identify of all participants in the communication chain directly and using different and validated means; and if we do all of this and still have doubts, to engage a security expert or peer before proceeding.  This simple SAVE mnemonic is useful for any of your reader’s to remember!

As cyberconIQ continues to grow and expand, what are your long-term goals and vision for the company’s impact on the cybersecurity landscape?

We are on a mission to help right the balance between attackers and defenders to help make the internet a safer place for all.  Today, crime-as-a-service is expanding rapidly and cybercrime is often a low cost, high reward venture with few legal consequences.  This has created a plague of loss, embarrassment and fear that we must arrest.  AI is going to make this even more profoundly felt globally as criminals get access to and exploit AI technologies against us before we even realize what is happening.  That is one reason that we introduced techellect.com, part of a suite of public service tools – all freely available for use by anyone – to help replace ignorance with knowledge and to reduce the fear of AI but also to help instruct users on maximizing its benefits, while avoiding unknown risks.

Cybersecurity is a collective effort that involves the entire organization. How can leadership and organizational culture play a role in promoting cybersecurity awareness and fostering a cybersecurity-conscious culture among employees?

In our studies, we have found that “tone from the top” is an essential ingredient to embedding a security 1^st culture in the organization.  Training alone cannot ever succeed in making cybersecurity everyone’s mission.  Instead, this must be actively fostered and supported and employees must see it, feel it and hear it as a continual priority if you want them to become engaged in and remain committed to the security mission.  As cybersecurity professionals or technologists, we also need to reconsider our language – for instance – “Zero trust”.  While we understand what this means as professionals and it is highly descriptive, how would YOU feel if someone said the only way to handle a problem is to not trust anyone?  While that is a bit of an exaggeration, it is not an exaggeration of the perception of this to a user who is not a technologist and only feels like more a part of the security problem, in this instance, versus part of the security solution.  So, we prefer the term “Absolute Confidence” instead!  And that subtle change also works.

The cybersecurity industry is constantly evolving, and competition can be fierce. How does cyberconIQ stay ahead of the curve in terms of research and innovation to maintain its unique position in the market?

We are data-driven and science-based.  We are continually assessing the threat landscape for new vectors, methods or hybrid combinations of attack architecture and testing them against styles and profiles striving to ensure that defenders have as much success defending as attackers do in attacking!

In your experience, what challenges do organizations typically face when trying to change employee behavior in regard to cybersecurity, and how can these challenges be overcome effectively?

Most of what constitutes security awareness training (SAT) is generally not actually designed by educators with a view of ensuring that the andragogy will actually effectively ensure an educational outcome.  What does that mean in plain speak?  Most of what is currently being done is boring, ineffective and most employees would rather have a root canal then do more security training.  So, how does that inspire them to change their habits? Does the combination of your training and then phishing simulations only catch them doing something wrong instead of doing something right – and if so, how should that make them feel?  Does the training over-simplify a complex problem at the risk of making it unimportant?  Or does it only raise fear instead of confidence in your employees that you do trust them as your last line of defense?  We prefer to think of what we do at cyberconIQ as education rather than training – we teach people about themselves in ways they find effective and inspire them to become part of a security 1^st culture with a mission of keeping themselves safer online – both at home and at work.  And guess what?  We must be doing something right because more than 78% of learners on our system voluntarily consume the user resources provided to them on our system NOT assigned to them as mandatory?  So with your current vendor, when was the last time you had an employee ask your for MORE security training instead of LESS?  That is what we can provide – proof that employees learn effectively, love doing it and that it efficiently improves your organization’s risk posture permanently.

Finally, what advice would you give to aspiring entrepreneurs and cybersecurity professionals who are passionate about making a positive impact in the industry?

Dr. James Norrie

Founder & CEO of cyberconIQ

Dr. James Norrie, Founder & CEO of cyberconIQ. Dr. Norrie has more than 30 years of experience in business management, psychology and the cybersecurity industry. He was the Founding Dean of the Graham School of Business at York College of Pennsylvania, and is currently a tenured faculty member at the school.

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In the realm of XAI technology, the paradigm of bridging the gap between human understanding and the extreme complexity of Artificial Intelligence (AI) has introduced explainability in AI, a.k.a. XAI, which is certainly a revolutionary approach that unveils XAI methodologies such as deep learning, decision-making, demystifying safety-critical systems, regulatory compliance, and risk management for human understanding. Such a trajectory of disconcerting eventuality emerges when there is a fusion of cutting-edge AI (XAI) technologies and cognitive transparency, where logic governs these algorithms through an aura of inscrutability, defying human comprehension and explanation alike.

❝Unchecked machine learning models possess the potential to transform into what she aptly termed “Weapons of Math Destruction.” She insightfully pointed out that as these algorithms ascend in efficiency, assuming an air of arbitrariness, they concurrently descend into a realm of unaccountability.❞ Cathy O’Neil is an adept mathematician and author.

XAI represents a transformative leap in AI technology that doesn’t just produce outcomes but also stays equipped with the ability to articulate the ‘why’ and the ‘how’ behind each derived decision. The ability of the XAI model stems from a transparent rationale and a comprehensible decision-making process encoded with the XAI strategies and efficiencies. 

Balance between XAI and Performance Trade-off

In this AI digital age, the demand for transparency and trust is paramount, especially in situations that necessitate the implementation of the social “right to explanation.” XAI methodologies allow for an inherent design that provides human-comprehensible explanations for the decisions they arrive at. Whether it’s an urgency in the banking sector due to the swift evaluation of AI techniques and algorithms, or whether it’s about approving a loan application, diagnosing a medical condition, or even deciding the course of autonomous vehicles, XAI’s explanation becomes the bridge between a seemingly enigmatic algorithm and the human need for deeper learning and understanding. 

Moreover, what’s contributing to the XAI’s complexity is the proliferation of big data, the rise in computing power, and the advancements in modeling techniques such as neural networks and deep learning. Notably, XAI’s implementation in high-risk business industries has helped teams within the organizations integrate AI and collaborate with their counterparts. 

2023 has also witnessed a notable uptick in the utilization of automated XAI machine learning (AutoML) solutions. In the current landscape, a range of models ranging from deep neural networks to decision trees have come into the AI tech realm. While simple-to-use models are far more interpretable, they still lack predictive power, whereas complex algorithms are critical for advanced AI applications in high-risk business sectors such as banking, securities trading, cybersecurity, and facial or voice recognition. In addition, the adoption of off-the-shelf AutoML solutions requires extensive analysis and documentation.

Today’s AI world is a vital cornerstone that not only fortifies the integrity of AI-driven systems but also elevates the accountability and compliance standards governing their deployment. This cornerstone is none other than Explainable AI (XAI). With its profound impact on regulatory frameworks and algorithmic accountability, XAI has emerged as an essential bridge between the intricate pathways of AI-driven decisions and the imperative need for transparency and comprehension. Let’s explore the realm of XAI applications and its top five use cases in high-risk industries for business, empowering you to harness its potential and drive success in your organization.

1. Illuminating Financial Landscapes: Building Trust in Algorithms

In the intricate world of financial services, where risk assessment models, algorithmic trading systems, and fraud detection reign supreme, XAI emerges as a beacon of transparency. By offering transparent explanations for the decisions shaped by AI algorithms, financial institutions not only gain the trust of their customers but also align themselves with stringent regulatory requirements. The synergy between XAI and the financial sector enhances customer confidence, regulatory compliance, and ethical AI deployment.

2. Healthcare’s Guiding Light: Enriching Patient Care

In the realm of healthcare, XAI’s impact resonates deeply. XAI in healthcare explains diagnoses, treatment recommendations, and prognoses that empowers healthcare professionals to make informed decisions while fostering trust with patients. By shedding light on the rationale behind medical AI systems, XAI enhances patient care and augments the decision-making process, turning complex medical insights into comprehensible narratives.

3. Personalized CX: The Business Advantage

Businesses embracing XAI unlock the potential of tailored customer experiences. By elucidating the reasons behind recommendations or offers based on customer preferences and behaviors, companies deepen customer satisfaction and loyalty. XAI transforms opaque algorithms into transparent companions that customers can trust, fostering long-lasting relationships between brands and consumers.

4. Navigating Autonomy: Trusting Self-Driving Cars

In the pursuit of autonomous vehicles, XAI plays a pivotal role in ensuring safety and instilling public trust. By providing real-time explanations for vehicle decisions, passengers gain the confidence needed to ride comfortably in self-driving cars. XAI bridges the gap between the intricacies of AI decision-making and human understanding, propelling the adoption of autonomous vehicles.

5. Justice and Transparency: XAI in Legal Proceedings

Legal proceedings hinge on transparency and fairness, and XAI offers a solution. By providing interpretable explanations for legal decisions, such as contract reviews or case predictions, XAI streamlines legal processes while ensuring accountability. Lawyers save time, clients gain insights, and justice is served in a comprehensible manner.

Empowerment through Clarity: XAI’s Timeless Promise

In the midst of the complex AI landscape encompassing machine learning, neural networks, and deep learning, Explainable artificial intelligence shines as a beacon of human-machine symbiosis. It unravels economic trends hidden within massive datasets and deciphers intricate biological patterns, spanning fields from econometrics to biometry. Across e-commerce and the automotive industry, XAI’s elucidations grant consumers and stakeholders unprecedented insights into the decisions shaping their experiences.In essence, Explainable AI isn’t just a technological advancement; it signifies a paradigm shift that transcends digital frontiers to touch the core of human understanding. By shedding light on the inner workings of AI systems, XAI empowers individuals, organizations, and societies to harness AI’s potential with clarity and confidence. As technology’s influence continues to expand, XAI stands as a guiding light, ensuring that machine-made decisions remain understandable, accountable, and aligned with human values. With XAI, tech enthusiasts stride into a future where transparency and comprehension illuminate the path to AI-driven progress.

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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In fact, companies strategically scaling their AI are experiencing nearly two times the success rate and three times the return from AI investments compared to companies pursuing siloed proof of concept solutions. With AI tools becoming more advanced and human-like by the day, it’s clear that the tech is here to make the future of business more effective.

So much technology is evolving and becoming available to streamline information processing and other administrative tasks that more business leaders have felt compelled to adopt AI tools. As a result, AI and machine learning have inevitably harmonized with the corporate world—in ways you probably don’t even realize.

One of the areas that will benefit the most from AI technologies is business communications, taking internal comms to the next level.

The Rise of AI for Business Comms

At a glance, internal comms seem less compatible with AI than other departments, partly because tools helping with content creation are perceived more as a threat than a productivity enabler. It’s easy to fall into the “Robots are coming!” anxiety, but the truth is that while AI is here to assist in repetitive or research-based processes, it can’t replace the human touch that is required to make real connections and recognize communication nuances. 

In fact, in an era of digital transformation and the restructured workplace, AI and machine learning stand to improve some of the biggest internal comms challenges—including alleviating employees’ engagement fatigue—while keeping them updated and connected with relevant materials.

As more businesses invest in digital communications systems, a massive data cache is produced. As a result, the data captured includes workplace discussions, thought processes, employee preferences, and other actions that are ideal for applying AI technologies to better identify and classify the content that organizations communicate to their teams. At Poppulo, for example, we use natural language processing to identify which content topics resonate the most within an organization. Our AI tools help automatically categorize communications by theme, including learning and education, IT or HR-related topics, diversity, and inclusion, or other key themes at scale.

The goal should be for businesses to understand how their internal comms content is trending and where they need to adjust. And using an automated platform helps determine which topics spark the most engagement and how to organize categories based on levels of engagement.

For professional communicators, AI is another tool in their arsenal to help improve the flow of information between business leaders and their employees. These interactions can tell us much more about how and why business decisions are being made, enriching the insights provided in employee surveys. 

A good business communication strategy is vital for any organization’s success because understanding what employees expect is essential for thriving in the market. AI can integrate into almost any IC strategy and optimize the plan of action, from marketing to operations to customer service—the applications are nearly endless. According to an Accenture report, three out of four C-suite executives believe that if they don’t scale their AI capabilities in the next five years, they’ll risk going out of business entirely. For many business leaders, it’s time to fully embrace AI and implement it into IC.

What AI and Machine Learning Can Do For Your Business Communications

Although we’re not exactly at the “robots taking over the world” phase in AI and ML, these tools will take over laborious and repetitive tasks and provide insights from the data they capture. These insights will help internal communicators supply employees with information specifically for them, making them more effective in their roles. Some AI benefits include:

• Assisting in relationship building
• Creating a positive work environment
• Boosting efficiency with automation
• Increasing productivity
• Reducing misunderstanding

By finding and delivering relevant information faster than humanly possible, artificial intelligence will help streamline communications and improve the ability of businesspeople to make smarter decisions. Here are a few examples of how.

Boost Engagement and Advocacy

According to Gallup’s State of the Global Workplace 2022 Report, only 21% of the global workforce is engaged in the workplace—with the other 79% withdrawn due to poor communication from IC. Communication is a necessary part of business, with different employees having their own personal preferences and expectations. From a business perspective, it’s imperative to nail down the proper messaging to minimize confusion and improve company culture. Using machine learning patterns makes it much easier for communicators to create accurate user personas for ad targeting and understand when employees will best engage with messages.

Leveraging AI to help with content creation frees up valuable time for internal comms teams to do the more strategic parts of their role. They pay more attention to the things that add the most value to organizations, like alignment with departmental priorities, channel planning, and meeting employees’ growing needs and demands without half of the struggle.

With the incredible amount of data we have at Poppulo, our AI-enabled platform provides business leaders and managers valuable insights into their company, helping them identify and address any issues affecting employee morale or satisfaction. Our clients send over 200 million email communications monthly with higher-than-average open rates. Since we offer AI-generated insights, we’re reducing the time it takes our customers to categorize and analyze those emails.

Improve Personalization

Internal comms utilizing AI for redundant tasks and personalization can ensure employees receive relevant content consistently and on time. AI can be used to segment employees based on factors such as job role, location, and interests, helping internal communications teams to tailor content to specific employee groups. This can also help employees feel seen and more engaged with the organization. 

This approach saves time, and resources; and perfectly symbolizes the future of AI and IC—working hand-in-hand to streamline tasks yet deliver high-quality human communications. When an employee doesn’t need to do extra work just to find resources, information, and subject-matter experts to do their job, they feel happier and more productive, reducing the time spent searching through irrelevant information.

Enhance Customer Experience

AI and ML technologies augment the customer experience. These technologies allow you to deliver personalized and user-centric experiences catered to your target audience at every touchpoint. With all the advantages AI tools can bring to your business, the ultimate beneficiaries are the customers. Not only do they receive the payoff of your newly optimized business processes and productive teams, but they’ll also form a strong, positive relationship with your brand and be more likely to repeat business and bring referrals—a win-win situation for everyone.

Propel Internal Communications

Internal communication is the lifeblood of modern businesses, and automated advancements can bring your organization a competitive edge while augmenting internal operations. Crafting the right message for the right platform to garner maximum impact with AI and machine learning helps you understand what your employees want so that you can deliver on it—and then learn from that interaction to do it better next time. 

In the near future, AI will be able to take extensive content, summarize it for you—even to a few lines for digital signage, extrapolate a mobile post to long-form text, and even make quick adjustments to messaging that will drive and optimize impact and response. Machines can filter through data and determine what information is most crucial to your employees, customers, and bottom line. Ultimately, AI is an incredible tool that will make the jobs of communications teams easier and more effective. But AI depends on the humans at the helm to strategically execute and lead businesses to stronger relationships both internally and with consumers.

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