By Stephen DeAngelis
A couple of years ago, business journalist Ben Sherry observed, “As artificial intelligence continues to disrupt industries, the demand for systems that can interact more naturally with people has greatly increased.”[1] He added, “Problem is, creating such a system is no easy feat.” Around the same time Sherry was making his observations, technology writer Joe McKendrick was insisting, “Artificial intelligence needs to speak the language of business, not the other way around.”[2] I agree with both of them. In a recent article, I wrote, “Artificial intelligence (AI) has emerged as a transformative force in the business world, offering extraordinary potential to unlock new capabilities and competitive advantages across enterprise environments. Yet as organizations invest billions in AI initiatives to improve large-scale operations, many find themselves grappling with a sobering reality: AI projects are not guaranteed successes. Many, in fact, have failed to realize their anticipated business value.”[3]
This track record has made many business executives hesitant to forge ahead with AI projects. However, Yevhen Kupchak, Head of Business Apps Development & Delivery Manager at Energame, believes in the old adage, “He who hesitates is lost.” He explains, “AI is not just a tool but a powerful catalyst for change, shaping the future of work and production capacity. Companies and employees must prepare for this challenge by developing new skills and adapting to evolving conditions. AI offers enormous opportunities for those ready to embrace and leverage them. Don’t miss the chance to be part of this revolution. Start learning AI today, invest in training, and stay ahead to face the future with confidence.”[4]
Fostering AI Success
McKendrick notes, “Almost every business leader on the planet, 94%, believe AI will be critical to success.” Nevertheless, he reports the business world is full of “AI underachievers.” He writes, “Issues diminishing the impact of AI include challenges improving its business value and a lack of full executive commitment. Industry leaders and observers in the trenches agree that it is these organizational issues, rather than technical issues, that are holding back progress.” This isn’t really surprising. As Niccolo Machiavelli wrote centuries ago in his classic The Prince, “There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in the introduction of a new order of things, because the innovator has for enemies all those who have done well under the old conditions, and lukewarm defenders in those who may do well under the new.” The lesson to be learned here is that AI success relies on making the people who use it successful. Studies show that is not happening.
One such study was conducted by Dynata on behalf of Infragistics. According to Dean Guida, Founder of Infragistics, the study found, “The workplace continues to undergo a rapid transformation as we now see more employers implementing AI tools into their businesses to drive efficiencies and increase productivity. While employers have specific intentions for AI in the workplace, it’s clear that they’re not aligned with employees’ current use of AI. Much of this comes down to employees’ education and training around AI tools.”[5] In my article mentioned above, I stressed the importance of making sure all employees were comfortable using AI. I explained, “The true value of an AI project often hinges on its accessibility and usability for employees across the organization, not just data scientists. An AI solution that can be effectively leveraged by nontechnical staff has the potential to create widespread impact and drive significant value. When measuring business value, it’s crucial to assess the ease of adoption, the quality of the user interface and the level of training required for general staff to utilize the AI tool effectively. High adoptability leads to increased usage, which in turn generates more data and insights, creating a virtuous cycle of improvement and value creation.”
How do you create the virtuous cycle I discussed? Juho Kim, an associate professor at the Korea Advanced Institute of Science & Technology (KAIST), offers a few suggestions. The first suggestion he makes is ensuring that users are incentivized to use AI systems. He explains, “Unfortunately, one of the most common patterns we see in Human-AI interaction is that humans quickly abandon AI systems because they aren’t getting any tangible value out of them.”[6] He believes workers can be incentivized to use the systems if they play an integral role in helping to develop them. He calls this a “co-learning” development process. According to Kim companies and developers should also consider the social dynamics that are in play. How do users interact on a daily basis? How can AI improve and/or enhance these interactions? Does the AI system help the user satisfy the needs of the customer? As McKendrick observes, “The key is to make decision-makers more comfortable and knowledgeable about AI, build organizational support for AI, and keep the focus directly on how it can help the customer.”
To be truly useful, AI tools need to be routinely used. That only happens when users conclude the benefits of using it are demonstrable and repeatable. AI systems need to provide expected results in a familiar format. Rajesh Raheja, chief engineering officer at Boomi, explains, “Organizations are more likely to buy into AI-based approaches when they directly tie to demonstrable customer value.”[7] Kim also believes that AI systems need to be adaptable so they can continue to provide value as users’ needs change.
Concluding Thoughts
Guida concludes, “AI has the potential to change the way teams work by allowing them to trade in highly repetitive, mundane tasks for more high-level, strategic projects. But the only way to unlock AI’s full potential in the workplace is to properly prepare employees and companies’ overall operations. Employers and employees are certainly on their way, but there’s still a lot more to be done, starting with centralizing data, increasing education around AI and reskilling and upskilling employees through continued training.” As McKendrick observed, it’s “organizational issues, rather than technical issues, that are holding back progress.” Ensuring that people will be successful using AI solutions is the best way of ensuring that AI projects will be successful.
Footnotes
[1] Ben Sherry, “The Demand for A.I. Is Huge. The Hurdles to Widespread Adoption Are Even Bigger,” Inc., 1 December 2022.
[2] Joe McKendrick, “Artificial Intelligence Needs To Speak The Language Of Business, Not The Other Way Around,” Forbes, 27 November 2022.
[3] Stephen DeAngelis, “For AI Adoption Success, Focus On These Five Critical Value Drivers,” Forbes, 13 September 2024.
[4] Yevhen Kupchak, “The AI Train is Leaving: Are You Onboard?” Hackernoon, 18 July 2024.
[5] Editorial Team, “Employers Are Introducing AI: 77% of Workers Lost on How to Use It,” Inside AI News, 29 August 2024.
[6] Sherry, op cit.
[7] McKendrick, op cit.
