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Why Most Enterprise AI Investments Are Failing and What Leaders Must Fix in 2026
Sending employees to AI courses, subsidizing generative AI tools, organizing internal AI hackathons. Over the past few years, many companies have poured significant resources into chasing the AI wave and promoting AI adoption.
Former Google Taiwan Managing Director Lee-Feng Chien offers a blunt assessment. “2026 will be the reckoning year for enterprise AI.”
By reckoning, he means companies will begin seriously examining return on investment and real results.
“For the past three years, everyone has been empowering individuals and teaching employees how to use AI. That does not create true value for the enterprise,” Chien explains.
Chien observes that whether companies invest in employee training or purchase new tools, most employees reach what he calls an 80 point level. They learn enough to do their jobs more efficiently, leave work on time, or avoid overtime. Few push themselves to deliver 120 point performance for the company.
Yet companies invest in AI to increase revenue or reduce costs. “The goals of employees and the goals of the enterprise are fundamentally not aligned,” Chien notes.
When objectives are misaligned, AI investment produces limited results. Companies spend money training staff. Employees do learn to use AI. But overall competitiveness across the organization does not improve in a meaningful way.
Small and medium sized enterprises face an even more awkward situation. Even if AI improves recruitment efficiency, their hiring scale is limited. In the end, the cost savings are modest.
If companies want to build unique and irreplaceable competitive advantages in the AI era, Chien argues they must start by transforming their core capabilities.
He points to the example of a specialty chemicals company that used AI to analyze vast volumes of academic research. By extracting innovative combinations from scientific literature, the company identified improved chemical formulas. That not only strengthened the competitiveness of its core products but also generated tangible revenue growth.
Chien encourages leaders to ask a basic question. What is your core competitive advantage?
“If you are in chemicals, it is your formula. If you are in ecommerce, it is recommendation and pricing. If you are in finance, it is risk control and fraud prevention,” he says. “Only by applying AI to strengthen core capabilities can you unlock real monetizable business value.”
Once an organization identifies the right focus, how should it align company goals with employee goals?
Chien believes motivation and incentives are essential.
Companies should identify employees who are already using what he calls shadow AI, meaning AI tools used privately without formal approval. These individuals did not wait for corporate training budgets. They began exploring on their own.
“It is much easier for someone who understands the business to learn AI than for someone who understands AI to learn the business,” Chien observes.
After identifying these highly motivated employees, companies should assign them real business problems to solve and allow room for failure and rapid iteration.
Incentive systems must also evolve. If AI is framed purely as a way to cut headcount, resistance is inevitable.
“When the company earns more and employees earn more, then they will fully commit to making it work,” Chien says. Aligning incentives around new revenue creation rather than cost reduction is what truly synchronizes organizational and individual goals.
Looking ahead to 2026, technology will continue advancing and models will grow even more powerful.
“Ninety nine percent of people are afraid to move forward,” Chien reflects. “What we need now is an explorer’s spirit.”
Do's
Focus on transforming core products and use AI to achieve things that were previously impossible rather than simply improving efficiency.
Identify applications that can generate revenue and deliver measurable ROI. Creating new income streams matters more than reducing costs.
Dont's
Do not expect employees to automatically empower the organization. Align organizational goals with personal incentives.
Do not use cybersecurity as an excuse for inaction. Security matters, but the risk of not advancing AI may be even greater.
About Lee-Feng Chien
Born in 1963, Lee-Feng Chien holds a PhD in Computer Science from National Taiwan University. He previously taught in the university’s Department of Information Management and served as Deputy Director of Academia Sinica’s Institute of Information Science. In 2006, he joined Google Taiwan as its first employee and later became Managing Director. After retiring in 2020, he has focused on mentoring startups and currently serves as a board member of AI companies iKala and Appier, as well as an independent director at several publicly listed firms.
This article is republished with permission from Manager Today. Original article: 花錢讓員工學 AI，公司競爭力卻原地踏步？簡立峰揭企業 AI 投資的致命盲點
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