September 10, 2026 l Manila Bulletin
For more than two decades, companies have been told to transform or risk getting left behind. First came the internet, followed by mobile, cloud, social media, big data, and digital platforms. Each wave brought predictions that old ways of working would disappear and businesses would face disruption.
We called much of this digital transformation.
Now we are hearing the exact same language about artificial intelligence. Companies are forming AI teams, launching pilots, purchasing tools, and urging employees to adopt the technology. Executives worry competitors will move faster, and consultants are preaching transformation once again.
It feels familiar, but it is not the same.
Having spent years working in tech and advising organizations through several waves of change, one lesson continually resurfaces: technology is usually the easy part, while changing the organization is harder.
That proved true during the digital transformation era. Many companies spent heavily on enterprise systems, mobile apps, cloud platforms, analytics, and customer experience tools, yet installing new technology did not automatically transform their operations.
McKinsey highlighted this years ago. In a 2018 survey, more than eight in ten respondents reported undertaking digital transformation efforts over the previous five years, yet successful execution remained elusive. Shifting systems to the cloud did not magically make an organization digital either, as McKinsey warned that simply migrating legacy applications would fail to yield expected gains.
We learned that lesson the expensive way. A slow process put online is still a slow process. A bad customer journey converted into an app is still a bad customer journey. A silo moved to the cloud is still a silo.
AI is exposing this exact problem.
Companies purchase AI tools without first asking what work needs to change. Employees receive AI assistants but continue following identical approval chains, reporting structures, and procedures. Organizations proudly announce dozens of proofs of concept, only for executives to ask six months later where the revenue or savings are.
The numbers bear this out. Stanford’s 2026 AI Index reports that 88 percent of surveyed organizations used AI in at least one business function in 2025. Generative AI reached 53 percent population adoption in just three years, moving faster than either the personal computer or the internet.
Adoption, however, is not transformation.
McKinsey’s 2025 research revealed that nearly two-thirds of respondents had not yet begun scaling AI across their enterprise, with only 39 percent reporting an enterprise-level impact on EBIT. The companies extracting real value were not just installing tools; they were redesigning workflows.
This is where the parallel to digital transformation is strongest. Both demand changes in processes, skills, leadership, culture, and metrics. Both fail when treated strictly as IT projects, and both generate initial excitement followed by frustration when expected returns stall.
Yet there is a fundamental difference. Digital transformation mainly changed how work was done, whereas AI can change who—or what—does it.
When a company digitized a purchasing process, humans still made most decisions. When a bank launched mobile banking, employees still executed the underlying back-end activities. Moving systems to the cloud altered the infrastructure, but human roles remained recognizable.
AI crosses that line.
An AI system can draft reports, analyze datasets, answer customers, write code, review legal documents, recommend actions, and increasingly execute end-to-end task chains through autonomous agents. The technology is shifting from supporting human labor to performing it.
That makes the AI wave intensely personal. Employees rarely worried cloud computing would take their jobs, nor did they enter the office wondering if a CRM system would replace them. AI raises that concern immediately.
Then there is the issue of speed.
Traditional digital transformation initiatives often spanned years as systems had to be purchased, configured, integrated, and rolled out. AI enters an organization without waiting for executive approval. An employee can open a tool today and instantly alter how they write, research, analyze, or build. This creates a paradox where AI transformation can spark from the bottom up before leadership establishes a clear strategy.
It also introduces new risks. AI can deliver incorrect answers with absolute confidence, sensitive company data can easily leak into public models, employees risk over-relying on machine-generated output, and decision-making logic becomes harder to audit and explain. Governance can no longer be an afterthought added post-deployment.
The core lesson from digital transformation remains paramount: do not start with the technology, but start with the business problem.
Examine how work happens today. Ask why ten people touch a process that only requires three. Question why a report takes five days to generate. Find out why customers repeatedly call about the same issue. Only then should you determine where AI can eliminate friction, enhance decision-making, or unlock capabilities that were previously impossible.
Digital transformation proved that buying digital tech does not make a company digital. AI will teach us the exact same lesson: buying AI does not make a company intelligent.
The organizations that mastered digital transformation eventually stopped asking what technology they should buy and started asking how their business should work differently.
That question matters even more today.
The AI wave may look like just another tech cycle, but it isn’t. Digital transformation changed our tools; AI is changing the fundamental nature of work. Companies that grasp that distinction won’t just adopt AI faster—they will redesign themselves around it.
***The views expressed herein are his own and do not necessarily reflect the opinion of his office as well as FINEX. For comments, email rey.lugtu@hungryworkhorse.com. Photo is from Pinterest.