A Shift from All-Knowing Bots to Purposeful Automation
Why Have Enterprise AI Strategies Not Worked as Expected?
The growth of Artificial Intelligence in recent years has changed a lot of things, especially in big companies. Many businesses tried to build huge AI systems that could handle almost every task. But now they see that just making AI bigger does not always bring good results. In 2026, companies will change their plans. They will pick smarter ways to use AI in certain parts of their work instead of trying to cover everything.
Andy MacMillan, who runs Alteryx as CEO, says companies will start to use AI in narrower ways. They will fit it into daily jobs to make things run better. This change will touch who controls AI, how plans are made, and why data leaders matter so much.
In 2025, many companies pushed hard for giant AI agents that knew everything. Those big projects hit walls. They did not connect well with the usual ways people do their jobs. MacMillan points out that when AI does not match real needs in the company, it causes big problems. Many teams wasted time and money.
Next year, things will turn around. Companies will build smaller AI tools that fix exact issues in one area or department. For example, an AI might help only the marketing team send better emails, or it could check inventory for the warehouse crew. These tools won’t try to do too much. They will fit right into the steps people already follow each day. That way, everyone can see clear improvements fast—like fewer mistakes or quicker reports.
What Will the Future of AI in Enterprises Look Like?
In 2026, no one will chase after one huge AI system that does it all. Companies will add AI to tools they already use. The main idea is to put AI inside normal work steps. Standalone bots that sit alone often fail because they don’t match what the business really needs.
MacMillan talks about moving from just trying things out to actually getting work done. Firms will notice AI works best when it solves one clear problem and shows real gains. Think about a retail company using AI to guess how many products will sell next month—that saves money on extra stock. Or a bank might use AI to spot unusual transactions quicker, which cuts fraud losses. I’ve seen cases where simple AI in customer support reduced wait times by 30%. Those kinds of wins matter more than flashy demos.
Who Will Own AI Strategy in 2026?
The Shift in AI Leadership
For several years, IT teams held most of the money for AI work. That will change in 2026. As companies choose focused AI for specific areas, leaders in those areas will take charge. People in sales, marketing, or finance know the daily headaches better. They can spot where AI helps right away. IT folks are farther from the front lines, so they sometimes pick tools that don’t fit perfectly.
This means budgets will move. Departments will decide how to spend on AI. They can buy or build what matches their goals exactly. In one company I heard about, the sales head picked a small AI add-on for their CRM. It boosted close rates by 15% in just months. Stories like that will become common.
The Role of IT Departments in a Decentralized AI Landscape
Big companies often kept AI decisions in one central spot, usually with IT or a special AI group. But MacMillan expects more freedom for regular departments. Sales leaders or finance chiefs will pick their own AI tools and pay for them.
This setup lets teams move faster. They won’t wait for central approval that takes forever. A marketing team, for instance, could grab an AI that writes basic ad copy and test it the same week. The whole company becomes quicker to try new things. We will see more CFOs or Chief Sales Officers leading AI choices. They will focus on results that help their numbers directly—like higher revenue or lower costs.
Of course, IT still has a job. They will help with security, make sure data flows safely, and fix big technical issues. But day-to-day AI picks will sit with the people who use it most.
The Evolving Role of the Chief Data and Analytics Officer (CDAO)
How Will CDAOs Adapt to the AI Revolution?
The job of Chief Data and Analytics Officer faces big changes too. In the past, many data leaders said everything had to be perfectly organized first. They wanted clean data pipelines before starting any project. That slowed things down a lot.
MacMillan says 2026 will bring a more practical view. Perfect data almost never happens—real companies deal with messy spreadsheets, old systems, and missing info all the time. Waiting for perfection means missing chances.
Data leaders should start using what they have now. Even if only 70% of the data is good, an AI tool can still find useful patterns. One manufacturing firm I know built an AI predictor with imperfect sensor data. It still caught machine breakdowns early and saved them thousands in repairs. CDAOs need to think like that: get value fast instead of polishing forever.
The new goal is simple—help solve business problems today. Deliver reports or insights that people can act on right away. That shift will make data teams more popular inside the company. No more excuses about “we aren’t ready yet.”
A Future of AI with Purpose
2026 will be a turning point for how companies handle AI. The old days of huge projects that promised the world but delivered little will fade. Instead, teams will weave targeted AI into regular work. Results will show up in real numbers, like faster processes or happier customers.
Business leaders in each area will guide AI choices more than before. They know their challenges best. Data heads will learn to work with imperfect setups and still win.
When companies focus on AI that has a clear job, things get exciting. Tools become helpful partners, not complicated extras. This practical path should bring more success stories—and fewer regrets—across many industries.
