Gartner's 4 top technology directions in 2026

Gartner's predictions for 2030 already need updating. Not because they got them wrong in the first place, but because change is moving faster than anyone planned. Here are the 4 top technology trends in 2026, straight from Gartner's Sr Director Analyst, Tristan Iles, who shared them at this year's Gartner Application Innovation & Business Solutions Summit in London. 

This year's APPS agenda focused on how to adapt to the AI-native era, align innovation with business goals, lead cultural change, and cut costs while modernizing your application portfolio. Out of many strong sessions, we picked the one that shows where organizations should focus right now, from getting real value out of AI investments, through data and governance, to modernizing the application portfolio. 

1. Maximizing value from AI investments

AI experiments run independently across finance, HR, and other departments, with no coordination between them. Much of this work stays as siloed experimentation instead of generating real value. That's why, as Tristan Iles stated, most AI work done to date needs to be re-evaluated.   

Only 45% of organizations have a recognized AI or data strategy framework, and only 16 to 17% believe they train their workforce on AI well enough. Initiatives are scattered, and IT departments are struggling to create unified platforms that would integrate all of them.  

2. Addressing AI governance and data challenges

50% of senior leaders admit customer data has left their organization through unsanctioned AI tools while 59% of CSOs have found evidence of employees using AI features in SaaS applications that had not been officially sanctioned. 

The governance challenge is real. But as Iles pointed out, rules that are too strict just create shadow IT. When that happens, people bypass the controls instead of following them. Add poor data quality on top, which drives up token costs and model errors, and the problem compounds. 

3. Modernizing enterprise application infrastructure

Gartner predicts that 50% of applications will become obsolete unless they're rebuilt with AI features. As Iles explained, not every application needs an AI agent, but AI-capable architecture is now the expectation. 

The biggest blocker is technical debt. 50% of leaders admit it's a problem they aren't managing. Here's the twist Iles pointed out: AI turns out to be very good at fixing technical debt, which makes remediation one of its highest-value uses.

4. Build vs. buy considerations

Over the past 10 years, vendors piled feature after feature onto their platforms. The problem is that most clients use only a fraction of what they pay for. That gap is now driving the build-vs-buy debate. People look at all that unused functionality and start asking why they're paying for it. 

The bigger risk is lock-in. Bundling makes it worse, and even building your own solutions ties you to something eventually. So choose a vendor that gives you room to move. Look for one that lets you adopt what you need, keep your data portable, and run AI on your terms rather than theirs. 

Key recommendations

Iles closed with a simple filter for the AI-native era. Chase business value, not hype, and don't run AI without the data governance to back it. The same discipline applies to how you buy, how you modernize, and how you staff. Here's where to start:  

• Ask where the real business value is before you green-light any AI initiative, and pair every one with a data governance effort. 
• Evaluate vendors as platforms rather than picking modules off the edges, and watch for lock-in hidden in bundles. 
• Move from scattered experiments to one coordinated, platform-level strategy. 
• Frame modernization around business outcomes, innovation, and lower risk. That's what gets the board on board. 

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