Velocity Is the New Strategy in the AI Era
Two McKinsey studies published this year – the Global Tech Agenda 2026 and a new survey on resource allocation in the age of AI – arrive at the same conclusion from two different directions: in the AI era, the companies pulling ahead are the ones that move. Not the ones with the best predictions or the biggest budgets.
Read together, the two reports form a playbook. One tells you who should lead the change (technology leaders, now sitting at the strategy table). The other tells you how the change actually happens (fast, disciplined reallocation of money, people, and attention).
Here is what we took away from both – and what it means in practice for companies building software in 2026.
The CIO is no longer running IT. They’re running strategy.
McKinsey’s survey of more than 600 technology and business leaders found a widening split between two kinds of companies. In the first group, the CIO is still primarily modernizing infrastructure and defending a cost center. In the second – the top performers, defined as companies growing revenue and EBIT at 10%+ over three years – technology has become the growth engine itself, and the CIO is helping design the business, not just support it.
The numbers behind that shift are striking. Nearly two-thirds of top-performing companies say their technology leaders are deeply involved in shaping enterprise strategy. Almost half of top performers now co-create strategy continuously between business and technology teams throughout the year – roughly double the rate McKinsey measured in its previous survey. Annual planning cycles are quietly dying; iterative, quarterly business–tech alignment is replacing them.
Structurally, the winners are converging on product and platform operating models: cross-functional teams organized around customer outcomes rather than departmental silos, with fewer handoffs and decisions made in days instead of months. McKinsey points to DBS Bank, which reorganized into 30+ business-and-tech-led platforms and turned itself into one of the world’s leading digital banks as a result.
Our take: most mid-sized European companies don’t need to copy DBS. But they do need to stop treating software delivery as a procurement exercise. When your development partner sits inside your product teams, you get the same effect at a smaller scale: fewer handoffs, faster decisions, and technology work that maps directly to business outcomes.
AI is now the #1 investment – but budgets alone don’t move the needle
For the first time in McKinsey’s survey, AI has overtaken both cybersecurity and infrastructure modernization as the top technology investment priority. Half of all companies put it first; among top performers, that rises to 54%. And the money is following: 28% of top performers plan to grow tech budgets by more than 10% in 2026, versus just 3% of everyone else.
The catch is in the obstacles. A quarter of even the top performers admit they lack the data foundations to scale agentic AI securely. Nearly 33% of companies report talent and capability gaps and difficulty integrating AI into existing systems. And revealingly, top performers are far more likely than others to name change management – not technology – as their core scaling challenge.
McKinsey’s example here is instructive: Aviva, the UK insurer, deployed more than 80 AI models across its claims journey and paired that with a full operating-model and cultural transformation. The results: liability assessments 23 days faster, complaints down 65%, customer satisfaction up sevenfold.
There’s also a talent finding worth pausing on. Top performers are insourcing strategic technology capability and reskilling their own people, while laggards keep outsourcing commodity work and hoping vendors deliver transformation. At the same time, 40% of all companies are opening or expanding global delivery centers to reach international talent pools.
Our take: Choose partners that build your capability instead of renting you capacity. This is exactly why modern staff augmentation and nearshore models have evolved: embedded engineers who transfer knowledge, work in your workflows, and leave your team stronger – rather than a black-box outsourcing contract that keeps expertise on the vendor’s side of the wall. Romania’s engineering talent pool has become one of Europe’s most effective answers to precisely this equation.
The second study: being first matters less than being able to move
McKinsey’s July 2026 survey of 1,200+ executives adds the uncomfortable half of the picture. 40% of respondents believe their business model will need significant change within 3 years just to stay economically viable. Nearly the same share expect to be AI first movers – yet fewer than half of those aspirants have ever been first movers at anything.
What separates companies with a genuine track record of moving first? The research is blunt about this: first movers are better at committing resources despite uncertainty. They are more than three times as likely as late movers to reallocate at least 20% of their resources year over year. Ideas don’t create advantage; the capital, talent, and management attention behind them do. Apple didn’t invent the MP3 player – it reorganized around digital music before the market matured.
How do they pull it off? Four patterns from the data: They align on trade-offs, not just strategy – everyone knows what will be defunded, not only what will be funded. They decide on performance, not politics – killing yesterday’s priorities is the discipline most organizations lack. They use hard metrics (discounted cash flow, IRR) to compare unlike initiatives. And they correct course in frequent, small moves rather than rare, giant transformations. McKinsey’s geology metaphor is apt: many small earthquakes release less destructive energy than one big one.
What this means if you’re building software in 2026
Pulling the two studies together, four practical implications stand out for technology and business leaders:
1.Shorten your planning cycle before you grow your AI budget. Continuous business – tech cocreation predicts performance better than spend does. Quarterly reviews are a realistic first step.
2. Fund fewer things, harder. Reallocation is the muscle to build. If nothing in your portfolio got defunded this year, that’s a warning sign, not stability.
3. Buy capability, not just capacity. Whether you insource, reskill, or augment your teams, the test is the same: is your organization more capable after the engagement than before it?
4. Treat agentic AI as an operating-model change. Data foundations, integration, and change management are where scaling efforts stall – plan for them from day one.
The through-line of both reports is the same idea McKinsey closes with: the goal isn’t to be right the first time. It’s to shorten the cycle between action, feedback, and adjustment. Velocity is the new competitive advantage.
—
*Zeren Software helps European companies build that velocity: embedded nearshore engineering teams, AI integration, and custom software delivered inside your product organization, not outside it. If 2026 is the year your technology roadmap becomes your business strategy, let’s talk .
**Sources:** McKinsey & Company, “McKinsey Global Tech Agenda 2026” (February 2026) and “Why accelerated resource allocation matters in the age of AI” (July 2026).
