Editorial Team According to a recent McKinsey report, 80% of companies anticipate increasing their technology spending in 2026, a massive jump that shows a real pivot in how businesses think about their core operations. We’re talking about a fundamental rewiring, where tech capabilities are now seen as directly tied to market share and, frankly, survival. So with all this money flowing, how are the sharpest organizations actually getting a real business advantage out of it?
Key Takeaways
- Firms are pushing AI and generative AI into production to improve customer experience and find efficiencies, with a full 40% of companies already actively deploying it.
- Cybersecurity is still a top-three spending category for 70% of organizations, a reaction to tougher regulations and smarter attackers.
- The rush to the cloud is speeding up, as 60% of companies say they’ll be moving more of their workloads to public platforms over the next year.
- There’s a major talent gap for critical tech roles, and 50% of executives admit they can’t find the data scientists and AI engineers they need.
40% of Companies Are Actively Deploying AI and Generative AI
The move from AI science projects to actual, widespread deployment is the biggest story in McKinsey’s latest tech outlook. Four out of ten businesses aren’t just dipping their toes in artificial intelligence anymore. They’re embedding it, especially generative AI, deep into their operations. This is all about practical application. We’re seeing generative AI used to automate content creation for marketing campaigns, deliver personalized customer service that can actually scale, and even help developers write code faster. For example, a big financial institution I was working with just rolled out a genAI tool to handle the first draft of responses for their most common customer service emails, which lets their human agents spend time on the really tough, high-value problems. Their response times and CSAT scores shot up almost immediately, a clear ROI. The challenge for everyone else is no longer about *if* they should adopt AI, but *how* to scale it effectively without creating a mess. If you’re still stuck in the pilot phase, you’re already falling behind competitors who are banking the productivity gains and seeing happier customers.
Cybersecurity Remains a Top-Three Investment for 70% of Organizations
With data breaches becoming a constant, background hum of corporate life, it’s no shock that cybersecurity is a permanent top-tier investment. The McKinsey report backs this up, finding 70% of executives place it in their top three spending priorities. It’s about more than just checking a compliance box, even though new rules like Georgia’s Data Breach Notification Act (O.C.G.A. Section 10-1-910) are definitely turning up the heat. It’s about basic business survival. The threat actors are getting better, with sophisticated ransomware and state-sponsored attacks becoming the norm. So companies are finally spending serious money on advanced threat detection, rolling out zero-trust architectures, and actually training their employees. I’ve personally seen a huge spike in requests for penetration testing and incident response planning, especially from mid-sized companies in the Atlanta business district who used to think they were too small to be a target. The cost of a breach, in dollars and reputation, is so much greater than the cost of prevention. Any company still treating cybersecurity as a cost center instead of an absolute necessity for building trust and staying open for business is making a critical mistake.
“SpaceX announced revenues of $14.6 billion in its Q3 earnings on Tuesday, which it says was a growth of 161 percent year-over-year “fueled by our Super Intelligence (SI) segment and accelerated growth in Connectivity.””
60% of Companies Plan to Shift More Workloads to Public Cloud Platforms
The move to the cloud isn’t new, but the fact that it keeps accelerating, which McKinsey highlights, is the real story. A full 60% of businesses are planning to push even more of their core workloads onto public cloud platforms this year. That shows a much deeper buy-in that goes way past simple infrastructure-as-a-service. Companies are now tapping into advanced cloud-native tools for their data analytics, machine learning pipelines, and app dev. Just think about the scaling benefits: a retailer can handle the massive holiday shopping surge without owning racks of servers that sit idle for ten months of the year. The flexible, pay-as-you-go model offers strong cost benefits. But it’s not a magic fix. A good cloud migration demands a ton of planning, strong governance frameworks, and a real grip on cost management in a multi-cloud setup. Without that discipline, organizations get slapped with surprise bills and gaping security holes. The cloud can absolutely deliver, but you need a smart strategy and rigorous execution to see the benefits.
The Persistent Talent Gap: 50% Struggle to Find Qualified Tech Professionals
Even with all this new tech spending, there’s a huge roadblock: the talent gap. McKinsey finds that half of all executives are having trouble hiring qualified people for key roles, especially in data science, AI engineering, and cloud architecture. This is a serious bottleneck that directly stalls technology projects and slows down growth. I’ve seen it firsthand, projects get put on hold for a quarter or more just because the company couldn’t land one specialist in machine learning. Companies are in a street fight for a tiny pool of talent, which is jacking up salaries and making it impossible to hire quickly. To fix this, you have to attack it from multiple angles: invest heavily in upskilling your current people, build real partnerships with universities, and rethink your hiring requirements to look for aptitude instead of just a perfect resume. The idea that you can just buy technology to solve business problems is a fantasy. You absolutely need the right human expertise. The human element is everything.
Where Conventional Wisdom Misses the Mark: The Overlooked Power of Legacy Modernization
The McKinsey report is right to focus on AI and the cloud, but I think a lot of people are missing a huge, unsexy piece of the puzzle: legacy system modernization. So many big, established companies are still running their most important applications on tech that’s decades old. Everyone wants to talk about shiny “greenfield” projects on new platforms, but ignoring the massive technical debt buried in your legacy systems is a strategic blunder. The assumption is that these old systems are too hard or expensive to fix, so you have to replace them entirely. That’s often wrong. I’ve seen projects where a smart, phased-in modernization of an existing system delivered huge returns way faster and with less risk than a “big bang” replacement. For instance, just migrating an old mainframe’s database to a modern cloud-based one or wrapping legacy code in APIs can open up entirely new possibilities without a painful, multi-year rewrite. This method, often called the “strangler pattern” modernization, lets you transition gradually and see value along the way. It may not be as exciting as launching a new generative AI chatbot, but it fixes the fundamental plumbing and frees up budget you can then pour into those modern projects. It’s like building a brand new penthouse on a rotten foundation. Eventually, the whole thing is going to fail. The smart strategy is to balance your portfolio between exciting innovation and this kind of prudent modernization.
What’s the main takeaway from McKinsey’s latest tech report?
McKinsey’s outlook for 2026 shows a major increase in tech spending, with businesses focusing their money on practical AI deployment, cybersecurity defense, and accelerating their move to the cloud.
How are companies actually using AI now?
They’re moving past experiments and using AI and generative AI for real work, like automating marketing content, personalizing customer service at scale, and making software development more efficient.
Why is cybersecurity still such a huge investment?
It’s a top spending priority because threats are getting more advanced, government regulations are getting stricter, and companies see it as essential for basic operational survival.
What are the main roadblocks in moving to the cloud?
While adoption is high, companies struggle with controlling costs in complex multi-cloud setups, establishing effective governance, and maintaining security across all their cloud services.
How bad is the tech talent gap?
It’s a major bottleneck. The shortage of specialists in fields like data science and AI engineering stalls projects and slows growth, forcing companies to get creative with upskilling and recruiting.
