Technology is outpacing the ability of most organisations to plan for it. The biggest change is that AI is no longer a standalone project in an innovation lab. Today it is built into the way software is written, the way systems are secured and the way work is done across the business.
Many of the trends on this list are tied to that change. Some are the AI itself, some are the protection it needs, some are the infrastructure that has to keep up with it. These are the trends to watch right now.
Proactive & AI-Driven Cybersecurity
Phishing, ransomware and attacks on company systems have not abated, and attackers are now also using AI. That’s pushed security teams away from reacting to incidents and towards preventing threats before they land.
Analysts say that is expected to reshape budgets. CIOs are shifting from reactive defence to proactive protection and Gartner forecasts that pre-emptive solutions will make up half of all security spending by the end of the decade. A second layer is also emerging: protecting the artificial intelligence systems themselves. More than half of enterprises will deploy AI security platforms to secure their standard artificial intelligence systems in the next few years.
Confidential computing is all part of the same story. Gartner forecasts that more than 75% of operations on untrusted infrastructure will be protected while data is in use by the end of the decade. Essentially, security is moving from a wall around the system to built-in protection at every layer.
Agentic AI and Multiagent Systems
Chatbots provided answers to questions. AI agents finish tasks. They can plan out a sequence of steps, use tools and complete work with minimal hand-holding.
The next step is the agents working in teams. Multiagent systems allow modular AI agents to work together on complex tasks, thus improving automation and scalability. One agent could pull the data, another could check it against policy, and a third could trigger the next action. This can have a huge impact on business processes. Gartner has predicted that AI agents will facilitate about 90% of B2B transactions in the coming years.
The practical lesson for businesses is that an agent is only as good as the systems he or she can reach. You need clean data and solid integrations more than ever.
AI that is physical
For years, AI was confined to the screen. Physical AI brings it to the real world. It puts intelligence into drones, robots and industrial machines.
Picture warehouse robots that can adapt to a changing floor, inspection drones that can spot faults themselves, factory equipment that can adjust as conditions change. Early adopters include logistics, manufacturing, healthcare and agriculture. This is also the logical next step after the old “virtual worlds” hype. This makes the physical world smarter instead of asking people to live inside a digital space.
Digital Provenance & Data Trust
As artificial intelligence is able to generate text, images, video and code in seconds, one question becomes harder: where did this come from, and can we trust it?
The answer is digital provenance. It means to be able to verify the provenance and history of software, data and content. This is no longer simply a nice-to-have. Gartner warns that organizations that don’t invest in provenance capabilities could be exposed to billions of dollars in compliance and sanctions risks.
Blockchain and other ledger technology still have a place here, but as a silent infrastructure for verification, rather than the speculative hype of years past. Look for more scrutiny on software supply chains, content authenticity and audit trails.
Clean Technology and Green Computing
The push for sustainable living continues to fuel growth in renewable energy, electric vehicles, rechargeable batteries and smarter waste management. Clean generation is getting cheaper and solar, wind and hydro power continue to grow.
There is also a newer angle. The tech industry now has its own sustainability challenge, with AI and cloud workloads big power consumers. Efficient chips, better data centre design and software that does more with less computing are becoming real competitive factors. Green computing is no longer a sideline. That’s part of the planning of infrastructure.
AI-Native Development and Smarter Smaller Teams
A lot of companies are working in a remote or hybrid way now, so it’s not about collaboration anymore. The bigger shift is inside of engineering teams.
“AI-native development platforms allow small teams to build and ship enterprise software much faster.” Gartner says that by the end of the decade, “most organizations will reconfigure large software engineering teams into smaller, more agile teams augmented by AI.”
Moving towards specialization is another sign of maturity. Gartner predicts that in the next few years, enterprises will run more than half of their generative AI models in domain-specific applications. General purpose models are useful, but models that are tuned to a specific industry tend to be more accurate, cheaper to run and easier to keep compliant.
Modernization and Hybrid Computing
All of these trends have one thing in common: they build on top of existing systems. Most businesses don’t start with a clean slate. “They have core applications that have been running reliably for decades and they need those systems to work with AI, the cloud and modern interfaces.
That’s why hybrid architecture is trending. Gartner predicts that more than 40% of leading enterprises will deploy hybrid computing architectures in critical workflows, compared to 8% today. Organizations don’t replace everything, they connect, modernize and extend what they have.
The world of technology is moving at a breakneck pace and the companies that benefit the most are the ones with solid foundations underneath. If you’re trying to figure out how your own systems fit into these trends, our AI + Modernization approach is a good place to start.