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The next phase of enterprise AI is likely to be less about asking a chatbot a question and more about giving AI a task and letting it work through multiple steps. Think about an AI system that doesn't just draft a customer email but also: 1. Checks the customer record 2. Identifies the issue 3. Prepares a response 4. Sends it for human approval Or imagine an AI coding agent that can help developers work through parts of the software development process. This shift toward agentic AI is already happening. Microsoft's research describes emerging “human-agent teams,” while Deloitte's 2026 Tech Trends report identifies agentic AI and the rise of a “silicon-based workforce” as a major enterprise trend. What does this mean for businesses? The challenge won't simply be buying an AI agent. It will be figuring out: Where should an AI agent actually be trusted to act? And, where does a human need to stay in control? For example, an AI agent might be appropriate for organizing information, preparing routine reports, or handling defined workflow steps. But decisions involving sensitive customers, financial consequences, or significant business risk may still require human oversight. The most effective approach will likely be human-led, AI-assisted workflows, rather than completely handing business processes over to autonomous systems.
AI might look effortless on a screen.
Behind every AI-powered business tool, however, is a lot of computing power, data, and infrastructure.
As businesses move from experimenting with AI to using it at scale, the technology bill can become a very real concern.
Deloitte notes that although token costs have fallen dramatically, some enterprises are still facing AI-related infrastructure bills reaching millions of dollars a month.
That changes the conversation.
Businesses are increasingly considering a mix of:
Instead of assuming that one infrastructure model will work for everything.
Why should business leaders care?
In 2027, the question may shift from:
“Can we use AI?”
to,“Can we afford to run this AI efficiently at scale?”
For enterprise leaders, technology strategy and financial strategy are therefore becoming increasingly connected.
A business might find that using an AI model for a small pilot is affordable, but scaling that same system across thousands of employees could create very different infrastructure costs.
This makes cost, scalability, performance, and business value important considerations before an AI system is rolled out across the organization.
AI is giving businesses new ways to detect suspicious activity, analyze huge amounts of data, and respond to threats faster.
But there's a catch.
The same technology can also create new vulnerabilities.
Businesses are already dealing with issues such as:
Deloitte describes this as an AI security paradox: AI can create new risks while also becoming an important tool for defending against them.
This could make cybersecurity one of the biggest technology priorities for 2027.
AI adoption without AI security is a risky shortcut
As businesses give AI systems access to more information and workflows, protecting those systems cannot simply be something companies add at the end.
For example, an organization introducing an AI tool for customer service needs to think about what customer information the system can access.
Similarly, an organization using AI for internal operations needs to understand what company data employees are putting into external AI platforms.
The practical question businesses should ask before adopting an AI tool is:
“What information will this system access, and how will that information be protected?”
AI adoption without AI security could become a very expensive shortcut.
4. Physical AI Could Take Technology Beyond the Screen
For years, enterprise AI has mostly lived inside software:
That's changing.
AI is increasingly being connected with robotics and physical systems, creating what is often called physical AI.
Think about:
Deloitte lists the convergence of AI and robotics as one of its five major technology trends for 2026, suggesting that businesses should pay attention as AI increasingly moves from digital environments into the physical world.
What could physical AI mean for businesses?
For businesses, this could eventually mean smarter operations rather than simply smarter software.
A logistics company, for example, could use intelligent robotic systems to improve warehouse operations.
A manufacturing company could use AI-powered systems to respond to changing production conditions.
But physical AI also raises bigger questions around:
When AI starts influencing real-world processes, businesses need to think about much more than whether the technology works.
They also need to consider what happens when it doesn't.
Here's the trend that could matter more than any individual technology: that companies may start redesigning their workflows around technology instead of simply adding technology to old workflows.
That's an important distinction.
Imagine a business taking a slow, complicated process and simply adding an AI tool to it.
The process may become slightly faster.
But its underlying problems remain.
Now imagine redesigning the entire process around what humans and AI are each good at.
That's a much bigger change.
Deloitte's 2026 research makes a similar point: organizations gaining the most from AI are increasingly redesigning operations rather than simply automating existing processes.
And there are already signs that enterprise AI is moving beyond experimentation. A 2026 study of S&P 500 companies found that 11% had deeply integrated AI into business processes in 2025, up from 5% in 2022.
What should businesses actually do?
Instead of asking:
“Where can we add AI?”
businesses should ask:
“Where can technology create a measurable improvement in the way we actually work?”
For example, a business could examine an internal process that currently requires several manual handoffs.
Rather than simply giving employees an AI tool, it could redesign the workflow so that:
This is where technology becomes a business transformation tool, rather than just another software subscription.
The technology landscape of 2027 probably won't be about finding one magical tool that transforms everything.
It will be about several technologies developing together:
What Should Businesses Do Before 2027?
Businesses don't need to adopt every emerging technology simply because it is receiving attention.
A better starting point is to evaluate current business problems.
Ask:
This approach can help businesses avoid one of the biggest technology mistakes of 2027:
Whether a business needs specialized technology expertise, support for a specific project, or resources to scale its technology capabilities, access to the right talent can make technology adoption more practical.
The larger lesson is that technology transformation requires more than software.
It requires the right people, processes, expertise, and infrastructure working together.
The technology landscape of 2027 won't be about finding one magical technology that solves every business problem. It will be about making smarter decisions about where technology belongs—and where it doesn't.
Businesses should watch the rise of AI agents, the growing importance of AI infrastructure, the evolving cybersecurity landscape, the emergence of physical AI, and the shift toward redesigning workflows around human-and-machine collaboration.
But there's an important reality check. AI adoption doesn't automatically equal business success.
Businesses still need to connect technology investments to measurable outcomes.
So, if there's one lesson businesses should take into 2027, it's this:
Because in 2027, being the company with the most technology may not matter.
Being the company that knows what to do with it might.
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