Generic AI Won’t Differentiate You. Build Your Own.

A little lightning bolt, a “Copilot” badge, an “Ask AI” field. They’re everywhere now: in Zoom, CRMs, customer service tools, Google Workspace. Every vendor has sprinkled in generative AI like adding a new flavour to an existing product. Summarize, translate, categorize: these out-of-the-box features target the masses. For overwhelmed teams, these tools are valuable. But no one has rethought their business strategy because of a copilot in PowerPoint.

The numbers back this up: McKinsey reports that over 65% of companies are using generative AI, but only 23% see a significant impact on their bottom line. Adoption is exploding, buttons are multiplying, but value remains concentrated among a minority doing something different.

When AI Actually Does the Work

At those organizations, it’s agents that make the difference.

An agent isn’t just a smarter chatbot. It’s software with a clear mandate, plugged into your data, capable of acting within your tools, under your supervision. You’re not asking it to “explain” something: you’re asking it to execute part of your process.

Take content creation. An agent can ingest a highly technical white paper, extract the key arguments, and automatically produce a series of ten LinkedIn posts, three blog articles and an email prospecting sequence. It doesn’t just write: it applies your brand voice, inserts the right UTMs and adapts visuals to each platform’s specs, ready for your manager’s approval. Same foundation model as a copilot, but supercharged by your data, your systems, your business context. In short, agents adapt to you.

The same principle applies to localizing campaigns for different markets, automated competitive intelligence or real-time sentiment analysis. The agent reads, searches, compares and executes within the parameters you’ve set.

The difference isn’t that AI suddenly becomes more intelligent. Agents often use the same language models (like GPT, Claude, or Gemini) as off-the-shelf tools. What changes is the scope you let them operate in. The moment an agent directly touches how you serve a customer or handle a file, you’re no longer in accessory territory. You’re in real advantage territory.

Building Without Reinventing the Wheel

Many assume they need specialized labs with massive budgets to build their own agents. While that might have been true months ago, those days are over. Building an agent no longer means reinventing AI.

What you really need to do is examine your processes in depth. How operations actually unfold, where things get stuck, the shortcuts no one has documented. That’s how you figure out where an agent can drive efficiency gains. Next, you need to ask the key people working on the ground to write down the rules or practices that until now only existed only in their heads (that’s how AI learns what it needs to do). Finally, you decide when in the process the agent makes suggestions, when a human makes the call, and crucially, how you correct errors along the way.

The goal isn’t to write code anymore: it’s to write intentions and guardrails in business language. That’s why companies succeeding with AI form teams that include both operations people and tech people. An external partner can be useful here: not to sell another miracle platform, but to help you go from “we could do something with AI” to “this agent does this work, in this system, with these measured results.”

From Generic Button to Capability You Own

Generic features like summarizing or reformatting a table are table stakes. They provide useful gains, but they’re not true competitive advantages. The real game is played where a well-oiled conversion mechanism can drive profit growth. Where faster response times can transform customer relationships. Where avoiding an error can save serious money.

Deciding to deploy one or two agents at the heart of these processes is a shift in posture. You’re no longer just buying a product: you’re building a capability that speaks your language, respects your constraints and only has value within your organization.

You can keep buying AI features like add-ons or you can ask a different question: which agent should actually do part of the work for us, in our tools, by our rules? That’s when AI stops being window dressing in a deck and starts being worth talking about.