Chad Hetherington

Enterprise AI isn’t exactly new. But how seriously organizations are beginning to take it might be.

We’re starting to see organizations move beyond individual productivity gains and think about AI as something that can be built into the way the business operates. There’s some interesting evidence of that shift.

In August, CNBC reported that OpenAI’s enterprise business had overtaken its consumer business in revenue. According to CFO Sarah Friar, OpenAI entered 2026 with roughly a 60/40 consumer-to-enterprise revenue split, but enterprise adoption grew considerably faster than expected.

That’s noteworthy for a company that became one of the biggest names in technology because hundreds of millions of individual people started using its chatbot.

Enterprise organizations have moved beyond just asking employees to use AI, connecting it to internal knowledge, building it into existing systems and even creating their own agents.

With that in mind, knowing how to use AI well at an enterprise level is becoming mandatory. Here are 7 things that can help unkink the lines as enterprises continue to move away from experimentation and into real, revenue-driving AI use cases.

1. Start With a Problem, Not an AI Tool

This sounds pretty obvious, but it’s surprisingly easy to do the opposite.

When a new, exciting AI tool comes out, people see the demo and are excited about what it can do. Then the organization starts looking around for somewhere to use it, because it looks cool or revolutionary.

But that’s really just experimentation again, and we’re already mostly past that. There’s nothing inherently wrong with continuing to experiment, but it’s probably not the way to get the most value from enterprise AI.

So, start with the work itself by asking things like: Where are people losing time? Which processes involve a lot of repetitive work? Where does information regularly get stuck? What takes five steps that could realistically take two?

From there, figure out whether AI belongs somewhere in that process.

There’s also a big difference between using AI to speed up one task and redesigning a workflow around it.

Say a marketer uses AI to summarize a research report. Great. They’ve probably saved some time.

But what if AI could gather information from approved sources, compare it with existing content, identify gaps, help create a brief and prepare an initial draft for an expert to review?

You’ve just changed how the work gets done, and that’s where enterprise AI gets interesting.

2. Give Your AI Something Useful To Work With

Public AI models know an enormous amount about general, public information. They don’t automatically know everything about your company, like:

  • Product nuances.
  • How your sales team qualifies leads. 
  • Internal terminology.
  • Previous projects.
  • Customer history.
  • Brand guidelines.
  • Any other weird little rules your organization has accumulated over the past 10, 20 or 50 years.

Much of that information is exactly what makes AI more useful inside an enterprise.

Connecting AI with internal knowledge can turn a generic assistant into something much more relevant to the work employees actually do day-to-day. That might include granting tools access to things like CRM data, product documentation, internal knowledge bases, brand guidelines, previous work/marketing campaigns or approved research sources.

But remember: More context isn’t automatically better context.

AI needs access to the information that’s relevant to the task, not everything all at once. Organizations need to be deliberate about the context they feed tools, both for efficacy and security.

3. Organize Governance Sooner Rather Than Later

Governance isn’t the most exciting part of AI implementation and strategy, but as the tech becomes more embedded in all areas of enterprise work, it’s increasingly necessary.

70% of technology executives said teams across their organizations were deploying technology faster than IT could track it, according to new research from IBM. Only 11% said they were fully prepared for AI agent deployments at scale.

This is reminiscent of our own research. In our survey of 163 marketers, 81% reported using AI in their processes, but only 42% said they had a formal AI policy to reference.

Good enterprise AI governance should make some basic things clear, such as:

  • Which tools are approved.
  • What data employees can share with them.
  • What systems AI can access.
  • What AI agents can do without approval.
  • What requires human sign-off.
  • Who owns the systems.
  • How the systems are monitored.

The more capable and autonomous AI systems become, the more important it becomes to figure these things out quickly before issues arise.

4. Decide Where Humans Actually Need To Be

Removing humans from a process isn’t — or shouldn’t be — the end goal of most AI initiatives. The goal should be figuring out which parts of the process AI is good at and which parts still benefit from a person.

AI can be very good at processing large amounts of information, spotting patterns, summarizing, generating drafts and handling repetitive tasks. But humans are still very useful when context, judgment, expertise, creativity and accountability matter, which is pretty much everywhere.

Sometimes that means letting an AI agent complete an entire low-risk task on its own. But other times it might mean it does 80% or 90% of the work before handing it over for quality checks, editing and finalization.

For higher-risk actions, like external communications, updating customer information or making financial decisions, you probably want explicit human approval built into the workflow.

There’s no universal human-in-the-loop formula for things like that. The important thing is to make that decision intentionally rather than assuming more automation is always better.

5. Watch Out for AI Tool Sprawl

You’ve likely heard about SaaS sprawl. Well, it’s happening all over again with AI.

Here’s how that sprawl might look in practice:

One team prefers ChatGPT, another uses Claude, someone else has a Gemini subscription, another department buys a specialized AI platform, and various employees build their own automations. Different tools get connected to different company systems.

Suddenly, you’ve got a lot of AI floating around.

That doesn’t necessarily mean every organization should standardize on a single model or platform. There are perfectly good reasons to use different models for different jobs.

The problem is when nobody really knows what’s being used, and for what — or why.

Enterprises should have some visibility into their AI stack: which models and tools are active in the organization, what they’re connected to, what they cost and how difficult they would be to replace.

That last part is worth thinking about, because AI is still changing quickly. The model that’s best for a particular task today may not be the best choice a year from now.

Building workflows that are unnecessarily dependent on one model, vendor or platform could eventually make it much harder (and more expensive) to change course later.

6. Teach People More Than How To Prompt

We’ve moved well beyond that AI training centered around ‘How to write better prompts.’ At least, most should be beyond that.

Being good at using AI in an enterprise environment increasingly means understanding the whole workflow around it. Most importantly, they need to see how AI applies to their actual work.

Giving 5,000 employees access to an enterprise AI platform doesn’t automatically give you 5,000 productive AI users.

Show people useful workflows. Give them examples. Create templates. Document successful processes. Give teams a way to share what they’ve figured out.

Because there’s a huge difference between one employee discovering a clever way to save two hours every week and turning that discovery into a repeatable workflow that saves 500 employees two hours every week.

7. Measure What AI Is Actually Doing for the Business

If you’re spending a significant amount of money on enterprise AI, eventually someone is going to ask a very reasonable question: Is it working?

“Our employees sent 10 million prompts last month” isn’t a particularly satisfying answer.

AI usage can tell you whether people are adopting the technology, but it doesn’t tell you whether it’s making the organization better.

The metrics that matter depend on the workflow and will be different between organizations. But generally, it’s KPIs that can define real improvements:

  • How AI is reducing the amount of time it takes to resolve a customer service request.
  • How much more collateral your marketing team can produce without increasing headcount.
  • The time salespeople spend talking to prospects rather than doing admin work.
  • How fast devs can ship products and updates.

Whatever the use case, try to connect AI adoption to an outcome the business already cares about.

Enterprise AI Is Becoming Less About the AI

There’s something slightly ironic about where enterprise AI appears to be heading.

As the technology gets more powerful, the model itself may become a smaller part of what separates successful enterprise AI programs from unsuccessful ones.

Most large organizations will have access to good AI models, many of which will be the exact same ones.

Everything built around them will be the biggest differentiator, such as the quality of the data, workflows, integrations, governance and training.

That’s also why enterprise AI adoption is becoming a much bigger project than buying some licenses and telling employees to start experimenting. Looking ahead, it’s increasingly about figuring out what AI should do and building the systems around it to make that work at scale.

Note: This article was originally published on contentmarketing.ai.