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Welcome to the Friday edition of our newsletter. We spend Fridays going deeper into tools and trends related to generative AI (and Tuesdays sharing news updates). This week, Professor Snider talks about a next phase in how many companies use AI.

There are two ways most companies can get better results from AI almost immediately.

The first is to make their people better at using AI. Teach employees to write better prompts, understand what the tools can do, and find ways to incorporate AI into their daily work.

The second is to make AI better at understanding their company. Give it access to the right information about customers, processes, goals, past decisions, and how the business actually operates.

We've spent most of the past few years focused on the first one. It's what we've spent a lot of our time teaching at Innovation Profs. But I'm increasingly convinced that the second one represents an even bigger opportunity for businesses.

The next big unlock for AI won't come from writing better prompts. It will come from giving AI better context.

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Think about the difference between these two requests:

Without context: “Create a marketing plan for our company.”

With context: “Review our past three marketing campaigns, our customer research, our sales results, and our upcoming product launches. Based on what's worked in the past, recommend a marketing plan for next year.”

The first prompt might produce a perfectly reasonable marketing plan. But it's probably going to look a lot like the plan AI would create for any other company.

The second has the potential to produce something genuinely valuable because it's grounded in what makes your company unique.

Think about all the information that exists inside a typical company.

There are documents stored in SharePoint or Google Drive. There are customer conversations in a CRM. There are spreadsheets tracking sales and performance. There are emails explaining why decisions were made. There are meeting notes, brand guidelines, strategic plans, and years of institutional knowledge.

And then there's all the stuff that lives only in employees' heads.

Now think about how much of that information your AI tools can actually access and use. For many companies, the answer is probably not much.

You can have the most powerful AI model in the world, but if it doesn't know your customers, your processes, your goals, or what you've learned from past experiences, there's a limit to how helpful it can be.

AI is getting better at understanding what we're asking. But it still needs access to the information required to give us a useful answer.

That's why I think companies need to start thinking more seriously about their context gap: the distance between everything their organization knows and what their AI tools can actually use.

The solution isn't as simple as giving Copilot access to SharePoint or connecting ChatGPT to Google Drive.

Companies need to think about a few important questions:

Giving AI access to thousands of documents doesn't automatically mean it understands your company. It needs the right information, not just more information.

And the information needs to be trustworthy.

Imagine asking AI to create a proposal for a customer. It finds your pricing guidelines, but they're from 2023. It finds an old presentation describing services you no longer offer. It finds two conflicting versions of your brand guidelines.

That's not necessarily an AI problem. That's an organizational information problem.

It's also a leadership challenge. It involves how teams share knowledge, how decisions get documented, who is responsible for maintaining information, and whether the organization has a culture of making knowledge accessible.

You don't need to reorganize every file in your company tomorrow.

Instead, pick one task your employees perform regularly that requires knowledge about your business.

Maybe it's preparing a proposal for a customer, responding to a complicated service question, onboarding a new employee, or planning a marketing campaign.

Now ask yourself what information someone needs to do that task really well.

Where does that information live? Is it accurate and up to date? Can your AI tools securely access it? Is there important context that exists only in someone's head?

Work on closing those gaps. You might be surprised how much more useful AI becomes when it has the same information your best employees rely on.

Need help? Download this worksheet to walk you through the process.

We've spent the last few years asking companies an important question: Are your employees getting good at using AI?

We'll continue asking that question. Individual AI skills still matter, and they're going to matter for a long time. But I think there's another question businesses need to start asking:

Have we made our company easy for AI to understand?

Because increasingly, the value you get from AI won't just depend on how well your people use the tools.

It will depend on how well you've prepared your organization to work with them.

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