I work with CEOs and investors in content, data and workflow companies, mostly in education, science, healthcare and business information.

Some of these companies tend to think in products: a journal or a database, a textbook or a course, a subscription, an assessment. That is natural, because products are what they build, price and sell. But customers don’t want a product for its own sake. They want a job done.

A teacher wants pupils to reach their learning goals, to save preparation time, and to be confident the materials are high quality and cover the curriculum. A scientist wants to know what is already established, judge the evidence and decide what to investigate next. A hospital wants its staff to have the right information at the moment they decide with a patient about a treatment. A business wants to know what a new regulation means for it and what it has to do.

The work around the content

Whatever the field, professionals go through broadly similar steps. Here is a simplified version for three of them.

Step	Teacher	Doctor	Scientist
1. Understand the objective	Define what pupils should learn	Establish the patient’s needs and goals	Define the research question
2. Assess the situation	Check prior knowledge and difficulties	Take history, examine and gather findings	Review existing evidence and available resources
3. Access relevant knowledge	Consult curriculum, materials and teaching approaches	Consult clinical evidence and guidance	Consult literature, methods and datasets
4. Interpret in context	Judge what this class needs	Interpret findings for this patient	Develop an explanation or hypothesis
5. Decide what to do	Choose the lesson and support	Agree investigations or treatment	Design the study or experiment
6. Do it	Teach and set work	Deliver care	Conduct the research
7. Evaluate what happened	Assess understanding and progress	Review response and outcomes	Analyse results and uncertainty
8. Adjust	Reteach or change the approach	Revise the assessment or care plan	Refine the hypothesis or method
9. Conclude	Close the topic and identify gaps	Close an episode or arrange continuing care	Draw and communicate conclusions

Content and data are often used at one or more of the steps above. What happens around that is interpretation: working out what the evidence means for this pupil, this patient or this experiment, and what to do next. Until now that has been done mostly by the professional, often in consultation with the client. Professionals are skilled and know the situation, but nobody can hold the whole body of evidence, including the latest insights, in their head, and most are short of time.

AI can now help with that interpretation, at several steps on the journey. I don’t suggest it replaces the professional’s judgement at steps 4 and 5 (in healthcare, within the limits regulation sets), but it could significantly support it.

At Sanoma I held the view that Sanoma Learning was not a content company but a workflow company. I believe education is among the least digitalised professional segments, and yet one where the publisher is most embedded in the professional’s core workflow, which gives it high potential for value creation. Our focus was on student learning outcomes, and time is one outcome teachers care about. Based on teacher feedback, we estimated that for every €100 a school spent on our solutions, teachers saved time worth roughly €1,000, because we supported their core workflows. That is about a 10x return on the school’s spending.

For much of the past, the value of the information sat mostly in the content itself. In the future, I expect it to sit more in how well a solution helps the customer reach the outcome they care about, in their own circumstances. In other words, the context and interpretation around the workflow will become increasingly important.

Past value was largely determined by inputs. Future value will be determined by outcomes.

Excellent content that knows nothing about the situation in which it is used is worth less than content that fits into the work and supports a good outcome.

Take a teacher with a pupil who keeps making the same mistake with fractions. The pupil’s answers are data. Working out what she has misunderstood takes interpretation. Choosing an explanation and exercises draws on content and didactics. Setting the work and reviewing progress has to fit into the teacher’s day. That is content, data, interpretation and workflow in one small task.

Where AI could create value

I see five places, and I would welcome challenge on the list.

• How can we improve the product?
• Which customers or services could become viable?
• How can we improve commercial performance?
• How should we distribute and license our content?
• Where could we reduce development and delivery costs?

Creating value does not guarantee capturing it.

Customers may benefit from competition, regulation, new procurement models or spending caps, so they may capture much of the value created. Existing and new competitors may cut prices. The value may end up split across many players. The commercial task is to see where these changes could make a material difference to the performance of the business, and what to do about them.

Which platform?

One of the questions I find most important commercially is where the customer will do the work. Is it in your product, or on another platform with your content inside it?

You can build a good business either way, but they are different businesses. In the second, someone else holds the customer relationship and largely sets the terms on which you earn. The more the value moves towards outcomes and context, the more that question matters.

What I think

First, many content companies have a lot to build on: subject expertise, editorial judgement, IP, brands, distribution and customer relationships. In education, pedagogy and didactics are central. In science and healthcare, it is the quality of the evidence and how it is assessed.

Why does a customer choose you? A trusted brand may help. High-quality proprietary content, including data, may be hard to replace, especially if much of the new competition is AI-slop. Deep understanding and embedding in core customer workflows can be a powerful moat. A product used every day may be hard to leave. How much does each matter, and could AI change that?

Second, AI could cut the time and cost it takes to prepare an edition, adapt material for another market or update a product when requirements change. Who will keep those savings? Unless you can sustain a lower cost position than your competitors, it’s possible much of the financial benefit might end up with customers. Competitors will make the same savings, and customers will want to see them in the price. The companies that keep more will be the ones whose products become more valuable (i.e. provide better outcomes), not just cheaper to make.

Third:

Ideas of what you could do with AI are not scarce. The hard thing is to decide what you should do. And the really hard thing is to actually do it.

There is plenty to work with. The task is to choose where AI can improve the customer’s work and the economics of the business, and to invest accordingly.

Why I’m writing this

My career has taken me from scientific research (bioinformatics) to academic publishing, then to leading Sanoma Learning, and since then to board and advisory roles in education and information businesses. AI is part of many of my conversations with CEOs and investors.

In the posts that follow I will look at what customers pay for, who gets the money when costs fall, what protects a business and who holds the customer relationship, and which AI investments deserve funding. I will also look at how people develop expertise when AI does more of the work, what CEOs still need to understand themselves, and what investors should ask before and after a deal.

I expect some of my views to change along the way. I would be interested to hear where yours differ.

A question for you. If a customer asked what they get back for every euro they spend with you, what would you tell them? Would they agree?