Professional content is in a hard reset. As AI pushes zero-click answers and attention splinters, value is shifting from volume to outcomes, trust, and time saved. In a frank conversation, 67 Bricks CEO Jennifer Schivas and CredSpark’s CEO Lev Kaye argue for a new playbook, one that integrates insights into workflows, links content to data, and treats audiences as collaborators whose declared needs matter more than inferred clicks. They explore where the biggest opportunities lie, the pitfalls to avoid, and how the most successful companies will be thinking about value three years from now.
Lev: I see a couple of AI-driven trends converging.
Jennifer: I define it in terms of what it enables the end user to do. For me, value isn’t measured by page views or download counts. It’s measured by outcomes:
Why is unlocking value in professional content such a critical topic in today’s attention economy?
Lev: I see a couple of AI-driven trends converging.
- The explosion in AI-powered ‘zero-click’ search raises the bar on all creators/publishers of professional content; you need to be the authoritative, preferred information destination for your audience, with content that can inform business decisions, not merely answer quick questions.
- There are very few professions which seem immune to disruption by AI in the next 3-5 years. Professionals will recognize that they need to learn/adapt, and they’ll look to trusted sources for career guidance.
- Integration into workflows – professional audiences don’t just want information, they want insight at the right moment, in the right context, saving time and reducing friction.
- Data-driven products – by linking content with data, publishers can open up new forms of value, from richer analysis to predictive services that customers can’t get elsewhere.
- Shifting from content to capability – it’s not about delivering articles, it’s about enabling professionals to solve problems faster, work smarter, and operate with confidence.
When you think about ‘value’ in professional content, how do you define it?
Jennifer: I define it in terms of what it enables the end user to do. For me, value isn’t measured by page views or download counts. It’s measured by outcomes:
- Does this content help a professional make a better decision, faster?
- Does it reduce risk or give them confidence in a complex situation?
- Does it uncover opportunities they wouldn’t otherwise see?
In an era of misinformation and AI-generated noise, the brand and reputation behind professional content is a critical part of its value
But there’s another layer too: trust. In an era of misinformation and AI-generated noise, the brand and reputation behind professional content is a critical part of its value. When users know they can rely on the accuracy and integrity of a source, that trust amplifies every outcome the content supports.
In my opinion, outcomes delivered are the true currency in today’s market. If professional content saves time, improves accuracy, or creates a competitive advantage, then it has high value. If it doesn’t, it risks being just more noise.
Lev: I define it as nothing less than content which contributes to one’s professional success. I don’t see a meaningful distinction between informing professionals, on the one hand, and helping advance their careers; a well-informed professional is both going to perform well for their organization and raise their career trajectory. So I think ‘value’ in professional content comes from the ability to make professionals better at their jobs. Which, btw, usually requires more than merely throwing content at them and hoping it sticks!
The other key component of content value is trustworthiness, which is not nearly mentioned enough in discussion/debate about AI. Consumers have rapidly adopted AI, generally trusting the technologies and their creators. But every generative AI tool represents a financial investment, and was built with an agenda which may or may not align with users’ needs. And of course, these tools are probabilistic in nature, only making statistical guesses as to the correct/helpful output. So professionals – with reputations staked on the trustworthiness of AI content & related products – need to assess which, whether and when they can trust AI tools on three levels:
Lev: First, an extremely common mistake is assuming that the relevance of your content is obvious to a given audience member. Someone’s job title, prior event attendance or online behavior such as emails opened doesn’t tell you much about what’s on their mind. A huge amount of measurable behavior is taken as an indication of intent, but nearly all the time, intent is being inferred, not actually declared by that individual.
- Trusting AI’s Accuracy
- Trusting AI’s Intentions
- Trusting AI’s Benefits
What are some of the most common mistakes you see organizations make when trying to increase the value of their content?
Lev: First, an extremely common mistake is assuming that the relevance of your content is obvious to a given audience member. Someone’s job title, prior event attendance or online behavior such as emails opened doesn’t tell you much about what’s on their mind. A huge amount of measurable behavior is taken as an indication of intent, but nearly all the time, intent is being inferred, not actually declared by that individual.
Despite how lovingly we’ve crafted our content, we can’t assume its value is obvious to the average busy and distracted professional.
Second, everyone (myself regularly included) makes the mistake of falling in love with their own content. Most people assume their own kids or pets are obviously terrific, yet we know from experiencing our friends’ kids and pets that they’re not universally 100% lovable. By the same token, despite how lovingly we’ve crafted our content, we can’t assume its value is obvious to the average busy and distracted professional.
Third, content producers need to think of their audience as collaborators, not passive receptors. The ground truth in your professional vertical isn’t the sole possession of our editors, no matter how expert they may be. The best practices, innovations, and emerging trends are to be found in our audience members. We need to capture their ideas and input, when doing so is relatively easy these days.
We need to give our audiences engaging ways to understand the relevance and the value of the content to their own careers. We cannot simply assume that a clever video thumbnail, report title, or event session summary will be enough.
Jennifer: One of the most common mistakes I see is equating “more volume” with “more value.” Publishing greater amounts of content often just creates more noise. The real value lies in making existing content work harder for users.
Another mistake is focusing inward rather than outward. Too many organizations optimize for their editorial or production processes, rather than asking: what problems does this solve for my customers? That disconnect can mean a lot of effort without real impact.
A third pitfall is overlooking usability and integration. Even the highest-quality content won’t drive value if it isn’t structured, accessible, or embedded in the workflows where professionals actually need it.
And finally, many organizations treat content as an end product, not a foundation. In reality, content can be enriched, combined with data, and repurposed into new services that deliver far more utility than a standalone article or report ever could.
Jennifer: Since this has been our core business for nearly 20 years, it’s hard to pick just one example! With Chemical Watch, we implemented semantic fingerprinting to connect content and users in unique ways. That not only powered new product development and personalized user experiences, but also drove a 40% increase in repeat revenues and was a core strategic asset in their successful sale to Enhesa. One of the most striking examples is our work with The Economist Intelligence Unit. Together, we created Viewpoint, a platform that brings together enviable content and world-class data in one place. The results were transformative: a 95% retention rate, and that year they rose to >£50m annual recurring revenue. Just as importantly, it gave EIU’s clients precision tools to weigh risks and opportunities across markets, countries, and industries. The flexibility of the platform proved crucial. For example, when the Ukraine–Russia war broke out, we were able to help EIU rapidly launch a new theme page, combining tailored content and search capabilities so clients could navigate fast-changing risks at a critical moment. We’ve also helped the British Medical Journal create a flexible ‘Knowledge Base’ that underpins their Best Practice platform and app. The new data structure enabled BMJ to launch the world’s first comorbidities tool to be used in a professional setting at the point of care, improving patient outcomes and cutting costs. Nearly 90% of surveyed users said Best Practice had a direct impact on their clinical practice. What ties all of these together is simple: when organizations truly get to grips with their existing assets and structure them in the right way, the value they can unlock, both for their users and their business, is transformative. Lev: CredSpark’s work with clients is all about unlocking the power of asking questions to create more personalized, relevant experiences for their audiences. An easy win for our clients is more efficiently connecting audience members to the content they’re interested in, as in the example of Fusable (a leading audience data provider for industrial markets) which saw a jump from 1% to 45% of readers clicking through to a recommended article once they implemented our interactive strategy to learn more about their audience. Another client, Instinct, which keeps veterinary staff current with practical insights and real world advice, has used CredSpark’s platform to repurpose existing content as interactive content, resulting in more than 2 million engagements with that content in its new form. That same client sees 27% of their audience engage with daily polls on their home page, making that home page a daily destination for readers. I’d love to say what CredSpark does is rocket science, but really it’s just the effective application of common sense. An audience is composed of humans, and humans tend to appreciate being approached conversationally, vs. having content blasted at them.
Lev: We think of two categories of data: Behavioral and Declarative. Behavioral Data is ‘where did someone click, scroll, open, download, etc.’ and is mostly captured passively by content creators. Declarative Data is gathered from asking direct questions of those who interact with your content: What do they know? How well do they understand a given topic? What are they interested in? What concerns them? Behavioral data capture online began in the late 1990s and is now commonplace. But it’s not sufficient to give us a well-rounded sense of an individual professional, and the risk of misinterpreting online behavior – to assume it means something it does not – is very high. That’s where Declarative Data comes in as an additive. The combination of the two – Behavioral and Declarative – is what gives content producers the ability to capture what’s most important to audience members, and adapt existing content based upon that intelligence. More data = More guidance in editing = More value to the audience. Jennifer: On its own, content is static. Data is live, dynamic and endlessly flexible, if collected and architected properly. Bringing the two together, marrying information and insight, opens the door to new ideas, new products and new revenue. So, instead of publishing once and hoping for impact, you can use data to continuously refine, personalize, and extend what you offer – and crucially, reduce the time it takes for users to get value. For example, you might use engagement data to restructure long reports into modular formats that readers actually finish; tailor insights and guidance by surfacing content based on a user’s role and past behaviour; or turn popular conference sessions into year-round intelligence products linked with external datasets. Done well, this creates offerings that are not only more useful but also harder to replicate, because they’re grounded in your unique combination of content and data.
Jennifer: We’ve already seen AI make a tangible difference across our client work, from podcast generation and interactive chat interfaces, to automated claims checking in complex pharma information sets, and using event data to extend engagement year-round. In all of these, the biggest gains come when AI accelerates human expertise rather than replaces it, which is why we focus on human-in-the-loop products that keep outputs accurate and trusted. Increasingly, we’re working in areas that move beyond efficiency into predictive and prescriptive decision support: tracking sentiment on key topics at scale and recommending how to adjust messaging before a trend breaks, monitoring regulatory changes and flagging the operational implications and next steps, and even generating adaptive business plans with realistic actions and budgets.
Can you share an example where unlocking content’s value had a measurable impact?
Jennifer: Since this has been our core business for nearly 20 years, it’s hard to pick just one example! With Chemical Watch, we implemented semantic fingerprinting to connect content and users in unique ways. That not only powered new product development and personalized user experiences, but also drove a 40% increase in repeat revenues and was a core strategic asset in their successful sale to Enhesa. One of the most striking examples is our work with The Economist Intelligence Unit. Together, we created Viewpoint, a platform that brings together enviable content and world-class data in one place. The results were transformative: a 95% retention rate, and that year they rose to >£50m annual recurring revenue. Just as importantly, it gave EIU’s clients precision tools to weigh risks and opportunities across markets, countries, and industries. The flexibility of the platform proved crucial. For example, when the Ukraine–Russia war broke out, we were able to help EIU rapidly launch a new theme page, combining tailored content and search capabilities so clients could navigate fast-changing risks at a critical moment. We’ve also helped the British Medical Journal create a flexible ‘Knowledge Base’ that underpins their Best Practice platform and app. The new data structure enabled BMJ to launch the world’s first comorbidities tool to be used in a professional setting at the point of care, improving patient outcomes and cutting costs. Nearly 90% of surveyed users said Best Practice had a direct impact on their clinical practice. What ties all of these together is simple: when organizations truly get to grips with their existing assets and structure them in the right way, the value they can unlock, both for their users and their business, is transformative. Lev: CredSpark’s work with clients is all about unlocking the power of asking questions to create more personalized, relevant experiences for their audiences. An easy win for our clients is more efficiently connecting audience members to the content they’re interested in, as in the example of Fusable (a leading audience data provider for industrial markets) which saw a jump from 1% to 45% of readers clicking through to a recommended article once they implemented our interactive strategy to learn more about their audience. Another client, Instinct, which keeps veterinary staff current with practical insights and real world advice, has used CredSpark’s platform to repurpose existing content as interactive content, resulting in more than 2 million engagements with that content in its new form. That same client sees 27% of their audience engage with daily polls on their home page, making that home page a daily destination for readers. I’d love to say what CredSpark does is rocket science, but really it’s just the effective application of common sense. An audience is composed of humans, and humans tend to appreciate being approached conversationally, vs. having content blasted at them.
What role does data play in creating more valuable content offerings?
Lev: We think of two categories of data: Behavioral and Declarative. Behavioral Data is ‘where did someone click, scroll, open, download, etc.’ and is mostly captured passively by content creators. Declarative Data is gathered from asking direct questions of those who interact with your content: What do they know? How well do they understand a given topic? What are they interested in? What concerns them? Behavioral data capture online began in the late 1990s and is now commonplace. But it’s not sufficient to give us a well-rounded sense of an individual professional, and the risk of misinterpreting online behavior – to assume it means something it does not – is very high. That’s where Declarative Data comes in as an additive. The combination of the two – Behavioral and Declarative – is what gives content producers the ability to capture what’s most important to audience members, and adapt existing content based upon that intelligence. More data = More guidance in editing = More value to the audience. Jennifer: On its own, content is static. Data is live, dynamic and endlessly flexible, if collected and architected properly. Bringing the two together, marrying information and insight, opens the door to new ideas, new products and new revenue. So, instead of publishing once and hoping for impact, you can use data to continuously refine, personalize, and extend what you offer – and crucially, reduce the time it takes for users to get value. For example, you might use engagement data to restructure long reports into modular formats that readers actually finish; tailor insights and guidance by surfacing content based on a user’s role and past behaviour; or turn popular conference sessions into year-round intelligence products linked with external datasets. Done well, this creates offerings that are not only more useful but also harder to replicate, because they’re grounded in your unique combination of content and data.
Where have you seen AI make the biggest difference – and where is the potential still untapped?
Jennifer: We’ve already seen AI make a tangible difference across our client work, from podcast generation and interactive chat interfaces, to automated claims checking in complex pharma information sets, and using event data to extend engagement year-round. In all of these, the biggest gains come when AI accelerates human expertise rather than replaces it, which is why we focus on human-in-the-loop products that keep outputs accurate and trusted. Increasingly, we’re working in areas that move beyond efficiency into predictive and prescriptive decision support: tracking sentiment on key topics at scale and recommending how to adjust messaging before a trend breaks, monitoring regulatory changes and flagging the operational implications and next steps, and even generating adaptive business plans with realistic actions and budgets.
The more we can help professionals turn signals into actions...then the more essential our products will become.
Looking ahead, I see real scope to build on this. The more we can help professionals turn signals into actions – whether that means adjusting strategy, responding faster to change, or navigating volatile markets – then the more essential our products will become.
Lev: We can assume the vast majority of AI chatbot queries are from consumers, where the risks/cost of inaccuracy are relatively low. But when it comes to professional uses of generative AI, accuracy matters a great deal. 2024 was the dawn of RAG (Retrieval-Augmented Generation) in AI, basically the integration of high-quality reference data to guide and make LLM outputs more context-specific and therefore useful. But RAG-enabled AI tools require a significant amount of time and expertise to build, and are highly customized.
This year, we’ve seen the dawn of MCP, or Model Context Protocol, which appears to be the first interoperability standard between AI models, databases, and software applications. If MCP lives up to its early promise, it should make it far easier & cheaper than RAG for owners/creators of professional content to develop AI-powered methods of interacting with & leveraging their content. This means that content owners can more quickly introduce, adapt, and personalize products, and can do so within their own control, not subject to the permissions & cost structures of the AI giants.
Lev: Nearly 10 years ago on a panel, I told a room of professional publishers that I saw them instead as teachers, and there were audible gasps. The reason we can instantly recall our favorite teachers and mentors, even decades later, is because they gave us the information, guidance, and encouragement to shape our futures
Looking three years ahead, what will the most successful professional content companies be doing differently from today?
Lev: Nearly 10 years ago on a panel, I told a room of professional publishers that I saw them instead as teachers, and there were audible gasps. The reason we can instantly recall our favorite teachers and mentors, even decades later, is because they gave us the information, guidance, and encouragement to shape our futures
The best professional content companies will expand their remit and think both longer-term and more holistically about fostering professionals’ growth.
To become as formative and essential to their audiences as great teachers, the best professional content companies will expand their remit and think both longer-term and more holistically about fostering professionals’ growth. If they’re a B2B publisher, they’ll get more active in professional education and micro-credentialing. If they organize events, they’ll think about how to engage and inform their attendees year-round, not just for 3 days in a large hall. If they’re a professional association, they’ll more regularly ask questions of their membership on important topics, and package the aggregate answers into helpful benchmarks several times a year, not just once after the annual member survey.
Jennifer: The one constant is that people will always want to know if they’re making the best decisions. The professional publishers who will survive will be those who have created integrated customer experiences that provide that guidance and certainty. Part of that is finding ways to help busy professionals sift through the noise so they don’t miss something vital. If all you’re doing is spitting out content that someone could easily get via ChatGPT or similar, it’s not enough. You have to be the source of truth for your niche, and provide an essential service that it is unthinkable for people to do their jobs without. And you have to make it easy – no one will be sitting and reading multi-page white papers any more, they’re barely doing it now! Plugging in to all the various ways your customers want to learn and serving them what they need at the point in which they need it, is the best way to guarantee your place in the future.
Jennifer: If your content vanished tomorrow and your customers could still do their jobs, you don’t have value. You have noise. Lev: Treat your audience as you’d treat your mother: Reach out regularly and ask questions, think of how best to help them, and try to make them proud.
Any final thoughts?
Jennifer: If your content vanished tomorrow and your customers could still do their jobs, you don’t have value. You have noise. Lev: Treat your audience as you’d treat your mother: Reach out regularly and ask questions, think of how best to help them, and try to make them proud.
Lev Kaye is the CEO and Founder of CredSpark. Previously holding executive roles in education technology, publishing, and related services in the education industry. He also has experience working for a variety of tech companies including startups, mid-size and Fortune 100 firms.
Jennifer Schivas
Jennifer Schivas is the CEO of 67 Bricks. She holds advisory and non-executive roles with Cadmore Media, Enterprise Oxfordshire, and the Oxfordshire Economic Partnership Board. She is also a regular conference speaker and chairs Renewd’s AI Strategy Council.
