Thor Olof Philogène, Co-Founder and CEO of Stravito – Interview Series: A Return Conversation

Thor Olof Philogène is the CEO and Founder of Stravito, the Insights Intelligence Platform global brands trust to turn knowledge into confident decisions that drive growth and accelerate market impact.

In our previous interview, we discussed Stravito Assistant as a conversational, AI-powered interface that allows teams to search, explore, and interact with their own internal research and insights. One year later, how are enterprise teams actually using Assistant day to day, and what has surprised you about how it fits into real workflows?

A year ago, teams were using Stravito AI Assistant primarily to find and surface research faster. What’s changing is how teams use it to validate strategic assumptions before major decisions, and synthesize evidence across markets before committing to a direction.

Early adoption was driven by time saved. Now, with Deep Research Agent, Stravito AI Assistant autonomously plans multi-step research, analyses full reports in parallel, cross-checks findings, and delivers fully source-cited synthesis grounded exclusively in a company’s own data. The result is analyst-depth answers providing evidence that decision-makers can stand behind.

Since we last spoke, Stravito has expanded its use of AI to support more dynamic ways of working with insights, including features like AI Personas. How are customers using these capabilities in practice, beyond early ideation or experimentation?

Stravito AI Personas turns static segmentation studies into interactive consumer profiles grounded in a company’s proprietary research, so teams can pressure-test everything from packaging to campaign concepts to product ideas, before major budgets are locked in.

For example, Lavazza Group integrated Stravito AI Personas in its marketing and innovation process, building consumer Personas from thousands of interviews. Already they have refined packaging and campaign decisions.

What once required weeks of stop-and-go validation can now begin with focused working sessions before moving on to further testing, keeping outputs rooted in proprietary research from the beginning. The intent is to reduce risk earlier, iterate more effectively, and prioritize stronger ideas before investment is committed.

As more AI-driven functionality has been introduced into the platform, what new questions are enterprises asking around governance, oversight, and accountability, and how has Stravito adapted to those concerns?

The question enterprises used to ask was “what can it do?” Now it’s “can we stand behind it?” For Stravito, governance has always been a priority and that shows in how the platform is built.

Generic AI is built on internet data with no business context. Stravito is built entirely on a company’s own validated research. No internet scraping or shared sources.

For example, with Deep Research Agent, the research plan is visible before analysis begins and every conclusion is fully source-cited, so any answer can be traced back to the original source. Stravito also meets the highest enterprise security and data privacy standards, with ISO 27001 certification, SOC 2 Type II attestation, and a contractual guarantee that customer data will not be used to train large language models.

On accountability, AI handles synthesis and research planning. People handle judgment, strategy, and the final call.

Many organizations struggle not with adopting new tools, but with embedding them into everyday decision-making. What have you learned about change management when rolling Stravito out across large, global organizations?

The organizations that embed Stravito most successfully do three things well.

They set a clear expectation from the start: no major decision gets made without the intelligence the business already owns. When that becomes the standard, using the platform stops being optional.

They invest in internal champions. When the people others look to are drawing from the platform during business reviews, planning discussions and innovation conversations, adoption follows.

And they pair access with enablement and support. Teams need guidance on how to ask the right questions and know what to do with the answers. That is what turns a platform people have into a platform people use.

Stravito positions itself as a single source of truth for market and consumer insights. In reality, enterprises often have fragmented research spread across multiple systems. How successful have customers been at consolidating that knowledge, and where do they still encounter resistance?

Often the research exists. Business, consumer and market intelligence is sitting across systems, regions and functions. The problem is that it is not connected, so major decisions on innovation, expansion and marketing get made without it. That is what Stravito solves.

Consolidation happens in stages. Organizations start with their highest-value research and expand from there, with each stage building the commercial case for the next. Heineken is a good example, by bringing their intelligence together in one place, they redirected CMI time away from finding research towards more strategic, higher-value work.

If resistance surfaces it is usually organizational rather than technical. The organizations that move the fastest and most effectively have clear ownership and internal champions who can bring different regions and functions on board. The commercial case, decisions made faster with better evidence, is what moves that conversation forward.

One of Stravito’s goals has been to democratize access to insights beyond traditional research teams. Over the past year, how has this broader access changed the relationship between insights teams and business stakeholders?

When more teams can engage directly with consumer and market knowledge, insights get into decisions earlier. Stakeholders are no longer waiting for static reports. They are interacting directly with their own intelligence to shape decisions, from positioning and packaging to where to expand next.

This has also changed what insights teams spend their time on. With less time spent fielding requests and finding research, they are increasingly working as strategic advisors, making sure evidence shapes the decisions that matter most.

Personalization is often cited as a major advantage of AI in enterprise software. How are Stravito customers tailoring insights discovery by role, region, or function, and what impact has that had on engagement with research?

When teams see research that is relevant to their decisions, they engage with it earlier and use it more consistently. A global category lead has different priorities than a regional marketing director, and Stravito understands that.

Collections are one of our most used features. They work like playlists, letting teams group research by topic, region or function, for example, a collection dedicated to onboarding or sustainability. Stravito’s AI then continuously surfaces further relevant research based on what is already in the Collection, so teams stay across what matters without having to go looking.

As AI becomes more embedded in insights workflows, how are organizations deciding when to rely on AI-generated context versus when to defer to human expertise, particularly for high-stakes decisions?

The organizations doing this well have a clear division of labor. AI handles the heavy lifting of synthesis and research planning. People handle judgment, strategy and the final call.

In practice, tools like Deep Research Agent in Stravito AI Assistant compress the time between question and evidence, delivering fully source-cited answers grounded in a company’s own research. AI Personas let teams test assumptions against real consumer perspectives before investment is committed. Both are designed to strengthen the foundation on which decisions get made.

Yet, the output is the starting point. People decide what the evidence means, weigh the trade-offs, and own the final decision.

Integration is a recurring challenge in enterprise environments. What patterns have you observed among customers who successfully integrate Stravito with their existing analytics, research, or knowledge management systems?

The organizations that see the most impact are clear on what Stravito is for. Not another repository, but the layer that connects existing insight and brings it into the decisions that need it.

In practice, Stravito works alongside the research and analytics providers customers already use, bringing business, market and consumer intelligence together in one place so the knowledge sitting across systems can reach the decisions that need it.

Looking ahead, based on what you have observed over the past year, what do you think enterprises still underestimate about operationalizing AI in insights management, and how is that shaping Stravito’s product direction?

AI can surface answers instantly. What enterprises underestimate is everything that happens next. The cost of acting on a wrong insight is too significant to treat the output as the conclusion.

The organizations that pull ahead will not be the ones that automate the most. They will be the ones that use what they already know to make better calls, faster, and with more confidence.

That is what shapes our product direction. For example, Deep Research Agent delivers the rigor and verifiability that major decisions demand. AI Personas bring consumer perspective into the process before investment is committed. Both have been designed to ensure no major decision gets made without the intelligence an organization already owns.

Thank you for the great interview, readers may wish to read our previous interview or to visit Stravito.