AiThority Interview with Thor Olof Philogène, Founder and CEO at Stravito

With enterprise AI innovations forcing business leaders to rethink workflows and productivity, Thor Olof Philogène, Founder and CEO at Stravito comments on the future of AI and enterprise ops and what’s in store in this AiThority Interview:

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Hi Thor, tell us about yourself and the story behind Stravito?

I have spent my career building companies at the intersection of technology and commerce. I sold my first company to SecondLife, where I led monetization, before joining iZettle as part of the leadership team ahead of its acquisition by PayPal in 2018.

Across those experiences, and speaking to leaders across industries, one pattern kept coming up. Organizations were not short of research or data. The problem was that it never reached the decisions that needed it most. Intelligence sat scattered across systems and regions, and by the time it found the right person, the moment had often passed.

That is the problem Stravito was founded to solve. Not a lack of research, but the gap between the intelligence organizations already own and the decisions it never reaches. Major calls on innovation, expansion and marketing get made every day without the full picture, not because the evidence does not exist, but because nobody could connect it in time.

Since 2017 we have been building a platform that changes that. Today, more than 100 of the world’s largest brands use Stravito to ensure the intelligence they already own actively shapes the decisions that drive commercial outcomes.

With enterprises moving from using AI as a productivity layer to a more intelligent layer: what AI adoption trends will dominate how AI is used down the line?

Enterprise AI is entering a new phase. The question is no longer “What can it do?” but “Can we stand behind it?” Three trends are shaping that shift.

Trust is becoming the deciding factor. Organizations are increasingly scrutinising where AI outputs come from and whether they can be traced back to validated sources. Generic AI built on internet data is finding it harder to earn a place in high-stakes decisions. The systems that are gaining ground are those built on a company’s own validated research, where every answer can be traced back to a source a leader can cite and defend.

Alongside that, AI spending is moving from experimentation into operating budgets. That shift brings a simple question with it: which decisions does this actually improve? The organizations pulling ahead are those that have stopped asking what AI can do, started asking where it makes the most difference, and are building accordingly.

Ultimately, enterprise adoption will be defined by defensibility. The systems that endure will be those designed to support decisions leaders can confidently stand behind.

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How can business teams looking to build AI driven processes keep a better human – automation balance with reduced rift?

The organizations doing this well are clear about where AI adds value and where people must remain central. AI handles the heavy lifting of synthesis and research planning — analysing large volumes of information, surfacing patterns, and getting from question to evidence faster than any team could manually. People handle judgment, strategy, and the final call.

That clarity matters because it changes what people spend their time on. Instead of searching for and pulling together research, experts can focus on interpreting what the evidence means, weighing the trade-offs, and guiding the decisions that follow.

The goal is not automation for its own sake. It is better decisions, made with both the rigor and speed of AI and the judgment of the people who own the outcome.

What tips and best practices would you share as more business heads start using AI within real time workflows and across functions?

The organizations that see the most value tend to focus on a few practical fundamentals.

Clear expectations around decision-making are essential. When teams understand that major discussions, planning processes, and cross-functional initiatives should be informed by the intelligence already available to the business, AI becomes part of how work gets done rather than an optional tool.

Strong internal champions also play an important role. When the leaders others look to begin drawing on AI-driven insights during strategy conversations or innovation discussions, broader adoption tends to follow quickly.

Access must also be paired with enablement and support. Teams need guidance on how to ask the right questions, interpret outputs, and act on the answers. That is what ultimately turns AI from something people technically have access to into something that becomes embedded in everyday workflows.

Five AI innovators and innovations you’d like to highlight in this Q&A before we wrap up?

Legora is a company close to home, Swedish-founded, and one of the clearest examples of what it looks like to build AI for a specific high-stakes professional domain rather than trying to be everything to everyone. They have taken legal work and shown that AI built around professional workflows. Proof that deep vertical AI built in Europe can compete at the highest level.

ElevenLabs is changing how AI communicates. Voice is still an underrated modality in enterprise AI, and ElevenLabs is defining what natural, expressive, and trusted AI voice sounds like. As AI moves deeper into workflows, the interface layer matters enormously.  

Cognition and their Devin platform represent the shift from AI that answers questions to AI that executes tasks. Agentic AI that can reason across multiple steps, take actions, and complete complex workflows is the next major inflection point for enterprise. The opportunity is figuring out where that autonomy creates real value and where human judgment must remain central.

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