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As we wrap up our third week at the STRAT7 AI Innovation Lab, I’m struck by how quickly things evolve in the AI space. What seemed cutting-edge just three weeks ago is now commonplace, and what seemed impossible is now on our roadmap. This rapid evolution brings both excitement and challenges.
Mode 1 projects bear fruit
Our Mode 1 projects —those focused on enhancing existing systems with AI capabilities—have started to deliver tangible results. The improvements to our internal data processing systems are already showing efficiency gains, with some of the heavy lifting being performed by AI rather than using up valuable researcher time. These aren’t flashy transformations that make headlines, but they’re the kind of practical improvements that create real business value.
Mode 2 prototyping continues
Meanwhile, our rapid prototyping efforts for Mode 2 projects—the more experimental, future-focused initiatives—continue at full speed. We’re continue to elaborate what we’ve already developed and a looking a new distinct prototypes to bring to the business, each exploring different ways AI can solve previously intractable problems. The energy in the lab during these prototyping sessions is palpable, with cross-functional teams bouncing ideas off each other and iterating at a pace that would have been unimaginable in our pre-AI Lab era.
The build vs. buy dilemma
One of our Mode 1.5 projects (sitting between enhancement and innovation) has entered unexpected territory this week. This project—one of my personal favourites—is facing a fundamental reconsideration due to our “build vs. buy” principle.
When we established the Lab, I frequently said, “With the pace of change in AI, if a capability is truly valuable, just wait and someone will build it.” This week, that principle came home to roost. A new solution has emerged in the market that accomplishes about 80% of what our project aimed to deliver.
I now face the dilemma of potentially halting a project I’m personally invested in and transforming it into an integration initiative instead. It’s a bittersweet moment—validation that our direction was sound, but also a reminder that in the AI space, being right isn’t always enough; timing matters tremendously.
The growing importance of Partnerships
This situation has highlighted something I’ve been thinking about increasingly: the critical importance of a robust partner ecosystem. In my previous roles at a large Systems Integrators, I had an extensive network of partners and clients to collaborate with, share insights, and create mutual value.
At STRAT7, we’re building this network from the ground up, developing value-shared partnerships with both clients and vendors. These relationships aren’t just about technical integration—they’re about combining expertise, sharing risk, and creating solutions that none of us could develop alone.
Effective partnerships in the AI space require:
- Shared value creation, where both parties benefit beyond the transaction
- Technical compatibility, with clear integration pathways
- Alignment on ethical AI principles and practices
- Long-term commitment, recognising that AI development is a journey
- Knowledge exchange that enriches both organisations
Are you interested in exploring partnership opportunities with the STRAT7 AI Innovation Lab? Whether you’re a potential client with unique AI challenges, a technology vendor with complementary capabilities, or a research institution looking to bridge theory and practice, we’re keen to connect. The most valuable innovations often happen at the intersection of different perspectives and expertise.
Brief hiatus ahead
On a personal note, I’ll be taking a short break over the next couple of weeks to appreciate the daffodils in Wordsworth country. There’s something poetically fitting about contemplating these golden harbingers of spring while also reflecting on the blooming possibilities of AI. The lab will continue its work in my absence, and I expect to return with fresh perspectives inspired by both nature and poetry.
What partnerships have you found most valuable in your AI journey? What collaborative models work best when innovation is happening at such a rapid pace? Share your thoughts and experiences in the comments below—I’ll be catching up on the conversation between admiring those daffodils!
