We set about understanding industry‑specific business challenges with the goal of accelerating the time it takes to bring Applied AI to regulated and complex markets.
The Importance of deeply understanding business requirements
Over the past few months you may have noticed we haven’t been at every AI event. Instead, we shifted our focus away from engaging with technologists and went deep with domain experts in healthcare, oil & gas and more. The objective was simple: learn what’s really needed. That exercise reshaped our entire approach to getting Applied AI into production.
All things being eventually equal — resources, skills, tooling, capability and capacity — the only competitive advantage is time. Applied AI systems compound in value, and first‑movers can establish a significant lead.
Executives don’t care about the model that runs underneath the outcome; they care about repeatable, manageable, adaptable results. And none of them need yet another standalone app. Industries already suffer from app‑sprawl they view as an exposure but tolerate as a necessary compromise. Applied AI should be invisible — seamlessly woven into existing workflows. Humans still hold experience, knowledge and logic that remain immensely valuable.
When defining a problem we lead with pain, impact, exposures and mitigations. Most executives can quote the impact (and spend‑to‑date) on a problem immediately; few technologists can with the same rigour.
Key lessons from going deep with industry experts
1. Delivering Applied AI into production requires a new approach
Our foremost objective is to ship outcomes, not proof‑of‑concepts. Drawing on the scar‑tissue of industry veterans we created a methodology that starts with the business requirement, maps to system blue‑prints, then delivers to production. That rethink led to new tool‑sets you’ll hear more about soon.
2. Create an invisible user experience
Operators possess a wealth of tacit knowledge that isn’t always captured directly as data. By embedding AI inside existing systems and keeping humans over the loop, we amplify that knowledge instead of replacing it.
3. Define problems in full context
Internal silos and external regulation shape every solution. We read the regulations (or have a model do it, then validate with experts) and bake those rules straight into the models.
4. Beyond chatbots
Chat agents are great for the last mile, but real magic happens upstream through chain‑of‑thought, retrieval and other specialised agents. Single‑focus, highly‑tuned models beat one mega‑model that tries to solve everything for complex enterprise use‑cases.
5. Navigate vendor hype
The market is flooded with AI‑washing. Executives want clarity over flashiness. Focus on specific, measurable outcomes today — but architect for the next two projects so your results compound.
What’s next?
In the coming weeks we’ll share more about the tools, patterns and trends we’ve observed, and why a different approach to Applied AI is needed as the ecosystem matures.