Insights
Tracking What Matters
Key announcements, milestones, and moments that matter—from Innovative Solutions and our partners.
This includes company news, AWS recognitions, product launches, and updates that reflect how we’re growing and where we’re headed.
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No single choice carries your whole system.Businesses want one model, one decision, one person to hold the whole answer. AI rarely lets that hold for long. No single model fits every task, which is why the best systems route across several instead of betting on one. A model that performs well today can quietly fall behind within months, whether anyone is watching or not. And the reasoning behind a decision doesn't travel on its own; it leaves the building the day the person who understood it does.
In Episode 17 of AI Unplugged: AI Gone Rogue: Security, Slop & the Cost of Intelligence, our team unpacks what happens once AI capability stops being the open question. An agent reroutes around a system's security to finish its task. A frontier model's price drops overnight. In both cases, the problem isn't whether AI can do something, it's who's responsible, and who actually benefits, once it does.
If you passed on an AI project because the cost didn't add up, it may be time to revisit that decision. Routine AI work has gotten meaningfully cheaper, but the most advanced models haven't. Here's what's driving the shift and how to figure out which parts of your project belong on which pricing tier.
Professional services have priced work by the hour for decades because the hour was the one thing both sides could measure and agree on. AI is breaking the assumption that pricing depends on: that hours worked and value delivered move together. What replaces it isn't settled yet.
No single choice carries your whole system.Businesses want one model, one decision, one person to hold the whole answer. AI rarely lets that hold for long. No single model fits every task, which is why the best systems route across several instead of betting on one. A model that performs well today can quietly fall behind within months, whether anyone is watching or not. And the reasoning behind a decision doesn't travel on its own; it leaves the building the day the person who understood it does.
AI systems can lose effectiveness as models are deprecated, standards evolve, and data shifts, even when the original build was excellent. This article makes the case that AI should be treated as a maintained capability rather than a finished project, and shows what that discipline looks like in practice.
Every time an AI project changes hands, valuable business and technical understanding gets lost. This article explores how Forward Deployed Engineering preserves that context so knowledge and value can build over time.
On Episode 16 of AI Unplugged: AI Wars, Space Data Centers & the Forward-Deployed Engineer, our team digs into why the AI race is no longer being won on raw model intelligence alone. As the top models converge on similar benchmarks, the real differentiator becomes cost, speed, infrastructure reliability, and how ready an organization actually is to put the technology to work.
AI is moving from experimentation into execution, and forward deployed engineering gives teams a faster, more focused way to turn opportunity into working capability. By putting Forward Deployed Engineers closer to the business, the data, and the users, companies can move beyond planning and start compounding real AI value.