Research and Innovation: AI Agents
Continuous scanning across eight technology domains, the research to revenue chain, and why declaring a result stays human.
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The purpose of this function is not to know about technology. It is to know earlier than the market, and to convert that lead into something sellable before it stops being a lead.
What is scanned continuously#
| Domain | What we are watching for |
|---|---|
| AI | Capability crossing the threshold from demo to dependable |
| LLMs | Cost, context, reliability. All three move fast and independently |
| Cloud | Pricing shifts and primitives that make an old architecture obsolete |
| Cybersecurity | Attack techniques before they are common, defences before they are standard |
| Databases | The boring domain where the biggest cost differences hide |
| Enterprise technology | What large buyers are being sold, and what they are actually adopting |
| Automation | Where the boundary between rules and judgement is moving |
| Emerging technology | The category most likely to waste a year, watched with the most scepticism |
Scanning is cheap and produces noise. The value is entirely in filtering, and the filter is one question: would knowing this early change a decision we are going to make anyway? If not, it is interesting rather than useful.
The chain this function exists to run#
Research → Knowledge → Product → Service → Revenue
Each arrow is a conversion, and each has a failure mode:
| Arrow | Fails when |
|---|---|
| Research to knowledge | Findings stay in someone's head. Nothing is written, so nothing compounds |
| Knowledge to product | Published, admired, never built. The most common stall |
| Product to service | Built, then never packaged into something buyable |
| Service to revenue | Sold once, delivered heroically, never repeatable |
Most research functions fail at the second arrow and never notice, because publishing feels like output.
The four agents#
Scanning agent#
Watches the eight domains, filtered by the "would it change a decision" test. Dated, sourced, ranked, and short.
Prior-work agent#
Searches our own negative-results record before a question is accepted, so we do not pay twice to learn the same thing. This is the cheapest agent here and the one with the best return.
Experiment agent#
Runs a designed experiment and logs every parameter. Execution is separated from interpretation deliberately, because a team that both runs and reads its own experiment finds what it hoped for.
Replication agent#
Re-runs a positive result independently before it leaves the function. Most reversals happen here, which is precisely why the step exists.
What stays with a person#
- Choosing which questions are worth answering. That is a bet on where the company is going.
- Setting the kill criterion, and applying it when the result disappoints.
- Declaring a result, especially a positive one.
- Deciding to productise, which is a commercial decision.
- Publishing. Research published under our name is our reputation, and it will be checked.
The discipline that makes this real#
A negative result is a success. A documented no is permanently cheaper than a slow yes, and it is the only output that stops the company spending the same money again in eighteen months when everyone who remembers has moved on.
Functions that measure themselves on positive findings quietly stop reporting negative ones, and then the record that makes research compound stops existing.