What does purpose-built AI for delivery look like?
General-purpose AI can write great summaries and passable first drafts. The open question that implementation teams should be asking is: what would AI that was made for delivery look like, and what's missing currently?
Where we are currently
If your team uses Claude or Copilot, you already know what it's good at. Point it at a transcript and it'll give you a great summary. Ask for a first draft of a document and the draft is decent, fast, and getting better every quarter. Writing things faster is basically solved, and any vendor claiming otherwise is selling lies.
Where are projects actually losing money?
Spoiler: the cases we see the most usually have nothing to do with document drafting.
Work nobody agreed to pay for. One small request in a meeting is fine, another over Slack might be too, but one too many and you end up losing money. Individually, most of these asks are easy enough that the team just does them. Often nobody is consistently checking them against what was sold, and over a long engagement, enough small additions can turn a profitable project into an unprofitable one as scope unknowingly creeps.
Time spent re-learning what the team already knew. Implementations are rewritten during the handoffs: the delivery team that picks up the project after it's sold spends redundant time re-discovering the meaning behind different decisions and reconstructing context that had at one point been known.
Lessons that disappear. Every project produces tons of super useful data: how long a certain feature took vs. what was estimated, which assumptions are usually true over a large set of implementations, how different clients make decisions. This is information that model providers and competitors don't have. This invaluable proprietary information typically stays in disparate tools and people's heads while the next engagement starts from zero.
The Promised Land
We've worked with our customers to address these issues by orienting Datafruit to solve for three outcomes.
- Preventing scope creep
- Preserving information through handoffs
- Rigorously building firm-specific proprietary context
Here are the capabilities that we believe are necessary for useful delivery-specific AI.
| General-purpose AI | Datafruit | |
|---|---|---|
| Engagement memory | Each output is disconnected from the last. | Maintains persistent engagement state across the life of the project. |
| Requirements capture | Summarizes well, but summarization isn't a delivery primitive. | Turns requirements into durable objects and tracks how they change. |
| Scope integrity | Has no concept of what was actually sold. | Maintains scope as a live baseline and checks new asks against it. |
| Traceability | Outputs carry weak provenance. | Ties requirements, decisions, and deliverables back to their source. |
| Continuity across people | Context still lives within individual contributors' minds. | Makes the engagement itself the system of record. |
| Continuity across engagements | Each project's lessons largely disappear after they are done. | Retains proprietary context so future work benefits from past delivery. |
| Governed action | Drafts, then leaves execution to a person. | Takes approved actions inside the systems where delivery actually happens. |
If you're interested in learning more about adopting a purpose-built system for enterprise software delivery, book a demo!