Identify one operational bottleneck, map the workflow and decisions involved, and determine whether process redesign, standard automation, AI, or no intervention is appropriate.
Dave Chapman
AI, Agents & Automation for Real Business Work
Operational AI
Find the bottleneck. Prove the fix. Then decide.
I help teams solve one frustrating workflow at a time: the delay, the rework, the constant handoff, or the decision that nobody can trust. I diagnose what is actually happening, decide whether software, automation, AI, or a process fix is the right intervention, and build a small working prototype before any bigger commitment.
AI-assisted support triage cut first-response handoff to under 20 minutes.
5+years building operational systems
2-6weeks to working prototype
1workflow per diagnostic
1go or no-go decision gate
What This Is Useful For
Where this creates measurable value
Workflows where delay, rework, and inconsistency are costing real money.
- Teams repeating the same decisions every day by hand.
- People re-investigating the same issues from scratch.
- Work that bounces between systems, spreadsheets, and inboxes.
- Processes where key context is hard to find when needed.
- Too many handoffs causing delay and inconsistency.
- Delivery slowed by avoidable process friction.
- Manual work left over because older automation could not handle gray areas.
How you can work with me
Start with one workflow, not an AI transformation programme.
The path is simple: diagnose the bottleneck, test the smallest useful intervention, and only then decide whether to implement.
Build and test a small working solution against real workflow inputs before committing to a larger implementation.
Turn proven prototypes into operational systems, or provide the architecture, evidence, and implementation plan for the client’s internal team.
Engagements start small, are scoped around one workflow, and are priced before work begins. Diagnostics and prototypes are bounded. Implementation follows proof.
How I Work
Five steps to a justified go or no-go
Built to reach a clear invest-or-stop decision, not a slide deck.
- 01 Understand the workflow problem
Identify the bottleneck, the cost of delay, and what a good outcome actually looks like.
Outcome: One workflow scoped
- 02 Decide what software can safely do
Work out which decisions software can make and which should stay with people.
Outcome: Ownership clarified
- 03 Design the smallest workable intervention
Map the handoffs, exceptions, review points, and failure paths before building anything.
Outcome: Clear next-step design
- 04 Prototype with real inputs
Build a working slice in the actual workflow, not a demo disconnected from the job.
Outcome: Prototype runs live
- 05 Measure and decide
Check quality, cost, and operational impact, then decide whether to stop, iterate, or implement.
Outcome: Go/no-go decision made
What I Can Build
Implementation patterns I repeatedly prototype
Practical patterns used to remove bottlenecks and reduce decision latency.
Why Dave
A practical bridge between strategy and implementation
The edge is linking business problems to practical system design and fast implementation.
Software engineer
+ Systems thinker
+ Former business owner
+ Prototype builder
- Software engineer designing systems, integrations, and operational tools that real teams actually use.
- Former business owner with hands-on experience in logistics, workflow pressure, and operational decision-making.
- Build-first approach: test reality before committing to large programs or expensive transformation work.
- Currently working with AI, automation, and prototype development in real operational contexts.
How I Think
Principles for practical AI adoption
Technology choices must be accountable to operational outcomes.
AI should remove work, not create more admin.
Use the simplest reliable system that gets the outcome.
Keep humans where judgment and accountability matter.
If standard automation is better than AI for a task, use standard automation.
Code is a means. Business outcomes are the goal.
Decision Boundary
When AI is not the right tool
Practical constraints that keep systems reliable and accountable.
- When deterministic rules solve it more reliably and with lower risk.
- When accountability requires human judgment that cannot be delegated.
- When process quality issues should be fixed first.
- When skilled human handling is still cheaper than building and maintaining fully fledged software.
Proof Standard
How prototype quality is made inspectable
Evidence gates used before any recommendation to scale investment.
- Each prototype includes boundaries, override points, and failure paths.
- Prototypes are tested with real inputs, not happy-path demos.
- Quality is inspectable before deeper automation is considered.
- If a reviewer cannot inspect why a decision was made, the workflow is not ready to scale.
Work Evidence
Recent proof structures
Each case shows the problem, design boundary, implementation, and measurable result.
AI-assisted support ticket triage
Median first-response handoff dropped to under 20 minutes in pilot runs.
- Problem
- Support teams re-read similar tickets and escalated too early.
- Decision boundary
- Automation classifies and drafts routing; team leads own final priority decisions.
- Mechanism
- Rerouted mixed-quality inbound tickets into confidence-ranked lanes.
- Constraint
- Low-confidence tickets always route to manual review. No silent auto-close.
Agentic development workflow with human checkpoints
Pull request cycle time fell 28% on bounded task classes.
- Problem
- Teams lost cycle time to repeatable setup, regression checks, and boilerplate updates.
- Decision boundary
- Agents can propose and implement scoped changes; merge decisions stay human-owned.
- Mechanism
- Reduced review latency by automating repeatable implementation and verification loops.
- Constraint
- All generated changes require deterministic checks and reviewer approval.