AI Strategy and Roadmap
Work out what to build, in what order, before spending money on any of it.
- Best for
- A leadership team with more AI ideas than capacity, or a program that needs restarting
- Timeline
- 4 to 8 weeks
AI implementation for operations teams
Your AI pilot works.
It's just not in production.
We take stalled AI projects from demo to live operations, integrated with the systems you already run. You own the code.
AI Readiness Review
45 minutes
You leave with
- Two to three use cases, scored on value and feasibility
- An effort estimate for the one worth doing first
- An honest cost range, including when the answer is do not build
Whether or not we work together.
Chemicals and petrochemicals, plastics, construction materials, food manufacturing, engineering and industrial services.
Anthropic Certified Partner
Week one to production, on one page.
Every phase ends at a point where you can stop. This is the whole shape of an engagement, including the part where we tell you not to build.
Week 1
Readiness review
A 45-minute conversation, then a short written assessment. Two to three use cases scored, one recommended.
You get
Scored use cases and an honest cost range
Weeks 2 to 4
Production Readiness Assessment
Data, systems and process detail on the recommended use case, scored against the six controls. Fixed fee, quoted before it starts.
You get
A score out of 18, a build plan, or a written recommendation not to build
Weeks 4 to 16
Build and integrate
Staged releases into the systems your team already uses. Each phase ends at a point where you can stop.
You get
A working system, and everything needed to run it
Ongoing
Operate
Monitoring, drift alerting, retraining and a monthly report. Or your team takes it, which is the point of the handover.
You get
Monthly review in your operating metrics
What we build, and what it takes.
Grouped the way an engagement actually runs, so you can see where you would start.
Decide
Before anything gets builtData Foundations
Fix the data problems that would otherwise surface halfway through a build.
- Best for
- Anyone whose AI effort has stalled on data quality, access, or a system that will not give it up
- Timeline
- 4 to 12 weeks
Build
The system itselfProcess Automation
Stop paying people to retype invoices, route approvals, and chase exceptions.
- Best for
- Three or more people doing repetitive document or data work
- Timeline
- 3 to 10 weeks
Custom AI Development
Build the thing that does not exist off the shelf, on your own data.
- Best for
- A defined use case that a general-purpose tool has already failed at
- Timeline
- 6 to 20 weeks
Integrate and run
Where most AI investments quietly failSystems Integration
Put AI inside the ERP and CRM your team already works in, not beside them.
- Best for
- Anyone whose AI pilot works in a browser tab nobody opens
- Timeline
- 4 to 16 weeks
AI Operations
Keep it working after launch, and know the moment it stops.
- Best for
- Anyone with an AI system already in production and nobody watching it
- Timeline
- Monthly, ongoing
Six ways this goes wrong. The control we use for each.
Most organisations have already paid for at least one of these lessons. Listing failures without countermeasures is commentary, so here is what we actually do about each.
We call these the Six Controls. Scoring an organisation against them is the Production Readiness Assessment, and the rubric it is scored against is published in full rather than kept in the room.
The pilot never scales
A model that performs in a sandbox usually needs re-engineering for production volume, edge cases, latency and integration constraints. Most vendors stop at the pilot.
The control
Production constraints are written into the acceptance criteria at the start, and the pilot runs against real data volumes rather than a curated sample.
The data was not ready
Preparation, cleaning and governance are consistently underestimated. Building on poorly structured data creates debt that surfaces after the money is spent.
The control
Data readiness is assessed in the first phase, before any architecture decision, and it can stop the project. That is what the first phase is for.
Nobody changed how they work
A system that changes how people work needs role definitions, training and escalation paths. Without them, adoption stalls and the tool is bypassed regardless of how well it performs.
The control
Enablement is a phase with its own deliverables, not a training session at the end. The people who will use it are in the room during design.
The model decayed and nobody noticed
Models degrade as real-world data drifts from what they were trained on. Performance erodes silently, and it usually surfaces as a costly mistake.
The control
Monitoring and drift alerting ship with the system, not after it. Retraining is scheduled from day one rather than triggered by an incident.
The vendor owns the outcome
Black-box systems cannot be inspected, modified or migrated off. That is structural dependency, and it is a real risk if the relationship changes.
The control
Source code, model weights, prompts, and infrastructure configuration transfer to you. No proprietary runtime. This is a contract term, not a promise.
Success was measured in the wrong units
Projects judged on model accuracy or latency lose leadership confidence even while working correctly, because those are not numbers anyone is accountable for.
The control
Success criteria are set in the metrics your operation already reports: cycle time, cost per transaction, labor hours, error rate.
- Absent Score 0, Absent.
- Informal Score 1, Informal.
- Defined Score 2, Defined.
- Operating Score 3, Operating.
You own the code. We own the outcome.
Three things that are true here and are checkable, rather than five that every competitor also claims.
-
01
Everything transfers to you
Source code, model weights, prompts, infrastructure configuration and documentation. No proprietary runtime, no licensing dependency, no API that holds the capability hostage. It is a contract term, not a value.
-
02
One team from assessment to production
Advisory firms commonly design a strategy then hand off to an implementation partner at the point of highest risk. The person who scopes your engagement is the person accountable when it goes live.
-
03
Measured in numbers you already report
Cycle time, cost per transaction, labor hours, error rate. Success criteria are agreed before the build, in the metrics your operation already tracks, not in model accuracy scores nobody is accountable for.
Delivery record.
Both describe systems work rather than AI work, which is deliberate. It is the same delivery discipline, and it is the record these two clients put their names to.
Ebrahim was an invaluable asset as we transitioned from a legacy system to a complex cloud-based ERP. His willingness to understand our business allowed us to get up and running more quickly and efficiently than expected.
Abstraction Advisors did an excellent job understanding our business, evaluating options, and helping put together a suite of technologies that would meet our needs.
What we see in the field.
AI Strategy
Why 78% of Enterprise AI Pilots Never Reach Production — and How to Be in the Other 22%
The failure rate for enterprise AI initiatives is well-documented. Less discussed is what the successful 22% consistently do differently. After dozens of AI implementations, patterns emerge — and most are operational, not technical.
ImplementationThe Data Readiness Assessment: What to Do Before You Build Anything
The most expensive mistake in AI implementation is discovering data problems after the project is already resourced and underway. Budget has been allocated, vendor agreements signed, timelines communicated…
AI OperationsModel Drift Is Inevitable. Here’s How to Manage It Before It Manages You.
Every AI model in production will eventually degrade. The question is whether you find out from your monitoring system or from your customers.
What you are agreeing to.
Selling risk mitigation while carrying none of it would be a strange position to hold. These are the terms, stated before you ask for them.
Fixed fee on phase one
The assessment is quoted as a fixed fee before it starts. No hourly overrun on the part where the scope is least certain.
You can stop at the end of any phase
Each phase ends with a written review against criteria set at the start. Stopping there is a normal outcome, not an awkward one. If the assessment says do not build, that is what it will say.
You own what we build
Source code, model weights, prompts, infrastructure configuration and documentation transfer to you. No proprietary runtime and no licensing dependency.
MSA and NDA on request
A standard mutual NDA is available before the first conversation, and a master services agreement before any work begins.
Bring us the pilot that stalled.
Forty-five minutes. We will tell you what it would take, what it would cost, and whether it is worth doing at all.
You'll leave with 2 to 3 scored use cases, an effort estimate, and an honest cost range, whether or not we work together.