Operational process inventory
Every meaningful repeated workflow in scope, with frequency, hours, systems, and owner recorded in one place.
With TitanWave
Know where AI pays before you spend a dollar on it
An operational assessment that names the processes where AI produces real return, the ones where it will not, and the order to do them in. Written down, vendor neutral, costed.
Most failed AI projects were not badly built. They were badly chosen. The team picked a use case because it demoed well, not because the underlying process was frequent, rule-bound, and expensive enough to justify automating.
A blueprint fixes the choosing. Before anything is built, we assess how your operation actually runs, where hours are consumed, where data already exists in a usable form, and which processes tolerate a handoff.
Then it says no out loud. A blueprint that recommends everything is a sales document. The value is in the processes we tell you to leave alone, because that is where the money would have disappeared.
We deliver blueprints with TitanWave, our sister company, whose entire practice is vendor-neutral AI transformation advisory. Lingows brings the marketing, frontend, and automation delivery capability. TitanWave brings the assessment discipline. If the blueprint concludes that a process should stay manual, that is what it says.
What it is
What the blueprint contains
A decision document, not a capability tour.
It starts with a process inventory. Every meaningful repeated workflow in the departments in scope, with frequency, hours consumed, systems touched, and who owns it. Most companies have never had this written down in one place, and it is useful on its own.
Each process is then scored on the four factors that actually predict success: how often it runs, how much time it consumes, how clearly the rules can be stated, and what an occasional error would cost. High frequency plus clear rules plus low error cost is where AI pays. Anything else gets flagged.
Feasibility is assessed honestly. Is the data reachable. Does the system expose an interface. Is there someone internally who can say what a correct output looks like. A perfect-fit process with unreachable data is not a candidate yet, and the blueprint says so.
Then sequencing and cost. Which project goes first, what it costs to build and to run, what payback looks like, and what has to be true for the second project to follow. Priced ranges, not a single optimistic number.
It is vendor neutral by construction. The blueprint names capability and cost profiles, not a mandatory product. If the right answer is a tool you already own, that is the recommendation.
Fit
Who this is for, and who it is not for
We would rather say no early than sell a program that cannot work.
Right fit
- Leadership is being asked for an AI strategy and does not want to guess at one.
- You have budget for one or two initiatives and need to know which ones.
- A previous AI project underdelivered and you want to understand why before trying again.
Not the right fit
- You already know the workflow and want it built. Skip the blueprint and go straight to automation.
- You want a document that justifies a decision already made. The assessment may not agree.
- Nobody internally can commit time to interviews. The blueprint depends on how the work really runs, not the org chart.
Deliverables
What you receive
A written blueprint you can act on, or hand to another firm.
Opportunity scoring
Each process scored on frequency, hours consumed, rule clarity, and error cost, so prioritisation is arithmetic rather than opinion.
An explicit do-not-automate list
The processes where AI would cost more than it returns, named directly with the reasoning attached.
Feasibility and data readiness review
Whether the data is reachable, whether the systems integrate, and what has to change before a candidate becomes buildable.
Sequenced roadmap with costs
What to build first, second, and third, with build and run cost ranges and the dependencies between them.
Measurement plan
The baseline to capture now and the metrics that will prove or disprove return, defined before anything is built.
How we run it
How a blueprint is produced
Interviews and observation first. Recommendations last.
Step 1: Scope and stakeholder mapping
We agree which departments are in scope and identify the people who actually run the work, not only the people who manage it.
Step 2: Process discovery
Structured interviews and observation of how the work truly happens, including the workarounds nobody documents.
Step 3: Scoring and feasibility
Each candidate is scored and checked against data availability, system access, and error tolerance.
Step 4: Roadmap and readout
A written blueprint with sequencing, costs, and a measurement plan, presented to leadership with the rejected candidates explained.
What is an AI blueprint?
An AI blueprint is a vendor-neutral assessment of an organisation's processes that identifies where AI will produce measurable return, where it will not, and in what order to build, with cost ranges and a measurement plan attached.
- Candidates are scored on frequency, hours consumed, rule clarity, and the cost of an occasional error.
- It includes an explicit do-not-automate list, which is usually the most valuable section.
- It is produced before any build commitment, so the spending decision is made on evidence.
Where this connects
Where this connects
The blueprint decides. These are the programs that deliver.
The first project a blueprint recommends is usually built as automation workflows against the exact process the assessment scored highest.
Where the recommendation requires a real interface and permission model, that is delivered through application frontends rather than a standalone tool nobody signs into.
Blueprints frequently conclude that the constraint is skill rather than software, which points to AI training and enablement before any build begins.
The measurement plan is implemented by our analytics practice so the baseline exists before the first change ships.
Questions
AI blueprints questions we get asked
Decide with evidence, not with a demo
Start with a scoping conversation about which departments belong in the assessment.