HOME/AI STRATEGY CONSULTING

AI Strategy Consulting

The AI strategy consulting company that tells you what not to build

BinaryBrill is an AI strategy consulting company for teams holding a list of AI ideas and no reliable way to rank them — we score each use case, run the build-versus-buy calls, and hand you an AI roadmap for enterprise delivery with owners and ROI framing. You get AI adoption consulting from in-house senior engineers who also ship AI systems, so the advice is grounded in delivery reality rather than slideware.

Book a free consultation See our work

A senior engineer replies within 24 hours — not a sales rep.

Why AI plans stall before a line of code is written

You have twenty AI ideas and no honest way to rank them

Every idea has a champion, so every idea is "high priority". Without a shared way to score value against data readiness and delivery difficulty, the loudest voice wins the budget rather than the strongest case, and the shortlist changes every time a new tool trends. Six months later the money is spent and nobody can say what it bought.

The data the plan depends on isn't actually there

The strategy assumes clean, unified, well-labelled data behind each idea. In reality it is spread across three systems that disagree, half of it is free-text, and the field everyone treats as authoritative is overwritten nightly by an integration. Plans built on that assumption fail at the first build, not at the whiteboard.

Pilots multiply and nothing reaches production

A department runs a proof of concept, it demos well, and then it sits there — no owner, no roadmap, no route through security and procurement. The next quarter someone starts a different pilot. Effort accumulates, production value doesn't, and the board starts to suspect AI is a money pit.

"AI" is on the board agenda with no target attached

A budget got approved for a buzzword rather than an outcome. There is no defined problem, no baseline to improve on, and no measure of success, which makes the spend impossible to defend later. That is exactly the condition that produces an expensive vanity project with a good demo and no return.

What an AI strategy consulting company should actually give you

Strategy is only useful if it survives contact with your data and your engineers. We produce assessments and roadmaps you can act on and defend — and we tell you plainly which of your ideas is a database query wearing a costume.

Scoring, not opinions

Every candidate use case is scored on the same three axes — business value, data readiness and delivery difficulty — so the shortlist reflects evidence rather than whoever argued hardest. You get a register you can take to a budget meeting and defend line by line.

Build, buy or wait — with the reasoning written down

For each idea worth pursuing we say whether to build it, buy an existing product, or wait until a dependency is ready, and we show the working. "Wait" is a real recommendation here: some ideas are sound but blocked on data or platform gaps that must close first.

Grounded in delivery reality, because we also build

Our advice comes from people who ship AI systems, not from a deck. That is the difference between a roadmap that looks impressive and one that survives a sprint — we size difficulty against how these builds actually go, and we will tell you when the honest answer is not to build at all.

A roadmap with owners and ROI framing, not a wish list

The funded items get sequenced, assigned to a named owner, and framed against the value they are meant to return, alongside a governance model for approving future AI features. What you receive is a plan a delivery team can pick up, not a slide that reads well and directs nobody.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

AI Opportunity Assessment & Use-Case Scoring

The core of our AI adoption consulting: we take your full list of ideas and score each on business value, data readiness and delivery difficulty using one comparable scale. The output is a ranked register you can defend to a budget committee, not a gut-feel shortlist that changes with the news cycle.

Every candidate scored on value, data readiness and delivery difficulty on the same scale
A ranked, defensible register rather than a list ordered by who argued hardest
Blunt identification of ideas that are really a report or a database query
Written rationale attached to each score, so the ranking survives challenge

AI Roadmap for Enterprise Adoption

We turn the scored register into an AI roadmap for enterprise delivery — sequenced, owned, and framed against the return each item is meant to produce. As an AI transformation consultant partner we plan for dependencies and capacity, so the roadmap reflects what your teams can actually deliver rather than an ideal-world Gantt chart.

Funded use cases sequenced with named owners and ROI framing
Dependencies and delivery capacity built into the sequence, not assumed away
Quick wins separated from foundational work so momentum and groundwork both get funded
A plan a delivery team can pick up, written to be revisited each quarter

Data & Platform Readiness Assessment

Most AI ideas fail on data long before they fail on models. We assess the data and platform each candidate depends on — coverage, quality, ownership, and whether the pipelines and access exist — and produce the gap list that must close before any build is worth starting.

Per-use-case gap analysis of the data each idea actually requires
An honest read on data quality, coverage and contradictions across systems
Platform and access dependencies identified before, not during, a build
A prioritised remediation list so the groundwork is scoped and sequenced

AI Governance & Risk Modelling

Once you start approving AI features you need a repeatable way to decide which are acceptable. We define the governance and risk model — who signs off, what evidence they require, where a human must stay in the loop, and how each feature is assessed against frameworks such as the NIST AI RMF and the EU AI Act.

An approval process for AI features with clear sign-off ownership
A risk model mapped to recognised frameworks, not invented in-house
Defined points where human review is mandatory rather than optional
Guidance your teams can apply to future proposals without us in the room

Build-vs-Buy Advisory

For every idea worth pursuing, the next question is whether to build it, buy a product that already does it, or wait for a dependency to be ready. We give a clear recommendation with the reasoning shown — and because we also build, we know when a vendor tool is genuinely the cheaper, faster call.

A build, buy or wait recommendation for each viable use case, with the working shown
Total-cost-of-ownership comparison across build and buy options, including running cost
Honest "wait" calls where an idea is sound but blocked on a dependency
Vendor and platform shortlists where buying beats building

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

AI platforms we assess

Claude
GPT-4 class models
Llama
Mistral
AWS Bedrock
Azure OpenAI Service
Google Vertex AI

Data & platform foundations

Snowflake
BigQuery
Databricks
dbt
Postgres with pgvector
Apache Airflow

Governance & risk frameworks

NIST AI Risk Management Framework
EU AI Act
ISO/IEC 42001
Model cards
Data protection impact assessments

Assessment & roadmap methods

Weighted use-case scoring
RICE prioritisation
Total-cost-of-ownership modelling
Capability mapping

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Discovery and idea inventory
We gather every AI idea on the table, the business question each is meant to answer, and what is really driving it — a genuine bottleneck, a competitor's press release, or a board request. Naming the intent behind each one is what lets us judge it honestly later.
You get: A written idea inventory listing every candidate use case, the business question behind it, and the outcome its sponsor expects.
02
Data and platform readiness review
We assess what data and platform capability actually exists against what each idea assumes. This is where costumes come off: an idea that needs unified customer history is only as viable as the state of that history across your systems.
You get: A written readiness assessment with a data and platform gap list per use case, and the specific dependencies that must close before each build can start.
03
Score and recommend
Each candidate is scored on value, data readiness and delivery difficulty, then given a build, buy or wait recommendation with the reasoning made explicit. We are direct about the ones that are reporting problems dressed up as AI, or a database query with a chatbot on top.
You get: A scored use-case register with a build / buy / wait recommendation and written rationale for every item, ranked into a defensible priority order.
04
Roadmap and governance
The funded items are sequenced into a plan with named owners and ROI framing, and we set the governance and risk model your organisation will use to approve AI features from here on — who signs off, what evidence they need, and where a human must stay in the loop.
You get: A sequenced roadmap with owners and ROI framing, plus a written AI governance and risk model your teams can apply to future proposals.

Where we've applied this

Healthcare

Deciding which AI ideas are viable given clinical data governance, and where a human must stay in the loop before any of them reach a patient-facing workflow.

Finance

Prioritising use cases against model-risk rules and regulatory scrutiny, so budget is committed to what can pass review rather than what demos well.

Retail

Separating genuine AI opportunities from reporting and data-quality problems that a dashboard would solve more cheaply than a model.

Logistics

Sequencing forecasting and automation ideas against the data you can actually capture today, so the roadmap doesn't depend on telemetry you don't collect.

Manufacturing

Assessing whether vision and predictive-maintenance ideas are supported by the data the line already produces, before anyone budgets for a build.

Professional services

A roadmap for adopting AI across delivery and back office without betting the firm on a single unproven pilot, with owners for each step.

Questions buyers ask us

We already know what we want to build. Do we still need AI strategy consulting?

Maybe not, and we'll say so on a first call rather than sell you an engagement you don't need. Strategy consulting earns its fee when you have several competing ideas and no shared way to rank them, when you're unsure whether the data behind an idea can support it, or when the board wants a defensible plan rather than a single pilot. If you have one well-defined problem and the data to back it, you may be better served by going straight to a build.

How is this different from just hiring you to build the thing?

This engagement produces documents, not software. The deliverables are written assessments, a scored use-case register, a sequenced roadmap and a governance model — the decisions that come before a build, made with evidence. A build engagement is the opposite: code, pipelines and a deployed system. Many clients do strategy first to decide what is worth building, then bring us back to build the items the roadmap prioritised, but the two are separate pieces of work with separate deliverables.

What do we actually receive at the end of the engagement?

Written artefacts you can act on and defend: an idea inventory, a data and platform readiness assessment, a scored use-case register with build / buy / wait recommendations and rationale, a sequenced roadmap with owners and ROI framing, and an AI governance and risk model. No slideware theatre — the register and roadmap are working documents a delivery team can pick up, and the governance model is something your teams keep using after we've gone.

What drives the cost, and how long does an engagement take?

The main drivers are the number of use cases to assess and how many data sources and systems sit behind them — reviewing readiness across a tangle of disagreeing systems takes longer than assessing a handful of ideas over one clean warehouse. A focused assessment and roadmap is usually a few weeks; a broader enterprise-wide engagement covering many candidates and a full governance model runs longer. We scope it to your list rather than sell a fixed package, and we tell you the shape before you commit.

Will you actually tell us not to build something?

Yes, routinely, and it's one of the most valuable things we do. Some ideas are reporting problems that a dashboard solves more cheaply, some are database queries with a chatbot bolted on, and some are sound but blocked on data that doesn't exist yet. Killing or deferring a weak idea early is far cheaper than discovering its flaws six months into a build, and because we also deliver AI systems we can tell the difference from experience rather than caution.

What happens to our internal information during the engagement?

It stays yours and stays confidential. We work under an NDA, we only look at the data and documents needed to assess readiness, and nothing is used outside your engagement. Where an assessment needs us to inspect systems directly we agree the access scope in writing first, and we don't move data out of your environment to do the review. The deliverables describe your data's condition; they don't republish it.

When is strategy consulting the wrong spend for us?

When you already have clarity and just need delivery, an advisory engagement is an unnecessary detour — go and build. It's also the wrong spend if the organisation isn't ready to act on a roadmap: a plan with no one empowered to fund or own the items becomes another document in a drawer. We'd rather flag that risk up front than produce a roadmap you can't execute. If the blocker is organisational rather than analytical, consulting won't fix it.

Can you help us execute the roadmap afterwards?

Yes, and it's a common path — but there's no obligation to use us for delivery, and the roadmap is written to be handed to any competent team. Because our advisers are engineers who build AI systems, the transition from strategy to build is short: the difficulty scores and dependency work carry straight into delivery. If you'd rather execute in-house or with another partner, the deliverables are vendor-neutral enough to support that.

Who runs the consulting, and where are they based?

Our own in-house senior engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted. The people advising you are the ones who also build AI systems, which is what keeps the strategy grounded in delivery reality rather than in theory, and a senior engineer replies within 24 hours of your first message.

Tell us the AI ideas you're weighing up

Send us the shortlist and whatever you know about the data behind each idea. A senior engineer replies within 24 hours with a first read on which ones look worth scoring, which look blocked on data, and which are a database query wearing a costume — including when the honest answer is not to build.

Book a free consultation Browse all services