AI Consulting · Enterprise AI Strategy
Your board wants an AI plan and your pilots are still in a spreadsheet
Banao builds enterprise AI strategy end to end: we audit your operations for viable AI use cases, size the value, rank them by effort and return, and produce a funded roadmap with the build plan and the team to execute it.
The strategy is not a PDF that lives in a shared drive. Every recommendation connects to a technical feasibility check, a data readiness assessment, and a build-or-buy decision — so the first item on the roadmap can go into development the week after sign-off.
Banao— We built and ran an enterprise AI strategy for our own 300-person operation before advising anyone else on theirs.
What an enterprise AI strategy engagement covers
A strategy that can be acted on requires more than a framework. It requires audit, assessment, and a build plan.
Use-case audit and prioritization
We map your workflows against AI capability categories, score each candidate on data readiness, effort, and expected return, and rank the shortlist so the first sprint starts on the highest-value item.
Technical feasibility assessment
For each prioritized use case, we check whether your data, infrastructure, and integrations can support it — and what has to change before it can ship.
Build, buy, or configure analysis
We assess whether a use case is better served by a custom model, a fine-tuned foundation model, or an off-the-shelf product — with total cost of ownership for each path.
Funded roadmap with milestones
A phased roadmap with delivery milestones, team requirements, and cost estimates that your CFO can approve and your engineering leads can execute against.
AI governance and risk framework
Ownership, audit trails, model update policies, and escalation paths — so your legal and risk teams have the controls they need before any model acts on live data.
Measurement and success criteria
We define the metrics that prove each use case worked — cost per unit, cycle time, error rate — before building starts, so evaluation is never an afterthought.
Change management and capability plan
A plan for training affected teams, managing the handoff from existing workflows to AI-assisted ones, and building internal capability so you are not dependent on outside help indefinitely.
Vendor and partner selection support
Where the strategy calls for a vendor or platform, we write the criteria, run the evaluation, and assess the shortlist — so the decision is based on your actual requirements, not a demo.
Why enterprise AI strategies fail before they ship
Most enterprise AI strategies stall at the same point: the gap between the slide deck and the first engineering sprint. The deck identifies the right areas but does not answer the questions that turn strategy into a roadmap — which use case goes first, what the data actually looks like, who builds it, and what success means in measurable terms.
We close that gap inside the strategy engagement itself. Feasibility, data readiness, and build planning are not follow-on work — they are part of what we deliver, so the strategy produces a roadmap that a team can pick up and execute without a separate discovery phase.
Strategy and execution in one engagement
We do not hand off a strategy to a separate build team. The engineers who will build the first use case are in the room during the strategy phase — so what we plan is what can actually be built.
Grounded in your data, not industry benchmarks
Every recommendation comes from what we find in your actual systems — your data volumes, your integration surface, your current tooling — not from a generic maturity model applied from the outside.
Ranked, not just listed
We produce a prioritized shortlist with an explicit rationale: use case A before use case B because of data readiness, effort ratio, and dependency. No long catalogue of possibilities with no ordering.
What makes enterprise AI strategy different at scale
A five-person startup and a five-thousand-person enterprise have access to the same AI capabilities. The difference is that the enterprise has existing systems, compliance obligations, procurement processes, and organizational change risk — and those factors determine which AI strategy is achievable, not which one looks best in a framework.
We design for the constraints your organization already has. That means the strategy respects your ERP vendor relationships, your data sovereignty requirements, your IT security review cycle, and the change appetite of the teams who will have to work differently. A strategy that ignores those constraints does not survive contact with your organization.
Designed for your compliance reality
We account for your procurement cycle, vendor approval process, and data-handling obligations in the roadmap phasing — so the strategy does not stall the first time it meets your legal team.
Works with your existing technology investments
We identify AI opportunities that sit on top of your current ERP, CRM, and data infrastructure — because adding net-new systems for every use case is rarely what the business can absorb.
We built an enterprise AI strategy for our own operation first
Banao is a ~300-person engineering services company. Before advising any client on enterprise AI strategy, we ran the same process on ourselves: audited our workflows, ranked the use cases, built the business case, and executed the top items.
Vikaas, our AI demand generation system, and InterviewGod, our AI candidate screening system, came out of that internal strategy process. We measure their performance against the criteria we set before we built them — that is the standard we apply to client engagements.
- VikaasAn AI system from our own strategy process — runs Banao's demand generation.
- InterviewGodAn AI system from our own strategy process — screens Banao's engineering hires each week.
Where we deliver enterprise AI strategy
India
Bangalore and Chandigarh hold the delivery bench, so strategy engagements start quickly and build work runs close to the engineers who execute it, under the DPDP Act.
UAE and GCC
From Dubai we build enterprise AI strategy for GCC enterprises, with data handled inside UAE boundaries under the PDPL where client policy requires it.
US and UK
For US and UK clients we design to SOC 2 and UK GDPR expectations, with the governance framework and audit logging their risk teams require.
When you don't need a full enterprise AI strategy engagement
An enterprise AI strategy engagement is the right call less often than the vendor market implies. We will tell you if one of these fits instead:
- You already know what to build: if your team has a specific, well-scoped use case with clear data and a ready build team, you need a proof-of-concept sprint, not a strategy engagement.
- You need to move in weeks, not months: strategy engagements are thorough. If the board wants a live system in six weeks, a focused sprint on one use case will get further than a full strategy first.
- Your organization cannot absorb the change: if change management risk outweighs the AI opportunity, fixing change readiness before investing in AI planning is the better use of budget.
- You have an existing strategy: if you have a recent, grounded AI audit and roadmap, updating it costs less than starting over — and we will tell you that in the first conversation.
How we start — prove the most uncertain part first
We do not quote an enterprise AI strategy engagement off a brief. We first establish what is actually uncertain and what the data looks like.
- AI Discovery Sprint2 weeks · fixed price
We audit two or three of your highest-candidate workflows, assess the data, and produce a prioritized use-case shortlist with feasibility notes and a build cost estimate — yours to keep. If you proceed to a full strategy engagement, the Sprint is credited.
- Enterprise AI Strategy
Full audit, prioritized roadmap, governance framework, build-vs-buy analysis, team plan, and measurement criteria. Delivered in four to eight weeks depending on scope.
- Roadmap execution
We field the engineers to build the first use case or cases, with the strategy team available to adjust the roadmap as the build surfaces new information.
Frequently asked questions
What is an enterprise AI strategy and what does it produce?
An enterprise AI strategy is a structured process that identifies where AI can improve your operations, ranks those opportunities by value and feasibility, and produces a funded roadmap your teams can execute. The output is a prioritized use-case shortlist, a phased roadmap with cost estimates, a governance framework, and the first use case in a build-ready state.
How long does an enterprise AI strategy engagement take?
A Banao enterprise AI strategy engagement typically runs four to eight weeks after a two-week Discovery Sprint. The Sprint covers two or three candidate use cases in depth; the full engagement covers the organization's top ten to fifteen candidates and produces the complete roadmap and governance framework.
How do you prioritize which AI use cases go into the roadmap?
We score each candidate use case on four dimensions: data readiness, technical feasibility, expected business value, and implementation effort. The shortlist is ranked on that scoring with an explicit rationale — not on what sounds most advanced or what a vendor is selling.
What data and access do you need from us to run the engagement?
At a minimum, we need access to the process owners for the candidate workflows, a sample of the data those processes produce, and a view of your current technology stack. We work under NDA from the start and can operate within your data security requirements, including no-export restrictions.
Do you build what you recommend, or do you hand off to another team?
We build it. Banao has a ~300-person engineering bench, and the engineers who will build the first use case are part of the strategy engagement. You do not hand off a deck to a separate team — the same people who produce the roadmap execute the first sprint.
How is this different from AI strategy consulting at a large firm?
The primary difference is delivery. A large consulting firm produces a strategy and then leaves. Banao produces a strategy and then builds what it recommends — and is measured on whether the built system performs against the criteria set in the strategy. That accountability changes how we write the strategy.
Can you build an enterprise AI strategy for a regulated industry?
Yes. We have worked in financial services, healthcare-adjacent, and manufacturing contexts where data handling, auditability, and change approval requirements are strict. The governance framework and data handling sections of the strategy are written to the specific regulatory obligations of your sector.
What does an enterprise AI strategy engagement cost?
The Discovery Sprint is a fixed price and gives you a prioritized shortlist and feasibility assessment for two or three use cases — enough to know whether a full engagement is warranted. The full strategy cost depends on the number of business units, candidate use cases, and governance complexity. We scope that during the Sprint.
Bring us the AI opportunities your team has been circling
We will spend 45 minutes on your top two or three candidates and tell you which one is worth building first — and what a Discovery Sprint to prove it would take.
Book a 45-min scoping call