Practical AI strategy. Built into production.

PlainThink is an AI-first consultancy for enterprises. We decide where AI pays, build it inside your stack and governance, and hand it over to a team that can run it — explained in plain terms, with no slideware and no lock-in.

Book a conversation Take the readiness assessment
3–8 weeksTime-boxed engagements
Week 3–5When value first shows
In your stackBuilt with your teams, owned by them

Where enterprises get stuck with AI.

The technology is rarely the hard part. Most organizations stall in one of three places. We bring a practical strategy for each — and then implement it with you.

1

Adoption

Pilots that impressed in a demo but never changed how work gets done. We redesign the workflow around the tool, so people actually use it.

2

Team readiness

Teams unsure what AI is for, what's allowed, or how it fits their role. We train by role, on your real work, and leave a playbook behind.

3

Prioritization

A long list of ideas and no agreed order. We score every use case on value, feasibility, data and risk, and give leadership a portfolio it can fund.

Decide. Build. Hand over.

The same three steps in every engagement. Strategy only counts when it runs in production, so we stay through implementation — then leave your people able to carry on without us.

Decide

Agree the scope, review the evidence and decide which use cases deserve a build — with baselines and acceptance criteria set before any work starts.

Build

Put one workflow or model into daily use, connected to your systems, with evaluation and human review, tested against a baseline.

Hand over

A scorecard, runbook and handover: documentation, ownership, access controls and team training. We check in once, a quarter later.

The AI Lab, on hire.

A senior, hands-on team for a fixed number of weeks. Each engagement has defined outputs, clear acceptance criteria and the week you should expect to see value — so you know what you are getting before we begin.

3–4weeks

Focus Sprint

For leadership teams with too many AI ideas and no agreed order.

  • Week 1 — interviews and inventory of live and proposed use cases
  • Week 2 — scoring on value, feasibility, data and risk
  • Week 3 — ranked portfolio with business cases for the top three
  • Week 4 — leadership readout, a scoped pilot with acceptance criteria and a 90-day plan
Value shows up — Week 3A ranked, costed use-case portfolio your leadership can approve.
5–6weeks

Adoption Lab

For organizations that have picked a use case and need it working, with people using it.

  • Weeks 1–2 — workflow design with the team that will use it
  • Weeks 3–4 — working pilot in your stack, tested against a baseline
  • Week 5 — role-based training and adoption playbook
  • Week 6 — results review and scale-up decision
Value shows up — Week 4A working pilot in the hands of real users.
8weeks

Capability Program

For enterprises that want AI running in a function and a team that can carry it forward.

  • Weeks 1–2 — prioritization and design
  • Weeks 3–5 — pilot built and tested with users
  • Weeks 6–7 — team enablement, evaluation and governance
  • Week 8 — handover: scorecard, runbook, named owners. We step away.
Value shows up — Week 5Pilot live by week 5; your team operating it alone by week 8.

Week by week

Wk 1
Wk 2
Wk 3
Wk 4
Wk 5
Wk 6
Wk 7
Wk 8
Focus Sprint3–4 weeks
Inventory
Scoring
Portfolio ready
Readout
Adoption Lab5–6 weeks
Design with users
Pilot live
Enablement
Scale decision
Capability Program8 weeks
Prioritize + design
Pilot live from week 5
Enable + govern
Handover

Green marks the week value is expected to show. Timings are typical; they depend on scope, data access and integrations and are agreed at scoping.

Know what you're getting.

Defined outputs

Clear acceptance criteria agreed before implementation begins.

A working system

Configured integrations, evaluation and appropriate human review.

A performance scorecard

Baseline metrics, success measures and a plan to evaluate impact.

A runbook and handover

Documentation, ownership, access controls and team training.

How ready is your organization for AI?

The Enterprise AI Readiness Assessment is fifteen questions for a leadership team to answer together. It scores five dimensions, shows where you are most likely to stall, and recommends which AI Lab engagement — if any — fits.

15 questions · about 10 minutes · results on screen, instantly

Start the assessment

Sample result

52 /100

Stage 2 of 4: pilots running, adoption uneven, no shared prioritization.

Strategy & prioritization64
Leadership & operating model52
People & skills41
Data & platforms58
Governance & risk35

Suggested next step: Focus Sprint, 3–4 weeks

Illustrative example, not a real client.

Measured, so results are real.

Each change is tested against a baseline, so results are real — not assumed. Adoption only lasts if people trust the output, so every engagement includes the evaluation and governance work that lets a risk committee say yes: test sets built from your real cases, LLM-as-a-Judge scoring, clear human review points, and a usage policy written in plain language.

Evaluation · Guardrails · Usage policy · Audit trail

Himanshu Sinha, founder of PlainThink

Enterprise depth. Practical scope.

Himanshu Sinha founded PlainThink after leading data science and AI transformation across several large enterprises, bringing emerging technology into teams that had to make it work. Along the way he learned that AI succeeds or fails on people, priorities and trust long before it fails on models.

  • Led data science and AI transformation at several large enterprises
  • Member, Forbes Technology Council
  • Founder, AI Safety & Security (aisafetysecurity.org)
  • Builds LLM-as-a-Judge evaluation frameworks

More about Himanshu

Start with one real business problem.

Thirty minutes with a senior practitioner to find the practical next step — and whether you need us for it.

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