Futurelab AI strategy consulting runs in three steps: diagnose goals, capability and constraints; prioritise use cases on value, feasibility, readiness and risk; and mobilise with a roadmap, owners and measures. Our engineers can then build what the roadmap selects.
AI strategy consulting
We help leaders focus on the few AI opportunities that deserve attention now, and build a credible path to capability and deployment.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Trusted by teams at PepsiCo · ITC · SBI · Syngenta · Piramal Foundation
Every candidate use case is scored on the same four lenses, so trade-offs are made in the open. Pick a lens to see how the ranking changes.
The business impact if it works: time saved, revenue protected, decisions improved or risk reduced, in terms the board recognises.
The data, integrations and technology involved, and whether a working version is weeks or quarters away.
A clear owner, people with the skills to use it and a process that can absorb the change.
Privacy, accuracy, compliance and reputation, and the controls a responsible rollout would need.
A useful strategy ties technology to business priorities, organisational readiness and what can responsibly be executed.
A portfolio filtered by value, feasibility, readiness and risk, so the first moves are the ones that matter most.
Clear decisions on talent, tools, partners, governance and measurement, made before money is committed.
A staged plan that connects quick wins to durable capability, with owners and dates leaders can stand behind.
Clarity before scale Priorities, operating choices and a roadmap your team can act on, instead of another deck that sits in a folder.
From a focused leadership sprint to a multi-function discovery. Each one ends with decisions made, not just options listed.
A focused working session to set direction with the leadership team.
A ranked view of AI opportunities across functions, scored the same way.
An honest assessment of data, skills, tools and governance.
A phased plan from enablement to deployed products.
Three steps that move an organisation from AI interest to a plan with owners.
Understand business goals, current work, capability and constraints, through interviews, workflow mapping and data checks.
You get: A readiness picture and candidate use cases
Score use cases on the same lenses and make deliberate trade-offs with the people who own the outcomes.
You get: A ranked use-case portfolio
Create the roadmap, ownership model and first set of action plans, ready for training and build.
You get: A roadmap with owners and measures
Embedded deployment with the full advisory team: custom AI tools that shortened planning cycles and improved coordination.
Technology leadership and product delivery, from AI Sachiv at state scale to meeting intelligence and learning platforms adopted as a digital public good.
The AI strategy session was tailored and insightful. It helped sharpen our thinking as investors on what's next.
Harish R, Principal
Futurelab gave us a strategic lens on how AI could empower HR, leadership, and talent assessment.
Sandeep Jadhav, Director
Futurelab brought strategic depth to our conversations. AI in media and OOH will never be the same again.
Yuvrraj Agarwaal, Chief Strategy Officer
We now view AI not as a threat to financial advisory, but a lever for deeper trust and insight. Futurelab helped us get there.
Lovaii Navlakhi, Founder & CEO
The conversations were clear-eyed, practical, and inspiring. Exactly what India's agri and food industry needs right now.
Sanjeev Asthana, CEO
13+ years in marketing and growth leadership at Google, Paytm, Airtel and Amul.
13+ years across business strategy and technology transformation at Amul, Wipro, PepsiCo and Mondelez.
We work in three steps: Diagnose (understand goals, workflows, capability and constraints), Prioritise (score use cases on value, feasibility, readiness and risk) and Mobilise (a roadmap with owners and measures). Pilots and scaling follow, through forward deployed engineering and team enablement.
A typical engagement runs 8-12 weeks from discovery to first pilots. Scaling across the organisation can take 3-6 months depending on size and complexity.
Training builds skills. Consulting builds strategy. We often combine both, training teams while simultaneously designing the organisational roadmap for AI adoption.
We have delivered AI transformation across FMCG, financial services, education, healthcare, retail, and the social sector. Our approach adapts to your industry regulatory and operational context.
That is exactly what the Diagnose step is for. We help you identify the highest-impact opportunities and build a phased plan that matches your budget and timeline.
AI transformation consulting produces the strategy and the sequencing decision: which workflows to touch, in what order, under what governance. Forward AI deployment is the embedded engineering that follows and builds the chosen systems. Many clients buy the consulting first, then the deployment.
AI strategy consulting identifies where AI can change how an organisation works and in what order to attempt it. Done well it produces a sequenced plan tied to specific workflows and owners. Done badly it produces a maturity model. The test is whether the output tells you what to build first and how you will know it worked.
An AI consultant advises on what to do; an AI engineer builds it. The gap between the two is where most AI programmes stall, which is why the embedded model has spread: the same person does both. When engaging either, the useful question is whether the work ends in a recommendation or in something running in production.
A data scientist builds models and analyses data. An AI consultant works on how AI changes a workflow, which is mostly integration, process redesign and adoption rather than modelling. With modern foundation models, far less work is model-building than it was five years ago, and far more is getting the model connected to real systems and real people.
An AI transformation consultant works on the organisational side of AI adoption: which processes change, who owns them afterwards, and how teams are trained into new ways of working. It is distinct from technical implementation, though the two fail when separated. Futurelab pairs the two deliberately, because a redesigned workflow nobody adopts is not a deployment.
Hourly pricing is common for advisory work and uncommon for build work, where fixed-fee or retainer arrangements are the norm. Rates vary widely by market and by whether the engagement includes engineering. For delivery work, an hourly rate is often a warning sign: it prices time rather than the outcome, and it puts the risk of an unclear scope on you.
Large firms bring procurement familiarity, breadth and scale. Specialists bring engineers who will be in your workflow rather than a rotating team. The deciding factor is usually organisation size: if your project is small enough that a large firm would staff it juniors-first, a specialist team is likely to ship sooner and transfer more capability.
Yes. Smaller organisations are often better candidates for AI work than large ones, because there are fewer systems to integrate and decisions are made quickly. The approach is the same: one workflow, a named internal owner, a measurable before and after, and handover that leaves your team able to run it.
Measure it against the workflow you chose, not against AI in general. That means recording how long the process takes today, how often it needs rework, and what it costs to run, before anything is built. Teams that skip the baseline cannot demonstrate the gain afterwards, which is a common reason promising pilots fail to get renewed.
AI consulting is usually priced as a fixed fee for a scoped build, a monthly retainer for embedded engineering, or a day rate for advisory work. What moves the number most is how many systems the work touches, whether the data is already accessible, and how much change management the rollout needs. Futurelab scopes against a named workflow first, so the estimate reflects real integration work.
An agency builds and ships something; a consultancy advises and hands over a recommendation. The distinction matters because most stalled AI projects fail at the build rather than the strategy. Futurelab works as an embedded engineering team, so the people who scope the workflow are the people who write the code that goes into production.
Ask three questions: what have you put into production, who owns it after handover, and what happens when it breaks. Case studies describing pilots rather than live systems are a warning sign, as is any engagement that ends at a slide deck. Ask to speak to the internal person who inherited a previous build, not the sponsor who signed it off.
Yes. Futurelab works with Bengaluru organisations from its Jaipur base, remote-first with on-site work where the engagement needs it. Consulting engagements are scoped around a named workflow and an internal owner, so the arrangement works the same way regardless of which city the client sits in.
Look for a partner whose work ends in something running rather than in a roadmap document. Ask what they have put into production, who owns it after handover, and what happens when it breaks. In a market as crowded as Bengaluru, that filter removes most of the field quickly.
Rates vary more by what the engagement includes than by city. Advisory-only work is usually priced by day or hour; work that includes building is normally fixed-fee or retainer. If a quote is hourly for delivery work, it is pricing time rather than an outcome, which puts the risk of an unclear scope on you.
Where relevant, we give practical recommendations tied to the use case and operating context, including build-versus-buy calls.
Yes. Our forward deployed engineers can take priority opportunities through to implementation, so the strategy and the build stay connected.
Tell us where AI is on your agenda and what is holding it back. We will suggest a sensible first step, from a sprint to a full discovery.