Digital Transformation Advisory in London
Every digital transformation we've seen fail in the last five years has failed for the same reason: the AI tool was bought before the data was clean, or the automation was built before the process was understood. We start somewhere different.
Most digital transformation roadmaps fail not because of technology — because there's no data foundation underneath them. You can't AI your way out of a data quality problem. UK manufacturing clients are typically further along in data maturity than GCC or India counterparts — Power BI is more embedded, Azure adoption is more advanced, and data literacy in the operations team is higher. The gap is usually not the foundation — it's the intelligence layer. Predictive maintenance, demand sensing, automated exception management. The data is there. The models that act on it aren't.
What we hear from operators
The problems we solve
These aren't hypothetical pain points assembled from industry reports. They're observations from actual plant floors, warehouse ops, and finance desks — written down because they come up in almost every first conversation.
The roadmap exists. Nothing is getting built.
Most organisations have a digital transformation strategy document — produced by a big four firm, blessed by the board, filed somewhere on SharePoint. It identifies the right priorities. It doesn't explain how to sequence them, what the first 90-day deliverable is, or who owns what. Two years later, the strategy is still the strategy. A few pilots have happened. The transformation hasn't.
AI was deployed before the data was ready
Generative AI tools, predictive models, and automated decision systems all share the same dependency: clean, connected, current data. When organisations deploy AI on top of fragmented, unreliable data, they get AI that produces confidently wrong outputs. The failure of the AI gets attributed to the technology. It's a data problem. The AI just made it visible.
Technology is being selected before the problem is defined
Vendor demos create technology pull. A Databricks demo impresses the CDO. A Salesforce pitch impresses the CCO. Technology decisions get made based on capability demonstrations, not on a clear definition of the problem being solved, the alternative options evaluated, or the total cost of ownership over three years. The result is a technology portfolio that doesn't fit together and doesn't match the organisation's maturity.
How we work
Our approach
01
Assess where you actually are, not where you think you are
Our Digital Maturity Assessment scores your organisation across four dimensions: Data Foundation, Analytics & Decision Intelligence, AI & Process Automation, and Strategy & Organisational Capability. The result is an honest baseline — not a vendor assessment designed to create dependency on a specific technology. Most organisations score 1–2 levels lower than they expect. That's not a criticism. It's the honest starting point for a roadmap that works.
02
Build a phased roadmap with 90-day deliverables
A digital transformation roadmap that doesn't show a deliverable within 90 days won't get the budget for year two. We sequence the roadmap so that the first phase — usually a data foundation or a high-visibility operational dashboard — produces a tangible result quickly enough to maintain board confidence and internal momentum. Each subsequent phase builds on the previous one.
03
Technology selection based on requirements, not vendor relationships
We're not a Microsoft, Salesforce, or Databricks reseller. We work across the full technology landscape. Technology recommendations come from your specific requirements — data volumes, team capability, existing infrastructure, commercial constraints — not from partnership incentives. You get honest advice about where the open-source option is good enough and where the enterprise licence is worth it.
What changes
Outcomes
These are specific, measurable shifts — not benefit statements. Every outcome listed here has been achieved with a client.
From strategy to first live deliverable: 90 days or less
Every engagement produces something tangible within the first quarter. A working dashboard, an automated process, a data foundation — not a further planning document.
Technology decisions: demo-driven → requirements-driven with TCO analysis
Technology selection documented against defined requirements. Total cost of ownership modelled over 3 years. Alternatives evaluated. The decision is defensible to the board and to the team that has to live with it.
Roadmap adoption: shelf document → actively managed quarterly review
The roadmap is reviewed quarterly against delivery, adjusted for what has changed, and remains a live working document rather than a historical record of good intentions.
Technology stack
Common questions
What buyers ask us
These are questions that come up in almost every first or second conversation. If yours isn't here, it will be in the first call.
We've done a digital transformation before and it didn't deliver. Why would this be different?
Most digital transformation failures have one of three root causes: the data foundation wasn't built before the AI/automation was deployed; the technology was selected before the problem was defined; or the organisation didn't have a named owner with budget authority and accountability. We address all three explicitly — maturity assessment to establish the foundation, requirements-led technology selection, and a governance model with a named transformation owner before the engagement begins.
We're a mid-market company, not an enterprise. Is this relevant to us?
Digital transformation in mid-market manufacturing and FMCG is where most of the real opportunity sits. Enterprise-scale organisations have armies of IT staff and consultants. Mid-market companies have a 10-person IT team, a legacy ERP, and a board that has just approved a digital budget for the first time. The playbook is different — faster decisions, less governance overhead, higher tolerance for pragmatic solutions. That's the environment we're designed for.
How do you handle change management? Technology is only part of it.
Change management in data and analytics transformations is almost always about trust. Operations managers who've been running on their own data — their own Excel, their own reports — need to see the new system produce a number they recognise before they trust it. We design the rollout to create early wins with the people who are most sceptical, because if they adopt it, everyone else follows.
What's the Fractional Data Officer model and is it relevant here?
The Fractional Data Officer model is the engagement structure where Amit embeds as your senior data leader — setting the strategy, owning the roadmap, managing the vendors, and acting as the CDO you need but aren't ready to hire full-time. It's relevant if you've identified a data transformation need but don't have a senior data leader internally to own it. It's typically a 2–3 day per week commitment, structured to match your pace of change.
Ready to move
Start with a conversation, not a proposal
First call is 45 minutes. No deck. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No obligation.