Digital, AI & Data

You are automating a process that nobody should be running in the first place.

Your data lives in three systems that do not talk to each other.

You have an AI pilot that works in a demo and fails in production.

Your digital roadmap is a list of tools, not a sequence of decisions.

AI for business

Applied where a decision is already being made badly, not where the technology is interesting.

Data science and analytics

The measurement layer that tells you which markets and accounts are actually working.

Immersive experiences

Digital and physical experiences built for buyers who need to see the product work.

Rapid response surveys

Fast, structured field reads when you need a market answer in weeks, not quarters.

Digital marketing

Demand generation aimed at the buyer who signs, in the language they buy in.

Digital commerce

The commercial layer that moves product online across borders, tax and logistics included.

Marketing engineering

The systems, data and automation that make marketing accountable to revenue.

Translation and transcription

Commercial material rendered into the working language of the market by people who know the category.

What the work involves

A digital, AI and data engagement starts from the operating model, not the technology. If the underlying process should not exist, automating it makes it cheaper to do the wrong thing, so we fix the process first. The work covers AI for business, data science and analytics, immersive experiences, rapid response surveys, digital marketing, digital commerce and marketing engineering, but every use case is bound to a named decision a leader actually owns.

How the work runs

We begin by consolidating the data that exists and building the dataset that does not, through digital and face to face surveys, B2B panels and out of home research in the hardest markets. We then interpret the result into an action plan, not a deck. In steady state, the AI and analytics sit inside the operating model as a decision support layer, with each use case tied to a review date so the programme does not drift into a science project.

What changes when it is working

Leaders stop arguing from anecdotes and start arguing from a shared view of the market. The forecast, the competitor picture and the customer signal sit in one place, and the team trusts it enough to act on it. The technology serves the enterprise outcome instead of demanding its own programme.

We start from the operating model, not the technology. If the underlying process should not exist, automating it makes it cheaper to do the wrong thing. We fix the process first, then apply AI where it creates a decision the business did not have.

We build the dataset. In the hardest markets, Latin America, the Middle East and Africa, the information does not exist until someone constructs it. We run digital and face to face surveys, B2B panels and out of home research, then interpret the result into an action plan, not a deck.

When the work is productizable, lead capture, follow up, scheduling, review generation, unified messaging, an AI agent answering the phone, a platform gets you there faster than we will. Our sister company Sales Engage builds exactly that, and we refer clients there when it is the right call.

Every AI use case is bound to a named decision and a review date. If it cannot be tied to a decision a leader actually owns, it does not enter the build. That discipline is what keeps the programme inside the operating model rather than beside it.

Leaders stop arguing from anecdotes and start arguing from a shared view of the market. The forecast, the competitor picture and the customer signal sit in one place, and the team trusts it enough to act on it.

A manufacturer wanted to automate its order-taking process with AI.