AI Consulting Services

AI CONSULTING THAT STARTS WITH YOUR REVENUE ENGINE

You have been told AI will fix your reporting, your forecasting and your admin load. It will, once the system underneath it is designed for it. We tell you which of those two you need first, and what each one costs.

HubSpot Platinum Partner. Building on HubSpot for over 10 years.

WHAT IS AI CONSULTING?

AI consulting is the work of deciding where AI can reliably do something useful in your business, and what has to be true of your data and processes first. In a revenue context that means looking at the engine before the tools: which decisions are made from data, how clean and consistent that data is, and which manual work exists because the system was never designed to handle it. The tooling question comes last, because the same AI feature either compounds or misfires depending entirely on what sits underneath it.

Identifying where AI would remove real cost, not just look modern
Testing whether your data can actually support those use cases
Designing the data model and processes AI has to run on
Sequencing the work so each phase earns its cost

Most AI consulting stops at the opportunity list. You get a set of use cases, a tooling recommendation and a slide on what is possible, then the work of making your data trustworthy enough to use any of it lands back on your team. AI does not fail because the models are weak. It fails because it is pointed at data nobody trusts and processes nobody follows. We consult to a plan we can build, so the assessment names what has to be fixed first, in what order, and what it costs.

CAMBRIDGE SPARK | TOM PHILLIPS
Chief Operating Officer

"Digital Litmus helped us build the HubSpot foundations we needed to scale. We can now trust our pipeline data, our sales team is more effective, and marketing and sales are finally working in sync. We're ready to accelerate the next stage of our growth journey."

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SIGNS YOU NEED AN AI CONSULTANT

You are paying for AI features nobody uses

The licences include AI scoring, AI summaries and AI forecasting. Someone switched them on, the output did not match what the team already knew, and everyone quietly went back to the spreadsheet. The features are not the problem. They were pointed at records that contradict each other.

Your team uses AI privately and nobody knows what for

Reps draft emails in one tool, someone in marketing summarises calls in another, and none of it touches the CRM or leaves a record. It works for the individual and does nothing for the business, because the output never lands anywhere the next person can use it.

You cannot answer what AI would actually save you

The pressure to have an AI plan is real and the business case is not. Nobody can say which hours would come back, from whom, or what those hours currently cost. So the decision keeps getting deferred, or gets made on a tool demo rather than on your numbers.

AI PROBLEMS WE SOLVE

Your data cannot carry AI

Duplicate records, inconsistent stages, fields three teams fill in differently. Any AI you switch on inherits all of it and produces answers you cannot defend. We fix the data model and the processes feeding it, then turn AI on where it now holds up. Usually delivered as a RevOps Programme.

Manual work AI should own

Data entry, chasing updates, rebuilding the same report every month. The work exists because the system does not do it, and it scales with headcount. We find which of it AI can genuinely absorb, design the process around it, and put the hours back where they earn.

AI running with nobody watching

Scoring, summaries and forecasts are live, nobody owns them, and nobody has checked whether the output still matches reality. AI drifts faster than the rest of the engine because it compounds its own errors. We take senior ownership of it month to month. Usually delivered as Managed RevOps.

HOW WE WORK

Diagnose

We start with the Revenue Engine Assessment. Where your data is reliable enough for AI and where it is not, which manual work is costing you most, and which AI features you already pay for and do not use. You get the AI opportunities ranked by what they would save, not by how new they are.

Design

We design the engine AI has to run on. Data model, lifecycle stages, process and ownership, with the AI use cases specified against them. You get a sequenced roadmap that names which fixes have to land before which AI capability, and what each phase costs.

Build

We build to the design, in the roadmap order. Foundations first, then the AI on top of them, each phase live and in use before the next starts. Your team is trained on what the AI is doing, what it is not, and when to override it.

Grow

AI needs more oversight than the rest of the engine, not less, because it compounds whatever it is fed. We review the output against reality, extend the use cases as your process moves, and keep senior ownership on it.

WHY CHOOSE DIGITAL LITMUS FOR AI CONSULTING

We start with the engine, not the tool

AI specialists arrive with a stack to sell and work backwards to a use case. We assess what your revenue engine can actually support first, which sometimes means telling you the AI is not the first thing to buy.

We consult to a plan we can build

A recommendation from someone who will not be doing the work is a guess with a logo on it. We design what we can deliver, which keeps the roadmap honest about effort, sequence and cost.

Accredited on the platform your AI runs on

HubSpot Platinum Partner, building on the platform for over ten years. Most AI advice stops at the concept. We know what the platform will actually do, and where the limits are.

You get the senior person

The consultant who runs your assessment is the one who designs your engine. No junior handover after the sale, and no findings arriving second-hand from someone who was never in the room.

Priced in points, so scope is visible

Every item is sized before the work starts, so you can see what each phase costs and decide what is in and out. Scope changes become a conversation about points rather than an invoice you did not expect.

Clients who stay

We have retained clients continuously since 2014. AI drifts faster than anything else in the engine, so our commercial model depends on yours still producing answers you trust in year three.

Our Ethos

STRATEGIC, SYSTEMATIC AND INTEGRATED

Strategic

Without a governing strategy, AI gets pointed at whatever is easiest to automate. You end up with a faster version of a process that was wrong. We start with who you win, why you win and where the opportunity is, so the AI serves the commercial goal rather than the demo.

Systematic

AI that works when one person prompts it well is not a system. Neither is an output the team overrides quietly and stops mentioning. We build so the AI sits inside the process people already follow, and so its output is checkable by someone other than the person who set it up.

Integrated

AI is only as good as the platform, process and data feeding it, and those usually sit in three different silos. Integration is what turns AI from a feature you pay for into something that compounds, because every improvement upstream makes the output better.

SUPPORTING EDUCATION GROUP | Anthony Lee
Group CMO

"Digital Litmus have been integral in upgrading all SEG businesses onto the same, superior marketing automation platform. Taking advantage of HubSpot's automation workflows with the ongoing training and support of DL, we are beginning to optimise and personalise a number of critical customer journeys. More importantly, we are already seeing this translate into increased MQLs".

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TALK TO US ABOUT AI IN YOUR REVENUE ENGINE

On the call we will:

Tell us what you are trying to do with AI and we will tell you what it would actually take. That includes saying so when your data is not ready yet, and when the thing you want is cheaper to fix without AI at all.

Understand what you want AI to do commercially, not which tool you saw
Check whether your data can support it today
Name the one or two use cases worth doing first
Agree whether there is a case for working together

We will reply within one working day. No sequence, no follow-up chasing.

Get in touch

Frequently asked questions

What does AI consulting actually involve?

Assessing where AI can reliably help, checking whether your data and processes can support it, designing what has to change first, and sequencing the work. In practice most of the effort is in the foundations rather than the AI itself, because that is where the constraint usually sits.

How much does AI consulting cost?

It depends on the size of the engine and how much has to be designed before anything gets built. We price in points, so every item is sized before the work starts and you decide what is in and out of scope. The assessment gives you the cost of each phase before you commit to any of them.

Do we need our data cleaned up before we can use AI?

Usually yes, at least in the areas the AI will touch. AI inherits whatever inconsistency exists in your records and presents it with confidence, which is worse than no answer. The assessment tells you which parts of your data are already good enough and which have to be fixed first.

Which AI tools do you recommend?

We start with what HubSpot already does, because most clients are paying for AI features they have not switched on. Where a gap remains we specify what is needed and why, rather than arriving with a preferred vendor. The tool is the last decision, not the first.

Is this the same as RevOps consulting?

It is the same work with a different starting question. RevOps consulting starts from how your revenue engine should operate. This starts from what you want AI to do, and arrives at the same place, because AI is only as good as the engine underneath it.

Will AI replace people on our team?

In this work it replaces tasks, not roles. The wins are in data entry, chasing updates and rebuilding reports, which are the parts of the job nobody was hired for. We name which hours come back and from whom, so the decision is made on your numbers rather than on a claim.

We already use AI tools informally. Do we still need this?

That is usually the reason to do it. Individual use gets no leverage because the output never lands anywhere the business can use it, and nobody is checking it. The work turns private habits into something owned, consistent and reviewable.

How long before we see anything from it?

The assessment and design take weeks, not months. After that each phase goes live and gets used before the next starts, so the first improvements land during the programme rather than at the end. Whether that first phase is AI or the foundations underneath it depends on what the assessment finds.

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