AI Technology Consulting

AI technology consulting is the work of deciding where artificial intelligence and the technologies around it change a company’s economics, in what order, and on what evidence. It is a where-to-compete question before it is a technology question. This page covers the strategy and sequencing layer. The four capabilities in this practice carry that decision into business models, agents, machine learning and the operating model that has to run them.

• Since 1999demand, commercial and customer strategy
• 200+published case studies
• 10 officeson four continents
• No vendor tiesno model, platform or cloud partnership sits behind our advice
• The Interaction Fieldwritten by our founder

When companies bring us in

• There are forty use cases and no defensible order. Everything on the list is justifiable in isolation and nothing on it explains why these and not others.
• The pilots work and the P&L does not move. Funded activity, real prototypes, and no line from any of it to a number the board already tracks.
• Before a platform or model commitment. A standard is about to be set for the whole company before anyone has named the decisions it is meant to improve.
• The board has asked what the AI position is. The honest internal answer is a list of tools and a budget.
• Year two costs more than year one. Prototypes multiplied, nobody owns them in production, and each one argues for its own budget.

Why the use-case inventory does not work

Ask a room of executives to list AI opportunities and you will get forty. Ask which three to fund and the conversation collapses, because the list came from what the technology can do rather than from where the business needs to win.

That is a structural failure, not a facilitation failure. Inventories are supply-led: they start from capability and search for application, which produces a portfolio ranked by feasibility and enthusiasm rather than by economic consequence. The program ends up busy, defensible item by item, and disconnected from the numbers the board watches. Most executives never see the results the business case promised, and the pattern behind that gap is rarely the model.

We start from the other end. The first question is not what AI can do for you. It is where future demand will pool, and where you have the right to own it. A demand space is a recurring set of customer situations and moments, defined from the customer’s life rather than from your product taxonomy, and the valuable ones usually cut across the categories incumbents organize themselves around.

Once the demand architecture is clear, prioritization becomes tractable. You stop ranking use cases and start asking which decisions, interactions and workflows stand between you and a demand space you have permission to own, and which of those AI actually changes. That question has a defensible answer. “Which use case first” does not.

What our AI technology consulting services include

Five bodies of work. Most engagements need two or three, and the first phase decides which, depending on whether the constraint is direction, architecture, capability or accountability.

1. Deciding where AI changes the economics

The shortlist is the deliverable, and so is the list of things we advise against. We map demand spaces from behavioral and occasion-level evidence rather than asserting them in a workshop, name the decisions and workflows standing between you and those spaces, and test which of them AI materially changes. Every surviving candidate is traced to price, volume or capital intensity and from there to the spread between return on invested capital and the cost of capital. If an investment cannot be placed on that line, we say so before you fund it.

2. Model and architecture choices that stay reversible

Most organizations standardize on a provider early, usually for convenience, and find eighteen months later that they have built a strategy on a supplier relationship. Capability, cost and terms move quarterly, and an architecture that assumes one provider is a bet that one company stays ahead on every dimension indefinitely.

• Knowledge and data held in your architecture rather than in a provider’s format
• Models chosen per workload, with the choice designed to be reversible
• Evaluation built on your own tasks and edge cases, so you can tell when a model has stopped being the right one

We are not partnered with any model, platform or cloud provider, so the recommendation carries no commercial interest either way.

3. The operating model that has to run it

Most companies have AI projects and no operating model, which is why the second year costs more than the first and delivers less. We set who is accountable once a prototype works and which budget carries it, how funding moves from experiment to production without a new business case each time, and where governance sits inside the workflow rather than beside it, with escalation thresholds set on materiality, reversibility, precedent, exposure and the system’s own confidence. When the answer is a company-wide change program rather than a set of decisions, that is strategic AI transformation.

4. Prediction, where it changes a decision

Prediction earns its budget when a recurring commercial decision is made differently because of it, and not before. The scoping question is which decision is being made worse than it could be, who owns it, and what a better version is worth. Our machine learning, predictive analytics and AI strategy team builds the models once that is settled.

5. Agents, personalization and new business models

Two questions sit at the edge of most AI strategies: what should act on its own, and what should the company sell that it could not sell before. The first is a question about bounds, authority and the point at which a system hands back to a person, which is where our work on AI agents and customer personalization strategy starts. The second is a question about revenue architecture, and it belongs with AI business model innovation strategy, which asks how value is created and captured when the capability becomes available to your competitors at the same time.

How an AI technology engagement works

1. Establish the demand. Where future demand is pooling, which of it you have permission to own, and the decisions, interactions and workflows standing in the way. This is the step that makes the rest defensible.
2. Test the portfolio against it. Every funded and proposed initiative placed against demand and against economic effect, with a stated order, the reasoning written down, and an explicit list of what we would not fund.
3. Design the architecture and the operating model. Model and data choices made per workload and kept reversible, accountability assigned, funding path set, governance placed inside the workflow.
4. Connect it to the number. A measurement line from each funded move to price, volume or capital intensity, so the next funding round is argued in the units the board already uses.

Where this has been done

One client, two engagements, cited for exactly what the published work says and no more. KWS SAAT is a seed producer based in Einbeck, Germany, operating in more than 70 countries.

The first engagement was the portfolio test, run before AI was the question. KWS recognized that its digital effort had grown fragmented and disconnected from customer needs. We assessed the initiatives already running against a custom scorecard, benchmarked them against outside practice, and delivered a roadmap that realigned digital around customer engagement, actionable insights and platform feature priorities. That is step two of the process above, applied to digital rather than to AI.

The second engagement is where AI enters. KWS needed a cohesive roadmap aligning platforms, organization and emerging AI capabilities as grower expectations, data-driven agronomy and B2B procurement all shifted toward digital. We supported the company across digital transformation, technology enablement, customer journey mapping, AI strategy, business design, platform strategy and organizational alignment, and the work connected near-term digital improvements to a longer-term platform and AI vision. The published outcome is direction rather than a number: a clearer digital direction linking customer experience, platform strategy and AI capability, and foundations for more integrated customer engagement across KWS’s global agricultural markets.

What this does not show, and we will not claim: an AI implementation, a model in production, or a published financial return. It shows the sequencing argument on this page carried out inside a real company, by one client across two engagements rather than by two independent proofs. Read them in full: how Vivaldi accelerated KWS’s digital customer strategy and how we built the digital customer communication strategy beneath it.

Why companies choose Vivaldi for AI technology work

• We start at demand, not at capability. The order comes from where the business can win, which is the only version of the question that has a defensible answer.
• We have nothing to sell you downstream. No model, platform or systems-integration revenue sits behind the recommendation, so stopping an initiative costs us nothing.
• The case is made in financial language. We trace each investment to the spread between return on invested capital and the cost of capital, so the CFO and the CIO argue from one case instead of two.
• We will tell you what not to build. The exclusion list is usually the more useful half of the work.

How to evaluate an AI technology consultant

Worth asking of every firm on your shortlist, including us.

• Do they start from demand or from technology? A firm that opens with a capability map will hand you a use-case inventory.
• Who are they partnered with? A firm with a commercial relationship to a model provider or a platform is not a neutral advisor on which one you should use. That is structure, not dishonesty. Ask, and weigh the answer.
• Ask them to describe a use case they advised a client against. A firm that has never talked a client out of an AI investment has either been lucky or has not been advising.
• How does this reach economic profit? Ask them to draw the line from a proposed initiative to the spread between return on invested capital and the cost of capital. If the answer arrives in efficiency language only, you are buying cost reduction, which competitors will match.
• Who owns it after the pilot? If the proposal ends at a working prototype, the second year is your problem.

Strategy consultancy, systems integrator or platform vendor?

Where this work sits

This practice is the strategy layer of business technology strategy at Vivaldi, the firm behind the Demand Landscape and the Interaction Field. Two adjacent practices answer questions this page does not. If the constraint is that your teams cannot run AI work without outside help, that is a capability question and it belongs with AI innovation capability consulting. If the question is narrower, specifically where generative models create advantage rather than novelty, start with generative AI strategy. Our thinking on how the capital case is argued is set out in AI ROI and the CFO’s capital allocation problem.

AI technology consulting in the US and worldwide

Vivaldi is headquartered in New York and has a second US office in Los Angeles. Outside the US, our teams work from London, Zürich, München, Hamburg, Düsseldorf, Singapore, Buenos Aires and Lima. We work with US companies nationwide from our two US offices. Read who Vivaldi Group is and how we work.