The Three Elements of a Successful AI Business Case

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

The 3 Elements of a Successful AI Business Case

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A successful business AI case when selling AI products and services has to be strategic. Oftentimes, prospective buyers will bring in artificial intelligence to the conversation is because they need solve a problem with a short, measurable outcome, and the seller has to be able to bring that to the table.

AI vendors and service providers routinely anchor the potential of AI as a solution to an immediate problem – rather than being frank about the long-term AI maturity and experience that is more likely to be the real ROI of an AI project (see our Emerj Plus article How to Succeed with AI Projects – Lead with Strategy).

Plans and strategies are going to change as data science evolves, so it is important to leave room to pivot. If the vendor leads the buyer to expectations other than the very near term, that path might derail as soon as it hits real-world applications. 

The real world is a reality of data in terms of access, integration, and implementation of a project moving forward as well as stakeholders and some other factors that might not play out as expected.  

What then are the elements of a successful AI business case that a vendor can use in a pitch?

Making the Enterprise Business Case for AI – 3 Key Elements

While some of the elements of a successful AI business case might shift over time (specific recommendations, details about a data audit of the client company, etc), there are three elements that remain constant across any AI deal or project.

  • For AI Consultants, Service Providers, and Vendors: These are the elements you’ll need to convey in conversations with your client, and you’ll ideally need all three of these elements together in any pitch deck or final presentation. Bear in mind that discerning strategic and measurable outcomes will draw in large part from a strong understanding of the client’s own priorities.
  • For Enteprise Innovation Leaders / AI Champions: These elements will be necessary for making a compelling (and ethical) argument to the C-suite in order to push an AI project forward. You’ll want to collaborate with vendors, and determine the priorities of the buying stakeholders, in order to flesh out both measurable and strategic ROI.

Element 1: Strategic North Star

The strategic North Star refers to the aim of a project in terms of its alignment with the bigger picture goals of a company and how this is strategically valuable in an ultimate sense. In other words, while a vendor might anchor an AI business case on short-term goals, there is always an element of its long-term value to the top-tier goals of a company. 

For example, a company might believe that a better customer experience, higher loyalty, and increased retention is going to be the only way that it can be competitive in the market and survive in the next 10 years. Another company might be more concerned with its ability to strategically reduce a particular kind of financial or regulatory risk. These are issues that are high on the priority list and keeping all the executives up at night because they believe it is what is going sink the battleship.

These are discussed more thoroughly in our full report Generating AI ROI, also available for professionals in Europe, consultants and IT services professionals.

These high-level issues are big-picture concerns, hence the reference to the North Star, and it is not something that will go away in two years. A North Star refers to any issue that presents an ever-present direction, opportunity, or threat to an organization. The goal of a company is to find a way to reach the North Star by whatever means available.

The question is what will it take for a vendor to convince the customer that an AI solution will eventually lead to that North Star? Will it take some conversations? Who will ultimately decide on whether or not to subscribe to the solution offered by a vendor? It is important for a vendor to identify the decision-maker when making an AI business case.

The conversations with that decision-maker should be anchored in the role of a project in the big- picture goals of the company. The contract should at least reference the strategic value of an AI solution in reaching the North Star because even if the short-term benchmarks and measurable outcomes of the benchmarks fall short, the client knows the long-term transformation is in process. 

Using the element of the strategic North Star is necessary because implementing an AI solution is often messy. The strategic North Star gives the vendor a safety net in case projects go awry, which they usually do to some degree, by implying that it can deliver and align projects with longer-term value. In that way, the vendor is not only banking on some short-term outcomes that is seldom realistic for an AI project. 

Element 2: Strategic Toeholds

The second element of a successful AI business case is the smaller strategic values that a project can bring to the conversation. This could include:

  • 3-5 year goals – Such as revenue targets, percentage of revenue from specific market lines, market share goals, product release/development goals, etc.
  • Key differentiators for a business – Such as a strong focus on customer loyalty, or a specific set of product features, or a unique brand positioning.
  • Personal value to the stakeholder – A decision-maker may believe that ti’s personally important for them to support customer experience changes, or they may be interested in becoming known within the company (or for their future job resume) as an “AI innovator.”
  • Current thrusts of the company – Such as current technology spend initiatives. Ask: Where are resources and attention flowing now? Is there a way to hitch along on this existing area of focus and flow of resources?

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To illustrate, say a company wants to gain X percent market share. Does the project tie to that?

A vendor will first have to figure out the motivation of the buyer. The buyer might want to be seen as the innovator and champion of AI within the company and carry that banner. The buyer might see it as a way to advance a career. Motivation is an important thing for a vendor to know when dealing with a buyer as this will drive the direction for the three to five year goals of a project that will appeal to that buyer.

 The vendor will also need to figure out the key differentiators that the company thinks make them different now and will make them different in the future, the current thrusts of the company such as improving efficiencies or finding new channels for sales. One way or the other, all these strategic toeholds need to be relevant to the buying stakeholder.

That is not to say that the contract must primarily deal with all these strategic toeholds with the buyer. However, a vendor must frame the conversation along these lines. When the time comes to put together the final pitch deck for the contract, it is not necessary to sync all of it under strategic language, but anchor it in that strategic language, whether in the slides or the contract. 

This gives buyers a more realistic set of expectations for the AI project and still makes it appealing to the buyer. Even if it does not go as expected, as often happens in AI projects, the vendor will still be able deliver value to the buyer in a relevant way. 

Element 3: Measurable Outcomes

While the strategic North Star and buyer toe-holds serve as the broad strokes of an AI business case, the pitch must still tie in to measurable outcomes. When working with a client, it is important to be able to specify how data will deliver a desirable result. 

The result will depend on the expressed goal of the buyer, whether it is improving revenue in some way, increasing retention in some manner, amplifying efficiencies, or reducing costs or risks.

For example, if the AI business case involves an enterprise search-related solution that can help people find legal contracts that includes regulatory risk, the case can tie into that. 

The goal is to have a strong argument for realistically presenting measurable ROI and the means to deliver it in terms of data or a goal, given the capabilities of AI a presented in a demo or sandbox project.  Giving a time horizon is always a risk with AI, but a business case must include some type of schedule for delivery without guaranteeing it. 

These measurable outcomes must be included to make a successful AI business case. It would make the case stronger if case studies with named clients and concrete ROI are available. These case studies do not need to include details, but just a summary of the problem, solution, and results. This provides some wiggle room when it comes to pitching an AI product or service that might not produce precisely the same timeline or results. 

The value of case studies is illustrative. It shows a potential buyer that the AI solution was able to deliver measurable outcomes such as improved conversion rates or increased checkout flow. Including one or two case studies with named clients serve as helpful anchor points. 

The next step is to establish benchmarks and checkpoints relevant to a particular business case. In many cases, there is a steep learning curve into a particular sector as some of the key data is not available or lacks the features that are valuable for figuring out how to help a prospective buyer. These benchmarks are way points where further decisions are made about how to go forward from there.

The value of these benchmarks is two-fold.

  • They will convey thorough thought on the actual deployment roadmap and how integration and implementation will happen in the real world.
  • They provide some flexibility to pivot when the project runs into issues when some of the deliverables might be delayed or not possible, or when new opportunities come up that might contribute to the same goal in a slightly different way.

In other words, these benchmarks help to set the right expectations for the client. Like it or not, but most stakeholders and buyers cannot be expected to understand what a realistic AI deployment looks like, or what results are realistic. The job of the in-house AI champion, or of the AI service provider/vendor is to act as a catalyst to this understanding, as well as to the intended results of the project.


Experienced AI project managers know better than to guarantee outcomes to clients when presenting a business case because they know it is not realistic.  The best way to make a successful AI business case is to understand the elements that take the uncertainty of AI into account and set realistic client expectations so that projects can work in the real world. 

In the final analysis, a client wants is long term transformation in value and an AI services leader wants long-term, lucrative client partnerships. Approaching case creation employing key elements in a strategic way is a win-win for both parties.

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