How to Succeed with AI Projects – Lead with Strategy

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.

How to Succeed with AI Projects - Lead with Strategy

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Promises.

The wrong promises break trust and ruin business relationships. The wrong promises turn projects into failures.

“Wrong promises”, and subsequently “wrong expectations” are the cause of a huge amount of stress and wasted resources.

Promising short-term results (X cost reduction within X months, or X customer satisfaction improvement by X date) reinforces to stakeholders that AI is like IT. You plug it in, you set it up, and you’re good to go. But that’s a lie.

Of course, AI is not IT, and building executive AI fluency in your stakeholders is crucial. Luckily – you don’t have to convince anyone of anything or overtly ruffle any feathers – this “AI educating” can be done in small ways throughout the AI project planning process, and that’s my focus with this article.

Below, I break down how to integrate this idea of “leading with strategy” into an actual sales process. Whether you’re an external vendor or consult, or whether you’re an internal innovation or strategy leader, “selling” strategy is often the difference between a failed pilot and a success in building AI maturity and actually moving the ball forward. I’ll focus on doing this across three phases of an AI project:

  1. Project scoping
  2. Pitching to stakeholders
  3. Managing a pilot project

We’ll begin with the first phase:

Phase 1 – AI Project Scoping

Finding initial opportunity areas – should be anchored in measurable ROI (i.e. what is the cost savings, time savings, revenue improvement, customer service score improvement, etc). Stakeholders need the security of accountability, and projects need boundaries and objectives.

That said, smart innovation leaders (or smart outside AI consultants) should never take a desired measurable goal at face value and decide to spin up an AI solution for whatever first goal the client or executive has in mind. The fact of the matter is that measurable ROI can’t be turned into a promise. There are just too many unknowns and too many challenges to enterprise AI adoption.

“Band-aid” projects are intended to layer an AI “fix” and promise a short-term result. They’re the reason stakeholders get disappointed and disheartened when the inevitable challenges come (data infrastructure needs overhauling, subject-matter experts need to spend more time on the project than planned, etc). If there is no deeper strategic value to the project, and no expectation that measurable results should be couched under strategic objectives, then every challenge is a “setback” instead of a “lesson” on the path to AI maturity.

Scoping should not be about: Determining the right AI solution to deliver on the stated short-term goal of the client, without gathering context on the bigger business objective or reason why.

Scoping should be about: Determining the long-term strategic value or objective that the stakeholder’s initial request is based upon. Finding that long-term value not only builds commitment from the stakeholder (in funding and supporting the project), but it also allows you to find a broader (and probably better suited) range of AI applications to achieve the stakeholder or client’s true aim.

Seek to understand the broader strategic initiative or purpose of this project. Is it overall cost reduction? Is it better customer experience that is notably better than competitors? Is it preparing the company for a bigger transition into eCommerce? Determine the broader strategic initiative that the cost-cutting effort is related to, and determine a range of potential solutions that would help to enhance or build towards that long-term aim.

A stated goal (i.e. “automating chat conversations”) may serve as one of the stepping stones to that long-term aim, but if it doesn’t make sense as a realistic project, be frank with the stakeholder and provide your suggestions.

Align the strategic priorities of the company (3-5 year goals, current initiatives, digital transformation vision, etc) with potential AI projects that might help to achieve those goals. Filter this set of potential use-cases by based on what types of initiatives is most important to the buying stakeholders – as the image below illustrates:

Making the AI business Case

Phase 2 – Pitching an AI Project to Stakeholders

When it comes time to present solutions and win commitment for a pilot and project, the same philosophy applies: Lead with strategy.

Wrong pitch: Aims to “sell” the stakeholder on short-term measurable or financial ROI, often within a limited amount of time. No consideration given to long-term transformation, or any higher or loftier goals that the project will help to contribute to.

Right pitch: Presents AI solutions and measurable ROI aspirations with no pretense of guarantees, but only with the guarantee of learning (yes, we should research our suggested application to ensure that the measurable ROI benchmarks are reasonable, but we can in no way promise them). Short-term ROI is couched within the context of broader progress towards a larger, agreed-upon strategic value that was explored and made clear during the scoping phase.

As a general rule: Do not settle on delivering on a narrow, near-term, measurable aim without having that project be in a direct line of value with a long-term strategic goal or initiative that is of high value to the client or stakeholder.

AI usually shouldn’t be used to solve today’s issues. AI should be used to shoot towards critical, long-term strategic goals, building AI maturity and delivering occasional financial results along the way.

As I like to say: Solve today’s issues with IT. Tie AI to winning future market share and profitability. Promising otherwise is rarely the right thing to do.

Phase 3 – Managing an AI Pilot Project

If you sell an AI project on the premise of plug-and-play financial ROI, every adoption hurdle (and you’ll encounter many, especially in your first few projects) will feel like a “setback” – both to you and to the stakeholder.

Millions of dollars are wasted each month as stakeholders check in on their supposedly “plug-and-play” AI solutions (i.e. false promises), and realize that the short-term gains haven’t materialized.

What happens as a result?

Finding gets pulled, projects get canceled, and whoever was the “champion” of that short-term promise now looks more like a fool than a champion.

Managing AI projects needs to also lead with strategy. Expect pivots. Expect new hurdles to arrive, expect to re-set your benchmark for measurable or financial ROI, and expect to potentially change course or approach during the project – but expect to learn along the way (learn more about retained AI project learning in our Emerj Plus best practice guide called Critical Capabilities).

Every “failure” should inform our approach to achieve the long-term value we’ve established and agreed upon with the stakeholder.

Does that mean you have to set the expectation that you will never reach your measurable financial goals (i.e. improve customer retention rate by X%, decrease payment fraud false positives by X%, etc)?

No. It simply means that you hold all measurable results under the umbrella of the bigger strategic goal. This is easy to do in the AI pilot phase if you’ve held that frame during the scoping and pitch phase.

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