Amidst the uncertainty of COVID19, our polls and surveys of enterprise leaders have show two trends to be consistent:
- Enterprises are eager to automate processes, to streamline operations, and to reduce overhead in pursuit of resiliency and efficiency.
- All previous technology and business strategies are being redrawn.
AI champions face some new challenges: Budgets may be constrained, remote teams make some projects harder to execute, and scrambled strategies have wiped out many existing AI pilot projects.
New opportunities are on the rise as well: A dire need to digitize and automate, new constraints opening up new ways to serve customers and run operations, and scrambled strategies mean new potential opportunities to make AI part of the redrawn one.
A subset of AI champions and leaders will win this early wave of AI transformation, and they’ll gain experience and references that will start a flywheel of opportunity – as clients will prefer to work with the rare subset of advisors who have actually done it (see my previous article The AI Career Gap – AI Knowledge as a Career Accelerator).
It goes without saying that AI is not a fit for all businesses, all business processes, or all companies. Some firms are so small – or have been hit so hard by the economic consequences of the pandemic – that investments in AI aren’t a rational near-term choice.
Many of the firms who can or should still invest in artificial intelligence will be less open to certain kinds of AI initiatives, and potentially more open to others. In this article, I’ll address two ways that leaders can adapt to these changes to continue with fruitful AI transformations.
This article will be useful for two kinds of AI champions:
(Internal) Enterprise AI champions: These insights will help you bring the right AI projects to leadership, and present their value in ways that will (a) appeal to leaders, and (b) improve the likelihood of pilots becoming mature deployments.
(External) AI consultants and services firms: This article will help you craft your approach for presenting AI projects to new clients – or expanding existing client relationships with further AI initiatives.
De-Risking Enterprise AI Opportunities
Firms have unique questions and a unique new set of needs as they think about efficiencies, automation, and new AI strategies in a post-COVID world. While they are eager to build a new digital foundation, they are less willing than ever to go with an unproven approach or technology.
“Artificial intelligence,” as an abstract idea, is losing its luster.
Enough enterprises have wasted millions on useless AI pilot projects to realize that it isn’t magic. Of the dozens of financial services enterprises we’ve worked with, almost all of them admitted to two or three failed and ill-advised AI initiatives (often costing 9-18 months, and hundreds of thousands or millions of dollars) before realizing that they needed a genuine strategy, and a data-backed perspective. Many still don’t have a strategy, and they still need AI catalysts to align leadership and develop proper strategies for picking the right AI projects.
More than any other fact, clients want use-cases with ROI figures attached. To build trust and move a buying conversation forward (for an AI project, and AI-related service, or any AI transformation initiative), come to clients with the following:
- A representative set of use-cases within your client’s industry (including use-cases within different functions or departments)
- A representative set of general AI use-cases, or use-cases in adjacent industries
- Return-on-investment data for as many of those use-cases as possible
Anything less than a grounding in that information won’t convince and convert clients.
Finding ROI numbers is hard. First, companies don’t want to share true information about their results. Second, most AI initiatives are so early in their development that no ROI can be gleaned. Third, many projects cannot be benchmarked, because it was challenging to measure the percent efficiency gains, or attribute the exact lift in revenue from AI alone. When collecting use-cases, use the following rules of thumb and you’ll be farther along than almost all of your competitors:
- Only find use-cases with named customers (“Wells Fargo”, not “a global bank”)
- Only reference use-cases from “AI” companies who are most likely to genuinely be using AI in the first place
- Carefully sort-out use-cases with vague ROI measures, and resort to calling vendors or buyers for clarification when the numbers are too vague
The secondary research required to find actual success cases (not bloviated press releases or anonymous vendor claims) is significant, but it’s critical for re-risking AI projects and showing precedence of a similar use-case delivering real value. Now more than ever, most firms don’t want to be the guinea pig for novel AI use-cases.
Set Expectations Around the Value of AI Maturity, and Measurable Results
While enterprise leaders are in desperate need for efficiencies and cost-savings in the present, the AI champions who will capture (and deliver) the most value will be those who can create a plan that translates to long-term benefits, not just short-term promises (which are hard to deliver on for many reasons).
Understanding the following will give you a head start compared to the vast majority of consultants or internal AI champions:
One of the core goals of early AI initiatives is the develop a set of new foundational AI capabilities – what we call AI maturity – and to make it clear to the buying stakeholder that this maturity is part of the ROI of the project.
Advisors, consultants, and innovation leaders should bear the following set of eight Critical Capabilities in mind when deciding on AI projects:
Projects should begin with the question:
If this AI initiative is to succeed, which of the following Critical Capabilities will be improved, and how?
Hard, measurable ROI measures are challenging, and mature companies truly committed to digital transformation should decide the desired Critical Capability improvements of any AI project ahead of time.
In addition, almost all projects should be paired with measurable ROI goals, which generally fall into one of three categories:
- Revenue improvement (Conversion rate, cart value, customer lifetime value, etc)
- Efficiency improvement (Time reduction, headcount reduction, cost savings, etc)
- Risk reduction (Lower compliance risk, lower cybersecurity risk, etc)
Combining both learning goals and measurable ROI goals conveys a genuine understanding of the true challenges to AI deployment – and the requirement to build new foundations, instead of simply looking for short-term financial returns (which are often near impossible). Maintaining a conversation with a buying stakeholder purely on the promise of near-term financial ROI is a recipe for disappointment, and for projects that never make it to deployment.
A smart AI champion will select projects that can deliver important near-term measurable ROI, and also build important AI capabilities to enable strategic goals, and the future fruitful use of AI. Conveying the value of AI maturity-building is the focus of our Generating AI ROI strategy report.
Emerj for Aspiring AI Consultants
If you’re considering becoming an AI consultant and helping companies with their AI transformation – consider learning more about the Catalyst Advisory Program. Catalyst is a limited, application-only program for aspiring AI consultants – involving one-to-one coaching, peer advisory, and proven frameworks for:
- Determining AI services offerings that appeal to enterprise leaders
- Finding the right industries to serve with your services, and reaching them consistently
- Communicate the value and ROI of AI projects in a way that enterprise leaders understand
Learn more about Catalyst and submit an application on our Catalyst Advisory Program page.
