5 Steps To Discussing AI Business Value When Leaders Want Fast Results
5 Steps To Discussing AI Business Value When Leaders Want Fast Results
How To Avoid Getting Blindsided And Move Stakeholder Conversations Forward
A few weeks ago, I met with a prospect. After the initial small talk, the executive quickly changed the subject to discussing use cases for AI—a sure way to end up in a dead end. Why? Because where you can use AI in a business (and what you can use it for) is pretty limitless.
Sure, you can go down the list of capabilities (generation, summarization, translation, …), but it will rarely lead to a fruitful outcome if you leave it there.
I tried pivoting to business value and understanding where the challenges lie in the business. But we kept coming back to broad, abstract topics.
The Daily Challenge of AI Leaders and Consultants
Generative AI is a gift and a curse for AI leaders and consultants. Everyone can experiment with it, even on their phone. Early adopters are exploring copilots and code generation tools. But most start from a technology angle, rather than from a business value angle. Why should we explore copilots and code-gen? Why?
The other day, I caught up with a trusted leader in my network who had also worked with dozens of leaders during the previous machine learning hype. What he shared got me thinking:
“When we told our customers that we need to approach AI as a business topic, take stock of the system and data landscape, interview stakeholders, and identify common pain points, they told us to just give them the top three use cases for AI.”
It felt like a deja-vu. Just like the meeting I had the other week. In my experience, business leaders looking for shortcuts is a big reason why AI projects don’t go beyond a proof of concept.
You can learn more about the collaboration between AI leaders and business stakeholders (and how to strengthen it) in my upcoming book, the AI Leadership Handbook.
Pivoting the Conversation to Business Value
Pivoting that conversation away from “What can we use AI for?” is hard—especially if the person across the table is looking for immediate results in general terms.
If you are faced with a similar situation, try the following approach:
State where Machine Learning and Generative AI each excel
ML: e.g., classification, forecasting, outlier detection
GenAI: e.g., text generation, summarization, translation
Give a general example
ML: e.g., classify incoming customer service requests to ticket categories
GenAI: e.g., draft a blog post about your product
Ask to make it concrete. Maybe ticket classification and/or generating blog post are not the biggest concerns right now
Where could the business be more efficient?
What business goals are they trying to achieve in the next 12-24 months?
Give a concrete example
Digitizing the invoicing process with the help of AI.
Use customer service for product upselling.
But depending on the company’s size, additional stakeholder interviews or process intelligence tools might still be needed to find out: “What would it mean if you were able to accelerate your invoice process? How much faster could you collect the cash? What would investing that cash X days sooner yield?”
Pivoting from general purpose use cases to concrete business value is anything but easy. There is a fair chance that you will get stuck on #2.
In my case a few weeks ago, we were able to get more concrete and found a path to exploring AI in the context of business value. The next time you are in a similar situation with your stakeholders, pivot the conversation with these five steps.
Learn More in the AI Leadership Handbook
Stakeholder management is just one aspect that AI leaders need to master on the path to AI success. Collaborating closely with business leaders and domain experts and building a pipeline of AI ideas are additional dimensions to manage in order to invest in capabilities than deliver business impact.
In the AI Leadership Handbook (available now), you will learn about the 9 dimensions of successful AI programs—and the combined findings of more than 60 AI leaders.
In the AI Leadership Handbook, you will discover how to:
Leverage the full scope of an AI leadership role
Win (enthusiastic) buy-in from employees
Take a product-centric approach to building AI applications
Build a pipeline of high-value AI capabilities
Utilize AI ethically, safely, and sustainably
Spanning strategy, stakeholder management, collaboration, culture, ethics, data privacy, risk management, and technology, you will learn everything you need to know to become a confident and successful AI leader—and get it right on the first try.
What To Do Next
Whenever you’re ready, there are three ways I can help you and your organization:
Order your copy to get the latest information in the AI Leadership Handbook.
See where your organization stands on AI adoption with the exclusive AI Readiness Assessment.
Inspire your audience with a tailored keynote or workshop for leadership and technology teams about introducing AI into your business.
Accelerate your AI program or product with top AI leadership experience without the overhead.
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