Chapter 6. The Importance of Why

Why is AI valuable for people and businesses? Why can AI improve human experiences and business success? Why should AI be pursued with caution? Why do some AI initiatives succeed and others fail? And lastly, why do we need a new framework to help plan for and maximize AI initiative success.

It all begins with why. This chapter centers on the importance of defining the why for any AI vision and strategy, as well as the ability to generate a shared vision and understanding among key stakeholders using the why and great leadership.

Start with Why

It is critical to understand and be able to explain the why of things before focusing on the how and what. Nobody illustrated that better for me than Simon Sinek in his seminal book, Start With Why,1 and his related TED talk, “How Great Leaders Inspire Action”.

He argues that every business knows “what” they do and, sometimes, “how” they do it. But rarely do businesses and people know “why” they do what they do. This is a deep concept, and as Mr. Sinek points out, the why isn’t a result or outcome like increasing revenue (which is more of a goal or objective). The why is the belief, the grand vision, the true meaning.

Think about it. It can take companies a very long time, even with a lot of people involved, to come up with messaging around a company’s vision, mission, and value proposition. I’ve been through the process of developing all of this multiple times with different companies (including my own!), and it always amazes me how much complexity, volume of words, opinions, and back-and-forth dialog are required before finally getting to simple and effective messaging that actually speaks to the true why. A very apropos quote attributed to Mark Twain, is “If I had more time, I would have written you a shorter letter.”

Sinek argues that great leaders and businesses are great because they think, act, and communicate from the inside out; or in other words, they start with why. He goes on to say that people don’t buy what you do, they buy why you do it. It’s all about people understanding and buying what you believe, not what you do. Adopting this mindset and being driven by this concept is easier said than done.

Some people refer to the why with terms like “goal driven,” “outcomes driven,” or “benefits driven.” All of these are perfectly fine, but in my opinion only when viewed through the lens of start with why. I have extended this concept to everything I do on a daily basis. I try to start with why when speaking to colleagues, employees, family, and friends. With enough practice, it becomes quite natural, and people’s reactions, apparent interest, and understanding are more than noticeable. It pays dividends.

So why does starting with why matter in the context of AI and how can we use this concept to improve human experiences and business success? The reason is that the why, when fully defined and understood upfront, serves as the North Star or guiding light for everything else. It helps those creating an AI vision and strategy better and more easily explain the potential value and outcomes of AI initiatives. This hopefully then inspires, motivates, and excites everyone involved.

Product Leadership and Perspective

As a person who has held executive leadership roles in both advanced analytics (AI, machine learning, data science) and product management, I view many things through the lens of making great products. To make great products, a product leader must understand (nonexhaustive):

  • What makes products great

  • What constitutes great product design and UX

  • How to best map people’s problems to solutions that take advantage of technology

  • How to create a product vision and strategy

  • How to execute a product strategy to build and deliver great products

  • How to communicate a product vision to all stakeholders including the product development team

  • How to determine the ROI of products

  • How to properly assess markets and competition

  • How to measure product success

For the remainder of this book, I discuss AI-based innovation along with developing an AI vision and strategy through the lens of making great products as outlined. An innovation framework known as “Jobs to Be Done” presents an interesting perspective in that people hire businesses, products, and services in order to get a “job” done; or, put another way, to accomplish something. Similarly, we can think of AI as something that can be hired to get a job done.

Leadership and Generating a Shared Vision and Understanding

We have established that the why is critical, but now what? How do you use it, what is it used for, and how do you turn why into a successful business, product, or human experience?

For me it begins with what I refer to as generating a shared vision and understanding. I use this phrase regularly, and this capability is critical. A shared vision and understanding sounds simple and high level, but this is a very deep concept for me. Let’s discuss why, and set some relevant context for the remainder of this book.

There are two distinct parts to this: shared vision and shared understanding. A shared vision for me is where all stakeholders completely understand why a solution is being proposed and built and what the solution will be exactly. This includes outlining all intended benefits and outcomes.

A shared understanding is critical to properly setting expectations and is where everyone completely understands the following:

  • What the prioritized product roadmap looks like

  • Why the roadmap is prioritized in a particular way

  • How the solution will be defined and built

  • Development progress at any time (including potential blockers and risks)

  • The existence and status of any upcoming important milestones and deliverables

Issues, unmet expectations, misalignment, and failure can happen when stakeholders do not have a shared vision and understanding. The same is true when target users or customers do not properly understand many of these things, as well; most important among them the purpose, benefits, and how to use the solution.

A very important concept for me here is consensus versus collaboration. Seth Godin is quoted as saying, “Nothing is what happens when everyone has to agree.” That’s a great quote, and I couldn’t agree more. I’m a huge fan of collaboration—the appropriate people working together toward a common goal while soliciting, listening, and accounting for collaborator feedback.

But collaboration is not the same thing as consensus. In many cases, I have seen the mandate of consensus heavily derail or effectively destroy initiatives and products. This is because it’s very unlikely that everyone will agree about everything, and most stakeholders usually approach initiatives and products with different incentives and agendas. The typical result when consensus is required is that either nothing gets done or the final outcome is a far cry from optimal or originally intended. It is usually a highly compromised version of what it could have been with properly executed collaboration.

Summary

Tying everything together, the art of creating a successful AI vision and delivering successfully on it comes from the why, leadership, a shared vision and understanding among all stakeholders, and with collaboration over consensus. This is the formula to which I attribute many great successes.

Accomplishing all of this is much easier said than done and is difficult to teach. It’s more an art based on emotional intelligence, soft skills, and leadership than a rigorous set of rules. The skills that are most important here are visionary and strategic thinking, start-with-why thinking, effective communication and listening, emotional intelligence, empathy, expectation setting and management, and general leadership skills such as cultivating excitement, motivation, and meaning.

Now that we understand the importance of why, let’s turn our attention to how that relates to specific goals for both people and businesses and, particularly, how AI can help achieve them.

1 Sinek, Simon. Start with Why: How Great Leaders Inspire Everyone to Take Action. New York: Portfolio/Penguin, 2009.

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