There has been an emphasis in project-based organizations to create executive leadership around big data and artificial intelligence, and for good reason. But organizations need to make sure they don’t put the cart before horse. The discussion should not start with the tool (“Let’s throw AI at it”) or the solution (“Let’s collect lots of data”) and work its way backwards from there.
Just as with any digital transformation or project, leaders need to lead with the question, “What problem are we solving?”
To illustrate the problem, let’s look at it from the opposite perspective. People are quick to recognize that today’s industry leaders use a lot of technology and data, but how much do they not use? Even the most intelligent and technologically advanced organization does not use 100% of AI or collect 100% of data. Regardless of the organization, the majority of data is noise and the majority of technologies are redundant with each other or irrelevant to the business. For example, it’d be accurate to say that there are far more forms of AI that Uber doesn’t use than there are forms that they do use.
That’s why solving business problems doesn’t begin with AI or with data. It begins with what the problem actually is and how to solve it. From there, an organization determines what AI and BI is necessary.
Starting with the “Why” Questions
As demonstrated above, before an organization can figure out how to implement technology, they need to figure why they are implementing it. The answer lies in benefits management.
There are two types of benefits. There are benefits at the project portfolio level–that is, why the organization should invest their resources in the project in the first place. Then, there are benefits on the requirement level–that is, why each specification is included.
Oftentimes when Business Analysts get a list of requirements, every row is listed as high priority. Organizations cannot get the most out of their investments unless they can quantify the return, investment, and risk for each requirement so that they can set them in priority order. The same goes for projects at the portfolio level.
Who Is Accoutable?
The above begs the question: Where do these numbers come from? Anyone can plug a number in, but who is held accountable to it when it’s wrong?
The traditional structure of today’s organization have a critical role missing in the chain of command. To illustrate this, let’s revisit a real-world example from another post.
An agricultural supply company once developed a new product for inseminating cows. It was an effective product that allowed farmers to get much more bang for their bull. The marketing department estimated that if just one in every ten farm bought the product, they could make over $20 million off of it. The company invested millions in developing the product assuming they could make an almost 10:1 return on investment. But upon release, no one bought it. It was shelved with zero return, so the company lost all of its investment.
When this project cost the organization millions with no benefit, who is held accountable?
So long as the project was delivered on-time and on-budget, the project managers aren’t held accountable.
The Solution Architect did their job in building the cow inseminator. They can’t be the one who does benefits realization. They just build the solution based on the assumed parameters.
Whoever selected the project can point to the fact that the business case was strong, but it was operating on a faulty assumption.
Is the marketing department held accountable for providing the glowing numbers they did? It’s certainly unorthodox to hold marketing leaders accountable to that level of business intelligence.
Besides, it’s possible their assumption about market viability was sound at the time that they made the estimate. Is it reasonable to expect marketing to monitor the market and ensure no one else beats them to market, that regulations haven’t changed, and so forth? That’s likely not a question the organization thought to ask the Director of Marketing in their hiring process!
Conclusion: It’s a Trick Question
For most organizations, there is no answer. And there needs to be one.
So an organization shouldn’t just bring someone in who’s accountable to implementing AI with the premise the-more-AI-the-better. Nor should they bring in someone who collects data on the premise the-more-data-the-better. Businesses do not derive a competitive advantage by participating in trends, but rather by applying the AI and data necessary to address business challenges.
First, organizations need someone who is accountable to realizing the benefits of the organizational initiatives. From there, they can bring in the tools necessary to deliver the results that they’re accountable to.
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