The Missing Link Project-Based Organizations Need to Address (And It’s Not a Chief AI or BI Officer)
The Answer Isn’t ALWAYS AI and Big Data
There is a recent craze toward creating executive leadership around big data and artificial intelligence, but that is putting the cart before horse. Everything should start with the question, What problem are we solving? It should not start with the solution (“Let’s throw AI at it”) or the tool (“Let’s collect lots of data”).
To illustrate the problem with this, let’s tun the question on its head. Everyone is quick to point out the obvious, 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. In fact, it’d be accurate to say that there are far more forms of AI that Uber (for example) 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.
Figuring out the “Why”
So before you can get into the “How” of a project, you have to answer the “Why”. Benefits answer this question.
Benefits exist on two levels. The first level is the benefits for the project at large. This answers why you select the project in the first place in the portfolio process. The benefits also answer exist at the requirements level. This answers why each spec is included .
Oftentimes when Business Analysts get a list of requirements, every row is listed as high priority. It’s when you can quantify the return, investment, and risk of for each that you can set real priorities and make the most out of your initiatives. The same goes for the project portfolio process.
Who Is Accoutable to the “Why”?
Of course, the question is 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 the cow insemination project we discussed in our last post.
We once saw a project where an agricultural supply company had 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?
If the project was delivered on-time and on-budget, it’s not the project managers.
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 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 floated the number. 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 your organization thought to ask the Director of Marketing in their hiring interview!
Conclusion: It’s a Trick Question
The answer is that there is no answer for traditional organizations. And there needs to be one.
So an organization shouldn’t just bring someone in who’s accountable to implementing AI with the assumption that the more AI the better. Nor should they bring in someone who collects data and the more data the better. Because it’s not about having more of these buzzwords just t say you’re doing it, but about applying the AI and data necessary to address business challenges.
First, organizations need someone who’s accountable to realizing the benefits of the organizations initiatives. From there, they can bring in the tools necessary to deliver the results that they’re accountable to.