How to Treat Deliverables as Assets in AI and Data Projects
At the beginning of every project there should be two key aspects that work together to achieve a successful outcome: the fundamentals (the WHY) and the process (the HOW). Given these fundamentals, why do so many projects still fail?
This is often the result of over-focusing on the needs of CXOs or over-focusing on the needs of engineers and developers. Yes, both requirements are valid, but when it comes to the critical nature of data and artificial intelligence, a gap can arise that will derail even the most ambitious projects.
With this in mind, I decided to create a series of articles to provide CXOs with practical advice on how to successfully initiate, execute, and manage data and AI projects. This first article focuses on outcomes and understanding their value as a core value for short-, mid-, and long-term business outcomes.
basics — the why
Let’s start with a few principles required for any project – result(s) and success (or failure). Every project, whether it’s a solution, a new product or service, or a process improvement, has something to deliver when it’s planned, executed, or completed. From an operational point of view, however, not all results are a success.
So what makes a performance successful? Quite simply: it must add value to the company by increasing productivity and efficiency or creating more business opportunities to increase sales.
Data and AI projects are no exception. The goal is to define the needs and outcomes up front, but be flexible should business goals change. Again, he focused on strategic outcomes and business opportunities.
This is called the whysince we have to answer: why Do we even need a data and artificial intelligence project?
process – the HOuch
According to the definition of why, It’s time to define the process (the how) – the high-level version. Here’s some key advice: Treat a data and AI project as an asset to be acquired.
Just as we buy a computer, software license, car or property, there are things that need to be done:
- Gathering the right resources
- Recognize that depreciation is incurred
- Calculation of Return on Investment (ROI) and Return on Time Investment (ROTI)
To do this, here are some recommendations:
Define the ultimate purpose and goal of the data project.
They must not be too broad or extremely specific. In data and AI projects, nobody knows what is possible until the data is seen, the environment is understood and the capabilities of the AI systems are recognized. As mentioned earlier, be flexible in terms of scope, but not too flexible to cause scope increments—something very common in data projects.
Assign a timeframe for completing the project.
In my experience, any data project with a defined viable purpose shouldn’t take longer than 13 weeks or a quarter. Otherwise, it’s easy to get into the “infinite iteration process” to improve on what’s been previously achieved. This will result in losing sight of the original intent of the project and, in turn, the scope will creep.
Set a budget and allocate resources.
Much like organizing finances for purchasing a third-party product or service, even if your organization has an in-house data team, it is critical that all resources – human and technical – invest the time and resources involved the associated costs are fully taken into account and recorded. In other words, treat your data and AI teams as business entities that you will buy a product from.
Additionally, investments in these resources must be approved and sponsored by someone—ideally, a CXO. This way the project is formally backed by a decision maker with real skin in the game.
The right team has to be in place.
I’ll cover this topic in the next part of this series, but having the right team means more than just project managers, scrum masters, data engineers, data scientists, and data analysts. It also means having the right contacts from the different departments or business units that the project will affect.
Consider creating a center of excellence represented by multiple areas (stakeholders) within the organization to help with overall governance, compliance, security, validation and approval.
Final Thoughts
Think about the business impact of every project—even data and AI projects. The responsible teams need to deliver something specific that needs to add value to the organization – and all agreed in advance.
As a CXO, be open to following the advice of the technical teams. Conversely, the technical teams need to understand the CXO vision and business goals. Once you understand this, stick to the plan and don’t be afraid to demand a high level of quality and ROI/ROTI.
