AI initiatives embody more uncertainty (risk) than most projects because of the data!
Of course, every new business endeavor, or investment, has risk. Perhaps a new product fails to gain market acceptance, macro-economic trends shift unexpectedly, a pandemic closes down entire sectors of the economy, or poor market research leads to services without a buyer — the list goes on. These are risks organizations regularly contend with and seek to mitigate.
Most would agree the bigger the risk, the bigger the reward. At Elder Research we believe projects in the data science and AI space embody a greater risk of failure (as is indicated by the statistics cited earlier) than projects in other domains yet tend to yield greater results when implemented successfully. Why do AI/DS projects embody greater risks? Because of the data!
The Building Blocks of AI
AI (and data science and machine learning) projects are built upon a raw material (data) that is often captured, or designed, without any automated learning in mind. This is particularly true for legacy organizations that, for decades, have collected data for operations, without deep thought about its strategic value. Of course, that is changing now as leaders have come to recognize the strategic value good data can provide. However, the process of extracting that value, either through big or small projects, is one that contains significant risk because the starting point, the raw material, is initially opaque and requires analysis before meaningful AI development and data science can begin.
- much of it may be missing,
- in the wrong format,
- collected poorly or in a biased fashion,
- be un-representative of you want to measure,
- is not understood, be confusing or misunderstood,
- or not have the appropriate infrastructure to support analysis, development, deployment, and monitoring, etc.
These obstacles are not trivial and are usually quite challenging to overcome.
The Challenging Side of AI
Moreover, data shortcomings are not uncovered until after a project is launched. AI and data science projects involve a significant discovery process, one that can require investment in exploratory analysis before a complete understanding of what is feasible materializes. These uncovered flaws cause timelines to falter and necessitate additional investments in tools, processes, infrastructure, and development to overcome.
These after-the-start discoveries can lead to discouraging initial results which dampens enthusiasm among the business leaders who sponsor the projects. As blame circulates as to why these stumbling blocks appear it is important to remember that a fundamental ingredient in these projects is data, and it is not until we are long into the initial discovery phase until the data is sufficiently understood.
Lack of awareness of this fundamental dependency of data science and AI projects will inevitably lead to disappointment unless expectations are managed. Unfortunately, data science and AI projects tend to get lumped in together with their close cousins in software engineering and IT projects. Yet, the latter type of projects are more deterministic (think of building a wall brick by brick) and their raw material is much better known from the beginning. Data science and AI instead begin with an inherited raw material (data) that defines the starting point.
Artificial Intelligence is a tool for Data Scientist
Realizing that data science and AI projects operate differently than most other technical projects means they must be treated differently. It is important to buffer project deadlines to account for the inevitable discoveries – good or bad – that will take place. That will help expectations to be calibrated appropriately to avoid the death spiral of waning stakeholder buy-in and executive sponsorship.