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Is AI a Solution, a Technology, or a System…and Why Should I Care?

A recent article in Harvard Business Review (HBR) asks if AI is a system or is it a solution like so many organizations think?

An interesting question, but one that I would rephrase: Is AI a solution, is it technology that supports the solution, or is it part of a larger system? I have always thought of AI as supporting the digital transformation, which includes all the organizational changes that are needed to make use of digital technologies. So I have always thought of AI more broadly than either a solution or technology. The HBR article points out that 1) 80% of organizations surveyed are developing some sort of AI applications and that 2) companies that think of AI as a system rather than a solution will see their revenues grow by as much as a third over the next 5 years[i].

To understand why this might be the case, let’s consider a few possibilities:

If we think of AI as a solution, we need to be pretty clear about what problem it solves, or business need it addresses. For example, let’s say we need to be able to predict which customers will buy our new product. Sure, this sounds like a business need, but it really is a solution. Ah, you might be thinking., predict customer patterns = predictive analysis, so the solution I need is predictive analysis. No, predictive analysis is a way we can predict who will buy our product. It supports the solution. But what is the business problem? It might have to do with loss of market share, decreased revenues, or a number of other real problems.

So instead of:

  • Problem: We need AI to remain competitive
  • Solution: AI

We can think of it as:

  • Problem: Market share has decreased by x% since this time last year with resulting revenues down by $x
  • Solution: Ability to predict which customers will buy our new products to increase our customer base and to increase revenues.
  • Technology needed to support the solution: Software to analyze the data for customer buying patterns and predict customers who will buy our product

But will technology by itself solve our problem? Probably not. What about the related end-to-end processes that will need to change, the massive amounts of data needed to be analyzed and which predictions need to be made, which algorithms to use, the effect of AI on the organizational culture, the jobs that will be created and lost, the business decisions that will need to be made, the business rules to consider and much, much more..


When we think of AI as the technology part of a system, a system in its broadest sense, this starts to make sense. We know that we need to understand not only the technology, but all the context and processes surrounding the technology. When we analyze whole systems, we consider such things as:

  • Problem: In this case, loss of market share to competitors
  • Solution: Ability to predict which customers will buy our new product
  • Technology needed to support the solution: Software to analyze the data for customer buying patterns and predict customers who will buy our product
  • Processes: current processes and how they will change with the implementation of the solution
  • New roles and positions to create and hire for

We also know how to make organizations aware of such consequences as:

  • Wrong staff doing the work, such as creating the models
  • Dirty data leading to shabby analysis and incorrect predictions
  • Minimal acceptance by key stakeholders
  • Wrong people making business rules and other business decisions
  • Biases built into the predictive models

That’s one of the reasons why, I believe, taking a systems approach increases the chances for organizations to see growing revenues. Thinking of the entire system, not just the technology, allows for the distasteful but essential hard work of figuring this whole thing out. If we look at only the technology, we’re apt to fall into the myriad pitfalls that so many organizations fall into, and which lower the chances of successful outcomes.

How BAs can help

  • Understand the problem. We can help explain the difference between a problem and a solution in search of a problem and that a solution in search of a problem does not necessarily help an organization achieve its goals.
  • Ensure data is trustworthy. AI depends on trust-worthy data, data that is clean, that not only has a single source of record, but that comes from an agreed-upon source. That the data business rules are aligned with the organization’s goals and objectives.
  • Examine algorithms and the underlying data to see if there are built-in biases. BAs these days need to get up-to-speed on AI in its various forms (machine learning, predictive analysis, RPA, etc.). They need to educate themselves on the various algorithms that are used and the advantages and disadvantages of using one over the other from a business perspective. We need to ask really good questions to ensure the right algorithm is being used for the business need at hand. We need to ensure that the kinds of predictions and AI recommendations will not harm the organization’s ability to serve a variety of constituents. We need to look for underlying biases.
  • Help evaluate predictive tools to weed out any that intentionally or unintentionally promote biases. As BAs we can help the organization examine various measures of success and explain how subjective measures might insidiously shape a tool’s predictions over time. We can look at end-to-end processes and the input to and output from these processes to examine the data for underlying biases. And once we understand the organization’s “system,” we can work with software vendors to help ensure that the software itself is aligned with the organization’s goals and doesn’t have hidden built-in biases.

If, on the other hand, our scope is simply implementing the AI application, much of the needed business analysis could well be short-circuited, resulting in this sorry statistic—72% of executives said their company’s digital efforts are missing revenue expectations.[ii].

Organizations may want us to help them implement AI quickly, but they need us to help them avoid the consequences of falling into the common pitfalls, as so many organizations have done. In other words, we can do our part to help achieve the revenue growth projections when viewing AI as a system

[i] Taking a systems approach to adopting AI by Bhaskar Ghosh, Paul R. Daugherty, H James Wilson, Adam Burden

[ii] Gartner, 11/27/2018 HBR, Every Organizational Function Needs To Work On Digital Transformation

Elizabeth Larson

Elizabeth Larson, has been the CEO for Watermark Learning as well as a consultant and advisor for Educate 360. She has over 35 years of experience in project management and business analysis. Elizabeth has co-authored five books and chapters published in four additional books, as well as articles that appear regularly in BA Times and Project Times. Elizabeth was a lead author/expert reviewer on all editions of the BABOK® Guide, as well as the several of the PMI standards. Elizabeth enjoys traveling, hiking, reading, and spending time with her 6 grandsons and 1 granddaughter.