The application of artificial intelligence (AI) methods, technology, and solutions represent a fundamental shift in how people interact with information, and a huge opportunity for government agencies to improve outcomes.
However, a common misconception is that AI is “plug-and-play.” Perhaps because of this, according to McKinsey research, only 8% of companies use practices that enable effective adoption of AI.
For this reason, here is a test for AI readiness that we call the “6 pillars of AI.”
These pillars make certain that a finished AI product provides:
- The right solutions for its users, and
- Enduring value to the organization as a whole.
AI is most effective when an organization has centered all functions around using it. Project-based AI has its place, but the more that an organization shifts from seeing AI as a tool to seeing it as a broad methodology, the more the promises of AI will be realized.
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Therefore, in every AI project we recommend the use of these 6 Pillars of AI to ensure that the solutions developed and the transformations made achieve broad organizational goals and bring lasting value to the organization.
6 pillars of successful AI adoption and projects
1. AI is only of value if it improves something
Is AI the best approach to your problem, given the desired outcome and required investment?
Currently AI is a hot topic in government IT. It’s exciting, seen as forward-thinking, and generally looks bright and shiny. This causes organizations to get into trouble because deep analysis isn’t made on how AI will bring broad, long-lasting value.
The first question organizations should ask is:
- What do you want to accomplish, and how do you imagine AI can help you reach that goal?
The second question organizations should ask is:
- Based on that goal, are the costs of implementing AI acceptable? Is the business impact worth the cost to the business?
The cost of AI is so much more than its sticker price; truly adopting AI in order to realize its full potential requires transforming an organization’s culture, vision and strategy. Such broad transformation is not easy nor cheap, and thus needs to be taken into consideration when developing an AI strategy or planning an AI acquisition.
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2. AI is only of value if it augments human function
Is the process of using AI easier than the old way of doing things?
Though AI is supposed to make jobs easier and more efficient, an organization should carefully evaluate exactly how the proposed solution will do this. A valuable AI solution should decrease human dependencies.
In other words, if it takes your people longer to work with the AI solution, rather than to do the process “manually,” then the AI is not actually decreasing human dependency and is therefore not doing what it should fundamentally do.
If your AI solution is failing here, before concluding that the AI solution itself is the problem, your organization should evaluate the solution, including asking the following questions:
- How accurate is our data?
- How efficient is our data infrastructure?
- Are we using the right AI model?
- Is our team adopting and implementing the solution as intended?
This evaluation might uncover that your solution is valuable and will decrease human dependencies, if only your data were better, your infrastructure was efficient, or your team fully adopts it as intended.
3. AI is a human multiplier, not a replacement
Does the AI solution remove repetitive subsets of jobs, enabling your people to be more efficient, productive, and able to focus on higher value tasks?
While AI that has the intelligence to operate independent of human input is theoretically possible, the vast majority of companies that could effectively use AI today will use it in such a manner that still depends on people to guide its use and make decisions.
Don’t imagine that an AI solution will be able to replace a person or a team in your organization; instead, an effective solution should turn your people into “super humans” — enabling them to process, for example, twice as much input as before.
4. Data is the foundation of all AI operations
Has your leadership developed an infrastructure strategy that will enable AI success?
It hardly needs to be said that for a machine to learn, it must have data from which to learn — the more, the better. An organization’s AI solution is only as good as the quantity and quality of the data it’s built upon.
To store the necessary data, such organizations must have large computational power, access to data science expertise, and data sets on which to train models.
5. Data strategy is critical to AI benefits
What is the quality of your data?
Data infrastructure alone is not enough for AI to be effective, much less to produce desired benefits for an organization.
Data strategy overall involves creating processes to collect records (particularly results and outcomes), which in turn produce data. Data is the required input for machine learning, and the desired outcome of AI—predictive/explanatory models—are dependent on effective ML.
Once data has been gathered, it’s vital for the data strategy to outline how that data can be verified, cleaned, and structured so that the data is accurate and usable.
The principle “garbage in, garbage out” succinctly describes the importance of a well-formed data strategy and its place as one of our pillars of AI.
6. AI must produce a usable output
Did the AI model produce a usable output, and if so, is that output valuable for the organization?
Finally, the proposed (or developed!) AI solution must lead to a result that brings value — to the specific project in which it is implemented, and ultimately to the whole organization.
If, from the start, the effectiveness and scope of a proposed AI solution will be siloed or limited by an organization’s structure or culture, the value of that solution should be questioned. This doesn’t mean there isn’t value in small projects with quick turnaround and limited results — only that a successful AI output or even model is not the same as AI supporting a business process.
Together, these 6 pillars of AI facilitate agencies and contractors in making certain that:
- AI is the right methodology for the problems being faced
- The AI solutions developed are an effective approach to those problems
- The output from AI will ultimately lead to long-term value for the organization