There is a lot of hype surrounding data and AI.
The New York Times: “There are seemingly endless ways in which artificial intelligence is beginning to touch our lives, from discovering new materials to new drugs [...] to picking the fruit we eat”
Forbes: “AI and machine learning are at the top of many lists of the most important skills in today's job market. Jobs requesting AI or machine-learning skills are expected to increase by 71% in the next five years.”
Gartner: “[AI] is evolving rapidly through new techniques, dedicated infrastructures, and hardware.
However, the reality is more complicated.
The New York Times: “Artificial intelligence technology is promising, but it’s not a magic potion.”
Forbes: “AI implementation is not instantaneous. It takes preparation to ensure that the solutions you've chosen for your business are the right ones and that they will be capable of benefiting your business.”
Gartner: “The reality is that most organizations struggle to scale the AI pilots into enterprise-wide production, which limits the ability to realize AI’s potential business value.”
In 2019, Boston Consulting Group surveyed 2,500 executives about artificial intelligence. The results were damning. Although 90 percent of respondents agreed that AI represented “a business opportunity for their company,” 70 percent reported little to no gains from AI so far. Additionally, although 90 percent of respondents’ companies had invested in AI, more than 60 percent had failed to realize any gains from AI in the past three years.
One might ask: “Has AI return on investment (ROI) improved in 2022?” Unfortunately, no.
We have seen a growing awareness of AI and its applications. McKinsey & Company reported a six percent increase in AI adoption across industries in its latest Global Survey. However, McKinsey & Co. also noted significant contrasts between its “high-performer” respondents—“those who said that at least 20 percent of their organizations’ earnings before interest and taxes was attributable to their use of AI”—and others.
“High-performers” enjoyed significantly higher ROI than their counterparts. The source of their success? Engagement in certain data and AI “best practices”.
These findings echo an enduring pattern. More organizations are growing aware of and, subsequently, adopting AI. But few manage a significant ROI. And interestingly, these “high performers” consistently attribute their success to a shortlist of best practices.
Time and time again, we’ve come to the same conclusion:
Maturity in eleven key capabilities helps organizations meet their data and AI objectives.
We call the cumulation of these capabilities AIQ™. Similar to IQ, which represents a reasoning ability according to performance on problem-solving tests, AIQ™ measures an organization’s ability to leverage data and AI according to our in-house assessment.
Synaptiq’s AIQ™ assessment tests eleven capabilities, each of which represents a broad category of specific data and AI “best practices.” We chose a broad, overarching focus for this initial assessment because no two organizations are the same, so, no two are suited to the same best practices, even if they are striving for maturity in the same capabilities.
Consider “data governance” for example. This AIQ™ capability pertains to the framework by which an organization governs the use of its data assets. An organization with “good” data governance will have formal roles, policies, and metrics to set standards for the efficient, effective use of its data assets and measure progress toward these standards.
Although all organizations should incorporate these data governance practices in some form, no two organizations should incorporate them in the same form. For example, a law firm will have different business objectives and desired outcomes than, say, a hospital. Therefore, these two organizations will need to employ very different practices—practices specifically tailored to their unique needs—in order to achieve data governance maturity.
Consider the following capabilities, with your organization in mind. Although these broad capabilities should help any organization meet its data and AI objectives, your organization should approach them through the practices suited to you. Ultimately, the best practice is always that which best suits your needs.
We developed AIQ™ based on years of successful strategy work for our clients and partners. It’s a comprehensive methodology for leveraging data and AI: technologies that often go un- or under-utilized. Simply put, AIQ™ solves two common point points:
Organizations miss opportunities by failing to invest in data and AI.
Organizations fumble opportunities by poorly investing in data and AI.
AIQ™ ensures that an organization achieves maturity in the eleven capabilities—the foundation for data and AI-driven initiatives—before they invest in these technologies.
Synaptiq’s AIQ™ assessment can give your organization a measure of its overall maturity. However, for those hungry for more, we also work directly with organizations to determine the practices that will best serve their growth toward individual capabilities.
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
You can learn more about our story through our past projects, blog, or podcast.