Artificial intelligence (AI) adoption is accelerating, but trends are misleading.
IBM reports that 35 percent of companies use AI in 2022, up from 31 percent in 2022. Gartner predicts that AI software revenue will total $62.5 billion in 2022, an increase of 21.3 percent from 2021.
What are these trends hiding? The majority of AI initiatives end in failure.
Many organizations achieve a (very) positive return on investment from AI. Others fall short because their leaders don’t understand the challenges unique to AI. Together, we’ll navigate these challenges to ensure that your AI roadmap is failure-proofed to achieve your goals.
The following tips have helped Synaptiq clients across industries succeed in their AI endeavors:
How much will your AI roadmap cost to execute?
If an AI initiative is “uncharted territory” for your organization, answering this question may pose a challenge. We recommend that you begin by setting achievable business goals and establishing specific, quantifiable benchmarks for success. For example, does “solving” your problem mean…
If you have multiple goals, AI may be able to accomplish some for a reasonable cost, while others may require an alternative solution. We can refer to the former as “viable” use cases and the latter as “nonviable” use cases. Viability heavily depends on your data and your team’s ability to support a certain use case. If you don’t have that support, the use case is nonviable.
Organize your viable use cases in order of priority. Estimate the cost of executing an AI initiative to address each viable use case. Your budget is the sum of those costs.
Skepticism can shred an AI roadmap even before the journey begins. Here’s a term that every business leader should keep top of mind: cultural readiness. “Culture” is the shared beliefs, values, and assumptions that distinguish a team from a group of strangers. Your team is “culturally ready” for a change when their shared beliefs, values, and assumptions align with it.
For example, imagine two teams: Team A and Team B. Team A recruits for flexibility and rewards members for innovating. By contrast, Team B recruits for risk adversity and neither expects nor encourages members to innovate. Team B prefers “the ‘traditional’ way of doing things.”
Tasked with AI adoption, Team A will rise to the challenge. Team B will rebel against it.
An AI roadmap that disregards cultural readiness will fail when team members inevitably (i) resist execution and (ii) resist using the new technology post-execution.
Unused technology is a waste of investment, even if it works. Therefore, a failure-proof AI roadmap should include a detailed plan to improve cultural readiness (if necessary).
Finally, we suggest your AI roadmap incorporate a feasibility study: a low-cost, limited commitment “pilot” AI initiative execution. A feasibility study will fortify your roadmap by exposing unforeseen challenges and checking your costs, benefits, etc. estimates. Many AI adoptions fail before implementation because expectation doesn’t match reality. Feasibility studies are not optional (if you want to succeed).
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.
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