To realize AI’s full potential, organizations must be in it for the long game; a pursuit that requires patience, persistence, and strategic alignment. While quick wins are important, they won’t stand alone in delivering meaningful value; agile experimentation is a necessity, execution requires iteration, and early challenges are inevitable.
Protiviti’s inaugural global AI Pulse Survey highlights a compelling correlation between AI maturity and return on investment (ROI) as well as a disconnect between expectations and performance for many organizations in the early stages of AI adoption. The survey, which had more than 1,000 respondents, categorizes organizations from more than a dozen industry sectors into five maturity stages:
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Stage 1: Initial — Recognizing AI’s potential but lacking strategic initiatives.
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Stage 2: Experimentation — Running small-scale pilots to assess feasibility.
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Stage 3: Defined — Integrating AI into business processes.
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Stage 4: Optimization — Enhancing performance and scalability with data feedback.
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Stage 5: Transformation — AI drives significant business transformation.
Expectations from AI Investments
As organizations progress through these stages, their satisfaction with AI investments improves. In fact, of the 50% of survey respondents who indicated that they are in the early stages (initial or experimentation) of AI adoption, about 26% reported that AI investment returns fell below expectations.
Of course, not all AI experimenters are experiencing poor returns. Indeed, a majority report ROI meeting expectations, but the results showed a higher concentration of slightly exceeded or significantly exceeded ROI expectations among groups in the middle to advanced stages of AI adoption.
In reviewing what differentiates successful experimenters — those in the experimentation stage of AI adoption who reported exceeding ROI expectations — from those that did not, we find three compelling attributes:
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Focus on balanced key performance indicators (KPIs) and measuring success using a mix of financial and operational indicators, such as employee productivity, cost savings and revenue growth;
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Report fewer challenges with skills and integration, as they tend to invest in training, upskilling and cross-functional collaboration;
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Seek diverse support, including strategic planning assistance and data management tools, not just training.
One more thing: These successful experimenters also emphasized financial and operational outcomes more evenly, while others focused more narrowly on cost savings.
Challenges AI Experimenters Face
Many AI experimenters are struggling not because of unrealistic expectations, but more likely due to unclear objectives or misunderstood value potential. This challenge and difficulties with integrating AI into existing systems are the two biggest hurdles faced by organizations in the early stages of adoption (stages 1 and 2).
Integration issues peak in the middle stages of AI adoption, but they begin in the early stages. Interestingly, the challenge related to understanding the most impactful use cases is most acute in the earliest stage, dips in the middle stages, and resurfaces even at the highest levels of maturity, albeit for different reasons.
The AI experimenters, of course, are unsure how to apply AI strategically and technical compatibility remains a hurdle, unlike the more mature companies. Compounding these issues are unclear or conflicting regulatory guidance and difficulties with data availability and access, a foundational issue for effective AI deployment.
It is the lack of structured approaches, unclear project objectives, and unreliable data that often lead to underwhelming ROI for these companies in the early stages.
Redefining AI Success
In another interesting finding from the survey, we see that as organizations progress to stages 3 to 5, their success metrics evolve from cost savings and process efficiency to revenue growth, customer satisfaction and innovation.
The good news is that organizations starting out on their AI journey can course-correct by focusing on these success metrics. It starts with redefining AI success, which means moving beyond short-term wins to sustainable transformation.
Having a clear understanding of what you’re trying to accomplish with AI is critical from the outset. Without clarity on what AI is meant to achieve, and how value will be measured, they will struggle to unlock its full potential.
Early experimenters should seek to build a solid foundation by:
Asking Why? Why are you adopting AI? What specific problems are you solving?
Investing in data infrastructure is critical. This step should involve auditing existing data systems and implementing robust data governance frameworks. Organizations will be well served in considering cloud-based platforms for scalability.
Developing a robust integration strategy early. Many existing systems were not originally designed to support AI. To overcome this deficiency, organizations should be proactive in assessing and modernizing infrastructure to handle AI workloads in the initial phases. They are likely to find greater success if IT, data and business teams collaborate and there’s shared ownership of AI initiatives to ensure alignment and adoption.
Aligning AI strategies with business objectives and organizational culture: This is not just a technical step. It involves ensuring organizational readiness and managing cultural and operational changes effectively.
Turning AI Trials into ROI Triumphs
The research is clear: there’s tremendous ROI potential for early-stage companies that can test, learn and scale AI use cases swiftly. Yet, while speed is crucial to capturing value, it’s important to recognize that AI experimentation is ongoing, requiring continuous iteration.
To win, think big, act swiftly, and continuously evolve — never stop.
