Anand Mahurkar, Findability.Sciences, an award-winning AI expert, consults global organizations on Enterprise AI.
A common and oft-repeated question at the board or C-level in all my engagements as an AI consultant is consistently the “when” and “what” of return on investment (ROI)—a marked evolution from the days when AI initiatives were predominantly experimental or proof-of-concept. Today, AI has reached a level of maturity, and organizations that have begun to integrate AI technologies are beginning to witness promising outcomes.
Challenges In Assessing AI ROI
Despite the apparent potential benefits, decision-makers often hesitate, weighing whether the investment in AI will yield a positive return. The apprehension stems from the substantial costs and potential organizational disruptions associated with AI projects.
AI vendors typically cannot guarantee specific ROI outcomes, as the realization of benefits largely depends on the effective operationalization of AI solutions. For instance, AI’s ability to rank debt collection prospects based on their payment likelihood only translates to tangible benefits when collectors adjust their operations accordingly.
Industry Insights And Trends
In a recent GenAI survey by IDC, it was highlighted that life sciences, healthcare, professional services and media/entertainment sectors are poised for significant disruption over the next 18 months, with approximately 40% to 44% of technology purchasers acknowledging this potential. As overwhelmingly indicated by survey respondents, the primary aspiration for integrating AI revolves around enhancing operational efficiencies.
So, what constitutes the ROI for businesses considering investing in AI? This question necessitates a preliminary examination of the challenges enterprise leaders aim to address through AI, namely:
1. Increasing revenue
2. Reducing costs
3. Fostering innovation and improvement, which, in turn, drive revenue growth and cost reduction
Advancements in the three primary AI domains—predictive, interpretive and generative—promise to empower enterprises to operate more efficiently and cost-effectively. Predictive AI, for instance, enhances forecasting, leading to revenue growth through new customer acquisition, reduced attrition and optimized pricing. Interpretive AI reveals insights from documents and images, aiding in cost reduction, efficiency enhancement and risk mitigation. Generative AI, meanwhile, automates processes and fuels innovation.
Practical Considerations For AI Implementation
I consistently argue that successful AI implementation requires more than just technological capability; it also demands domain expertise and data. Leaders considering AI investments should leverage their industry knowledge to anticipate potential savings or revenue opportunities. For example, AI could expedite regulatory approval processes for medical device companies, reducing costs and accelerating market entry—thereby enhancing revenue and lowering expenses. Quantifying these benefits requires a detailed application of domain knowledge.
Organizations embarking on their AI journey should meticulously identify and quantify potential benefits. Likewise, AI providers should be transparent about costs and timelines, facilitating a collaborative pre-implementation analysis.
Encouragingly, IDC’s report also reveals that, on average, enterprises worldwide observe a return on their AI investments within 16 months, with approximately 68% of organizations reporting returns ranging from double to fivefold.
However, it’s important to acknowledge that about 30% of organizations do not see any returns. For these cases, I advise reassessing the intended use cases and goals. Unlike traditional IT projects, it’s perfectly acceptable in AI initiatives to pause, stop or pivot direction if the expected ROI is not materializing.
One intriguing thought is whether AI itself could eventually assist in predicting ROI outcomes before the commencement of projects, potentially revolutionizing the planning and execution of AI initiatives.
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