Return on Investment from AI Investments
Krishnan CA
SVP, CDO, Global Head. ET Young Leader. IIM-A Alumnus. MBA & B.tech (Gold Medalist). Author.
As businesses continue to invest in Artificial Intelligence (AI), the importance of ensuring a solid Return on Investment (ROI) has become a crucial point of focus for business leaders. AI has transformative potential, but without a strategic, methodical approach to measuring and optimizing returns, it can easily become an investment with little tangible benefit!
In this article I explore the critical elements leaders need to consider when managing AI investments, including establishing a clear ROI framework, conducting pilot programs, leveraging the Business Model Canvas, and structuring AI initiatives effectively within an organization.
1. Establishing a ROI Framework for AI Investments
Leaders must begin by setting up a robust ROI framework before investing in AI. This involves articulating clear objectives, identifying potential use cases, and laying out the financial and operational expectations from AI deployments. As the maxim goes, "what gets measured gets improved," which underscores the importance of defining measurable outcomes that can demonstrate the success of AI projects.
A key lesson learned from companies like Stimulr (Stimuler is a mobile app that helps non-native English speakers improve their conversational skills and fluency - https://stimuler.tech/about)— which saw tremendous success in the language learning domain— is that AI investments must be guided by well-defined outcomes. In Stimulr's case, the team recognized a gap in spoken English proficiency, which drove the development of an AI-driven solution tailored to address that need. With over 750,000 downloads and widespread recognition, the startup proved that AI, when tied to a clear business objective, can scale and deliver substantial returns.
However, success stories like Stimulr's are often the exception rather than the rule. A Gartner survey of more than 700 IT leaders revealed that nearly half of AI leaders face difficulties in estimating or demonstrating AI’s value. For example, companies adopting Microsoft Copilot struggled to quantify how AI tools like this would improve processes like writing newsletters, especially without access to internal data. This points to the need for a clear hypothesis upfront.
Leaders should commit to running AI pilot programs, with a structured approach to test hypotheses and validate assumptions before scaling AI initiatives. By setting milestones and measurable KPIs from the start, they can determine whether the expected outcomes are being achieved. If certain assumptions about AI’s impact are not validated during the pilot phase, the project should be shelved or re-evaluated. This approach not only mitigates risk but also prevents AI from becoming a massive expenditure with no measurable return.
2. Utilizing the Business Model Canvas to Guide AI Investments
The Business Model Canvas offers a powerful framework for guiding AI investments and measuring ROI. This tool helps leaders map out critical elements such as customer needs, the value proposition, and the revenue model, ensuring that AI solutions are not developed in isolation but are tightly integrated with the business’s broader objectives.
For example, companies like CNH Industrial (https://www.cnh.com/en-US/Our-Company), which manufactures farming and construction equipment, have used AI to develop solutions such as health monitoring systems for equipment and AI-powered chatbots for service technicians. By leveraging AI, CNH not only improved operational efficiency but also enhanced the customer experience through innovative AI-driven services.
The Business Model Canvas framework helps to answer key questions:
By using this structured approach, businesses can align AI initiatives with clear, profit-generating goals and track their return on capital employed (ROCE) more effectively.
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3. Structuring AI Investments for Maximum Impact
Once a clear framework is established, leaders must consider how to structure AI investments within the organization. Should AI be centralized under a single leadership team, or should individual departments have autonomy to develop AI-driven solutions tailored to their needs?
In most cases, a hybrid model works best. This allows for a centralized approach to governance, data policies, ethical guidelines, and regulatory compliance, while enabling business units to tailor AI implementations to their specific operations. By centralizing these foundational elements, organizations ensure that AI initiatives align with overall business objectives and comply with regulatory standards. Meanwhile, decentralizing execution allows each business unit to experiment and innovate according to their unique needs.
For instance, Apple’s success is often attributed not to its ability to invent new technology but to its skill in understanding customer needs and building scalable business models that serve those needs effectively. Apple’s approach shows that innovation must be customer-centric and aligned with strategic business goals. Similarly, organizations that scale AI successfully focus on integrating AI into customer value propositions rather than adopting it purely for technological advancement.
4. Measuring Success and Demonstrating AI’s Value
To track the success of AI initiatives and demonstrate value, leaders need to establish clear metrics that capture the financial and operational impact of AI. For instance, CNH Industrial set a target of saving 10,000 man-hours using GitHub Copilot, an AI-driven coding tool, and asked developers to manually track time saved. This practice underscores the importance of tracking ROI metrics over time, not only in financial terms but also in operational efficiencies and customer satisfaction.
Similarly, Stimulr's rise to success was closely tied to its ability to provide measurable improvements in spoken English skills through continuous feedback loops. The company’s ability to quantify customer engagement and demonstrate improvements in fluency and pronunciation helped to validate its AI investment and attract a broader user base.
By focusing on outcome-based metrics rather than subjective measures, organizations can gain a clear understanding of the value AI delivers and use these insights to refine and scale their AI initiatives further.
Conclusion
Scaling AI from experimentation to a profit-generating core of the business requires a structured, methodical approach. Leaders must establish an ROI framework upfront, run pilot programs with clear metrics, and leverage tools like the Business Model Canvas to ensure AI initiatives are aligned with business goals. A hybrid model—centralized in governance but decentralized in execution—can help balance strategic alignment with the need for business unit flexibility. Finally, a dedicated AI leadership team and a strong focus on measurable outcomes are critical for turning AI investments into tangible returns that drive business growth and innovation.
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Director Supply Chain & Procurement KPMG India | Business Consulting | Mentor | CXO Advisor
4 个月Krishnan to unlock AI's true potential and reap maximum benefits, businesses must strategically align AI initiatives with clear ROI metrics and focus on creating value by continuously optimizing to get impactful results.
Client Solutions Director
5 个月Good points. One other point (kind of covered but, I think, needs to stand out) is Relevance and Integration into present Business
Former Executive Vice President at Tata Consultancy Services | AI &? Intelligent Automation , Cloud , Cybersecurity & IT Infrastructure Services |Strategy | Advisory | Consulting
5 个月Dear Krishnan, very well articulated on the business framework for AI . One critical aspect missed out by most business leaders is the investment they have to make collecting " good data" from their existing Enterprise systems which could be a mix of old and new ! This often leads to the outcomes not to their expectations. The business model framework you have defined should probably take this into consideration.