What is Return on AI – and How Do Companies Measure It

In today’s competitive business landscape, the pressure to adopt Generative AI can be overwhelming. Companies everywhere are rushing to integrate these advanced technologies, often driven by a fear of missing out rather than a clear strategy for enhancement. But should businesses hop on the GenAI bandwagon just because everyone else is doing it?

To achieve meaningful outcomes, we need to evolve the conversation from “whether to adopt GenAI” to “how to adopt GenAI in a way that actually creates a business ROI.” We’ve come to call this concept “Return on AI” (RoAI). RoAI is about integrating AI thoughtfully, in a way that achieves measurable, impactful outcomes. How can you ensure that your investment in AI isn’t just an expensive experiment but a strategic advantage that delivers real value – and ideally, does so quickly?

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This article aims to demystify the concept of RoAI and provide you with a blueprint to measure the true impact of GenAI beyond the hype. We’ll explore why understanding and quantifying RoAI is crucial – not just for tech teams, but for anyone in a leadership role looking to make informed, strategic decisions about AI investments. After all, shouldn’t the adoption of new technology be as smart as the technology itself?

Let’s delve into how companies can adopt a more calculated approach to GenAI, focusing on measurable outcomes that align with their broader business goals.

Understanding Return on AI (RoAI)

The key emphasis here is that RoAI moves the conversation from AI as a cost to AI as an investment. This means looking at AI through the lens of strategic business returns, not just technical achievements. For instance, does the implementation of AI in your operations reduce costs or make your people more efficient? Does it drive revenue? Perhaps it enhances customer satisfaction or employee productivity? Or even more compelling, does it create new business models or revenue streams? These are the types of measurable outcomes that define RoAI.

To achieve RoAI, leaders need to look past the feel-good factor of employing the latest AI technologies, and instead shift toward quantifiable results that directly tie into the strategic goals of the business. The measurable aspects of RoAI can range from direct financial gains, such as revenue growth and cost reduction, to efficiency metrics like speed of service delivery and the number of tasks automated. These metrics provide concrete data to gauge the effectiveness of AI investments.

Without clear metrics, businesses risk navigating the complex landscape of AI adoption without a compass, potentially leading to investments that are misaligned with business objectives and fail to deliver expected results. Measurable outcomes allow for the continuous evaluation of AI strategies, ensuring they remain aligned with evolving business goals and market conditions.

Furthermore, quantifiable RoAI provides a framework for accountability. It helps businesses avoid the increasingly common pitfall of adopting “AI for AI’s sake,” where technology is adopted without a clear understanding of its business value. By focusing on metrics that matter, leaders can justify AI investments to stakeholders and pave the way for further AI integration where it can produce real benefits.

The Art of Measuring Return on AI

Now, let’s explore the different dimensions through which companies can measure the impact of AI, ensuring these technologies are not merely adopted but are strategically integrated to enhance performance and innovation. Quantitative measures form the backbone of RoAI, offering clear, numerical data that business leaders can use to assess the effectiveness of their AI investments. These metrics can include:

• Cost Reduction: How has AI helped in cutting operational costs? For example, corporate legal departments are increasingly finding ways to utilize AI for drafting and redlining contracts. They are finding this significantly streamlines the process, reducing the hours in-house counsel or costly external attorneys spend on these tasks.
• Revenue Growth: Can AI initiatives be linked to an increase in sales? Implementing AI-driven analytics for personalized marketing can lead to higher conversion rates and, consequently, increased revenue.
• Efficiency Gains: How much faster are processes with AI? Generative AI can accelerate research and personalization for SDRs, enhancing sales workflows by quickly analyzing customer data for tailored outreach. When paired with the expertise of sales professionals who review and refine outputs, this human-AI partnership ensures communications are both rapid and resonant.
• Accuracy and Quality Improvements: Has AI contributed to fewer errors or higher quality outcomes? Going back to our prior example of contract management, AI-driven tools can enhance the drafting and redlining of contracts by identifying inconsistencies and errors with greater accuracy than traditional methods. This precision of AI is amplified when combined with the oversight by legal professionals.

The Future of GenAI in Business: AI Where It Matters, People Where It Counts

The prevailing discourse around Generative AI (GenAI) often centers on its potential to replace human roles, with the promise of slashing operational costs as a major selling point. However, this narrow focus on cost-cutting misses the broader, more impactful benefits of AI. The key to unlocking true Return on AI (RoAI) lies not in replacing human workers but in amplifying their capabilities.

Consider the growing use of AI in contract management within corporate legal departments. AI’s ability to draft and redline contracts is undeniably efficient, but its greatest value emerges when paired with the expertise of legal professionals. Here, AI does the heavy lifting by automating the initial drafting and redlining processes, which traditionally consume a significant amount of time. It quickly synthesizes information to suggest contract terms, identify legal inconsistencies, and flag areas that require human judgment. Humans are not replaceable in this process – but the strategic use of GenAI allows legal experts to focus on higher-level strategic decisions and nuanced negotiations.

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Similarly, the evolution of SDR programs highlights how GenAI enhances sales processes. By automating the groundwork – areas like research and personalization – GenAI enables sales professionals to focus on direct interactions with prospects, where human engagement remains crucial. This allows SDRs to dedicate more time to understanding client needs and building relationships, crucial aspects that machines cannot replicate. This synergy between GenAI efficiency and human insight not only streamlines operations but also enriches customer interactions.

In other words, adopting AI with the aim to merely reduce headcount and operational costs is a short-sighted strategy that often leads to suboptimal outcomes. Instead, a more sustainable and impactful approach is to view AI as a tool to enhance and extend the capabilities of human teams. By doing so, businesses can achieve true business outcomes and meaningful Return on AI.

Conclusion

It’s clear that the true measure of success for AI adoption isn’t found solely in automation or operational cost reductions. Rather, it resides in how well AI can amplify and enhance human capabilities to drive meaningful business outcomes. As we’ve seen, when AI supports human efforts – whether in legal, sales, or marketing roles – it not only increases efficiency but also enriches the quality of work and the strategic impact of teams.

The future of AI in business calls for a shift in focus from merely employing these technologies to integrating them thoughtfully where they can make a substantial difference – supporting and extending human capabilities. This strategic integration ensures that AI investments are aligned with business objectives, empowering teams, enhancing decision-making, and fostering a culture of innovation. The key here is making smarter decisions, not just faster ones, and using AI to provide insights that lead to better strategies and more creative solutions.

Therefore, leaders are urged to champion AI not as a replacement for human talent but as a powerful ally to it. By doing so, they can ensure that their organizations not only survive the digital transformation but thrive in it, achieving a robust Return on AI. This is the path to not just adopting AI, but adopting it wisely and well – where it matters most, with people where it counts.

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