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Imagine a bustling retail company that has enthusiastically jumped on the AI bandwagon. Every week brings a new use case, each more exciting than the last—from chatbots promising better customer interactions to smart systems designed to streamline the supply chain.

However, as these use cases pile up, management finds itself in a chaotic technological landscape with no clear way of measuring which use cases are paying off. How can they pinpoint the return on investment (ROI) from each AI endeavor, prioritize their efforts, and turn potential into profit?

Best-case scenario, this uncertainty stalls AI adoption. At worst, it leads to wasted money and resources.

The ambiguity surrounding ROI is not just a financial concern; it fundamentally affects strategic decision-making across every level of the organization. When executives can’t clearly quantify or predict the benefits of AI initiatives, many become overly cautious, potentially missing out on technological advancements that could give their company an innovative advantage.

Conversely, a lack of clear ROI may drive some leaders to impulsively invest in AI technologies without a solid strategic foundation. This leads to misallocated resources and use cases that fail to align with the business’s core objectives or deliver compelling financial returns.

These challenges are compounded by the current “use case frenzy“ seen across many industries, where companies eagerly jump on the AI hype train without a structured evaluation plan or clear strategic alignment. This can lead to a scattered investment landscape, with resources stretched across many initiatives that aren’t delivering meaningful or sustainable value.

By evaluating the ROI of AI initiatives early on, you’re building a foundation for your organization to leverage emerging technologies sustainably and effectively. This value-driven approach paves the way for:

• Strategic alignment 
  Aligning investments with strategic business objectives prevents resources from being squandered on projects with limited impact. Having a clear strategic plan also helps stakeholders—from C-suite executives to operational teams—gain confidence in AI technologies, fostering a culture that embraces innovation rather than fearing it. 

• Informed decision-making 
  Quantifying the benefits and costs associated with AI projects helps you make informed decisions about how to optimize and scale these technologies. This allows you to allocate resources to the most impactful AI projects, maximizing returns and minimizing waste. 

• Proactive risk management 
  Understanding the potential ROI of AI initiatives helps in identifying and mitigating risks associated with technology investments, allowing you to address potential issues before they escalate.

• A culture of accountability 
  Establishing clear ROI metrics and tracking progress fosters organization-wide accountability, as teams are more likely to stay focused on achieving desired outcomes when success measures are well defined.

Addressing AI ROI is not just about justifying individual projects; it's about laying a foundation for sustainable growth and innovation in an organization’s journey toward AI adoption at scale. It’s about ensuring that AI investments contribute to long-term success and resilience.

At Slalom, we’ve developed a proprietary AI Value Calculator that helps you prioritize and monitor the value of your AI investments.

This robust approach to AI value analysis is composed of a series of steps designed to help you connect AI initiatives to ROI.

1. Define the problem:

Clearly identify specific business challenges and how AI can address them. Make sure your AI initiatives align with business needs by detailing issues, understanding stakeholder pain points, and outlining success criteria.

2. Establish success measures:

Set clear, quantifiable metrics for success early in the project, making it easier to evaluate ongoing performance. This could include cost savings, efficiency gains, customer satisfaction, and/or revenue growth. 

3. Collect relevant data:

Start by gathering the data that’s most essential for estimating the ROI of your AI initiatives. Focus on financial benefits and costs—including both initial implementation and ongoing expenses—without overcomplicating data collection.

4. Consider all costs:

You should also account for hidden and ongoing costs like maintenance, scaling, and training. This helps develop a comprehensive understanding of the total financial commitment for successful AI adoption while minimizing risks.

5. Prioritize use cases:

Rank AI use cases by ROI potential, then allocate resources to projects with the highest impact. This helps ensure you’re focusing on initiatives that drive significant business value and support strategic goals.