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From possibility to profitability: Connecting AI to ROI

By Raph Casadei
team meeting

Unlock the full potential of your AI investments with a strategic approach to manage costs, scale efficiently, and secure financial returns.

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.

Why address ROI right away?

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.

Prioritizing your AI investments

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 demo provides an early look at our AI Value Calculator—a proprietary tool that empowers Slalom customers to make informed decisions about their 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.

Provide a detailed description of the problem or opportunity you’re trying to address with that specific use case.

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. 

Enter the core KPIs or OKRs that will define success for the AI use case you’re looking to test.

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.

Determine who your primary users or stakeholders are, the predicted number of users who will interact with the solution, and any key outputs the solution would provide. Then—based on complexity and scope—estimate the effort to implement that particular use case, considering any potential risks involved.

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.

Provide a more specific description of potential benefits before breaking them down by measurable revenue increases, reductions in operational or production costs, and proactive cost avoidance. Then, estimate overall implementation costs as well as regular, ongoing expenses.  

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. 

Use the “Compare Use Cases” function to help prioritize AI solutions, taking into account the most critical factors that influence decision-making.

6. Refine ROI analysis:

For each individual use case, perform a deeper ROI assessment based on new data and feedback. By keeping AI initiatives aligned with business goals and adapting to changing conditions, you’re maximizing returns and mitigating risks.

We helped a Fortune 500 retailer prioritize AI use cases through comprehensive workshops, defining problem statements, establishing success metrics, and exploring use-case architecture. This strategic approach clarified the path forward and led to a successful proof of concept (POC) that transitioned into production, demonstrating tangible results and significant business impacts.

Let’s solve together.