Building a compelling business case for artificial intelligence (AI) is part strategy, part storytelling and part structured financial modeling. While technical teams may focus on what AI can do, business leaders must focus on why it matters — and to whom.

Being able to translate AI’s potential into real-world, quantifiable outcomes could be a critical component in getting your project approved. The most effective AI business cases frame the investment not just in terms of innovation, but also in terms of risk, efficiency, growth and long-term value.

That starts with a grounded understanding of the business problem that the technology will solve. Here are the steps to build a solid business case for AI.

1. Define the Business Problem

Before discussing technology as a team, clearly define the business pain point. AI solutions are most credible when they’re rooted in problems that have financial weight and organizational urgency.

  • Identify a challenge with measurable consequences: Look for areas where inefficiencies, manual work or customer experience gaps are dragging down performance. This could include poor forecast accuracy, high churn, long cycle times or agent inefficiency.
  • Quantify the cost of doing nothing: Help stakeholders understand the financial impact of doing nothing. Use real data to calculate the lost revenue, excess costs or missed opportunity tied to the problem.

Here’s an example: “Our company’s existing manual approach to demand forecasting results in inaccurate projections that lead to $4 million in excess inventory and $1.5 million in lost sales annually. By automating this function with AI, we estimate improved management of our supply chain, therefore better sales conversion.”

Clear, specific framing builds urgency — and sets the stage for meaningful ROI calculations.

2. Align to Your Strategic Goals

Aligning the initiative with your organization’s broader strategic goals shifts the conversation from “interesting technology” to “critical investment.”

To do this effectively, frame the AI solution as a direct enabler of goals that already exist, such as improving customer experience, driving revenue growth or increasing operational efficiency. Speak in terms that resonate with executive priorities, referencing language from the company’s strategic plan, if possible. The more closely your proposal reflects leadership’s stated objectives, the more credible and relevant it becomes.

Next, paint a picture of what success looks like. Describe the impact the AI solution will have on the business — what it improves, streamlines or unlocks. Then clearly outline the business outcomes you expect, focusing on measurable impact from several perspectives:

  • Financial: Lower support costs, higher conversion rates or increased revenue
  • Operational: Fewer manual tasks, faster response times improved accuracy
  • Experiential: Higher customer satisfaction, greater employee engagement, less churn

Here’s an example: “By implementing AI copilots, we’ll reduce call time by 10%, which equates to an estimated $1 million in annual savings, while delivering a more consistent experience across all channels.”

Select the business benefits that best apply to your conditions and that you can prioritize.

3. Match AI Solutions to Specific Needs

Mapping AI capabilities to your specific needs — and your level of customer experience (CX) maturity — is key to demonstrating value and proving ROI for any organization’s starting point.

To manage this, Genesys identifies six Levels of Experience Orchestration, each representing a different stage in a company’s ability to deliver intelligent, personalized, connected customer journeys. These levels provide a framework for matching the type of AI investment to the outcomes your business is ready to pursue, while balancing tradeoffs between operational efficiency and delivering people-centric experiences.

Focus areas might include automation to reduce manual effort, AI-driven augmentation to assist employees with faster responses, personalization or optimization to drive more proactive engagement.

By matching AI to the right level of readiness, you avoid overbuilding and instead create a roadmap based on ROI and impact, where value compounds as capabilities grow.

Analyze current costs, investments and business benefits: Analyzing your current cost structure and comparing it to a potential AI-powered model helps surface not only cost savings but also investment readiness.

Start by conducting a total cost evaluation. This includes implementation fees, ongoing expenses and the internal resources needed to support your current systems. When possible, compare this baseline to the more efficient model that’s enabled by AI. This analysis should reveal a clear estimate of potential hard savings.

Next, assess the AI investment itself. What is the cost of the proposed solution? What current tools or manual processes will it replace? And where does it reduce or eliminate redundant spend?

Finally, evaluate the business benefits through measurable financial metrics. Focus on:

  • Net Present Value (NPV) to understand long-term gain
  • Payback period to determine how soon you’ll recoup your investment
  • Sensitivity analysis to model best- and worst-case scenarios

This structured approach gives stakeholders the financial clarity and confidence needed to make data-driven decisions about AI investments.

4. Design for Future Integration Needs  

When evaluating AI solutions, it’s important to look beyond immediate functionality and consider how the technology will integrate within your broader ecosystem. Choose solutions that work seamlessly with your existing CRM, ERP and workforce tools to avoid costly disruption.

Look for platforms built with an open API architecture — these are more adaptable and easier to scale as business needs evolve. Also, ensure the AI can extend across multiple channels, including voice, chat and social.

Finally, review the provider’s strategic partnerships to ensure long-term alignment and support. Future-ready integration is key to sustaining value.

Once you’ve followed these four steps to prove your AI need, you can move onto project buy-in and clearly plan out implementation steps.

Create an Implementation Roadmap with a Realistic Timeline

A successful AI deployment starts with a clear, actionable roadmap. Begin by identifying your priorities — what needs to be implemented first and why?

Consider any dependencies within the AI solution that could affect the timing of business benefits. From there, break the project into phases with defined milestones. Be sure to account for activities that may require longer lead times, such as integration efforts or user training.

It’s also important to anticipate potential risks. Data quality, change resistance and user adoption can all delay value realization if not addressed early. Equally critical is ensuring you have the right people involved. Often, the biggest challenge isn’t the technology, it’s resource availability.

Key subject matter experts are frequently pulled between transformation efforts and their day-to-day responsibilities. A well-scoped roadmap not only outlines the work ahead but ensures that the right support is in place to see it through.

Gather stakeholder input: AI initiatives succeed when the right voices are at the table from the start. Engage contact center managers, IT teams and customer experience leaders early in the process to align priorities and uncover their needs. Understand how decisions are made, and which criteria matter most.

Just as importantly, listen to agent feedback. Their frontline insights often reveal opportunities that data alone can’t. Businesses that succeed with AI don’t just chase features — they focus on delivering measurable, customer-centered improvements.

Turn Abstract Innovation into Tangible AI Results

All these points discussed represent critical insights that are discovered during the customer journey to AI. And they’re important aspects to align on.

The Genesys Value Canvas is a strategic framework designed to help organizations clearly define and communicate the business value of their CX solutions. The Value Canvas is part of a broader methodology to align technology offerings — like AI-powered contact centers and omnichannel platforms — with the specific goals and pain points of each customer.

This collaborative tool can help to align stakeholders around what matters most: clear outcomes, real-world impact and a path to measurable success. Rather than diving straight into technology, it starts with customer challenges and guides teams toward prioritized, strategic solutions.

First, it maps common business issues like long wait times, high handle times or low customer satisfaction. And then it identifies root causes. This framing helps pinpoint which capabilities, such as AI automation, augmentation or analytics, will be most effective in addressing those pain points.

The canvas also supports impact analysis. Teams can identify which use cases can deliver the greatest value, with guidance on ROI modeling, cost savings and efficiency gains. Whether you’re using benchmarks or building a quantified business case through collaboration, the Value Canvas turns technical potential into business justification.

Importantly, the canvas supports lifecycle planning. From discovery to go-live, it outlines key decisions, goals, and phases, encouraging long-term value realization — not just quick wins.

It also facilitates value chain thinking. By connecting capabilities to business outcomes and layering in KPIs and ROI estimates, stakeholders can see exactly how an operational initiative supports strategic goals, like revenue growth or improved loyalty.

Finally, the Value Canvas builds stakeholder alignment, enabling technical and business teams to tell a unified story. In this way, you can summarize the “What?” “Why?” and “How?” of a transformation in a format that’s clear, actionable and grounded in outcomes.

From Insights to Action: Bring AI ROI Into Focus

The Value Canvas brings clarity and alignment to what can often feel like a complex AI investment conversation. At Genesys, we use it to map customer challenges to solutions — helping leadership clearly understand where and how value will be delivered.

Because it adapts to different levels of digital maturity and readiness, the Value Canvas enables teams to tailor their value story to resonate with both technical and business stakeholders. It supports informed, confident decision-making backed by real-world benchmarks.

Think of it as a blueprint for transformation. It can help to assure that every AI investment is tied to meaningful, measurable outcomes that grow stronger as your capabilities evolve.

Get details on why companies worldwide choose the Genesys Cloud™ platform for AI and automation. And then reach out to one of our experts to see how Genesys Cloud AI fits into your business strategy.