GenAI and ROI: Building the Business Case

The rising buzz around artificial intelligence and its capabilities continues to make headlines. GenAI's potential uses, from executing tasks with simple text prompts to creating entire presentations from a report, are becoming integral in various industries. However, before integrating AI or any innovative technology into their operations, organizations need to look beyond the novelty and understand its practical implications and their outcomes. The implementation of AI should be seen not just as a technology upgrade but as a strategic initiative that involves people, processes, and technology to unlock value and generate new revenue streams within an organization.

For successful AI implementation, organizations need to identify expected benefits, how to measure them, and which use cases offer the most value. By targeting areas where AI can boost performance and growth, companies can develop strong business cases with realistic ROI estimates.

Evaluating the expected ROI of any project is crucial, especially for AI initiatives. Identifying high-potential AI use cases helps direct efforts towards more rewarding solutions. Successful outcomes can then persuade key stakeholders and leadership to invest further in other AI projects.

Benchmarking data by AI4SP shows 3.5 to 10 times ROI in companies using generative AI, primarily in small and midsize businesses and among individuals at large enterprises. When examining AI's ROI, it's crucial to look beyond revenue and consider the equally significant indirect benefits. Some of these include:

• Increased time savings: AI-based solutions can cut the time required to complete a repetitive or complex task, leading to savings that could be significant enough to positively affect team productivity, customer response time, and average cycle length.

• Improved resource allocation: By automating tasks or enhancing employee efficiency through AI solutions, organizations can focus their human resources on higher-priority areas.
• Enhanced customer experience: AI-powered chatbots and tools can provide fast, reliable answers to customer inquiries without human intervention. In turn, this allows customer-service workers to focus on more complex issues they may not have had time to process otherwise.
• Advanced decision-making: AI analytics tools can quickly parse large volumes of data, delivering valuable insights about consumers, operations, and markets. This empowers businesses to make critical decisions based on relevant information, helping to increase revenue streams and respond to their operating environment.

Identify Necessary Investments

After identifying the right use cases for an AI solution, the next step is considering the investment required. While it might seem straightforward to quantify this by estimating development time, there are additional factors to consider.

Labor Costs

While one of AI’s benefits is its ability to enhance efficiency, it still takes knowledgeable, skilled professionals to reach that point. This is especially important during the building and setup phase of a new AI tool. Organizations will need to consider the cost of time and resource allocation that teams will require to get a new tool up and running, but it doesn’t stop there.

After being developed, AI models must undergo regular review, maintenance, and support to prevent a decline in accuracy over time. Additionally, human oversight is crucial to ensure that these AI systems are free from biases and do not develop biases as they evolve.

Process Reengineering/Automation Costs

When aiming to enhance an existing process with AI, organizations must consider initial expenses. This may involve:

• Reviewing current processes to spot improvement opportunities
• Defining the optimized process and how AI will assist
• Using robotic process automation software

Software Costs

Although certain cloud environments provide AI tools to support new project development, companies might still require additional software, including:

• A preferred development environment for languages like Python or R for creating custom models
• A suite of data analytics or visualization tools, particularly those that aid in feature recognition or include prebuilt functions for identifying basic statistical relationships between features
• Tools for development documentation and operational tasks

Hardware Costs

When the development team is engaged in on-premises model development, the organization might have to account for the cost of extra hardware required for project-specific training purposes. Depending on the type of software and the project's demands, companies may additionally need to purchase more advanced systems capable of effectively handling essential computational tasks or graphical renderings.

Cloud-Based Operational Costs

Companies might choose a cloud-based AI solution, as many cloud services come with built-in AI models and tools that facilitate development and integration. Operational costs will differ based on a company's experience and previous use of these tools. For instance, if the organization isn't yet in the cloud, it must consider the cost of data migration. If it's using only Microsoft 365, setting up an Azure tenancy is necessary. An existing Azure tenancy without a recent security assessment will also require budgeting for that evaluation. Additional general operational expenses cover maintenance, upgrades, and regular security assessments.

Integration Costs

Organizations aiming to incorporate new AI capabilities into their current enterprise resource planning (ERP) systems might face extra expenses. This often hinges on whether the ERP provider can or is willing to support AI features in their platform, or if the company has to manage the integration on its own.

Change Management Costs

The success of any new solution, AI included, hinges on user acceptance. Preparing those who will work with the AI solution can foster its adoption. Investing in change management helps ensure a smooth transition and builds early proficiency among users.

“Failing Fast” Costs

Adopting a "fail-fast" strategy is seen as an efficient lean development method to help reduce sunk costs. This step-by-step approach helps organizations spot problems early and make corrections before mistakes become expensive.

Although adopting a fail-fast strategy can prevent significant sunk costs in the future, it also incurs expenses related to time and resources spent on each development iteration. Companies can mitigate unexpected budget increases by dedicating a specific allocation for fail-fast initiatives, help to ensure that expenses remain manageable.

Quantifying Benefits

During the investment phase, many steps have easily calculated costs, but quantifying AI’s benefits can be complex. For instance, the value of time savings hinges on how the saved time is utilized. An employee using generative AI for note taking and report writing saves time, allowing them to tackle smaller projects or more complex tasks requiring experience. The organization must decide which is more valuable.

Time savings affect operational costs when the time AI saves is measurable. A manual task taking 15 minutes might only take seconds with AI; calculating this cost difference makes time savings clear.

However, AI doesn't eliminate the need for employees. Freed from manual tasks, they can focus on roles AI isn't suited for, such as handling complex customer service issues or nuanced creative projects requiring human oversight.

Enhancing Quality

AI and humans work well together, boosting productivity in organizations. Combining AI's efficiency with human insight leads to quicker quality checks, dependable production, and less material waste.

In areas like cybersecurity, AI helps understaffed teams by scanning data for threats, allowing security professionals to focus on solving and preventing issues. Cloud-based AI tools also offer regular updates, helping organizations stay resilient against new cyber threats.

Talent Acquisition and Retention

Just like employers, employees recognize AI’s potential. Companies that adopt these tools can attract and retain top talent by reducing repetitive tasks and allowing workers to focus on more stimulating tasks, creating an appealing work environment for skilled professionals.

Informed Decision-Making

AI's key advantage is its capacity to quickly sift through massive data and present it to decision-makers. This allows leaders to act on up-to-date, relevant information, positioning their organizations for success.

Conclusion

While the ROI of AI projects may not be immediately clear, the growing number of use cases and benefits strengthens the argument for its implementation. As AI progresses and integrates into daily operations, companies have numerous opportunities to invest in tools that tackle difficult areas and enhance productivity for the business, employees, and stakeholders. 

BDO Can Help

BDO understands that the key to realizing tangible ROI from AI initiatives lies in aligning them with your corporate strategy. We assist you in developing an AI strategy rooted in your organization’s goals, identifying the challenges and metrics that matter most—whether it's improving margins, reducing costs, expanding into new markets, or driving innovation. By crafting a strategic roadmap tailored to these objectives, BDO helps you demonstrate that AI initiatives are not merely experimental projects but integral to achieving your corporate targets.

No matter where you are in your AI journey, BDO can help you along the way.

¹ Gartner Press Release, Gartner Predicts 30% of Generative AI Projects Will Be Abandoned After Proof of Concept By End of 2025, July 29, 2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.