Skip to main content

Select your location

Search

The ROI of AI

3D illustration of spheres rolling in miniature track circuit

Successfully executing AI projects is a serious challenge for many businesses; the rush to adopt new technologies can sometimes take priority over crafting a well-designed strategy. 

IDC research found that 30% of AI projects fail because of a lack of an integrated development environment from experimentation to production. 25% of projects misfire because the business case isn't understood by stakeholders.

Unlocking the transformative benefits of AI begins with embracing a strategic mindset. Leaders should step back and focus on overarching objectives instead of being swayed by AI trends or rushing to implementation. Defining what they want to achieve in a clear, consistent way can help businesses determine how AI factors into their success.

Adopting new technologies introduces additional complexities for companies, including how to measure and improve return on investment. As organizations dive deeper into the intricacies of AI processes and tools and acquire more data, they can identify high-impact use cases to help maximize ROI.

Build your business intelligence muscle

ROI on AI takes more than just technology. It takes a team of people who can confidently work with data to make informed decisions, plus a culture of experimentation and innovation. Heather Ryan, Lead Data Strategist and Shadow Board Member at Kin + Carta, says it's critical to have good data foundations to work with newer technologies, especially AI. "AI can't fix bad architecture. It can help you transform and structure your data, and therefore speed up the time needed to fix it, but it can't single-handedly solve problems that stem from bad foundations." 

Data foundations, such as having the right architecture, good documentation practices, and strong data governance can help create a data center of excellence, which she adds, "will then breed experimentation and innovation, which is where you'll see success." 

Drawing on the example of Kin + Carta's data strategy work with Toyota Europe, Ryan explains that their centralized access and understanding of data enables teams to rally around challenges and leverage data insights to drive solutions. This strong foundation allows the exploration of emerging opportunities, like GenAI, which accelerated the recent development of an AI proof-of-concept to help with vehicle selection. "Speed to innovation relies on having the right data foundations: empowering teams to make informed decisions and drive progress," she says.

Organizations have two primary options to consider when it comes to creating a data center of excellence: 

The first aims to break down information silos where possible. This centralized approach creates a single source of truth and empowers decision-makers to access valuable insights for intelligent decisions and strategic pivots. 

However, there is an alternative perspective known as data mesh that advocates a decentralized approach to data management. With data mesh, data is distributed across different domains or teams, allowing each domain to have ownership and autonomy over its data. For larger organizations looking to rethink data strategy, it's key to consider both options and lay the right data foundations for delivering ROI.

Once strategies are developed and data is collected, organizations need individuals with expertise to analyze this data and understand what needs to be done. Kin + Carta calls this finding your "moments that matter"—the critical touchpoints where a customer's loyalty can be secured. This is where you should focus your efforts and unleash your creative thinking to maximize the potential for success in both the frontstage (customer experience) and the backstage (business outcomes).

3D illustration of spheres rolling in miniature track circuit

Cultivate a culture of experimentation

In addition to having the right individuals with data expertise, it's essential to foster a culture that embraces change. This mindset allows organizations to adapt to evolving market conditions and stay ahead of the competition. It encourages innovation, experimentation, and continuous improvement. 

Cultivating a culture of learning and knowledge sharing is crucial for long-term success. Organizations should provide opportunities for employees to enhance their skills and stay up to date with the latest advancements in AI and data analytics. This can be done through training programs, hackathons, workshops, and knowledge-sharing platforms. 

However, pockets of people in your organization will already be experimenting with new AI tools and incorporating them into their day-to-day. According to Ryan, the best way to harness this curiosity is to give people a platform to share their ideas, connect them with others doing the same, and give them the remit to scale across teams. When employees are encouraged to share their success stories, it promotes collaboration and learning while inspiring others to strive for excellence. She adds, "Your teams will solve your problems, you just need to give them the tools."

Another critical aspect of fostering a culture of innovation is the establishment of a feedback loop. It's important to regularly evaluate the effectiveness of AI initiatives, measure their impact on business outcomes, and gather feedback from stakeholders. This feedback helps organizations identify areas for improvement, refine their strategies, and make necessary adjustments.

Determine your key priorities

The significance of clear goals can't be overstated. When everyone in an organization understands what they are striving to achieve, it promotes a cohesive environment where people are aligned and moving in the same direction.

It's a delicate balance between the art of the possible and the need to identify practical use cases, whether customer-facing or internal-operations oriented. The true potential of AI isn't always fully known from the outset. That's why organizations need to make sure their efforts and outcomes align with overall business strategy.

"In order to demonstrate ROI, we need to know what the business focus is," Ryan explains. "Do we want the success of a single experience, or are we unlocking a core step towards a wider vision?" 

When thinking about how to set and measure AI goals, Ryan adds that it's important to think beyond the implementation of chatbots or basic automation. "Our horizons are broader now," she emphasizes. "It can be easy to focus on the short-term gain, but a good data strategy will view AI as a tool to deliver your wider vision. It's a piece, it's not the whole puzzle."

Set clear expectations

When leveraging AI solutions, consider both short-term and long-term perspectives. Convincing skeptics and addressing concerns about job displacement often requires showcasing the immediate benefits of AI through proofs of concept. These early proofs are stepping stones to gain buy-in and support for larger, more comprehensive AI projects in the future.

AI isn't a standalone solution, but a fast track to intelligent experiences, augmenting expertise to drive innovation and efficiency. However, assembling the right skills to execute and evaluate AI-powered solutions is a big challenge for organizations. Skilled professionals who can navigate the complexities of AI deployment are critical to maximizing the potential of AI.  

It's essential to find a balance when harnessing the excitement and potential of AI. While AI is generating enthusiasm and hype, organizations need to make sure they manage expectations. Openly communicating the limitations and risks of AI technologies can prevent unrealistic expectations and disappointment.

Once AI solutions are implemented, organizations must establish continuous evaluation and improvement mechanisms. Collecting and analyzing data generated by AI systems provides valuable insights for refinement and optimization. It enables organizations to identify areas of improvement, detect patterns, and make data-driven decisions that lead to even more intelligent experiences.

Starting with high-impact areas is also crucial to long-term AI value. Conduct assessments of existing processes and identify pain points and opportunities for improvement. Prioritizing areas of maximum impact supports smarter allocation of resources, leads to greater ROI, and builds a strong foundation for future success. 

Share this article

More like this

Show me all