As companies navigate implementations of new AI use cases, and the adoption of emerging technologies like generative AI (GenAI), defining accurate metrics for their success (or failure) can be complex. A financial metric like Return on investment (ROI), which quantifies the return relative to the cost, may not be best suited in these scenarios given the large initial investments.
There are other measures, such as time savings, productivity gains, and customer and employee experience improvements that can; however, provide early data points to determine if an AI project is on track. Already, increases in productivity, gains in innovation and upticks in employee engagement have materialized, setting the table for leadership and investors that a sustainable financial return will follow.
“In the creative domain, generative AI has produced new design ideas in minutes that previously would’ve taken many hours, shortening the time it takes to bring an idea to market,” said Kash Rangan, a senior equity research analyst at Goldman Sachs. And in code development, AI has automated low-level code writing, which has freed up developers to focus on more complex tasks.
At TELUS Digital, we are seeing firsthand in customer experience (CX) delivery how our AI and GenAI solutions are supporting human agents in numerous ways. This includes reducing Average Handle Time (AHT), which measures the average duration taken to resolve a customer call, consisting of talk time and any follow-up actions, and successfully resolving certain customer support inquiries without human intervention in order for them to be able to focus on more complex work.
The key to having an earlier indication of the impacts and progress of your AI investments before financial returns are apparent is by developing metrics that answer questions such as: How can AI support your employees and make them more effective and happier in their roles? What value can it bring to customer satisfaction? What efficiencies can you expect to see after implementation? And how does a responsible approach to AI design, implementation, deployment and governance, create value?
Setting the benchmark
Clear benchmarks play a critical role in maximizing GenAI investment success. Here’s what to consider:
Time-savings and cost-efficiencies: Time savings and cost savings can be important benchmarks for gauging GenAI’s success. According to BCG’s 2024 AI at Work survey, 64% of business leaders say they are starting to use GenAI to reshape their organizations and over half (58%) of the employees that use GenAI say they are saving at least five hours a week with the tool — time they’re using to perform more tasks or new tasks altogether, resulting in an uptick in productivity.
For TELUS Digital and our clients, we’ve seen it on a granular level through Fuel iX™, our proprietary enterprise-grade GenAI engine that we leverage to help brands deploy customized AI solutions. As one example, a GenAI-powered agent copilot developed for a client has significantly enhanced the ongoing training and support for frontline team members to respond to customer queries quickly and easily by combing through large knowledge bases and summarizing responses with source links. Notably, this has reduced new-hire training by five days.
Productivity gains: Productivity is another key metric and one of the promises of GenAI. According to a mid-2024 report by Goldman Sachs, GenAI is expected to automate 25% of all work tasks and raise US productivity by 9%.
For many enterprises, IT support within their business is an area ripe for AI adoption. By deploying AI-powered chatbots to handle common tech issues and queries, enterprises can provide faster resolutions as agents do not have to wait in a call queue to have their questions addressed, enabling them to get back to work fast while simultaneously improving the IT help desk experience.
We designed a Single Point of Contact (SPOC) copilot for a client that combines knowledge base data with GenAI. The self-serve tool launched in February 2024 and to date has resulted in a 35% reduction in calls to human agents, representing roughly 25% of SPOC’s operating budget.
Customer experience improvements: Another indicator of ROI to consider is customer satisfaction. By expediting, streamlining and personalizing support, GenAI can vastly improve the customer journey. For example, AI-powered search and machine learning recommendation engines can provide agents with a customer’s account history and best next-actions, which is the most effective or appropriate steps to take in a given situation – to address questions and complaints more effectively or provide relevant product recommendations. What’s more, new GenAI-powered support channels can be added to your customer journey, such as asynchronous messaging and voice-enabled apps and chatbots that further enhance customer satisfaction.
Environmental and social impacts: Measuring environmental and social impacts has become essential in today’s landscape, where brand loyalty is increasingly tied to responsible practices. AI models that produce biased outcomes can result in reputational damage, eroding trust and costing the company substantial revenue losses.
Investors, too, are prioritizing companies with strong governance frameworks that ensure AI systems are responsibly developed, deployed, and monitored for adverse impacts. Such frameworks not only support responsible practices but also demonstrate a commitment to transparency and accountability—key factors that can increase investor confidence and open up new funding opportunities.
While each of these metrics addresses unique business goals, collectively they provide a fulsome view of how well AI initiatives are supporting an organization’s strategic priorities. By measuring these diverse areas, companies can gain a clear sense of whether their AI investments are not only driving immediate operational improvements but are also contributing to long-term, sustainable success. This comprehensive perspective enables leaders to gauge the full value of AI in aligning with company goals, meeting stakeholder expectations, and preparing for future growth.
Making future-focused investments
With GenAI continuing to evolve quickly and as new entrants are flooding the space, it can be challenging for businesses to navigate. Notably, enterprises must ensure they don’t lock in to one particular vendor in order to remain agile and access the right AI at the right time.
Enterprises should seek out an experienced third-party vendor that offers:
Flexibility: The right solution is going to maximize flexibility without the need to lock in to a specific vendor. Enterprises should look for a partner that allows them to easily switch between the various providers as business needs evolve.
Trust: Find a solution that has the right privacy, safety, security, governance framework and data sovereignty features to operate in both shared and private environments. Privacy and data stewardship should be embedded into the design.
Control: Look for a solution that will allow for a centralized view and management of all GenAI applications and tools to ensure effective orchestration, no duplication of efforts, and governance and compliance frameworks are being followed and updated unilaterally across the enterprise.
Taking a thoughtful approach
Taking a thoughtful, strategic approach to measuring success in AI investments is crucial to unlocking their full potential. By establishing near- and mid-term objectives and metrics before expecting long-term financial returns, companies can create a roadmap for success that builds momentum while keeping future gains in sight. This approach also enables leaders to make informed adjustments along the way if initial results fall short of expectations.
Clear communication with stakeholders about realistic timelines for returns—whether that’s three, five or even 10 years—is essential for setting expectations and fostering patience. As Goldman Sachs shared, these early investments in AI are foundational not only for individual projects but also for the innovation that will fuel future initiatives.