I was walking to the office the other day when I bumped into Tam, our local ‘lollipop’ man (aka a crossing guard for those readers based in N. America), and he reminded me of the classic Aesop’s fable about the race between the tortoise and the hare.
Now, Tam has recently gone through some health challenges, and he was telling me that his doctor had informed him he was probably facing a recovery process that might take about two years.
Tam expressed some frustration at this, and the doctor reminded him that these things take time and he needed to focus on being more tortoise than hare.
Wise words.
I’ve been thinking about Aesop’s tortoise and the hare fable for a while now, particularly in reference to how organizations adopt and utilize new AI technology and how research from the likes of IBM, Accenture and MIT shows that they struggle to scale their pilots and deliver significant ROI.
As a reminder, in the classic Aesop’s fable, the hare is the undisputed favorite. He possesses the raw speed, the agility, and the flair to win. But his undoing is his overconfidence and his tendency to sprint without a long-term strategy.
Over the last couple of years, I believe we have witnessed a digital reenactment of this race, where boards have demanded immediate deployments of AI to prove innovation, and large enterprises have responded by scrambling to integrate AI into their customer service ecosystems.
However, in the complex world of enterprise customer service, speed can be a liability.
As a result, I believe, that the organizations that will actually win the AI race are the “tortoises”, those who prioritize disciplined, phased implementation over the frantic sprint of the hare.
AI Implementation Requires More Tortoise, Less Hare
Now, while this might feel like a useful analogy that could go some way to explaining some of the things happening in the enterprise space with respect to AI and customer service and experience, does it actually stack up in practice?
According to Katie Costanzo, President of CX at CSG, the answer seems to be a fairly resounding ‘yes’.
Constanzo told me that they are seeing this type of behavior play out “very clearly” and the cost of trying to be the hare is showing up in three very real ways:
Firstly, they are seeing many organizations rush into AI pilots to prove speed or innovation without a clear use case or outcome in mind, and that is resulting in wasted investment.
Secondly, when AI is deployed to deflect rather than help, then customers feel it, and this is eroding trust, both internally and externally, in the technology, and
Thirdly, many organizations that are taking more of a “hare” approach are optimizing for speed and novelty, without fixing what is genuinely broken in the experience. As a result, they are missing the real problem. If the journey is fragmented, automating it will just scale the fragmentation.
Moreover, while Costanzo concedes that the hare approach, for many enterprises, may be tempting and feel like the right competitive response, she adds that it often involves launching a high-profile, customer-facing AI application as quickly as possible to capture headlines or appease stakeholders.
In expounding on this, she said:
“The irony is that the ‘fast’ approach often slows organizations down. They spend the next 12 to 18 months undoing, re-platforming, or explaining AI decisions instead of compounding value.”
The organizations that are successful and are delivering significant improvements to customer outcomes, as well as return on investment, are those making deliberate choices.
Constanzo adds that what distinguishes winners from losers is discipline:
“They don’t start with AI for AI’s sake. Instead, they start with a specific customer problem they know is repeatable, measurable, and fixable.
“They gather data from multiple (20-30+ sources) to clearly define and quantify the problem. They’re clear on the outcome they want, like fewer repeat contacts, faster resolution, lower cost-to-serve; and they work backwards from there.”
For example, CSG helped one of the largest cable operators in North America reduce contact center activity by 18% by using agentic AI to provide customers with a personalized, clear explanation of their bill before a bill change occurs.
This not only resulted in a more transparent and easy customer experience but also delivered $2.5M in annual savings.
Clarity Over Speed
Reflecting on all of this, it seems that the problem at the heart of this issue is not just about the speed of approach, but also about mindset.
So, to help enterprises run a different race, Costanzo recommends that leaders and enterprises “stop thinking about AI as a race to be won and start treating it as a capability to be earned.
“That means slowing down just enough to be clear about what problem you’re solving and why.
“If you can’t clearly connect an AI use case to a customer outcome and a business metric, it’s probably not ready. Again, automating a broken or fragmented experience won’t fix it; it will likely magnify the problem.”
She goes on to say that when enterprises measure customer success – whether that’s fewer repeat contacts, lower churn risk, reduced cost-to-serve, and/or higher lifetime value – it is because “AI is grounded in those outcomes.”
In these instances, “business ROI becomes clear, and the momentum can be sustained – tortoise-style.”
While the hype surrounding AI can sometimes make it feel like the finish line is just around the corner, in reality, for a large enterprise, the ‘race’ never truly ends.
Customer service is built on a foundation of trust, and trust is earned through consistency, the very trait that allowed the tortoise to overtake the hare.
By taking the time to focus on and better understand the problem they are solving and why, while simultaneously developing their own capabilities, organizations will be able to build a secure, accurate, and agent-assisted AI strategy today that will ensure they do not have to rerun their race when a rushed deployment fails.
