Breathing New Life into Legacy Businesses with AI

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Introduction: 

I was hired and trained as a data scientist for IBM fresh out of university. Data science and data analytics were the hot topic at the time, as businesses were ready to become more data-driven organizations. I was so excited to unlock new customer insights and inform business strategy, until my first project. After 8 weeks of cleansing data and crunching numbers for 12 hours a day, it was glaringly obvious that I was entirely too extroverted for a pure data science role. I quickly moved into implementation consulting, where I was fortunate to see firsthand how businesses evaluate, implement, and adopt different types of process automation technology as the technology itself continued to evolve.That evolution led me to realize the other data and AI’s capabilities, primarily focusing on what they could do to the operations of businesses labeled legacy – not only for efficiency, but also for improving user service. These companies tend to be branded or perceived as slower to adapt, but they’re full of indisputable value waiting for the right ‘nudge.’

AI is providing that nudge because today, AI does more than automating boring work; it is changing how businesses perceive value. A century-old bank, a global manufacturer, and a regional insurer, these are just a few examples of businesses that are evolving their core AI technologies, improving their internal systems while retaining their rich history. 

This didn’t suddenly happen though, but there were many steps involved, each more groundbreaking than the last. So to truly narrate the current state AI had to evolve in, we need to wind the clocks back to a time when data wasn’t an inevitability but a luxury.

The First Wave: Data as a Project

Back in the infancy of data science within companies, their treatment of data resembled a whiteboard and marker experiment. Businesses seemed lost on what to do with data, and therefore assumed it required a project-like treatment with a start and end, or a PowerPoint presentation in mid — something that showcases interim findings. Along the way, gathering “let’s get a data scientist to look at this” comments became a norm embracing a carefree approach of one-single-to-multi-business domain change. 

While working for IBM, I was fortunate to witness this phenomenon in real time. Shifts were just beginning where organizations were changing from relying on gut feelings to data informed strategies but everything still felt labored. One of my clients from IBM still sits fresh in my head because they took the tin can approach a little too literally, printing out .txt files like word documents containing customer interactions and using scissors to post these documents in a conference room where they would visually calculate key metrics, calculators and highlighters in hand. Data science in its untapped, unrefined glory was radical.

The purpose wasn’t to create sustainable systems. Instead, it was to respond to prompts such as “What’s our churn rate?” or “Was this campaign successful?” These questions, while important in their own right, were evasive at best. Each project felt like a fleeting victory without much future potential. There was no reusable framework, no collaboration across teams, and definitely no foresight for what data could evolve into.

However, this preliminary wave had significance as it allowed companies to recognize the boundaries of instinct-driven decision-making and the usefulness of evidence. Although the work was done in stages, it rarely resulted in foundational changes, and even when insights did materialize, they were not capable of driving widespread change.

The Second Wave: Building a Foundation for Ongoing Innovation

Gradually, a new understanding seemed to surface,  one that moved data from being a tactical resource to a strategic asset. In this second wave, companies sought answers to more advanced inquiries. How do we use data to enable proactive decision-making rather than only responsive actions? How can we incorporate insights into the operational fabric of our company? 

This phase experienced shifts from companies such as Publicis Groupe. Rather than bringing on freelance data scientists on a contractual basis, they transformed their approaches by building internal ecosystems of expertise composed of multidisciplinary teams and fostering a spirit of innovation. Thus, the focus shifted from immediate problem-solving to laying the foundational systems for comprehensive future infrastructure. 

Moreover, data started to shift from the back-office functions to the forefront. Marketing, sales, product, and customer service functions received access to real-time dashboards, AI tools, predictive analytics, and a host of other utilities. Therefore the democratization of data accelerated to bring the power of AI data insights to the decision makers who worked directly with customers and crafted user experiences.

What also became clear during this phase was that not all parts of the organization required the same level of AI maturity at the same time. Some teams were set for complete automation; others just required clean reporting which was perfectly fine. The goal was not standard adoption; rather, it was movement. The most advanced thinking companies understood that the pace of change didn’t have to happen everywhere all at once, it just needed a starting point and careful cultivation.

This was the turning point when data began evolving from a department to a capability; it could now single-handedly drive continuous enhancements instead of relying on project-based wins. That is when the flywheel of innovation had commenced spinning.

The Current Wave: Reimagining Processes with AI

Today, we are experiencing a third and possibly the most impactful wave of change. AI is no longer limited to enhancing analytics and operational efficiency; it now rethinks the very framework of how businesses are structured and run. What was previously regarded as an expenditure is now considered a divisive competitive advantage.  

Consider what PolyAI and Simplyhealth have done. Simplyhealth, a UK health insurer, partnered with PolyAI to implement voice AI within their customer service channels. However, this integration went beyond implementing basic chatbots. The AI was ‘empathetic AI’ since it could understand urgency, recognize vulnerable callers, and make judgment calls on whether patients should be passed to a human auxiliary.  

Everyone saw the difference. There was less waiting around, better call resolution, and most crucially, those that required care from a member of staff received it. Nonetheless, AI did not take the person out of the process; it elevated the person into the process, allowing them to experience empathy and enable humanity to work alongside effectiveness.

Such a focus on building technology around humans is rapidly becoming a signature of AI change in today’s world. You see it with retail AI, which customizes every touchpoint in the customer experience. It’s happening in manufacturing with costs associated with breakdowns being avoided through predictive maintenance. And in financial services, it’s experiencing massive shifts as AI technologies offer personalized financial consulting, fraud detection, and assistance to those missing traditional support.  

In all these examples, AI technologies support rather than replace people. Customer service representatives are equipped with richer context, which augments their responses, freelancers are liberated from doing repetitive work, and strategists get help concentrating on the right resources. Therefore, today’s best AI use cases focus on augmenting human experience instead of reducing the workforce.

Conclusion: 

Too often is the phrase “legacy business” misused to describe something as old-fashioned or boring. But in fact, these are businesses with long-standing customer relationships and histories, enabling them to evolve in meaningful ways.  

Modern AI solutions don’t simply replace manual labor as the advancement from spreadsheets and instinct-based decisions to fully integrated AI systems is more complex. Businesses progressively adopt modern practices all while having a vision and patience in terms of cultural branding. Plus, legacy businesses are contemporarily evolving and keeping up with the pace, and many are leading the race.  

AI today is changing everything and has now become a culture driving system. It impacts the very way we collaborate, deliver services, value customers, and so much more. Whether implementing new business strategies, redefining customer support, or optimizing computer science logistics, AI is proving to be a propellant for transformation focused on humans.  

Further, visionaries and team members who witnessed this automated evolution firsthand felt unity through action, fervently participating as data table-aligned pilots meshed with algorithms and numbers. Reminding us that change isn’t all technical; it’s human. It’s intricate, fulfilling, and simply put: essential.

To sum up, the future businesses are not the newest; rather, they are the oldest that choose to develop with a strong sense of intention behind it. In that development, legacy is not a hindrance, but rather, a powerful resource.