Call me sentimental, but I find that there’s nothing like a classic song to reset me, to ground me.
Don’t get me wrong, there is plenty of new music in my playlist. But there’s something about a familiar tune popping up in the car on a sunny day – it’s not just nostalgia. There’s an energy that’s different when old mixes with new.
As a consultant, my work is often focused on The New – the latest technology, processes, and practices. But anything new has its roots in a predecessor, one that contributed so much that it started people thinking “what else can this do?”
AI is no different, though for some reason it feels different.
AI existed before ChatGPT, Alexa, and Siri. Companies have been using Traditional AI for years. Think of Traditional AI like The Beatles. Sure, Gen AI is the latest hit, but Traditional AI is the “Hey Jude” of the business world—timeless, reliable, and still delivering value with every play.
You don’t listen to The Beatles just because they’re familiar. You listen because they’re still relevant. And that’s exactly why Traditional AI deserves more attention.
It’s not just the fallback option; it remains a value driver for the right use cases in today’s business landscape.
Traditional AI vs. Gen AI: Complementary Forces
Before we dive into those use cases, let’s clarify the distinction: what exactly is the difference between Traditional and Generative AI?
Traditional AI covers machine learning, predictive analytics, and custom algorithms that have been foundational for industries like banking and insurance for years. Logistic regression models, for example, analyze historical data to spot fraud, making new transactions more secure. Risk assessment models help set policy premiums, using predictive analytics to evaluate a variety of data points that predict an individual’s or group’s likelihood of filing a claim. Traditional AI thrives on historical data and well-established algorithms to streamline processes and reduce cost. And it has proven itself dependable, scalable, and relatively simple to manage.
Gen AI, on the other hand, enables automation and personalization through its ability to process large amounts of data in real time and create relevant outputs – and it deserves its accolades for being truly revolutionary. Some examples include chatbots that can handle real-time conversations or platforms that create personalized content automatically. These capabilities are exciting, but also require costly infrastructure, continuous model retraining (due to model drift), and longer timeframes for realizing value.
Despite their differences, Gen AI and Traditional AI are not opposing forces.
When combined, they can deliver transformative business outcomes. Gen AI can enhance the customer experience by producing creative, personalized content and solutions, while Traditional AI quietly manages more operational tasks with tried-and-tested models. Think Lennon and McCartney—they may approach things differently, but they’re undoubtedly better together.
The Unmaximized Potential of Traditional AI
With all the hype around Gen AI, it can be easy to forget about the untapped opportunities Traditional AI presents for your business. In some cases, it offers lower-hanging fruit, driving results today with manageable effort. This can be particularly valuable when investment in Gen AI is resource prohibitive.
Traditional AI may not steal the spotlight, but it also has proven to be transformative, both intrinsically and as an enabler to Gen AI integration:
- Payments: Automated fraud detection based on predefined rules and historical data can offer immediate benefits, while building a foundation of patterns that enables Gen AI to adapt to emerging fraud trends.
- Banking: Predictive models can be used to optimize customer turnover predictions, with Gen AI enhancing customer engagement by generating tailored messaging based on real-time data.
- Insurance: Claims automation like rule-based algorithms combines with Gen AI natural language processing (NLP) to extract and analyze information from complex documents such as medical records, improving accuracy and speeding up approvals. Together they streamline the claims process—Traditional AI handles efficiency, Gen AI adds a layer of intelligence to interpret unstructured data
- Risk and Underwriting: Traditional AI historically streamlined risk assessments and underwriting processes both in insurance and bank lending. These applications remain valuable as companies transition toward Gen AI for dynamic policy generation and offer optimization that provides real-time, personalized recommendations based on customer behavior.
The Strategic Play
With the breakneck pace of AI innovation and so much value at stake, it’s tempting to charge headlong into incorporating AI into your business. But as with any technology, truly capturing AI’s value depends on a pragmatic approach – and a healthy dose of patience.
- Define Capability Needs. AI should be applied to areas where, once enabled, it transforms the way business is done. The right AI approach starts by solving actual business problems, not chasing hype. As with all technology strategy, your AI decisions should be grounded in strategic capabilities, organizational resources and readiness, and ROI considerations. Gen AI is not the silver bullet for every challenge. By aligning AI adoption with strategic business capabilities, you’ll be able to maximize both short-term value realization and long-term transformation.
- Get your Data Right. The success of any AI application relies on thoughtful investment in the fundamentals of data capability. A data strategy that builds quality and hygiene into a core data lifecycle and is supported by infrastructures and processes – including data governance – make its use sustainable. Building that foundation isn’t always easy, but I’ve seen my clients’ opportunities for AI multiply as they make progress.
- Tend to Existing AI Applications. Traditional AI continues to be a disruptive technology that can transform business (in a good way). Those capabilities will continue to mature and become more effective, so investing in their care, feeding, and improvement is critical.
- Integrate Thoughtfully. Gen AI capabilities are rapidly evolving, so be patient. Focus on leveraging proven capabilities (e.g., text generation and summation, virtual assistants, personalization) and avoid being the hammer looking for a nail. In some cases, Gen AI applications will replace machine learning and Traditional AI. In others, combining Traditional AI with the cutting-edge capabilities of Gen AI will further enhance business transformation and set you up for lasting success.
Traditional AI remains an essential driver of business value and transformation. While Gen AI may dominate the headlines—and rightly so—a balanced approach that leverages both technologies will yield the most sustainable long-term results.
Traditional AI isn’t just a relic from the past—so don’t just Let It Be.
Further Advisory helps companies strategize, plan, and deliver their most complex data challenges. From governance to analytics to A.I, we make strategy become a reality.
