Overcoming AI's "Day 2" problem: How managed services protect AI ROI

It’s time to talk about the elephant in the room: AI value realization.  

While success stories abound, disappointment is still all too common. According to Boston Consulting Group, 74% of organizations struggle to generate tangible value from their AI investments. By the end of 2025, an additional 30% of AI projects will be abandoned after the proof-of-concept stage.  

Beyond the statistics, we’ve all seen the infamous failures – chatbots giving away cars for a dollar, suggesting glue as a pizza sauce, and so on. In industries with low risk tolerance or tight budgets, even the fear of these outcomes can delay or derail AI adoption.  

So, where does this AI value realization problem come from? 

These sorts of outcomes reveal a fundamental misunderstanding: AI is not a static solution you can implement and expect to perform with the same level of success in perpetuity. Rather, it relies on ongoing management, tuning, and governance to achieve long-term success. For organizations that may not have the in-house capabilities to tackle each of these objectives, managed services is a critical life-line.  

But first, let’s take a look at two ways set-and-forget AI solutions can lead to major ROI problems.  

Reason #1: Data and concept drift 

AI models don’t just maintain their performance; they either improve through active monitoring and tuning, or they degrade over time. Many organizations understand this in theory but lack the resources or expertise to monitor AI effectively. As a result, data drifts, models lose accuracy, and performance suffers. This is often referred to as the “Day 2” problem of AI – the day after implementation, when the real work of maintenance and optimization begins.  

Reason #2: Initial outcomes are not the end state 

AI adoption can come with a significant initial cost in the form of new technologies, training, and implementation time. However, once these initial models and use cases are in place, scaling to adjacent use cases can dramatically improve ROI.  

For example, a bank might first set up an AI chatbot to help users navigate complexities within its app. With the initial infrastructure built out, this chatbot can then quickly expand to handle account setup, payment inquiries, and more. Each new use case increases overall AI containment and drives incremental ROI, making the initial investment pay off exponentially.  

How managed services bridges the gap between AI investment and AI ROI 

Managed services provide two key pillars for managing and maximizing AI: 

The technical side: AI observability (protecting your initial investment) 

This involves tracking consumer behavior and model performance over time. It means maintaining confidence thresholds tailored to your industry and use cases, and constantly monitoring knowledge base content updates or changes in user behavior to keep the solution relevant. AI observability is the continuous maintenance that prevents model degradation and protects your initial investment.  

The strategic side: Use case and approach evolution (growing your investment) 

Beyond observability, managed services teams can analyze customer and agent behavior to identify new, relevant opportunities to advance your overall customer experience (CX) strategy. They integrate emerging models and tools to ensure your AI stays ahead of the curve, constantly expanding its capabilities and value.  

Focus only on the technical, and your AI risks stagnation. Focus only on the strategic, and your AI will scale on shaky ground.  

AI managed services in action: A food delivery company 

To illustrate this point, let’s look at one TTEC Digital client example that shows how these two pillars of AI managed services combine to serve as the engine for ROI acceleration.  

A food delivery company had a goal of deflecting delivery driver calls away from its contact center. After the initial self-service chatbot implementation, it achieved impressive deflection rates across a few limited query types. Many organizations might have stopped there with modest ROI.  

However, this would have quickly led to declining returns. Why? Because policies change over time, which can create similar, but conflicting, information for the bot to pull answers from. Rather than watch the models powering these chatbots start to drift, TTEC Digital established a set of monitoring solutions that checked for information overlap and flagged it for closer review.  

Additionally, through a regular review of the conversation data, the managed services team identified even more self-service topics that could expand call deflection even further — delivering incremental ROI in the process. This proactive approach led to a 49% transfer rate reduction, equating to $3 million in operational cost savings year over year.  

The AI “Day 2” problem is real, and it’s why so many organizations fail to realize the full potential of their investments. By combining continuous AI observability with strategic evolution, managed services protect your investment from degradation, while also actively seeking new opportunities to grow. This proactive approach ensures your AI not only works today but continues to deliver significant value as the AI landscape continues to evolve at a lightning-quick pace.