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Customer Experience

Delivering Real Customer Experience Impact with the Power of AI

Christopher Barcelona
Over the last few years, organizations of all stripes and across all categories have run countless AI proof of concept initiatives.

In fact, 91% of mid-market firms are using or planning to use AI, but only 25% of organizations have truly integrated AI across their operations. In simple terms, the AI market is exploding, but there’s a massive disconnect between investment & experimentation vs. realizing tangible value. 

Many of these companies are stuck in “pilot purgatory” vs. effectively scaling their AI initiatives into meaningful enterprise applications. Why are these pilots failing, and what can companies do to reverse the trend? 

A Look at Why Many AI-for-CX Efforts Stall

When organizations decide to “do something with AI,” they often start with a capabilities-first mindset: large language models, chatbots, copilots, or recommendation engines. The question becomes “Where can we use this?” rather than “What do our customers and what does our business need? What are our key challenges and how might AI improve our ability to solve those problems?” 

That tech-first approach leads to a few common pitfalls: 

  • Pilots that never leave the lab because they’re not tied to a critical journey or business impact metric. 
  • Solutions that automate internal tasks but don’t meaningfully change customer or employee experiences. 
  • Good ideas that hit a wall when data quality, processes, or roles aren’t ready for scale. 

A more reliable pattern starts with customer experience and considers how AI can be a strategic enabler. That means clarifying the journeys and business challenges that matter most, the outcomes you’re targeting, and the specific pain points where AI could make a measurable difference both for your customers and for your business. 

The “Problems-First” Lens

A problems-first approach begs the asking of a different set of questions and then uses the answers to shape where, how, and whether AI shows up in the solution. Instead of starting with the capabilities of AI and searching for a use case, you start with the realities, the needs, and the problems that are facing your customers and your business. 

A problems-first lens asks: 

  • Which moments in our customers’ journeys matter most to their satisfaction and to our financials? Where do they decide to stay, leave, buy more, or disengage? 
  • Where are we consistently falling short—speed, accuracy, personalization, transparency, support, ease of use, risk mitigation? 
  • In those moments, should AI play the role of informing (better insight and decision support), augmenting (assist the people serving customers), or automating (take over repetitive, rules-based tasks)? 

Once you have those answers, you can get more specific. For a given journey, you can identify: 

  • The points where customers or employees are confused or forced to repeat themselves and ask whether AI could surface clearer information or guidance in context. 
  • Steps that are currently manual, slow, or error-prone for your teams, and ask whether AI could draft responses, summarize history, or recommend options without removing human judgment. 
  • High volume, low variation tasks that add little value when done by people and ask whether automating them would free capacity for more nuanced, relationship building work. 
  • Previously hidden or hard to see patterns or trends that allow you to get out ahead of recurring challenges and modify or adjust solutions to improve overall outcomes. 

Seen this way, AI is not a generic “layer” you smear across every channel. It becomes a set of targeted interventions in specific moments—shortening a wait, clarifying a choice, anticipating a need, or quietly fixing a process behind the scenes. 

Four High‑value AI Use Cases that Change the Experience

There’s no shortage of possible AI use cases. The ones that consistently deliver customer experience impact tend to cluster around a few themes—and create both engagement and efficiency gains. 

1. Personalization at Scale 

Here, AI is used to tailor products, content, and interactions across channels—web, mobile, contact center, and in person. 

  • On the customer side, that looks like more relevant recommendations, clearer decision support, and fewer irrelevant offers. Over time, this can drive satisfaction, product depth, and lifetime value. 
  • On the operations side, it can increase the effectiveness of existing campaigns and channels without linearly increasing spend. 

When personalization is layered on top of already improved journeys, the effects compound. 

2. Predictive Support and Proactive Service 

Rather than waiting for customers to report problems, predictive models can spot patterns that signal risk or need—billing inconsistencies, likely service failures, process anomalies—and trigger proactive outreach or self-service options. 

  • For customers, that means fewer unpleasant surprises, faster resolution, and a sense that the organization is paying attention. 
  • For teams, it can reduce routine inbound volume and free people up for higher value, more complex interactions. 

AI that feels helpful and not intrusive is crucial for both organizational adoption and customer satisfaction. 

3. Journey Orchestration and Next-Best-Action 

AI-powered orchestration focuses on what happens across touch-points rather than within a single channel. Models recommend the next best step, message, or channel based on behavior and context. 

  • Done well, this reduces dead ends and unnecessary friction in journeys like onboarding, renewals, or problem resolution. 
  • It also produces clearer insight into which interventions actually move customers forward, improving the ROI of marketing and service investments. 

This kind of orchestration only works when underlying journeys are deliberately designed; otherwise, the application of AI runs the risk of accelerating a fragmented experience.  

4. Operational AI that Customers Still Feel 

Some of the most impactful AI never shows up overtly for the customer. Automation, process optimization, and predictive analytics applied to internal workflows can remove bottlenecks that customers experience as delays, errors, or inconsistency. 

  • Customers notice shorter wait times, fewer mistakes, more consistent experiences, and more reliable promises. 
  • Organizations see productivity gains and cost reductions that can be reinvested in further improvements. 

The strongest programs connect these internal benefits back to specific customer journeys, so the link between “better operations” and “better experience” stays visible. 

Readiness: The Prerequisite

Many AI efforts falter not because the ideas are bad, but because the organization isn’t ready to support them at scale. In fact, only 13% of companies are fully prepared to implement their AI strategies. Three areas show up repeatedly: 

Taking the time to assess and address these layers—through efforts like a Data & AI Strategy Assessment—often determines whether a promising use case becomes a durable capability. 

Moving from Ideas to Impact

Across the AI-focused engagements we’ve undertaken for our clients, the organizations that see durable CX impact from the application of AI typically follow a specific path: 

  1. Clarify target journeys and outcomes. Identify and align upon key challenges and where improvements would matter most to customers and to the business. 
  2. Generate and prioritize use cases. Map AI opportunities against those use cases, then prioritize based on customer impact, business value, feasibility, and time-to-value. 
  3. Run focused, 90 day proof-of-concept initiatives. Select one or two high-impact use cases, implement proof-of-concept or pilot AI-powered solutions, and measure results. 
  4. Scale what works. Extend successful patterns to other segments or lines of business and keep investing in the readiness elements that support them. 

If you’re exploring where AI can have the most impact on your customer experience, get in touch. We can help you identify and implement the use cases that move the CX metrics that matter most to your organization.