CX Has a Framing Problem – Not a Fixing Problem.

 

Every few years, something new promises to “fix” CX. First it was CRM, then omnichannel, then design thinking, and now AI. Each wave brings real progress, but also a familiar disappointment. Despite better tools and more data, many experiences still feel fragmented, impersonal, and oddly exhausting for customers.

When that happens, the instinct is to fix harder. More technology, more programs, more dashboards. But at some point, it becomes clear the problem isn’t effort. It’s how CX is being framed.

Where CX usually starts

CX is almost always framed as an internal problem. We begin with what we can control: systems, processes, org structures, KPIs, skills. That makes sense. This is where authority and budgets live, and over time these internal capabilities come to define the CX strategy itself.

Customers, however, don’t experience internal capability. They experience effort, emotion, and expectation. That disconnect between how CX is designed and how it is lived is where CX quietly breaks.

What the research has been telling us for years

What’s striking is how consistent the research has been on this point, even if practice hasn’t kept pace. Work across service design, behavioral science, and CX measurement repeatedly shows that satisfaction is driven less by objective process quality and more by perception, emotion, and expectation.

For example, research published in FIR Journal describes experience as a combination of cognitive evaluation, affective response, sensory input, and behavioral engagement. Psychological theories of satisfaction reinforce this by showing how expectations, and whether they are met or violated, shape how experiences are remembered.

Customers don’t evaluate journeys step by step. They compress experiences into memories. Peaks matter more than averages. Friction weighs heavier than elegance. One confusing or effortful moment can outweigh several efficient ones. This isn’t a soft insight. It’s a structural one.

Why similar companies get very different outcomes

This helps explain why two organizations with similar technology stacks, similar journeys, and similar talent can perform very differently on CSAT, loyalty, and retention. One has designed from the outside in, anchored in customer psychology. The other has optimized from the inside out, anchored in internal logic.

The practical insight is straightforward. Experiences need to be designed to reduce cognitive friction, meet emotional expectations, and reinforce trust. This is where true customer-centricity begins. Not with empathy statements, but with an understanding of how customers think, decide, and remember.

What changes when psychology comes first

Organizations that start with customer psychology ask different questions. They try to understand where trust is built, where confidence erodes, where effort becomes unacceptable, and where expectations are forming long before an interaction begins.

Once those dynamics are clear, internal capabilities gain focus. Technology choices shift from feature breadth to perceived effort reduction. Journey mapping moves from documentation to accountability. Leadership conversations move away from reviewing CX scores and toward making deliberate trade-offs between experience, cost, and speed.

The work is still internal, but the framing is finally customer-centric.

AI as a framing test, not a solution

Technology, especially AI, is a good example of how framing determines value. There is growing evidence that AI can materially improve CX through personalization, prediction, and responsiveness. At the same time, research shows that customer acceptance of AI-driven interactions depends far more on trust, perceived usefulness, and emotional tone than on accuracy or speed alone.

Many organizations discover that AI amplifies intent. When intent is unclear, AI scales confusion. When intent is grounded in customer behavior and psychology, AI becomes a bridge between internal complexity and external experience.

Used well, AI reveals patterns that were previously invisible. Friction across channels. Unexpected effort spikes. Experience breakdowns that correlate directly with churn, repeat contacts, or cost to serve. That visibility is where CX starts moving from anecdote to economics.

From journey maps to management tools

The same principle applies to journey mapping. Research consistently shows that mapping alone does not improve experience. What improves experience is what organizations do with what the journey reveals, especially when it exposes uncomfortable truths about ownership, incentives, and handoffs.

When journeys are viewed through a psychological lens, where customers hesitate, repeat themselves, or feel uncertainty, they stop being diagrams and start becoming management instruments. But that only happens when leadership is willing to act.

Why CX efforts lose momentum

CX initiatives rarely fail because teams lack skill or intent. They fail because leadership attention is episodic. Without sustained sponsorship, CX becomes a reporting layer rather than a decision lens.

Studies of high-performing CX organizations show a clear pattern. CX earns durability when experience metrics are explicitly linked to business outcomes such as revenue growth, retention, and efficiency. When they are not, CX remains vulnerable to the next strategic priority shift.

CX as a value creation discipline

This is also why strong CX organizations look the way they do. The most effective teams blend analytical rigor, service design, behavioral insight, and operational realism. They understand that experience sits at the intersection of systems and psychology, and that optimizing one without the other leads to diminishing returns.

With recent advances in data and AI, CX is no longer just about experience quality. Framed correctly, it becomes a way to identify the few experience moments that disproportionately influence customer decisions to buy, stay, expand, or leave. When those moments are clear, AI-enabled insight can help prioritize use cases, quantify impact, and guide execution in ways that directly affect EBITDA.

A strategy-led solution lens

The opportunity for most organizations is not to deploy more AI in CX, but to use AI as a diagnostic lens grounded in customer psychology and monitored in near real time. The starting point is not new surveys, but continuous signals that already exist across the business: sentiment analysis of customer comments and conversations as they happen, live contact center transcripts, digital behavior patterns, repeat-contact velocity, escalation frequency, abandonment and hesitation points, and repeated clarification requests. These leading indicators surface rising effort, uncertainty, or loss of trust while the experience is still unfolding, not weeks later in survey results.

When this real-time psychological insight is overlaid onto established CX metrics such as NPS, CSAT, CES, and journey KPIs, it changes how those metrics are used. Survey scores are largely lagging indicators. Their value is in confirming impact, not discovering risk. The real advantage comes from using leading signals to intervene early, before sentiment hardens and behavior changes.

AI then helps quantify which experience moments, when monitored and managed in real time, have a measurable effect on retention, repeat purchase, and cost to serve. This is how CX shifts from scorekeeping to situational control, and from reporting to decision-making.

The reframing CX needs now

Seen this way, most CX challenges don’t need fixing. They need reframing. From customer psychology as the starting point to internal capability as the opportunity. From optimizing what is visible to influencing what is decisive. That shift is when CX stops being a perpetual initiative and starts becoming a strategic advantage.

Agentic AI – Why ROI breaks at the workflow level.

 

Most leadership teams are no longer debating whether to invest in agentic AI – the real question is why returns remain so uneven.

A recent McKinsey study (Sept ’25), drawing on 50+ real agentic AI builds, captures many of the now-familiar challenges – difficulty attributing value, unclear user pain points, fragile agent–human collaboration, and poor performance in low-variance, highly standardized workflows. From my own experience building and scaling AI systems, three lessons (from the study) stand out that I think need a deeper look-in from a strategic and operating standpoint.

1.  “Workflows (not agents) are the main source of value”

Most failed or underwhelming agentic implementations I’ve seen share the same pattern: agents are layered onto the tail end of broken workflows in the hope of quick wins. The result is surface-level optimization that doesn’t address deeper issues around data quality, usability, model training, or deployment discipline.

The true advantage comes from treating workflows as economic units – segmenting them across service delivery, product experiences, and process controls creates clarity on implementation and measurement. More importantly, it allows agentic AI investments to be tied directly to the performance KPIs and, ultimately, EBITDA – which is increasingly the lens through which C-suite is prioritizing AI spend, and rightly so.

2.  “Mapping processes and defining user pain points are critical to designing effective agentic systems”

Strong models don’t compensate for weak process design. Once workflows are clear, the differentiator is how well organizations map user pain points and instrument real touchpoints. Users need seamless experiences and painless troubleshooting, whenever needed.

Strategic innovations in the agentic systems that combine real-time inputs, journey-level measurement, and dynamic decisioning stand to make the most impact for the end users. This is where customer experience (CX) stops being a “soft” concept, and being the most direct indicator of customer success, translating into economic value and as a planning currency across industries – another C-suite lens for measuring AI program success.

3.  “Humans remain essential for oversight, compliance, and judgement”

Agentic AI systems introduce real complexity: data dependencies, security exposure, regulatory risk, and privacy concerns sit right at the core of service delivery. Bias, drift, and edge-case failures are still very much part of today’s reality, and human judgment remains essential for oversight, tagging, and de-risking. The cost of getting this wrong isn’t theoretical – it shows up quickly as customer harm, compliance exposure, or brand damage. Maintaining the right skills and capacity alongside agentic systems isn’t optional; it’s foundational to any sustainable AI roadmap.

As agentic AI moves from experimentation to scale, it becomes an operating model decision, and not a technology problem. Exactly why experienced AI practitioners need to be engaged with strategy and planning leaders – helping them think through where agents make sense, where humans must stay in the loop, and how both come together to create competitive differentiation.

For most leadership teams, the real opportunity lies in aligning strategy, workflows and human judgment - experiential lessons that deliver the value available with AI investments.

AI’s EBITDA Promise – And, why most companies fail to realize it.

A recent Bain & Company study (Sep ’25) cited 20–25% EBITDA gains from AI adoption, with agentic AI visions starting to take shape. While promising, these numbers largely reflect outcomes from tech-forward companies that have an advantage in mature foundational (data) and processing (talent) capabilities.

For most non-tech-forward enterprises, the challenge lies in translating the belief into reality – defining the right AI strategy, setting realistic ROI expectations, aligning investments to business outcomes, and procuring services that align with their overarching goals. In my view, this is where it becomes imperative to work with AI practitioners, who can collaboratively help “translate” the AI promise into an organization’s specific context. These experienced practitioners help outline AI roadmap for strategic advantage, define the right KPIs for ROI measurement, prioritize a step approach to a pragmatic AI investments philosophy, and can be a great "interlocuters" between the technology providers and the functional teams.

The same Bain study also highlights familiar barriers in data silos, IP and security risks, vendor lock-in, and missing standards. I would argue the reality is often worse – many organizations struggle with a fragmented understanding of the benefits AI can truly deliver (and how to measure it), difficulty prioritizing use cases with credible ROI, limited budget flexibility for experimentation, and executive hesitation to place big bets.

This is where AI practitioners can act as “human in the loop,” who helps “shape decisions” like; defining target use cases, outlining budget-talent requirements, creating flexible architectural views, and avoiding common pitfalls, while navigating fragmented data, modeling efficacy, application relevance, and the absence of industry-defining standards.

AI transformation isn’t a technology problem – it’s a business translation problem.

Program Management in AI Transformations: What We Are Really Managing.

 As I looked for a topic to close out 2025, I found myself returning to program management, particularly in the context of AI and digital transformation programs, where its importance is often recognized only after momentum starts to slip.

Few initiatives expose weaknesses in program leadership as quickly as AI. These programs cut across data, technology, operations, and business teams. Expectations are high, timelines are compressed, and assumptions often go untested. Yet the fundamentals of program management in these efforts remain underfunded and undervalued.

What I have repeatedly observed is that AI programs rarely stall because of algorithms or models – they stall because of misalignment around expectations, focus, and timing. Here are some more insights.

>  Managing expectations: where most AI programs quietly go off track

Expectation management in AI initiatives begins well before the first model is built. Many programs start with ambitious goals, but imprecise definitions of success.

A request for an AI feasibility assessment or pilot often carries unspoken assumptions – production-ready solutions, measurable ROI, or a clear path to enterprise scale. In reality, those outcomes depend heavily on data readiness, integration complexity, governance, and change adoption.

Data, in particular, is frequently overestimated. Teams often discover late that critical data is fragmented, inconsistently governed, or simply not usable in its current form. What was positioned as a quick pilot, struggles because foundational assumptions were never surfaced early.

What you are really managing is alignment – between AI ambition, data reality, scope, and achievable timelines.

>  Managing focus: discipline matters more than sophistication

AI teams are naturally inclined to explore. Models can be refined, features extended, and architectures improved almost indefinitely. Without discipline, this curiosity can quietly undermine outcomes.

I have seen AI pilots expand into sophisticated technical builds while losing sight of the original business question they were meant to answer. The result is often an impressive solution in search of a decision.

In transformation programs, focus matters more than technical elegance. Clear, decision-oriented outputs, impact estimates, risks, and operational implications create momentum. Deeper technical work can and should follow once value is established.

What you are really managing is focus – ensuring effort remains anchored to both business and technical possibilities.

>  Managing timelines: protecting sponsor credibility

AI programs are especially sensitive to delays. Dependencies on data access, privacy approvals, business inputs, and validation cycles introduce uncertainty that must be actively managed.

Delays become issues when their impact is not communicated clearly and early. Sponsors are then left explaining slow progress without the context or options needed to manage expectations internally.

In AI programs, transparency around timing does more than manage delivery – it protects credibility, both for the team and for the leaders championing the initiative.

What you are really managing is trust – and the sponsor’s ability to lead the transformation with confidence.

A closing reflection

AI initiatives, more often, lose momentum quietly, through misaligned expectations, drifting focus, and unspoken delays. The programs that succeed are designed to surface reality early, force disciplined choices, and protect trust as complexity increases.

That, more than any model or platform, determines whether a program delivers value.

The Evolving Landscape of Global Technology Delivery

 

In the last 20 years, most organizations have shifted their technology engagements with the global vendors (suppliers) – going from a “touchless” outsourced delivery to a “partnering” model and a shared ownership of the business success.

We have come a long way from back-office operations and call centers to active participation in executing growth and modernization strategies. Companies have adopted the outsourcing models at scale, while the vendors in-turn have evolved from resource augmentation to creating a pool of highly experienced industry experts, who partner with client organizations in building the platforms for shared success.

The benefits and the profits at the client organizations have soared, and so have the expectations to extract even more value from the suppliers. Internally, pressed for efficiencies, ROIs and savings targets, companies are looking for even greater stakes from the vendors, where the latter should not only partner, but also own the end-to-end program execution. In addition, vendors are expected to be geographically dispersed to provide that round-the-clock coverage and cost optimization. 

The vendors, however, have to keep pace with these increasingly complex expectations and their success will require strategic investment in two areas:

• Develop geographically disperse locations and some redundancies, with the resource pool in your top 5-10 core support functions. Client needs often pop up, without the runway to stand one up, and being able to stand up a team at a short notice is often the key to building that long-term relationship.

• Develop a talent pipeline that regularly feeds the need for the resources. This is highly relevant from a client perspective, when market fluctuations create short-term gaps and program delivery is jeopardized or delayed.

• For a more meaningful relationship, be ready to partner in client’s success on both operational efficiencies and customer-facing KPIs (revenue, CX, CSAT, etc.). Have client engagement managers and directors to be more conversant with the client and the industry, to provide fresh outside perspectives to problems that may seem like headcount issue, when it really is about a different way of doing solutioning.

With these three imperatives, I believe, vendors can greatly enhance the client-partner relationship, and build a more sustainable and mutually beneficial business model of shared success – consider product development, AI/ML applications, business process automation, and other back-office functions in the list. 

For vendors, do it now and showcase it. From a client’s perspective, we are looking for this proactive approach and a foundation to internally promote the partnership and commit to a long-term investment.

3 E's of Effective Leadership

Been in a few leadership seminars, and the oft-repeated terms reminded me of the article from a few years back, so decided to share it again. (originally published in March 2016).

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The greatest responsibility of a leader is to make their employees successful. This is, more often than not, the one trait that does not garner enough attention from those measuring leadership success. It is about how well the employees are flourishing under one's leadership!

Use the 3 E's to answer this question; perhaps a better measure than (or in addition to) the usual promotions, team size, numbers, etc.

• Empower your employees (go do it!) – set clear goals and expectations, but don’t stymie free thinking. Establish the culture where managers can think freely and are “empowered” to make decisions that move the project forward.

• Enable your employees (provide the required resources!) – get down from the “I know all” pedestal and learn about the daily struggles of a manager in balancing deliverables and getting  projects prioritized for delivery. Then make sure to provide the required resources and/or remove the organizational hurdles to “enable” employee success.

• Engage your employees (give a pat on the back more often!). Employees are more engaged when they are recognized. But, do it on a regular basis, not just once a year. Even the small achievements should be called out at weekly reviews or other team gatherings. It will go a long way in keeping them motivated and “engaged” towards the company’s success.

Just like the other assets within the company, employees also need nurturing, albeit with a human touch!

3 C’s of Consumer Centricity

Back into a few age old discussions on customer centricity and wanted to remember the foundations that formed the basis of organizational changes I was fortunate to have been a part of. (originally published in September 2014)

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Consumer-centric organizations are known to do all they can to engage with their customers and proactively cater to their needs. Often these will include product/service updates, social communications, systems to support consumer-centric strategies, etc. 

However, it is easy to feel burdened with multi-directional pulls on your consumer-centric strategy as tactical urgencies evolve in any business cycle. Keeping a framework of key consumer touch points is imperative in such situations to stay focused and continue to deliver on the promise of better than excellent engagement. I categorize these core consumer touch points into 3 areas – the 3 C’s of Consumer Centricity.

1. Content – what does a consumer see about your brand, products, services? Are you developing content that clearly communicates and reinforces the value of your products and services? Does this content remind the consumer how valuable their relationship is to the brand and vice-versa? Whether a consumer is using your products or not, we need to evolve our content strategy such that it caters to consumer need for information, before and after they make a purchase decision, and at the time of purchase, you will typically get the nod.
2. Choice – Do we offer choice in our products and services? Is our product strategy in-tune with the evolving market dynamics, namely, technological changes, consumer usage behaviors, market expectations, etc.? After we have successfully attracted consumer attention, we need to ensure we are ready to live up to the promise – great product and service. As and when consumers mature and place their trust in your brand, you need to be able to offer a portfolio that goes beyond the core product(s) and allows the customer to strengthen the bond.
3. Community – Speaking of bonding with brand, what better way than to draw customers into your community. Do you have a community that not only caters to customer queries, but also, provides valuable information to enhance your products/services? Social revolution has brought about a radical shift in organizations’ communications tactics, as to how frequently and what do consumers need to know. We need to proactively build this into our marketing strategy and invest in right resources to maintain it and keep it current. 

As we all know, consumer life-cycle starts well before they become our customers, and continues well beyond their first purchase. The 3 C’s provide a marketer’s view into this life-cycle and by creating the planning tools within this framework may just help simplify analysis, refine your marketing/communications strategies, maximize ROIs and contribute to your maturing as a consumer-centric organization.