AI Maturity Is Now a Valuation Variable. Most PE Portfolios Aren’t Ready.

Companies at the highest "AI maturity" levels traded at a median revenue multiple of 31x vs., 13x for those at the lowest (according to McKinsey’s 2026 study on 471 PE-backed companies across 31 industries). The report also accentuates that AI maturity has to extend beyond productivity (level 1) into revenue maximization (level 3 and above) to truly make an impact – wider embrace of AI results in more than twice the median revenue multiple when using AI only for productivity. The study has wide implications for the mid-market companies and PE investors that are shifting focus to AI as a valuation criterion, both for investments and exits.

Productivity AI Does Not Move the Multiple

The most common AI applications in PE-backed companies today — automating invoices, summarizing reports, drafting communications — are level one and level two activities. They reduce cost. They do not move the multiple, and per McKinsey, the valuation premium only becomes material at level three, where AI is embedded in the product or service itself. To take advantage of AI, companies need to think Revenue AI - transformations for growth, not just for efficiency.

The question I ask every Portco leadership is simple: which workflows in your business touch revenue directly, and what would it take to put AI there? In telecom, AI-driven churn prediction models are identifying at-risk subscribers and triggering personalized retention offers, resulting in 10% - 25% improvements across churn and lifetime value. In retail, Zara's use of AI embedded into production and distribution decisions, using real-time sales and social trend signals, resulted in faster inventory turn-rate and lower markdowns than competition. Both are using AI embedded in the revenue cycle, directly improving the margins.

Why Mid-Market Companies Are Especially Exposed

Large organizations have been building the foundations – clean data, insights & use cases, cross-functional impacts, governance and orchestration mechanisms – for such scaled AI applications in the revenue cycle. Most mid-market companies have none of that. Fragmented systems, inconsistent data, manual workarounds are just some of the foundational problems that mid-markets face in AI adoption and execution.

Three success points, common across industries, can help reverse the trend:

• Insights on existing data used to scope use cases based on business outcomes.
• ROI modeled at the workflow (use case) level.
• Named point of accountability to orchestrate roadmaps, governance, delivery and measurement.

I have implemented these at enterprise scale – targeting top customer segments for personalized offers, preferred call routing, digital self-service that saves a support call, are just some of the targeted use cases that drive revenue and engagement KPIs for any customer-focused business. By applying these success points to the top performing core areas of their business, mid-markets can exploit the AI value at level 3 implementation (margin driver per McK study).

The Hold Period Is Not Forgiving

The implied capital cycle for buyouts is now approximately seven years. Assessing AI maturity early at acquisition and adopting “success points” can reduce that capital cycle and provide the runway to accumulate the benefits of implementing an AI program – scored baseline, prioritized use cases, governance structure, and a documented track record a buyer can verify. The firms that start this work pre-acquisition/year-1 are better positioned to drive margin improvements and compress the hold period, without sacrificing the investment goals.

The single most expensive mistake I see is treating AI maturity as an amalgamation of AI productivity tools adopted during the hold period. For example, building a real-time customer journey support engine (for digital, retail and support channels) mandates diligent prioritization, model training, content targeting and governance framework. Revenue margin use cases require development and consistent proof-points over the hold period.

An AI maturity baseline taken at acquisition – scored against industry peers, with the highest-impact use cases prioritized and resourced from day one – is what defines the roadmap for the operating partner to build and scale inherent value through the hold period.

What the Market Is Actually Paying For

BCG's AI adoption research puts the performance gap plainly: companies in the top quartile of AI maturity are 2.3 times more likely to hit their value creation targets than lower-maturity peers. And only 11% of PE investors explicitly link AI progress to exit narratives – that is real value left on the table.

The narrative gap is the most solvable, yet most overlooked. In my experience, I have seen that data exists – it just needs to be organized, developed into use cases, and executed at the workflow level in order to realize the upside. I have helped structure this value-chain through: an AI maturity score at acquisition, workflow-level ROI, and an exit narrative that connects AI activity to verifiable EBITDA lines.

The data is clear on what the market is pricing. The question for every PE operating partner is whether AI maturity has been baselined at acquisition – and if the remaining hold period can accommodate the required AI transformations.

Beyond the Prompt: Why GenAI’s Limitations Are the Secret Roadmap to Agentic CX

The buzzword of 2026 is Agentic AI, but here is the cold truth: An agent is only as good as the Generative AI reasoning engine behind it. If you haven't solved the fundamental limitations of GenAI, you aren't building an "autonomous agent." Here are some points to close this gap. To lead in CX today, we must start addressing the structural gaps that separate a "chat" from a "resolution."

Here are a few that stand out:

1.       The Context & Continuity Gap – GenAI often suffers from "goldfish memory," losing the thread in multi-turn or multi-channel conversations. True personalization isn't just using a customer's name; it's remembering their journey.

o   The CX Reality: Customers hate repeating themselves. If your AI can't remember a detail from three messages ago, or from an email sent yesterday, it is a friction point that is affecting the lagging metrics (CSAT, revenue, costs).

2.       The "Hallucination" Liability – GenAI is designed to be "plausible," not necessarily "factual." It can confidently invent a refund policy that doesn't exist. Ground your agents in RAG (Retrieval-Augmented Generation) and "Human-in-the-loop" (HITL) guardrails to ensure the AI speaks only from your verified knowledge base.

o   The CX Reality: Trust is the only currency in CX. One confident lie from a bot can destroy years of brand equity.

3.       The Empathy Paradox – AI can mimic empathy, but it cannot feel it. In high-stakes moments, like a cancelled flight or a billing error, generic "I understand your frustration" scripts often backfire. Use AI to solve the problem fast, but keep a "Human Escalation" path for when the customer needs a heartbeat, not an algorithm.

o   The CX Reality: AI should handle the transaction, so humans can handle the emotion.

4.       The Real-Time Intelligence vs. Integration Wall – Most GenAI lacks real-time access to your back-end systems (CRM, Inventory, Billing). Without deep integration, your AI is just a fancy FAQ page.

o   The CX Reality: A bot that can explain your return policy is "Generative." A bot that can actually process the return is "Agentic."

5.       The Brand Voice & Bias Risk – Without fine-tuning, AI defaults to the "average" of its training data, often sounding robotic or, worse, reflecting hidden biases. If it doesn't reflect your unique tone, it is diluting your market position.

o   The CX Reality: Your AI is your brand, and the agents can only perform tasks that support that voice.

Key Conclusions for the CX Leader:

• Move from "Content" to "Action": The goal is to trigger the right response, as well as, the resolution.
• Data is the Guardrail: Solve your data silos before you scale your agents.
• Privacy is a Feature: In 2026, secure AI and compliance (GDPR, CCPA) build loyal customers, and is the backbone of a competitive advantage.
• The Hybrid Future: The most successful CX models will be AI-orchestrated, (not AI-only), where agents handle the routine and surface the complex to empowered humans.

The Bottom Line: Don't get distracted by the "Agentic" hype. Focus on fixing the GenAI foundations – context, factuality, integration and HITL. That is where the real ROI lives.

Digital and AI Transformations: Same Friction. New Frontier.

For two decades, Digital Transformation was the primary engine of enterprise evolution. We moved from digitizing data and processes, to improving ecommerce and marketing, and finally to mastering cloud, social, mobile, and early AI. Each wave forced enterprise change: reconfigured workflows, faster information flow, and new growth vectors for both incumbents and disruptors.

Today, AI Transformation has taken center stage, demanding a radical rethinking of core strategies across process, products, and customer engagement. The speed of innovation is relentless, the need for change is imminent, and while the outcomes can be visibly impactful – so are the failures. Business models are in flux. Applications are complex. Infrastructure demands are non-trivial. ROI is often ambiguous. Meanwhile, a growing ecosystem of AI vendors produces point solutions that perform in isolation but fail under real organizational constraints.

Many organizations are still digesting digital transformation, and now face an AI mandate layered on top.

Having led digital transformation for a mid-market and AI transformation for a global enterprise, I observed that while the scale differs, the obstacles are strikingly consistent. Smaller environments enable tighter alignment and clearer accountability. Larger ones offer resources and reach. Yet the nature of execution risk remains the same – success is found, less in technology, and more in navigating human and structural barriers.

The Shared Friction Points – regardless of the "Digital" or "AI" label, the friction remains constant:

• Executive Alignment: Securing genuine buy-in from the cross-functional leadership
• Process Innovation: Redesigning end-to-end workflows
• Fiscal Competition: Managing budgets against entrenched priorities
• Measurement: Defining clear KPIs and defensible ROI
• The Talent Gap: Upskilling the existing workforce while integrating new expertise
• Integration: Melding new tech into legacy architectural realities

Strategic Takeaways for the AI Era – adopting a practitioner’s lens to drive a sustainable AI transformation:

1.    Anchor the Vision in "Quick Wins" A long-term roadmap is essential, but decompose it into short-cycle wins. This builds the organizational "muscle" and executive confidence needed for larger capital outlays. Executives fund momentum, not intent.

2.    Prioritize Workflow over Technology Focus on a few high-priority use cases. A practitioner perspective ensures these are rooted in execution reality. Solve a specific problem, measure the attribution, and use that success as your internal marketing engine.

3.    Build the "Hard" Business Case The most common points of failure are poorly defined KPIs and a vague ROI. Define your financial burdens and benefit realizations upfront. Business cases built on clear KPI definitions are the easiest to prioritize and the hardest to cut.

4.    Address the Talent Deficit Early AI transformations require specific talent. Identify where you need external partners versus internal upskilling immediately. Include these costs in your ROI analysis to avoid "sticker shock" midway through execution. Talent lag is the most common, and avoidable failure mode.

5.    Design for Architecture, Not Just Features Upfront clarity on data, models, integration, governance and platform requirements will prevent downstream friction – understanding the "plumbing" is vital. Whether you use off-the-shelf models or custom solutions, ensure they fit into a scalable downstream vision.

The Bottom Line

A holistic approach builds the foundation, but "bite-sized" execution builds organizational muscle and ensures sustainability. In an era of overwhelming complexity, the right strategy is to win small, win fast, and scale what works.

VOC in the AI Era: From Customer Feedback to Strategic Advantage.

Voice of Customer (VOC) is often treated as reporting. 

For real strategic advantage, VOC should be a key input into strategy, operating decisions, and AI-enabled CX programs. 

This is even more relevant in the AI era, where computing and embedding customer feedback is essential for competitive differentiation.

Too often, VOC lives in decks filled with survey scores that confirm what we already suspect. Useful, yes – but insufficient if the ambition is true customer-centricity. To create meaningful differentiation, VOC must be embedded into how organizations prioritize investments, design experiences, and deploy data and AI at scale.

A proposed three-legged VOC model - designed to capture what customers say, how they feel and how they engage - can help build the foundations for creating strategic advantage.

1. Quantitative – What customers tell you
Traditional surveys still matter. They help track CSAT trends and identify strengths and gaps, provided the questions are designed for meaningful measurement. The real value emerges when structured data is combined with open-ended feedback and used as a directional signal, not the final answer.

2. Qualitative – What customers feel
Behavioral signals from service interactions, social channels, and digital journeys reveal emotional highs and lows, friction points, and unmet needs. When analyzed in the right product and journey context, and merged with quantitative feedback, these insights become far more actionable. This is where VOC starts informing experience redesign, personalization, and AI use cases.

3. Physical – What customers appreciate
Personal touches like, loyalty rewards, milestone recognition, gifts, birthday cards, etc., create memory and emotional connection. But these shouldn’t be random acts of generosity. When anchored in a robust LTV strategy and targeted to the right cohorts, physical engagement becomes a strategic lever for loyalty, advocacy, and long-term value creation.

The real shift happens when Quantitative, Qualitative, and Physical VOC are integrated into a unified data ecosystem. This enables AI-driven insights, improves “quality of experience,” and most importantly, aligns teams around what truly matters – delivering experiences customers genuinely value.

For leadership teams in an AI-driven enterprise, the real advantage comes when VOC actively shapes decisions, the AI agenda, and the growth strategy - not just a fancy insert in the customer feedback scorecards. 

AI for CX is About Insights, Not Just Automation.

In the previous post (Customer Experience has a framing problem…), I argued that most organizations optimize what they can control internally, while customers judge experiences through psychology, effort, and expectation. If that framing is correct, then AI should be employed as a way to connect internal capability with external behavior, and not just as an automation layer.

Once CX is understood as an outside-in, psychology-driven discipline, the next question I usually hear is: “So where does AI actually help?” It is a fair question, especially when AI pilots come and go without much to show for them. Instead of AI in CX as a technology upgrade, employing AI to “assess the right CX decisions” remains an under-utilized opportunity – it may hold the key to unlocking the true value. The following 4 foundational pillars may help unlock the magic of CX optimization. 

Analyzing cross-channel friction points 

Most CX organizations are drowning in data but starving for clarity. They have surveys, transcripts, logs, journey maps, dashboards. What they often lack is a way to connect experience signals to customer behavior and business outcomes in a reliable, repeatable way. This is where AI can be employed as an analytical and decision-making engine. It can surface patterns in friction across channels, shed light on moments that drive disproportionate churn or repeat contact, and formulate customer effort into cost-to-serve.

Understanding customer intent & sentiment 

The most effective use cases are anchored in customer psychology first. Further analysis of data can be used to compute intent and sentiment to answer customer question such as: Where do customers hesitate or abandon? Where do expectations break down most often? Which moments loom large for customers? AI can then help quantify these moments, connecting CX friction to lost revenue, preventable churn, or increased service cost.

Personalizing the effort before the offer

Many organizations use AI to personalize offers or messages. Fewer use it to personalize effort. Yet research consistently shows that reducing perceived effort has a stronger impact on satisfaction and loyalty than increasing choice or novelty. AI can help by predicting intent (as stated earlier), routing customers more intelligently, or preempting issues before customers feel the need to ask – delivering the operational efficiency rooted in customer need.

Dynamically managing journey orchestration

Journey orchestration is a key concept in employing AI-led analysis and change – identifying where journeys diverge, loop, or stall in real time. Organizations can focus on the few journey points, based on operational and financial ROIs, that can influence customer outcomes. AI is used to distinguish between what’s visible CX and what’s decisive CX.

Reframing AI to assess CX decisions offers real strategic advantage, by leveraging multi-source data to narrow the focus to a handful of use cases (with specific economic outcomes) that materially influence customer behavior. Further, by connecting customer psychology, AI-driven insight, and business strategy, it raises the bar for CX teams and AI practitioners to deliver against the stated enterprise transformation promises.

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.