Beyond the Pipe: Telcos Need a New Revenue Playbook for the AI Era

For two decades, the telecom growth story has followed one arc: build the network, sell the connection, defend the ARPU. That arc is flattening. Wireless connectivity is now a mature, commoditized product in most developed markets — and capital intensity keeps climbing while the revenue line struggles to keep pace.

At the same time, AI is creating an entirely new category of economic value, spanning compute and application — measured in tokens, inference cycles, and autonomous agent interactions — growing faster than connectivity revenue has in years.

"The question every telecom leadership team should be asking is whether they'll help define the business models for this new value chain, or simply carry the traffic for someone else's margin."

This isn't a call for telcos to become AI labs. It's a case for revenue diversification built on assets telcos already own: infrastructure, identity, and the most durable customer relationship in consumer technology. Below are three near-term opportunities that hold the promises — where the money actually is, what it takes to capture it, and the hurdles that will need to be overcome to achieve the transition.

The Diversification Imperative

Revenue realization per gigabyte has been falling for years as consumption outpaces pricing power. Meanwhile, network capex – 5G, fiber, now AI-ready infrastructure – keeps rising. This creates a margin squeeze that operational efficiency alone can't fix.

📊 Key stat: Enterprise AI spending is now the single largest category of enterprise digital-transformation investment through 2030 – larger than 5G, cloud infrastructure, or IoT combined.

"For telcos, AI purely as an internal efficiency tool will capture a fraction of the value on the table. Treating AI as a new revenue architecture is where telcos can redefine their growth trajectory for the decade ahead."

---

Three opportunity areas stand out as the clearest, most actionable starting points.

1️⃣ Real-Time Journey Prompts: One Intelligence Layer, Omni-Touch Experience Optimization

This isn't a single chatbot – it's a shared intelligence layer sitting underneath three different frontline experiences at once: the digital customer self-serving on the website or app, the retail agent helping a walk-in customer, and the care agent on a live call. All three run the same underlying mechanism in real-time: continuously synthesizing the knowledge base into ready answers, monitoring the live interaction for hesitation or friction signals, and surfacing the next-best-action before the user has to ask.

📊 Key stats:

·       Real-time agent-assist AI reduces average handle time by 27% by eliminating manual knowledge-base searching mid-interaction

·       Gartner projects "Connected Rep" / expert-assist technology will lift contact-center efficiency by up to 30% by the end of 2026 — without replacing a single human agent

·       Acting on hesitation signals before abandonment generates roughly 30x the ROI of traditional post-abandonment recovery, which sees open rates below 20%

"The mechanism is identical whether it's a customer stuck entering a credit card or an agent mid-call with a frustrated customer: watch the live signal, synthesize the knowledge base into an answer, and deliver it before the moment of friction becomes the moment of abandonment."

The real frontier, though, is bigger than any one of these tools. Most "unified customer journey" conversation today is about marketing personalization – stitching together browsing data for better targeting. Almost no one is solving the harder problem: real-time resolution continuity, where a customer's struggle on the website is instantly solved, or (if the customer chooses to) visible to the retail agent or care agent they talk to next – no re-explaining, no lost context. That's a genuinely greenfield space, gated less by ambition than by two real constraints: the technical maturity to unify live interaction data across systems never built to talk to each other, and the privacy discipline to do it with clear customer consent. It's a bet that is increasingly gaining relevance with AI advancements, and governable data and privacy constraints.

✅ Takeaway: The winning move isn't better chatbots or better agent scripts – it's collapsing customer self-service, retail assist, and care assist onto one real-time intelligence layer, closing a resolution gap the industry hasn't even started calling by name yet.

---

2️⃣ The Token Economy: Monetizing Compute, Not Just Connectivity

The least obvious opportunity, and arguably the most important. Telcos have spent decades monetizing bits. AI introduces a new unit of value entirely: the token, the basic unit of computation behind every AI query or agent action. This shift is no longer theoretical.

📊 Key stats:

·       Operators in leading Asian markets have already launched commercial consumer plans priced by token volume

·       National-level daily token consumption has grown more than 1,000x in under two years in the fastest-moving markets

·       Global token consumption is forecast to grow more than 20x by the end of the decade as AI agents move from novelty to default infrastructure

"This isn't about telcos building foundation models. It's about position in the value chain – producing inference near the edge, transporting it efficiently, and packaging it into services, rather than carrying someone else's AI traffic at commodity rates."

The asset that matters: Physical network infrastructure – central offices, fiber, edge real estate – is exactly the distributed compute footprint the AI industry increasingly needs, closer to users than hyperscaler data centers.

✅ Takeaway: The pricing model and technical standards for AI-token services are being set right now, by whoever moves first. Waiting doesn't preserve optionality – it cedes the standard to someone else.

---

3️⃣ Super-App Bundling: Wireless as the Gateway, Not the Product

Asia's leading operator-led "super apps," where a high-frequency core service – connectivity – became the gateway to cross-sell commerce, content, and financial services under one roof.

📊 Key stats:

·       One major Asian operator's digital-services revenue is growing meaningfully faster than its core connectivity revenue

·       ARPU has climbed steadily as customers adopt bundles combining connectivity with entertainment, cloud storage, and AI-powered features

"Most digital products spend years and real marketing budget earning a recurring, trusted relationship with users. Telcos already have one – mandatory, monthly, multi-year, touching nearly the entire customer base. The opportunity lies in converting share of a wallet relationship a competitor would have to build from zero."

✅ Takeaway: The bundling playbook isn't unproven – it's a repeatable model that needs a rethink on the new bundle strategies and how to build partnerships to scale it as a viable business model.

---

The Hurdles That Will Separate Movers from Watchers – No credible strategy skips the hard part:

·       Legacy technical debt – billing and network-ops systems weren't built for real-time AI orchestration or token-level metering

·       Organizational silos – network, product, CX, and pricing functions must co-design one customer-facing product, not four separate ones

·       Trust and regulatory exposure – every play here leans on data and identity sitting inside tight privacy and telecom-secrecy regulation

·       Cultural resistance – reframing the core product from bandwidth to compute/intelligence requires a genuine mental-model shift at the leadership level, not a bolt-on

·       Competitive timing risk – some markets already have commercial token products and super-app ecosystems; waiting cedes the model to whoever moved first

---

The Bottom Line

The telecom industry has weathered commoditization before – long distance, SMS, data plans – and the operators that came out ahead each time found a new axis of value before the old one fully collapsed. AI is that axis now.

"The operators willing to treat infrastructure, identity, and customer relationships as a platform – not just a utility – will define the next decade of industry economics. The ones who wait for the business case to become obvious will be buying their way into a market someone else has already priced."

The strategic frameworks above are directional – the real value is in translating them into a sequenced, capital-efficient roadmap specific to a given operator's assets, regulatory footprint, and competitive position. If you're a telecom leader weighing where to place these bets first, that's a conversation worth having and you are ahead of the curve – for now.

Portcos Are Being Scored on AI Maturity. Get Ready for the Conversation.

My earlier post on AI Maturity made the case that AI maturity is now priced into exits. This one dives into what buyers are specifically evaluating, and what it actually takes to build a position that survives the scrutiny.

In 2025, 28% of global M&A activity was AI-related, 76% of buyers are already using AI in their own due diligence, and 83% expect it to improve post-merger integration. The buyers across the table from your Portcos at exit are running structured AI maturity assessments. The question is how to prepare your companies for that conversation.

What the Assessment Actually Covers

A structured AI maturity evaluation scores a company across several key dimensions: AI strategy alignment, use case deployment depth, governance and decision rights, data infrastructure quality, talent and capability, technology architecture, and ethical and regulatory compliance.

The distinction that matters most is that AI maturity is not a measure of software purchased. A company with three well-run, well-governed use cases embedded in the revenue model is well ahead of one focused solely on deploying the latest technology. Maturity measures whether AI is changing how the company works and showing up in results. Buyers know how to tell the difference. And they are pricing it accordingly.

Why Mid-Market AI Programs Stall Before They Can Be Scored

Enterprise organizations institutionalized AI discipline through Centers of Excellence — dedicated teams with cross-functional mandates, executive sponsorship, and the organizational staying power to move from pilot to production at scale. That is the north star model: a centralized, accountable function that owns prioritization, governs execution, and measures outcomes against business ambitions — not just technology adoption.

For $20M–$500M PE-backed companies, that discipline is rarely formalized. The operating partner is stretched across a portfolio, and the portco CFO is under pressure to show AI progress without a clear path to begin. The result is point-solutions adopted function by function, without a connecting logic or shared accountability — progress that doesn't compound and doesn't tell a coherent story at exit.

The answer is not to build a COE. It is to execute with COE discipline: a smart, sequenced AI roadmap with clear ownership, governed delivery, and a measurement framework calibrated to the company's EBITDA goals. That is an achievable version of the north star — and precisely the kind of structured approach that translates enterprise-scale AI experience into mid-market results.

Four check-points for a Credible AI Roadmap

For a company heading toward exit in the next two to four years, the real question is if AI investments create a narrative with and EBITDA attached. What builds that credibility can be broken into 4 buckets:

• A scored baseline. A structured AI maturity score to evaluate, benchmarked against industry peers, and can outline the before-state of the value creation story – a verifiable starting point.
• A prioritized use case portfolio. Ranked by business impact, not technical novelty, with ROI modeled at the workflow level before any build decision is made – workflow level models, not category-level estimates, survive diligence.
• A named owner with governance that holds. The use cases that reach production are the ones with a named owner, pre-defined success criteria, and accountability that outlasts the launch energy – the structural gains are easy to show and defend.
• An exit narrative connecting AI to EBITDA. This needs to be built over the hold period, as in a claim that AI drove margin improvement means something supported by two years of documented measurement. 

A Note on Where I've Seen This Work

Much of what I've described above is the framework I've been applying through kriAItiv — starting with a structured AI maturity diagnostic benchmarked against industry peers, translating that into a prioritized roadmap with EBITDA impact modeled before a dollar is spent, and staying engaged through execution until the gains are measurable and the exit narrative holds up.

The firms that get this right, start at acquisition, so there is a recorded baseline that anchors the value-creation story at exit. 

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 states 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.