LogiSense Corporation blog

Why Monetization Is Becoming the Contact Center Bottleneck

Ryan Susanna — Mon, 30 Mar 2026 12:25:36 GMT

The modern Contact Center is undergoing one of the most significant transformations in its history. Artificial intelligence is no longer a future concept for CCaaS providers. It is actively reshaping how customer interactions are handled, how agents work, and how value is delivered at scale.

AI-powered self-service, real-time voice analytics, agent assist, sentiment detection, automated workflows, and proactive engagement are quickly becoming standard expectations rather than differentiators. Enterprises are investing aggressively to modernize customer experience, reduce operational friction, and meet rising customer expectations.

Yet beneath this rapid innovation lies a growing and often overlooked issue.

While CCaaS platforms are delivering more intelligence, more automation, and more measurable value, many are still monetizing their services as if nothing fundamental has changed.

The Monetization Gap in the Modern Contact Center

The 2026 January Market Study: Emerging Contact Center Technology shows that Contact Center and CCaaS leaders now expect measurable returns from technology investments within months, not years. AI initiatives are no longer pilots. They are operational, revenue-impacting, and deeply embedded into day-to-day service delivery.

Despite this shift, many CCaaS pricing models remain anchored in:

Flat-rate subscriptions
Per-seat licensing
Static feature bundles

These approaches were designed for predictable usage patterns. AI-driven Contact Center environments are inherently variable.

AI introduces fluctuation across:

Call duration and call volume
Voice processing intensity
Real-time analytics consumption
Automated resolution rates
Agent assist usage
Multilingual and cross-region interactions

When pricing and billing remain static while usage becomes dynamic, value creation and revenue capture begin to drift apart.

Why AI Changes the Economics of CCaaS

AI does more than improve efficiency or service quality. It changes how Contact Center services are consumed.

In a modern CCaaS environment:

Customers often begin with AI-driven self-service
Voice AI handles intent detection and routing
Transcription and sentiment analysis run continuously
Agent assist tools surface guidance in real time
Post-call automation generates summaries and insights

Each of these steps generates usage events. Each event carries cost, compute, and business value.

Most billing systems, however, were never designed to:

Track granular AI-driven usage in real time
Rate events dynamically across services
Apply contract-specific pricing logic
Support hybrid pricing models combining subscriptions, usage, and outcomes

As a result, CCaaS providers frequently absorb AI-related costs or bundle advanced capabilities into pricing models that fail to reflect actual consumption.

The Risk for CCaaS Providers

This disconnect creates tangible business risk.

Margin Pressure –

AI services scale with usage, not seats. Without usage-aware monetization, operating costs rise faster than revenue.

Commercial Inflexibility

Enterprise customers increasingly expect pricing aligned to consumption or outcomes. Rigid billing systems make it difficult to support these models without heavy customization.

Slower Product Innovation

When monetization systems cannot support new pricing structures, product teams delay or limit launches. Innovation slows not because of technology constraints, but because revenue systems cannot keep up.

Over time, billing becomes the limiting factor in CCaaS growth.

Why Traditional Billing Systems Fall Short

Legacy billing platforms were built for a different era of the Contact Center.

They assume:

Predictable call volumes
Static service definitions
Limited event types
Simple subscription or seat-based pricing

AI-driven CCaaS environments demand:

High-volume event ingestion
Real-time rating and mediation
Contract-specific pricing rules
Support for tiered, usage-based, and outcome-driven models
Transparent, auditable billing for enterprise customers

Without these capabilities, CCaaS providers face growing operational complexity and increasing exposure to revenue leakage.

When Value Creation Is Not Enough

Delivering better customer experiences is no longer the challenge. Capturing the value of those experiences is.

As CCaaS platforms introduce AI-driven capabilities, the gap between what is delivered and what is monetized widens unless pricing and billing evolve alongside the product.

This misalignment creates friction across the organization:

Product teams struggle to commercialize innovation
Finance teams lose visibility into margins
Customers question pricing transparency
IT teams absorb complexity through custom workarounds

Over time, this friction undermines scalability.

What CCaaS Leaders Should Be Asking

As AI becomes foundational to the Contact Center, CCaaS leaders must reassess their monetization readiness.

Key questions include:

Can we track and rate AI-driven usage at scale?
Can we support customer-specific pricing without creating operational debt?
Can we launch new AI services without rebuilding billing logic?
Can Finance see margin impact in near real time?
Can customers clearly understand and trust their invoices?

If the answer to any of these is uncertain, monetization is already constraining growth.

Monetization as a Strategic Capability

The next phase of CCaaS competition will not be decided by AI features alone. It will be shaped by how effectively those features can be commercialized.

This requires:

Event-driven billing architectures
Flexible usage-based pricing models
Contract enforcement at scale
Real-time visibility into consumption and revenue

For CCaaS providers and Contact Center platforms, monetization can no longer be treated as a back-office function. It must evolve into a core platform capability.

Those that modernize early will be able to:

Launch differentiated AI services faster
Align pricing with value delivered
Protect margins as usage scales
Support enterprise complexity without excessive customization

Those that do not risk delivering more value while capturing less revenue.

Final Thought

AI is redefining the Contact Center. CCaaS platforms are moving quickly to meet customer expectations and operational demands.

The question is no longer whether AI belongs in the Contact Center.
It is whether monetization systems are ready for the scale, variability, and complexity AI introduces.

How Companies Monetize AI

Marcos closed by urging leaders to sit with their teams for thirty minutes and test each framework against their own product. Determine where AI fits in your customer experience, identify the right hybrid model, and validate whether your value story is complete. The fundamental principle is simple. You should be paid more as your product does more work. Customers should feel safe adopting and scaling that work over time.

AI has created a rare moment in pricing history. The opportunity to capture value is significantly higher than previous eras. The companies that win will be those that price the experience, quantify the real outcome, and meter the right value with clarity and confidence.

To dive deeper into how companies are monetizing AI today, make sure to watch Marco’s full recording of his keynote presentation.

Salesforce’s Journey to Usage-Based Pricing

Ryan Susanna — Mon, 23 Mar 2026 13:30:34 GMT

Enterprises across the technology landscape are rethinking monetization as AI, automation, and new consumption patterns reshape customer expectations. Salesforce offered one of the most practical and transparent perspectives at the Usage Economy Summit 2025, openly sharing what worked, what failed, and what is still evolving as they move from traditional seat based pricing to usage and agent driven models.

Their experience matters. It reflects the reality many providers now face. Pricing strategy is no longer a theoretical exercise. It requires a coordinated shift across people, processes, policy, and technology, all happening while markets move faster than any annual planning cycle.

This article breaks down Salesforce’s journey and highlights what vendors can learn as they evolve their own monetization strategies.

The Shift: Why Traditional Pricing Could Not Keep Up

Salesforce began by acknowledging a simple truth. Traditional seat and license pricing was capping growth and eroding value perception. Customers often used only half of what they purchased. AI amplified this problem. Cost to serve became highly variable. GPU and infrastructure expenses could swing unpredictably as employees experimented with agents and LLMs.

Three major forces pushed Salesforce toward new models:

AI created variable costs that old pricing models could not absorb.
Usage patterns became the clearest reflection of value, not feature libraries.
Technology cycles accelerated beyond the pace of annual roadmaps.

This required a fundamental rethink of how Salesforce packaged, priced, billed, and measured value.

Breaking Traditional GTM: People, Process, Policy, and Technology

Salesforce emphasized that shifting to usage based pricing is not a pricing project. It is a business transformation that affects every function.

1. Financial Strategy and RevRec

Pre commit models, rollovers, contractual overage rates, and dynamic consumption created RevRec complexity that traditional processes were not prepared for. Forecasting, recognition, and pricing governance had to evolve together.

2. Data Scale and Billing Infrastructure

Usage telemetry from agents, APIs, and actions required mediation, metering, and rating at massive volume. Billing and platform constraints quickly surfaced.

3. New Product Categories Without Historical Data

Agent based metrics such as actions, conversations, and resolutions had no precedent. Without historical data, budgeting and modeling were difficult for both Salesforce and its customers.

4. People and Incentives

Moving sellers from seat based compensation to consumption based compensation required a mindset shift. Without transparency and clear incentives, field confusion grew.

These challenges forced Salesforce to rebuild key foundations that would allow usage models to scale predictably.

Building the Trust Layer: The Foundation for Usage Monetization

Transparency became the core principle of Salesforce’s usage transformation.

Digital Wallet – A near real time usage portal gave customers visibility into their consumption. This single feature significantly increased adoption and confidence, especially for pay as you go models.
Threshold Notifications – Customers received alerts at key usage levels, removing fear of unexpected bills.
Usage Calculator – Prospects could estimate spend before committing, reducing anxiety for new workloads.
Observability and Compliance – Given the volume and financial impact of usage data, Salesforce invested heavily in monitoring, reconciliation, and multi system controls.

These investments had measurable commercial impact. High growth segments adopted usage models faster, revenue leakage decreased, and Salesforce discovered new monetization levers based on actual consumption patterns.

The New Models: From Overages to Flex Credits

Salesforce now operates several monetization models across its portfolio:

Overage based models in Marketing Cloud
True up models in Slack
Agent based models across the new AI portfolio
Pay as you go, pre commit, and flex credits for emerging use cases

The complexity of managing multiple models across a single quote became a field challenge. This drove Salesforce toward standardization and simplification.

Flex credits became a strategic priority. The vision is flexibility for customers and accurate P&L attribution for each product GM. Achieving this requires innovations such as super entitlements, transparent multipliers, and consistent rules for swaps.

What Worked for Salesforce

Salesforce was refreshingly honest about where they succeeded.

Customer Zero – Using their own technology internally surfaced edge cases early and helped validate the product before external rollout.
Transparency Increased Adoption – Showing every multiplier, calculation, and usage detail built trust and accelerated expansion in high growth customers.
Usage Exposed Value and Roadmap Priorities – Salesforce discovered exactly which features customers relied on, allowing smarter investment and pruning.
Revenue Leakage Was Identified and Reduced – Clear measurement surfaced significant gaps that were invisible in seat based models.

What Failed or Slowed Progress

The failures offer important lessons for any vendor considering a similar shift.

RevRec and discount policy lagged pricing innovation.
Legacy SKUs conflicted with new models and slowed migration.
Sellers initially resisted consumption based compensation.
Field teams were overwhelmed when multiple products used different pricing models in the same quote.
The lack of historical usage data made early conversations with customers difficult.

Salesforce emphasized that missteps are inevitable, as long as the organization commits to continuous adjustment.

The Road Ahead

To move further into the usage economy, Salesforce is focusing on three priorities:

1. Enterprise scale flex credits

A unified consumption currency that still preserves visibility for each product GM.

2. Next generation estimator tools

AI driven forecasting tools to help customers avoid both over commit and under commit.

3. Redefining enterprise metrics

ARR alone cannot represent value in an agent based world. Salesforce is exploring new metrics such as net consumption growth and net new order value.

Final Thought: Usage Is a Signal of Value

Salesforce closed with a simple insight. Usage is not just a billing metric. It is the clearest indicator of customer value, product relevance, and revenue opportunity. Vendors who treat usage purely as a meter risk missing the strategic advantage it provides.

Enterprises that embrace transparency, build trust, simplify models, and align internal incentives give themselves the foundation to scale in the AI and usage era.

Salesforce’s journey illustrates that usage based monetization is not theoretical. It is operational, and cross functional. It can accelerate growth, and it demands a new way of thinking about GTM.

Watch the full recording of Salesforce’s session to see how their team navigated this transformation in real time and learn directly from the challenges and decisions behind their pricing evolution.

Why Traditional Telco Billing Breaks in the AI Era

Ryan Susanna — Mon, 16 Mar 2026 11:15:33 GMT

Artificial intelligence is not simply adding new features to digital services. It is changing who consumes services, how those services are accessed, and when value is created.

In AI-driven environments, consumption is increasingly initiated by non-human actors such as AI agents, automated workflows, and machine-to-machine processes. These actors authenticate through identity tokens, operate continuously, and make autonomous decisions in real time.

For Communication Service Providers, this shift exposes a growing disconnect between modern service architectures and the legacy billing systems expected to monetize them.

Billing was built for users. AI is built on identities.

From Users to Identities: A Structural Shift in Digital Consumption

Historically, billing systems assumed a stable model:

A human user
A predictable service
A recurring subscription or a simple consumption counter

AI breaks every one of those assumptions.

Modern platforms authenticate identities, not users. Every API call, AI inference, model execution, or automated action is validated through identity tokens issued by an identity provider. These tokens define what an entity can access, for how long, and under what conditions.

As AI adoption accelerates, CSPs and SaaS providers are seeing:

Explosive growth in non-human identities
Continuous, machine-driven service consumption
Bursty, unpredictable consumption patterns
Fine-grained authorization replacing coarse entitlements

This is no longer an edge case. It is becoming the dominant operating model.

Why Traditional Telco Billing Systems Struggle

1. They Were Designed for Periodic, Human-Initiated Usage

Legacy billing platforms assume consumption can be summarized, aggregated, and billed after the fact. AI systems do not behave this way.

AI agents consume services continuously, often at millisecond intervals, and often without human intervention. Traditional billing architectures struggle to ingest, rate, and reconcile this volume of real-time events without introducing latency or revenue leakage.

2. They Do Not Understand Identity as a First-Class Concept

In AI-centric systems, identity is not a security layer that sits outside monetization. It defines entitlement, access, and consumption.

Most billing systems lack native alignment with identity frameworks. They do not natively correlate consumption to:

Token scope
Authorization context
Delegated or “on-behalf-of” access

As a result, finance and revenue teams are left billing aggregated consumption without clear lineage to the identities that generated it. This undermines transparency, auditability, and trust.

3. They Cannot Keep Pace With Architectural Change

CSPs and SaaS providers are modernizing their platforms around:

Microservices
Event-driven architectures
API-first design
AI orchestration layers

Security teams are simultaneously responding to evolving standards from organizations such as National Institute of Standards and Technology, which are tightening expectations around token validation, signing keys, and non-human identity governance.

Billing platforms that operate as batch-oriented back-office systems simply cannot keep up with environments where access decisions, service execution, and monetization must occur in near real time.

The Hidden Gap Between Security and Revenue

Most organizations treat security and monetization as separate conversations.

Security leaders focus on access control, risk, and compliance.
Finance leaders focus on revenue recognition, pricing, and margin.
Architecture teams focus on scalability and resilience.

AI collapses these boundaries.

When identity tokens become the gatekeeper to services, they also become the natural meter for consumption. If access is granted in real time, consumption must be captured in real time. If consumption is captured in real time, pricing and billing can no longer be static or delayed.

This is where traditional billing models break down and where forward-looking CSPs and SaaS providers are rethinking monetization architecture entirely.

Monetization in an Identity-Centric World

In AI-driven environments, monetization must evolve to reflect how value is actually delivered.

That means billing systems must be able to:

Ingest high-volume, event-level consumption tied to identity
Rate consumption dynamically based on context, scope, and policy
Support non-linear pricing models such as consumption bands, thresholds, and outcomes
Align product, security, and finance teams around a shared source of truth

This is not simply about charging for more consumption. It is about enabling business models that match how AI-powered services operate.

What CSP and SaaS Leaders Should Be Asking Now

For leadership teams, the question is no longer whether AI will change monetization. It already has.

The more important questions are:

Can our billing systems accurately monetize consumption created by AI agents and non-human identities?
Can we price services that are dynamic, autonomous, and event-driven without introducing revenue leakage?
Can our security and architecture decisions support growth rather than constrain it?

Organizations that fail to align identity, architecture, and monetization will find themselves delivering more value while capturing less of it.

Closing Perspective: Architecture Is Now a Revenue Decision

AI has turned identity from a security detail into a core business primitive. Every authenticated interaction represents potential value, but only if it can be measured, rated, and billed accurately.

For CSPs and SaaS providers, the next phase of transformation is not just about building smarter platforms. It is about ensuring those platforms can be monetized at the same speed and scale at which they operate.

Billing is no longer a back-office system. In the AI era, it is part of the architecture.

AI Pricing Lessons from Telco

Natalie Louie, Head of Product Marketing & Pricing at RightRev, joins Tim Neil to unpack what telecom learned the hard way about usage based pricing and why those lessons matter now for AI, SaaS, and infrastructure driven businesses.

Drawing on decades of experience in SMS, voice, and carrier pricing, Natalie explains why unlimited plans, opaque costs, and discount driven sales motions quietly destroy margins as usage scales. Watch the podcast now.

Monetizing AI Without Losing Pricing Power

Ryan Susanna — Sun, 08 Mar 2026 18:33:01 GMT

Artificial intelligence is unlocking one of the greatest value shifts in modern software history, yet many companies are losing pricing power before they ever reach production. At the Usage Economy Summit 2025, Marcos Rivera, CEO of Pricing I/O, delivered a practical and candid session on how to avoid this mistake. Drawing from more than 500 pricing and packaging projects across leading B2B SaaS companies, he outlined clear frameworks that help teams capture value confidently while navigating the messy economic realities of AI.

AI Is Changing the Rules, Not the Purpose of Pricing

Marcos opened by highlighting a simple truth. Pricing is still pricing. The objective remains capturing value in a fair exchange. What has changed are the mechanics that surround value. The industry has moved from selling access in the on prem era, to selling activity in the cloud era, to selling action in the AI era. Systems now perform work on behalf of users, which increases the percentage of economic value that vendors can capture. The opportunity is larger, but the path is far more complex.

Unit economics in AI do not behave like traditional SaaS. Recurring revenue is blended with reoccurring usage. Services now operate as high margin premium layers. Gross margins swing dramatically based on model choice, frequency, and task type. Even more challenging, most AI pilots never reach paid production. The outcome is clear. Companies must approach AI monetization with intention, discipline, and a willingness to experiment with structure while avoiding chaos.

Pitfall One: Your Value Is Framed Too Low

Many companies default to a narrow time savings narrative when articulating AI value. Marcos warns that this weakens pricing power immediately. Buyers do not pay for AI. They pay for meaningful business outcomes, and studies show that the AI label does not increase willingness to pay or trust on its own.

To strengthen value articulation, Marcos introduced the REAL framework.

The REAL Framework

Revenue: Show how AI helps customers win, grow, or keep more business.
Expense: Move beyond hours saved. Demonstrate cost advantages and competitive speed.
Avoidance: Highlight risks or future expenses the customer avoids by adopting your solution.
Lift: Subtract the friction required to adopt and reach time to value.

This framework forces teams to quantify a complete value story. It also reinforces a key point. If your implementation time drops from months to weeks, your pricing power has improved. Revisit it.

Pitfall Two: Packaging Is Too Complex

Packaging AI capabilities is where companies often fall into confusion. Some teams bundle AI at no charge. Others create add ons without rationale. Many simply imitate competitors and hope it works. Marcos argues that packaging must follow the experience, not the hype.

Experience Packaging Framework

Underscore the core: If AI makes the core product faster or smarter, include it in base plans and adjust prices accordingly. Slack adopted this path by folding AI into its Business Plus plan and raising the price by twenty five percent.
Upgrade the more: If AI solves a more advanced version of an existing job, offer it as a premium tier or paid add on. ClickUp uses this structure successfully with its AI Brain offering.
Unlock the new: If AI creates an entirely new product or TAM, package it as a standalone capability. Intercom’s Finn agent is a prime example.

The lesson is straightforward. AI should be packaged where the customer experiences the value, not where it is most convenient for the vendor.

Pitfall Three: Charging for the Wrong Thing

AI introduces a fundamental shift in how work is performed. It is no longer only humans driving value. Systems perform tasks independently, which makes usage and outcomes natural pricing candidates. Marcos believes hybrids will dominate this transition.

The BAM Hybrid Framework

Every hybrid model contains three layers.

Base: The platform or foundational service fee.
Allowance: Entitlements, thresholds, or credits that create predictability.
Meter: The unit of value to charge for. This must align to customer value and protected cost.

From these layers, five common models emerge, ranging from simple access tiers to flat plus usage combinations, all the way to credit based systems and pure pay as you go. Marcos cautions that the meter is where the most risk lives. Choose the wrong metric and you distort adoption. Choose the right metric and you unlock scalable value capture.

Creating Psychological Safety in Usage Pricing

As usage increases, customers become more anxious about variability. Marcos presented a simple mental model called the 3P framework that reduces fear and promotes adoption.

Predict: Provide real time visibility into usage and burn.
Prevent: Align usage periods with reset periods so spending patterns feel intuitive.
Protect: Use caps, guardrails, and true forward adjustments to avoid surprising bills.

Psychological safety is essential. If customers do not trust the model, they will not scale the product.

The Call to Action

To dive deeper into how companies are monetizing AI today, make sure to watch Marco’s full recording of his keynote presentation.

AI in CCaaS and the Monetization Gap

Ryan Susanna — Mon, 02 Mar 2026 13:21:21 GMT

Artificial intelligence has become the defining force reshaping Contact Center as a Service. For Communication Service Providers, this shift goes far beyond improving agent productivity or customer satisfaction. AI is fundamentally changing what CCaaS delivers, how value is created, and how revenue must be captured.

The challenge for CSPs is not whether AI belongs in the contact center. That decision has already been made by enterprise buyers. The real question is whether existing pricing and billing models can keep pace with the value AI now generates.

AI transforms CCaaS from a call-handling platform into an intelligence-driven enterprise service. Only consumption-based monetization allows CSPs to capture that value at scale.

AI introduces real-time insight, automation, and predictive capabilities directly into customer interactions. Transcription, sentiment analysis, intelligent routing, agent assistance, and post-interaction analytics are no longer optional features. They are becoming table stakes.

However, most CCaaS pricing models are still anchored to seats, licenses, or bundled packages that were designed for a pre-AI environment. As a result, CSPs are delivering more value while pricing as if nothing has changed.

The Cost of Inaction

Flat pricing absorbs AI cost while giving away AI value.

AI-driven CCaaS introduces variable cost structures that do not align with fixed pricing:

Compute and inference costs scale with interaction volume
AI usage varies by channel, language, and complexity
Enterprise demand fluctuates with campaigns, seasons, and regions

When AI capabilities are bundled into static pricing, CSPs face margin pressure as adoption grows. The more customers rely on AI-driven features, the more costs increase, without a corresponding revenue mechanism.

This is not a new lesson for CSPs. Similar patterns emerged in messaging, data services, and IoT. CCaaS is following the same trajectory, but with greater speed and complexity.

AI Creates New Consumption Signals Inside the Contact Center

AI introduces measurable and billable consumption events that did not previously exist.

Examples include:

Minutes of real-time transcription and speech processing
Per-interaction sentiment and intent analysis
AI-generated summaries, compliance checks, and quality scores
Predictive routing decisions executed in real time
Advanced analytics queries across historical interaction data

Each of these activities represents consumption of compute, data processing, and intelligence. Treating them as bundled features obscures both cost and value.

Leading CCaaS platforms such as Genesys Cloud, NICE CXone, and Talkdesk already position AI as central to their differentiation. The next competitive shift will be how transparently and flexibly that AI is monetized.

Why Communication Service Providers Have an Advantage

CSPs are not new to consumption-based businesses. They already operate at scale with high-volume, event-driven services and complex enterprise contracts.

This gives CSPs a structural advantage in CCaaS if they apply the same discipline used in connectivity and consumption-based services to AI monetization.

AI-driven CCaaS pricing enables CSPs to:

Align revenue directly with consumption and delivered value
Support customer-specific pricing without inflating product catalogs
Introduce premium AI tiers without disrupting existing contracts
Protect margins as AI adoption scales across enterprises

In this model, AI becomes a monetizable service layer rather than an untracked cost center.

The Operational Constraint Holding CSPs Back

The primary barrier to AI-based CCaaS pricing is not customer resistance. It is operational readiness.

AI-driven CCaaS requires billing platforms that can:

Mediate and rate millions of real-time usage events
Apply flexible pricing logic per customer and per contract
Support blended models combining seats, usage, and outcomes
Evolve pricing models without rearchitecting the entire system

Many legacy billing systems were built for predictability, not dynamic consumption. As a result, they constrain how CSPs can package and monetize AI capabilities, even when demand exists.

The Strategic Decision Facing CSPs

CSPs offering CCaaS face a clear choice.

They can treat AI as a bundled enhancement and accept growing margin pressure. Or they can treat AI as a monetizable service layer and align pricing with how enterprises actually consume intelligence.

AI is already reshaping customer expectations inside the contact center. The next phase will reshape how CCaaS services are bought, measured, and paid for.

For Communication Service Providers, CCaaS is no longer just a customer experience platform. It is a proving ground for how intelligence-driven services will be monetized across the broader portfolio.

Those who align AI innovation with consumption-based monetization will not only sustain profitability. They will define the future of CCaaS in the AI era.

AI Pricing Lessons from Telco

CPQ and Usage Based Models

Ryan Susanna — Mon, 23 Feb 2026 16:59:45 GMT

Modern pricing has evolved far beyond traditional product catalogs. Today’s offerings are complex assemblies of compute, data, power, software, and services. This transformation places immense pressure on organizations to maintain cost visibility, safeguard margins, and price with confidence in a marketplace defined by volatility, accelerating innovation, and unpredictable consumption patterns.

At the Usage Economy Summit 2025, Daniel Kube, CEO at servicePath™ underscored a crucial truth. Organizations can no longer depend on intuition or outdated Configure Price Quote methods when their products incorporate elements such as compute cycles, cooling infrastructure, power consumption, software layers, and embedded AI. Navigating this new landscape requires precision, transparency, and an operational approach that blends deterministic accuracy with intelligent automation.

The Need for a Cost First Mindset

In complex B2B environments, large deals often carry layers of hidden cost. Power, cooling, tokens, data retrieval, hardware, services, and third-party fees all sit beneath the surface. Without explicit modeling of these inputs, sales teams are flying blind. Daniel proposes expanding CPQ into C-CPQ, which brings cost into the configuration process and ensures every quote includes a full economic profile.

This approach gives RevOps visibility into deal profitability, payback timelines, and P&L impact before approval. It provides guardrails that prevent margin erosion while allowing sales teams to move quickly.

AI Helps, but Accuracy Still Demands Structure

AI accelerates analysis, improves guided selling, and supports deal qualification. It can harvest knowledge from senior engineers and help new sellers propose valid configurations. However, AI also introduces unpredictable costs. Token-based billing models can generate unexpected expenses, and certain tasks can trigger high usage without warning.

This reality makes it essential to fuse AI insights with deterministic controls. Pricing precision, configuration logic, and margin protection must sit inside strict rules that guarantee five nines accuracy.

Guided Selling Reduces Risk and Increases Consistency

Outcome based pricing is only successful when sellers can properly qualify consumption patterns. Guided selling uses structured questions and embedded expertise to help teams propose offers that protect both customer value and vendor economics. It also removes mundane tasks from technical staff and supports faster sales cycles.

Clear Insight Creates Strategic Advantage

Dashboards that map costs, margin, and expected cash flow for each deal give leadership an accurate picture of risk and return. Service contract histories show how customers evolve over time and help forecast renewals or expansions. Together, these tools turn uncertainty into informed decision making.

Conclusion

The pace of change in the AI and usage economy is accelerating. Organizations that combine deterministic pricing discipline with AI driven guidance will price with confidence, avoid hidden risks, and capture more profitable revenue. The firms that invest in cost visibility, guardrails, and adaptive architecture will be the ones that thrive in the next generation of monetization.

To explore Daniel’s full insights on achieving precision, resilience, and profitability in modern pricing, watch the full recording of his presentation.

Why Monetization Will Define the Next Era of Telecom

Ryan Susanna — Tue, 17 Feb 2026 09:42:02 GMT

The telecom industry has always been comfortable building the backbone of the digital world. Networks, reliability, coverage, uptime. These are things communications service providers do exceptionally well.

But today, that foundation is no longer enough.

As artificial intelligence reshapes how enterprises operate, monetize, and compete, telcos are being forced to confront an uncomfortable question about their future. Are they destined to remain connectivity providers, or can they evolve into AI-enabled platforms that deliver higher-value services and outcomes?

This is not a technology question. It is a business one.

And at the center of it sits monetization.

Infrastructure Is Necessary, but It Is Not a Differentiator

There is no question that telcos have made massive investments in modern infrastructure. 5G, fiber, edge computing, and cloud-native cores have transformed what networks are capable of delivering.

Yet, despite these investments, many operators find themselves under constant pricing pressure. Margins are thin. Competition is fierce. And too much value is still being captured by companies that sit on top of telecom infrastructure, not by those who built it.

The uncomfortable truth is this: infrastructure enables value, but it does not define it.

Value is defined by how services are packaged, priced, delivered, and monetized.

AI Raises the Stakes and Exposes Old Limitations

AI presents telcos with a genuine opportunity to move up the value chain.

From sovereign cloud and compliant data services to AI-enabled networking, edge intelligence, and enterprise platforms, the potential is real. Many operators are already experimenting, piloting, and investing.

But AI also breaks traditional assumptions about billing and pricing.

AI services are unpredictable, variable, and unevenly consumed.

They are driven by usage, events, outcomes, and contracts that vary from one enterprise customer to the next.

This is where many transformation efforts quietly struggle.

When Innovation Moves Faster Than Monetization

Telcos talk a lot about modernization, but monetization systems are often left behind.

Legacy billing platforms were built for simpler times. Subscriptions. Fixed plans. Predictable usage. Limited product variation.

AI does not work that way.

When billing systems cannot support flexible pricing models, real-time usage, or complex enterprise agreements, innovation slows down. Finance teams become cautious. Product teams compromise. Sales teams struggle to explain value clearly.

AI initiatives may move forward, but revenue does not follow at the same pace.

That is not transformation. That is friction.

The Real Divide Is Monetization Maturity

The difference between telcos that remain connectivity providers and those that become AI-enabled platforms will not come down to who deploys AI first.

It will come down to who can monetize it effectively.

Monetization maturity means having the ability to:

Price services based on usage, value, or outcomes
Launch new offerings without re-engineering billing every time
Enforce complex enterprise contracts with accuracy and trust
Understand revenue, margin, and exposure in real time

Without this foundation, even the most ambitious AI strategy will struggle to scale.

From Carrying Traffic to Creating Value

Telcos do not lack relevance. They lack leverage.

The future of the industry is not about abandoning connectivity. It is about building on it.

Operators that treat monetization as a strategic capability will be able to turn AI into differentiated services, not just internal efficiencies. They will be able to move faster, price smarter, and capture more of the value they help create.

Those that do not will continue to compete on price, even as their networks become more advanced.

Why This Matters Now

The question facing telcos is not whether AI matters. That debate is already over.

The real question is whether the business systems behind AI are ready to support what comes next.

Because in the AI economy, innovation without monetization is not progress. It is a risk.

AI Pricing Lessons from Telco

Agentic AI and the New Economics of Monetization

Ryan Susanna — Tue, 10 Feb 2026 10:06:14 GMT

The global technology landscape is shifting from a mature SaaS economy to a fast-evolving AI economy defined by unprecedented capital investment, rising compute demands, and a rapidly expanding class of intelligent agents. Dr. Ali Arsanjani’s presentation at the Usage Economy Summit captured the scale of this transformation and its implications for how businesses must rethink value creation, cost structures, and monetization.

The message is clear. AI is no longer an enhancement to existing offerings. It represents a structural change in how labor, compute, and intelligence are measured, priced, and monetized.

The Economic Shift Behind Agentic AI

Enterprise AI development is supported by massive investment. By some estimates, cumulative global capital expenditure on data centers exceeds seven trillion dollars. Two thirds lies in compute architectures. The remainder is consumed by power, land, and the infrastructure required to train, deploy, and operate advanced models.

This scale of investment is reshaping the unit economics of software. Traditional SaaS models were built on near zero marginal cost. AI breaks that paradigm. Every inference has a cost. Every workflow has a token budget. Compute becomes a proxy for labor.

Businesses cannot assume that current AI cost structures will remain sustainable. Nor can they rely on generic models to deliver differentiated value. The future belongs to companies that build domain-specific intelligence and package it through monetization models that align usage, value, and cost.

Agents Are Redefining How Work Is Done

Dr. Arsanjani explained that modern LLMs have transitioned from static information retrieval systems to “thinking models” capable of reasoning, planning, and making complex decisions. These models form the foundation of agentic AI: systems that perceive their environment, formulate plans, coordinate with other agents, and execute tasks on behalf of humans.

This shift introduces new economic patterns:

Labor becomes elastic because digital labor can be scaled with compute rather than headcount.
Workflows are decomposed into task units that can be reassembled into hybrid human–agent processes.
Compute becomes a form of workforce capacity, requiring tight cost governance.
Domain-specific agents become a primary source of competitive differentiation.

The companies that master these patterns will redefine productivity and expand margins.

Why Monetization Must Evolve Alongside AI

As agentic AI grows, monetization must evolve beyond static subscriptions. The economics of AI favor models that directly connect compute cost to customer value. Three principles emerge.

1. Usage-Based and Hybrid Pricing Will Become Essential

AI introduces variable cost structures. Prompt execution, token consumption, and compute cycles fluctuate based on workflows. This makes usage-based and hybrid monetization models critical.

Vendors must meter the value their agents deliver and align pricing to consumption. Token-based pricing, workflow-based pricing, and compute-tier pricing will become foundational elements in enterprise monetization strategies.

2. Domain-Specific Agents Create Pricing Power

Generic models are becoming commodities. The real value lies in agents tuned on proprietary data and tailored to specific industries such as financial services, supply chain, telco, or healthcare.

Domain-specific agents allow companies to:

Charge premium prices for specialized intelligence.
Introduce new monetizable modules and expansions.
Differentiate on capability, speed, and accuracy rather than raw model size.

This supports higher NRR, stronger expansion paths, and more defensible pricing.

3. Governance and Cost Transparency Will Influence How AI Is Sold

Token budgets, inference cost caps, and controllable computing limits become monetizable features. Customers will not tolerate unpredictable AI costs. They will expect clarity, governance, and guardrails.

This opens opportunities for new revenue models, including:

Predictable commitment tiers for compute.
Add-on governance features priced as premium modules.
“Performance guarantees” tied to cost savings or output efficiency.

A New S-Curve for AI Adoption and Monetization

Traditional technology adoption follows a slow-start S-curve. AI reverses this. Innovation moves from zero to sixty almost instantly. The middle phase is volatile. Plateaus come faster.

This compressed S-curve forces companies to treat AI investment like an options portfolio rather than a linear roadmap. Small, compounding bets on agentic workflows, domain models, and automation paths create future revenue opportunities.

Those who wait for perfect clarity will be left behind. Those who build early operational muscle will shape customer expectations and define the pricing standards of the next decade.

Building a Monetization Strategy for the Agentic Era

To compete effectively, businesses need a clear economic and operational blueprint:

Reassess your unit economics with compute as a labor force multiplier.
Decompose jobs into task-level workflows that can be metered, automated, and monetized.
Design pricing models that track usage, cost, and measurable outcomes.
Consolidate your AI technology stack and build domain-specific agents that deepen differentiation.
Implement governance, auditability, and cost controls as part of the monetization package.
Treat AI initiatives as a portfolio of strategic options rather than single high-risk projects.

AI will only deliver profit if the value story, pricing model, and cost governance are engineered with the same rigor as the technology itself.

Conclusion

Dr. Arsanjani’s perspective highlights the most important strategic truth of the AI era. Intelligence is becoming a scalable economic resource. Compute is becoming labor. Agents are becoming digital workers.

The companies that thrive will not simply adopt agentic AI. They will monetize it intelligently. They will align cost with consumption. They will differentiate through domain expertise. They will build governance into their value proposition.

Most importantly, they will design pricing models that reflect the real economics of AI, enabling sustainable growth in a world that is moving faster than ever before.

Watch Dr. Ali Arsanjani’s full presentation to understand how agentic AI, compute economics, and next-generation automation will reshape monetization strategies and long-term competitive advantage.

Why Monetization Is the Defining Challenge for Telco Leaders

Ryan Susanna — Tue, 03 Feb 2026 07:35:48 GMT

The telecom industry is entering a decisive phase. Network innovation is accelerating, enterprise demand for digital transformation is rising, and new technologies such as eSIM, AI, and next generation wireless are expanding what operators can deliver. Yet for many telco leaders, the central question is no longer what is possible, but what is monetizable at scale.

Across boardrooms and executive forums, service innovation and monetization have become inseparable topics. The reason is simple. The industry’s next wave of growth will not be driven by connectivity alone. It will be driven by the ability to translate complexity into revenue without increasing operational friction.

This article explores four major industry themes shaping telecom strategy today and explains why monetization capability has become the critical differentiator for operators looking to lead rather than follow.

Turning Enterprise Digital Transformation Spend into B2B Growth

Enterprises are investing heavily in digital transformation across cloud, AI, automation, data analytics, and connected infrastructure. For telecom operators, this represents a generational opportunity to move beyond connectivity and into higher value B2B services.

Private networks, edge computing, managed IoT platforms, AI enabled services, and industry specific solutions are increasingly part of the telco enterprise portfolio. However, these offerings introduce commercial complexity that traditional telecom operating models were not designed to handle.

Enterprise customers expect:

Contract specific pricing and service levels
Flexible usage based and outcome based charging
Multi year agreements with negotiated terms
Integration with broader digital ecosystems

For telco leaders, the challenge is not delivering the technology. The challenge is monetizing it without slowing down innovation or introducing revenue leakage.

Operators that succeed in enterprise B2B will be those that can support bespoke deals at scale, enforce complex contracts accurately, and launch new services without lengthy billing transformations. Monetization agility becomes a growth enabler rather than a constraint.

eSIM, IoT, and the Economics of Massive Scale

The global adoption of eSIM and the continued expansion of IoT are reshaping the economics of telecom services. Connectivity is no longer tied to individual subscribers. It is embedded in devices, machines, vehicles, and infrastructure.

This shift introduces new realities:

Millions or billions of endpoints with low individual ARPU
Highly variable usage patterns
Global deployments spanning multiple regulatory environments
Device lifecycle driven pricing models

In this environment, profitability depends less on pricing levels and more on operational efficiency and precision monetization. Manual processes, rigid catalogs, and subscriber centric billing systems simply do not scale.

For telco leaders, eSIM and IoT success requires monetization platforms that can handle:

High volume, real time usage data
Event driven and consumption based pricing
Dynamic device states such as active, dormant, testing, or seasonal
Enterprise wide visibility into usage and revenue

Without this foundation, IoT growth risks becoming a volume driven business with diminishing returns.

Reshaping Consumer Growth with 5G, AI, and Experience Based Services

On the consumer side, 5G and AI are opening the door to new service categories and differentiated experiences. Faster speeds and lower latency are table stakes. Real growth will come from how these capabilities are packaged, priced, and personalized.

We are already seeing the shift toward:

Experience based pricing rather than generic data plans
Application or feature specific usage models
AI powered services that justify premium pricing
Context aware and dynamic offers

The challenge for operators is keeping monetization in sync with innovation. When billing systems lag behind service development, operators are forced to simplify offers, delay launches, or leave revenue on the table.

Telco leaders who want to unlock sustainable B2C growth must ensure that pricing and monetization capabilities evolve at the same pace as network and device innovation. Agility in monetization becomes essential for differentiation in increasingly saturated markets.

Preparing for 6G: Innovation Only Matters If It Can Be Monetized

According to GSMA Intelligence, 89 percent of operators believe that 6G will enable new services and business models. This optimism reflects the industry’s expectation that future networks will be AI native, programmable, and deeply integrated with digital ecosystems.

History offers a cautionary lesson. Previous network generations often struggled to deliver the expected return on investment, not because of technical limitations, but because monetization models failed to evolve.

6G is expected to enable:

AI driven and context aware services
Network and compute offered as programmable platforms
Advanced sensing, digital twins, and real time intelligence
Multi party ecosystems with shared value creation

These models demand flexible, future ready monetization infrastructure. Without it, innovation risks becoming a cost center rather than a growth engine.

For telco leaders, the question is not when 6G will arrive, but whether their organizations are building the commercial foundations today to monetize it tomorrow.

Monetization as a Strategic Leadership Priority

Across enterprise services, IoT, consumer innovation, and future networks, one theme is consistent. Complexity is increasing, not decreasing. Pricing models are becoming more granular. Contracts are becoming more customized. Ecosystems are becoming more interconnected.

This places monetization at the center of telecom strategy.

Leading operators are reframing monetization as:

A strategic capability, not a back office function
A prerequisite for innovation, not a downstream concern
A source of competitive advantage, not just operational support

Those who invest in flexible, usage driven monetization platforms position themselves to move faster, experiment safely, and capture value from new services without compromising financial control.

Final Thoughts for Telco Leaders

The next phase of telecom growth will not be defined by who builds the fastest network or deploys the most advanced technology. It will be defined by who can convert innovation into measurable financial outcomes.

Enterprise digital transformation, eSIM and IoT scale, 5G and AI driven consumer experiences, and the promise of 6G all point in the same direction. Monetization is no longer optional. It is foundational.

For telco leaders, the opportunity is clear. The operators that master monetization in the age of complexity will be the ones that shape the future of the industry rather than react to it.

AI Pricing Lessons from Telco

Defining Success with Go-to-Market Clarity

Ryan Susanna — Mon, 26 Jan 2026 14:56:09 GMT

The pace of change across the technology and SaaS landscape continues to accelerate. New products emerge quickly, AI transforms workflows, and buying behavior shifts toward digital-first experiences. Yet despite this evolution, Sangram Vajre argues that the single biggest determinant of success in 2026 will not be a new product launch, a breakthrough AI enhancement, or even a new sales motion. The deciding factor will be something far more foundational: internal clarity on go-to-market.

At the Usage Economy Summit 2025, Sangram delivered a compelling reminder that go-to-market is not a department. It is the business itself. Every executive, every leader, and every team member plays a role in shaping how a company wins, expands, and retains its customers.

The Go-to-Market Problem No One Talks About

Sangram opened with a simple exercise: define go-to-market. As attendees quickly discovered, the answers varied dramatically. Marketing leaders emphasized demand and brand. Sales leaders focused on pipeline and conversion. Product leaders highlighted roadmap and capabilities.

This inconsistency is pervasive across the industry. In Sangram’s research, spanning startups and billion-dollar enterprises, every executive described go-to-market differently. When teams cannot agree on the definition of the problem, they cannot align on the solution.

The first strategic takeaway is therefore straightforward:
Every organization must formalize its own definition of go-to-market.

This definition will depend on the company’s operating model. A product-led business will define GTM differently than a category-creator or a sales-led enterprise. What matters is alignment, not uniformity.

NRR: The Metric That Predicts a Company’s Future

Sangram reinforced a point that resonates strongly across the Summit: net revenue retention is the single most important indicator of business health.

His research shows that companies operating below 100 percent NRR are on a downward path. Companies at or above 120 percent NRR, especially in private markets, represent rare but elite performance. These high-performing organizations consistently outperform peers regardless of product category, market segment, or pricing model.

Every leader must therefore know their company’s NRR, understand its drivers, and align their work to improve it. Customer expansion and retention are no longer optional. They are existential.

Growth Happens in Stages: Problem, Product, Platform

Sangram introduced a simple but powerful maturity model:

Problem – proving you are solving a meaningful pain.
Product – achieving product-market fit and repeatability.
Platform – expanding across products, personas, and markets.

Nearly every successful company progresses through these stages. Yet many organizations stall between product and platform. According to the research, 80 percent of companies that fail to transition into platform status eventually decline or disappear.

The implication is clear: growth strategy must be tied to the company’s stage, and executive teams must be aligned on where they truly are.

The GTM Operating System: Eight Questions That Drive Alignment

Sangram revealed the Go-to-Market Operating System, an eight-question framework used by high-performing companies with exceptional NRR. These questions force disciplined thinking and remove ambiguity.

The first question alone reshapes most strategic conversations:
Where can we grow the most?

Not “where can we grow” but “where can we grow the most.” This qualifier forces teams to discuss market size, margins, customer value, ICP focus, and the real trade-offs required.

Each of the eight questions serves as a governance tool. They create a structured cadence for leadership teams to align, debate, and make decisions. According to Sangram, the best organizations do not rely on goals alone. They rely on systems.

Real-World Examples: How Great Companies Operationalize GTM

To demonstrate how these principles manifest in practice, Sangram shared several case studies:

Notion

Once on the verge of collapse, Notion rebuilt its strategy around community-driven adoption and radical ease of use. Their central GTM question became: What is our point of view?

HubSpot

HubSpot won by defining a category rather than competing in an existing one. Their differentiated POV became the cornerstone of their go-to-market system.

Snowflake

Snowflake transformed the sales motion by removing procurement barriers and encouraging customers to try the platform before paying. This usage-first model reshaped cloud adoption and set a new standard for frictionless enterprise selling.

Across all examples, the theme is consistent. High-performing companies align around a clear GTM system, not isolated tactics.

The Market Is Changing: Services, Usage, and Invisible Interfaces

Sangram outlined three macro-shifts shaping the next era of business:

Rise of services as software – usage-based models and hybrid monetization are becoming the norm.
Shift in buying behavior – digital self-service is increasingly preferred, even for high-value deals.
Rise of invisible platforms – interfaces will become embedded, automated, and AI-native, changing how products are built and used.

The future will reward companies that build workflows instead of static products and that operate from a unified internal operating system rather than siloed departmental plans.

A Blueprint for 2026 Planning

Sangram closed with several directives for leaders preparing for 2026:

• Define go-to-market and make it explicit.
• Know your NRR and align your work to improve it.
• Use the eight-question GTM Operating System to guide planning.
• Adopt a platform mindset and build for expansion.
• Develop a differentiated point of view.
• Focus on ROI in the customer’s mind, not your own metrics.
• Recognize that AI is not the strategy. Alignment is.

The companies that succeed will be those that replace ambiguity with clarity and replace siloed goals with shared systems.

Sangram’s full presentation brings these ideas to life with clarity and energy. I encourage you to watch the session and hear directly from him how your organization can build a stronger go to market foundation for 2026.