LogiSense Billing Blog

AI Is Rewriting Telecom Infrastructure Economics

Ali Naqvi — Mon, 08 Jun 2026 13:44:22 GMT

Artificial intelligence is beginning to place new pressures on enterprise infrastructure, but not in the way many expected.

Much of the AI conversation still revolves around massive foundation models, billion-dollar GPU investments, and speculative “moonshot” projects. Meanwhile, a quieter transformation is already underway inside enterprise environments. AI capabilities are increasingly embedded into everyday business systems such as collaboration platforms, analytics tools, search, security operations, development workflows, and customer engagement applications.

The result is that AI is no longer isolated to centralized experiments. It is becoming operational infrastructure.

That shift matters because AI-enhanced applications behave differently than traditional enterprise workloads. They consume more compute resources, generate more traffic, require lower latency, and place greater pressure on distributed infrastructure environments. As enterprises scale AI usage across departments, telecom operators and infrastructure providers are beginning to face a more fundamental challenge:

AI is changing the commercial characteristics of connectivity itself.

AI Traffic Is Not Traditional Network Traffic

For decades, telecom networks were largely optimized around predictable usage patterns. Even as cloud adoption accelerated, many enterprise traffic models remained relatively stable from a commercial standpoint.

AI workloads introduce very different dynamics.

Agentic AI systems increasingly operate across multiple environments simultaneously, coordinating APIs, applications, databases, cloud services, and edge systems in real time. Video-driven AI applications are generating enormous upstream traffic volumes from cameras, sensors, and industrial systems. Interactive AI experiences, including real-time collaboration and immersive digital environments, are placing far tighter latency requirements on both network and compute infrastructure.

This creates a more volatile infrastructure environment where:

traffic patterns become less predictable,
workloads become more burst-oriented,
latency becomes commercially sensitive,
and compute consumption fluctuates constantly.

A one-second delay in an AI-generated transcript may be acceptable. A one-second delay affecting industrial automation, operational technology systems, or customer engagement workflows may not be.

The network is no longer simply transporting data. It is increasingly participating in business-critical AI execution.

Performance Is Becoming a Monetizable Service

One of the most important shifts in the AI era is that infrastructure performance itself is becoming commercially valuable.

Historically, telecom connectivity was often sold through relatively static commercial models based on bandwidth tiers, fixed contracts, and recurring subscriptions. AI changes that equation because not all traffic now carries the same operational importance.

AI inference workloads, edge processing, and real-time automation systems require differentiated treatment. Enterprises will increasingly expect infrastructure providers to deliver:

low-latency AI connectivity,
prioritized network paths,
geographically optimized inference routing,
workload-aware orchestration,
and performance guarantees tied to business outcomes.

This creates entirely new monetization opportunities for operators.

Future telecom offerings may include:

premium AI workload routing,
latency-based service tiers,
edge compute consumption models,
AI API monetization,
dynamic bandwidth allocation,
and usage-based charging tied directly to AI infrastructure consumption.

In other words, AI is accelerating the shift from fixed telecom economics toward dynamic consumption economics.

Legacy Billing Models Were Not Built for AI Infrastructure

While network modernization receives significant attention, the monetization layer often receives far less scrutiny.

That may become one of the biggest operational challenges facing telecom operators.

Many existing quote-to-cash environments were designed around relatively static service models:

fixed circuits,
recurring subscriptions,
predictable usage thresholds,
and limited pricing variability.

AI infrastructure environments are far more complex.

Operators may soon need to support:

hybrid recurring and consumption pricing,
burst-based charging,
AI transaction billing,
API usage monetization,
edge compute consumption,
geographically differentiated pricing,
and dynamic SLA-based commercial models.

This dramatically increases the complexity of mediation, rating, invoicing, entitlement tracking, and revenue recognition.

Without modern monetization infrastructure, operators risk creating a disconnect between the technical capabilities of their networks and their ability to commercialize them effectively.

That disconnect creates operational friction, billing complexity, and ultimately revenue leakage.

The Rise of AI-Native Monetization

The telecom industry is entering a phase where infrastructure flexibility alone is no longer enough.

Operators must also develop commercial systems capable of monetizing highly dynamic AI-driven environments.

That requires a shift toward AI-native monetization capabilities such as:

real-time usage mediation,
scalable event processing,
dynamic rating engines,
customer-specific pricing logic,
hybrid billing models,
and near real-time visibility into infrastructure consumption.

This is particularly important as AI ecosystems become increasingly distributed across cloud, edge, and enterprise environments.

As enterprises scale AI adoption, they will not simply consume more connectivity. They will consume infrastructure differently.

The operators that succeed in the AI era will not just provide bandwidth. They will provide monetization-ready infrastructure capable of supporting highly variable, performance-sensitive, usage-driven business models.

The AI era is not simply rewriting telecom infrastructure architecture.

It is rewriting telecom economics.

How AI is shaping monetization

At the Usage Economy Summit, panelists from Five9, 8×8, and Vonage discussed how AI-driven innovations are influencing dynamic pricing models, usage-based billing, and service monetization.

Watch the recording now, and gain insights into the evolving landscape of SaaS monetization, the role AI plays, and how companies can offer more personalized and scalable pricing solutions in the future.

Designing Flexible Pricing Models

Ali Naqvi — Mon, 01 Jun 2026 13:58:06 GMT

The pace of innovation in AI and agent driven capabilities has created an entirely new operating reality for software companies. Features are shipped faster than organizations can operationalize them, customer expectations evolve overnight, and the pressure to monetize emerging capabilities grows stronger each quarter.

Himanshu Mishra’s presentation at the Usage Economy Summit 2025 addressed this accelerating landscape with a clear message. Monetization models will only succeed when the execution layer has a seat at the table from day one.

Innovation Has Outpaced Operational Readiness

For years, product and engineering teams controlled the innovation pipeline. They discovered customer needs, built features, and then handed the work to go-to-market teams to take to market. That flow already strained operations. The arrival of rapid AI feature development has amplified the dysfunction.

Capabilities are now built and released at speeds that overwhelm internal systems. Billing teams, finance teams, RevOps, success, and support often learn about new capabilities only when customers begin to consume them. The result is margin risk, revenue recognition issues, billing complexity, and customer confusion.

Himanshu cautioned that this is no longer sustainable. Uncontrolled innovation introduces real financial impact. AI workloads generate variable cloud costs. New features require metering. Revenue must be recognized differently. Promises made in the market must match what back end systems can support.

Start With Customer Value Before You Build

A major theme of the presentation was the importance of front loading value definition. Before teams build or price a capability, they must answer three critical questions.

What material outcome will the customer realize.
Will the new capability help them increase revenue, reduce expense, or execute work faster or with higher accuracy. If the answer cannot be quantified, the team should not propose outcome based pricing.
How will this affect the customer’s own margins.
AI features often increase a customer’s cloud consumption. Pricing must reflect the full economic system, not just the vendor’s cost structure.
What is the operational impact on the customer.
Every new capability affects their people, their workflows, and their technology stack. If this impact is not understood, monetization decisions become disconnected from reality.

This value-first lens shifts the conversation away from inside out engineering excitement and toward a disciplined assessment of customer benefit and feasibility.

The Execution Layer Becomes the Unifier

Value and cost only work when the execution layer connects them. Billing systems, revenue platforms, product and usage data, sales insights, and customer support environments must align early in the monetization cycle. Without this alignment, pricing models fall apart in the real world.

Himanshu emphasized a simple truth. If you cannot meter it, bill it, recognize it, support it, and scale it, then it is not a monetization model. It is just an idea. The back office is no longer a cost center. It is now a strategic control point for customer value delivery and company margin protection.

The Innovation Roundtable: A Framework for Cross Functional Monetization

As a remedy for the chaotic AI innovation cycle, Himanshu introduced the Innovation Roundtable. This is not a theoretical model. It is a practical, eight part framework designed to bring the entire organization together early.

Participants include:

Product
Engineering
Marketing
Competitive intelligence
Sales
Customer success
RevOps
Finance, internal IT, and billing

This group becomes the guardian of margin and the voice of customer reality. Its role is to ensure that innovation does not happen in isolation and that monetization decisions do not become disconnected from what customers can understand, what sales can sell, and what systems can support.

The Innovation Sandbox: How to Test and Validate New Models

Himanshu’s second framework, the Innovation Sandbox, provides an actionable path for experimentation.

Define what value you are monetizing, how the customer wants to buy it, and whether you can meter and bill it.
Test through pilot groups or self-service channels. Run A and B tests between different pricing constructs such as credits, pay as you go, tiers, or tokens.
Validate that the model works across rev rec, billing, and support before any large scale rollout.

This approach removes guesswork and replaces it with evidence. It also helps teams avoid costly missteps, such as introducing usage or AI charges that destroy margin or confuse customers.

Hybrid Pricing Will Dominate for Years

While many companies aspire to pure usage models, Himanshu explained that most will remain hybrid for the foreseeable future. Seats will shift from a headcount model to an access model. AI driven features will create secondary usage streams. Very few companies will operate with both primary and secondary usage models because revenue dilution becomes a risk if too much value shifts to consumption based billing.

The right path is simple: start with a small usage component for AI capabilities and scale it only when the model is proven.

The Call to Action

Himanshu closed with a direct challenge to the audience. Enablement teams are no longer observers. They are central to the revenue engine. Billing, finance, RevOps, customer success, and support now share equal responsibility with product and sales in shaping monetization strategy.

He left attendees with three mandates.

Build consensus early.
Move beyond the back office mindset. Step into GTM 2.0 with confidence.
Assemble a cross functional team.
Elevate your presence and bring the Innovation Roundtable to life in your organization.
Execute with discipline.
Launch only what you can operationalize. Keep models simple. Deliver value at scale, not chaos at scale.

Final reflection

In an era where AI accelerates both opportunity and complexity, organizations that succeed will be those where innovation and monetization are tied directly to the capabilities of the execution layer. Himanshu’s message is clear. The companies that win are the ones that elevate enablement, safeguard margins, quantify value, and bring every monetization decision through a disciplined, cross functional operating model.

This is the new foundation of profitable growth in the Usage Economy.

Pricing as a Product: Designing Flexible Models that Win Deals and Scale

Pricing isn’t just a number, it’s a strategic capability built for scale. In a world where volatility is the norm, modern companies are designing flexible pricing models that support growth, whether through new products or M&A.

This session from The Usage Economy Summit 2025 with Himanshu Mishra explores how to align pricing with product design, go-to-market strategy, and roadmap planning to build frameworks that win deals and evolve with the business. Learn how to experiment, adapt, and make pricing a core part of your growth engine.

AI Is Stress-Testing Modern Quote-to-Cash Systems

Ali Naqvi — Mon, 25 May 2026 13:50:51 GMT

Artificial intelligence is rapidly changing how software is built, delivered, consumed, and monetized. But while much of the industry conversation has focused on AI capabilities, copilots, and productivity gains, a far less discussed issue is beginning to emerge behind the scenes:

AI is stress-testing modern quote-to-cash systems.

A recent webinar from MGI Research highlighted an increasingly important reality facing SaaS providers, telecom operators, cloud platforms, and AI-driven businesses: many monetization systems were designed for predictable subscription economics, not dynamic AI-driven consumption models.

And that difference matters more than many organizations realize.

Traditional SaaS Economics Were Predictable

For years, software companies largely operated within a relatively stable commercial framework:

Per-user subscriptions
Fixed monthly recurring revenue
Predictable infrastructure consumption
Linear pricing models
Standardized contracts

Traditional quote-to-cash systems evolved around those assumptions.

The operational model was relatively straightforward:
quote the product, provision the user, generate the invoice, recognize the revenue.

But AI changes the economics completely.

AI Introduces Variable Consumption at Scale

AI workloads are fundamentally different from traditional SaaS usage patterns.

Infrastructure costs fluctuate based on:

Model inference
Token consumption
GPU utilization
Agent activity
API requests
Workflow complexity
Autonomous task execution

In many cases, the cost to serve one customer can vary dramatically from another, even within the same pricing tier.

That creates a new challenge for finance, operations, and product leaders:
How do you monetize AI profitably while maintaining operational control?

This is where traditional quote-to-cash systems begin to show strain.

The Rise of the “AI Margin” Problem

One of the more important themes emerging in the AI monetization conversation is margin visibility.

Historically, SaaS margins were relatively easy to model. Companies could forecast infrastructure costs and revenue growth with reasonable confidence.

AI introduces much greater unpredictability.

A customer generating heavy AI workloads may consume significantly more infrastructure resources than anticipated. Another may use advanced AI functionality far less than expected. Meanwhile, infrastructure pricing itself continues to evolve rapidly.

As a result, organizations are beginning to ask more operational questions:

Which AI services are actually profitable?
Which customers generate disproportionate infrastructure costs?
How should AI usage be rated and billed?
How should hybrid pricing models be structured?
How do we explain variable AI charges clearly to customers?
How do we forecast AI revenue accurately?

These are no longer just pricing questions.

They are quote-to-cash questions.

Why Legacy Monetization Systems Struggle

Many monetization systems were originally built around static subscriptions and standardized recurring billing models.

AI monetization introduces a level of operational complexity those systems were never designed to handle.

Organizations are increasingly encountering challenges such as:

Limited usage visibility
Rigid pricing structures
Disconnected mediation and billing systems
Difficulty handling hybrid contracts
Delayed rating processes
Poor invoice transparency
Revenue leakage risks
Inconsistent reporting between product, finance, and operations teams

Even companies experimenting aggressively with AI products often underestimate the operational infrastructure required to monetize those products effectively at scale.

Launching AI capabilities is one challenge.

Monetizing them sustainably is another entirely.

Hybrid Monetization Models Are Becoming the New Reality

As AI adoption accelerates, many organizations are discovering that traditional “one-size-fits-all” pricing approaches no longer work.

Instead, businesses are moving toward hybrid monetization models that combine:

Subscription pricing
Usage-based pricing
Consumption drawdowns
Overage models
Entitlements
Tiered usage structures
Outcome-based pricing
Prepaid and postpaid combinations

This creates significantly greater complexity across the quote-to-cash lifecycle.

Pricing logic becomes more dynamic.
Contracts become more customized.
Usage data becomes more granular.
Billing becomes more variable.
Revenue recognition becomes more complicated.

The operational demands increase rapidly.

AI Is Elevating Monetization Infrastructure Into a Strategic Priority

The companies that succeed in the AI era will not simply be the ones that innovate faster.

They will be the ones capable of monetizing innovation efficiently, transparently, and profitably.

That requires infrastructure capable of:

Collecting and processing granular usage data
Supporting dynamic pricing models
Managing hybrid monetization strategies
Delivering finance-grade accuracy
Reducing revenue leakage
Providing operational visibility into AI margins
Scaling monetization alongside AI adoption

In other words, the AI era is elevating monetization infrastructure from a back-office billing function into a strategic business capability.

The Future of Quote-to-Cash Is Becoming Consumption-Aware

AI is accelerating a broader shift that has already been underway across SaaS, telecom, cloud, and digital services markets.

Businesses are moving beyond static subscriptions toward more dynamic, consumption-aware commercial models.

That transition affects every stage of the quote-to-cash lifecycle:

Pricing
Quoting
Usage mediation
Rating
Billing
Revenue recognition
Reporting
Customer transparency

The organizations that modernize these systems early will likely have a significant operational advantage as AI-driven business models mature.

Because ultimately, AI is not simply changing software products.

It is changing the operational and financial systems required to monetize them.

From Static Pricing to AI Orchestrated Monetization

Discover how AI is transforming pricing, bundling, and customer experiences into real-time, dynamic revenue engines.

In this episode, Hemant Soni explains why static pricing can no longer keep up and what is replacing it.

ARR in the AI Era

Tim Neil — Tue, 19 May 2026 13:01:50 GMT

Annual Recurring Revenue has long been the North Star for SaaS valuation and financial discipline. Yet as business models evolve with usage based pricing, consumption billing, managed services, and AI powered products, many companies treat ARR loosely, and in some cases incorrectly. Ben Murray’s presentation at the Usage Economy Summit 2025 offered a detailed and practical roadmap for restoring rigor to ARR while adapting it to the realities of modern software and AI monetization.

ARR Is Not Broken. Revenue Models Are Evolving.

Ben’s central argument is straightforward. ARR remains one of the strongest indicators of predictable value creation, but companies often undermine their own numbers through poor revenue categorization. He emphasizes that metrics are not failing. It is the business models that have become more complex.

Subscription, usage, consumption, processing, overages, and recurring managed services now sit side by side. Without proper segmentation, a CFO cannot understand gross margins, retention behavior, or the true quality of recurring revenue. Ben stresses that the modern tech P&L must clearly separate revenue streams and map COGS to each stream with precision. Only then can leadership understand profitability dynamics and defend their numbers during valuation or due diligence.

What Public Companies Reveal About ARR Construction

To understand current practices, Ben reviewed 167 SEC filings and 92 global press releases. Patterns emerged that are increasingly important for private companies preparing for future rounds or acquisition.

Pure subscription ARR remains the cleanest model. Many companies continue to calculate ARR as MRR multiplied by twelve or last quarter’s revenue multiplied by four. Hybrid models, such as subscription plus usage, are becoming more common. Companies often annualize usage revenue by taking the last ninety days or the last thirty days of usage and extending it to a twelve month run rate.

A growing number include recurring managed services in ARR. While Ben considers this a debatable move, he notes that many CFOs choose to include it if the services are contracted and recurring. Pure usage companies, however, rarely attempt to transform usage revenue directly into ARR due to volatility and limited predictability.

The takeaway is clear. If revenue streams are not separated and ARR is not constructed with care, companies risk misleading themselves and their investors.

ARR Quality, Margin Visibility, and Business Model Discipline

Ben encourages leadership teams to ask hard questions about the nature of their recurring revenue. Is the revenue IP powered or people powered? Does scale require significant human effort? How do margins differ across streams?

With the rise of AI, these distinctions matter more than ever. Serving AI products can carry significant variable costs, particularly when relying on third party LLMs. Gross margin analysis must be tied to the structure of each revenue category, or the company will not know whether it is scaling profitably.

AI Monetization: What Public Tech Leaders Are Actually Doing

Investors have moved past AI hype. They now look for evidence of revenue contribution, adoption, and measurable outcomes. Wall Street’s questions consistently focus on five areas: AI revenue, adoption rates, usage tied to value, margin impact, and product readiness for monetization.

Murray showcases several strategies that leading enterprises are using to turn AI from a buzzword into a revenue engine.

Charging price uplifts for AI features and driving higher ACV
Moving to metered work units instead of token consumption
Demonstrating outcomes such as reduced call abandonment or faster wrap times
Creating new security categories around AI risk
Defending data moats by embedding AI into established networks and systems
Bundling AI within existing product families to bypass lengthy procurement cycles

These examples illustrate that AI monetization is no longer theoretical. It is measurable, traceable, and increasingly tied to concrete operational improvements.

Reporting AI ARR and Proving Real Traction

Some public companies now report AI ARR separately, especially when it grows faster than total ARR. This transparency gives investors confidence that AI initiatives are contributing to retention, expansion, and new logo acquisition. Ben notes that companies able to demonstrate AI driven expansion or pipeline acceleration are receiving stronger market recognition.

For private companies, this signals a shift. Boards and prospective investors will soon expect similar clarity.

Outcome Based Pricing: Slow but Emerging

Despite frequent debate, outcome based pricing is still early. Only one public company explicitly described moving from seat based to outcome based pricing in its filings. Ben advises teams to be realistic. Unless outcomes can be quantified and trusted, outcome based pricing will remain limited and selective.

What Finance Leaders Must Do Next

Ben closes with a disciplined framework for ARR in the AI era. CFOs and CEOs must:

Define revenue streams clearly and publish an internal ARR policy
Track margins by revenue stream to understand scale and sustainability
Monetize AI with purpose through uplift pricing, work unit billing, or embedded SKUs
Measure adoption and usage to prove real customer value
Track AI contributions to pipeline velocity, ACV, retention, and expansion
Prepare for deeper due diligence as investors scrutinize ARR quality and AI economics

Final Thoughts

Ben’s presentation is a reminder that the fundamentals of finance still matter. ARR remains a powerful signal of value, but only when constructed with integrity and supported by a clean revenue architecture. AI presents an opportunity for meaningful growth, provided companies can measure and monetize it with discipline.

For teams preparing for their next stage of scale, the message is simple. Bring clarity to revenue, prove real adoption, and show how AI drives measurable impact. The companies that do this well will earn investor confidence and expand their valuation advantage in the years ahead.

The Agentic AI Cost Problem in Telco

Adam Howatson — Tue, 12 May 2026 13:24:24 GMT

Agentic AI is being positioned as the next major efficiency lever for telecom operators. Autonomous agents that can troubleshoot network issues, optimize routing, resolve billing disputes, upsell intelligently, or even orchestrate service provisioning promise a step change in operational productivity.

But there is a structural issue emerging beneath the hype.

Agentic AI is not simply another software license. It is not even comparable to traditional automation. It behaves more like a digital workforce that continuously consumes compute, calls models, interacts with multiple systems, and triggers cascading workflows. For telecom operators operating on already compressed margins, this introduces a new and potentially underestimated cost layer.

In telco, where cost discipline is survival, that matters.

Why Agentic AI Is Fundamentally Different for Telecom

Traditional AI deployments in telecom have typically been narrow:

Fraud detection models

Churn prediction algorithms
Traffic optimization engines
Static chatbots

Agentic AI changes the equation. These systems:

Plan multi-step actions
Interact across OSS, BSS, CRM, provisioning, billing, and network systems
Make iterative LLM calls to reason, validate, and execute
Trigger real downstream financial events

In a telecom environment, this means an agent could:

Analyze a billing dispute
Retrieve usage records
Check contract terms
Calculate credits
Initiate adjustments
Log compliance documentation
Communicate resolution

Each of these steps may involve multiple model calls, database queries, API interactions, and logging events. Multiply that by millions of subscribers, and you no longer have a tool. You have a cost engine.

The Real Cost Drivers for Telcos

1. Token Explosion at Scale

Telecom operators do not operate at startup scale. They operate at national or global scale.

If an agentic AI handles:

500,000 support interactions per month
5 to 15 reasoning cycles per interaction
Multiple contextual lookups per cycle

Token usage can escalate rapidly.

Unlike a static chatbot with limited branching, an agent makes recursive calls. In a high-volume environment like prepaid top-ups, roaming queries, IoT provisioning, or wholesale billing disputes, costs can grow non-linearly.

If pricing models remain opaque, operators risk margin erosion at scale.

2. Infrastructure and Orchestration Costs

Agentic systems require:

GPU-backed inference capacity
Vector databases
Observability pipelines
Logging and tracing
Workflow orchestration layers
Failover and fallback mechanisms

Telecom infrastructure is already complex. Integrating agentic AI into legacy BSS and OSS stacks adds engineering overhead and maintenance layers.

Unlike consumer SaaS companies, telcos often operate hybrid infrastructure environments with regulatory constraints, regional data residency requirements, and strict audit controls. That adds cost and friction.

3. Compliance and Regulatory Exposure

Telecom is one of the most regulated industries in the world.

Agentic AI making billing adjustments, provisioning changes, or promotional offers introduces:

Regulatory risk
Audit complexity
Dispute management exposure
Revenue assurance concerns

An incorrect automated credit issued at scale is not a minor bug. It is a financial liability.

The true TCO must include compliance validation, audit trails, explainability layers, and human oversight frameworks. These are rarely factored into initial AI budgets.

4. Revenue Assurance and Margin Impact

Telecom operators already struggle with:

Revenue leakage
Rating inconsistencies
Discounting complexity
Partner settlement errors

Agentic AI interacting with billing systems without structured guardrails can introduce new leakage vectors.

For example:

Misinterpreted contract tiers
Incorrect roaming classifications
Usage misalignment in IoT state-based plans
Wholesale rating inconsistencies

If monetization logic is not tightly controlled, AI automation can unintentionally amplify existing complexity rather than reduce it.

The Hidden Risk: AI Usage Growing Faster Than Revenue

One of the most concerning dynamics in telecom is this:

Network usage often grows faster than ARPU.

The same risk applies to AI.

If:

AI interaction volume scales rapidly
Agents are deployed across multiple channels
Internal teams use agents heavily

But monetization and cost tracking are not aligned, operators could face a scenario where:

AI consumption scales
Infrastructure spend rises
Margin remains flat

This is not hypothetical. Telecom history already provides a lesson.

Unlimited data plans destroyed per-GB pricing discipline. AI could repeat the same pattern if cost per interaction is not tightly measured and aligned to value.

What Telecom Leaders Should Do Now

1. Treat Agentic AI as a Monetized Resource

Every AI interaction has a cost. That cost must be:

Measured
Allocated
Controlled
Forecasted

Operators should build cost-per-interaction dashboards from day one.

2. Align AI Costs with Revenue Models

If AI agents:

Drive upsell
Reduce churn
Improve ARPU
Accelerate provisioning

Then ROI must be tied to revenue lift, not just automation savings.

Without revenue alignment, AI becomes an expense center rather than a growth engine.

3. Architect for Guardrails First, Autonomy Second

In telecom, financial logic cannot be probabilistic.

Agentic systems should:

Call deterministic rating engines
Respect contract enforcement rules
Validate before executing monetary actions
Log every financial decision

Autonomy without monetization governance is operational risk.

4. Build Monetization Infrastructure That Can Support AI

As AI introduces new billing dimensions, operators will need systems that can handle:

Usage-based AI pricing
Token-based consumption tracking
Hybrid subscription plus AI event models
State-based IoT billing triggered by AI decisions
Near real-time rating for AI-driven interactions

Legacy flat monthly billing platforms will not support this complexity.

AI is not just a technology upgrade. It is a monetization transformation event.

The Strategic Question for Telcos

Agentic AI is not a distant concept for telecom operators. Its adoption is inevitable. The real issue is not whether it will be deployed, but whether it will be governed with financial discipline.

Operators must decide if they will properly instrument it, actively manage its cost curve, align it to monetization outcomes, and protect already pressured margins.

Telecom has lived through multiple waves of margin compression. Unlimited pricing models diluted revenue predictability. OTT players reshaped value capture. 5G required heavy infrastructure investment before returns fully materialized. Each wave demonstrated the same principle: scale without monetization control erodes profitability.

Agentic AI presents a similar fork in the road. It can become a margin amplifier, driving operational efficiency, accelerating service innovation, and enabling intelligent, usage-aligned monetization. Or it can evolve into an uncontrolled cost layer, where AI consumption grows faster than revenue and quietly compresses margins.

The differentiator will not be the sophistication of the model. It will be monetization discipline.

If telecom leaders apply the same rigor they use for network economics, rating precision, and revenue assurance, agentic AI can become a strategic advantage. If it is treated as another automation layer without financial guardrails, it risks becoming the next invisible expense that scales with volume.

In telecom, scale magnifies everything. That includes AI cost.

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.

What Investors Expect From GTM Teams in the AI Era

Ryan Susanna — Mon, 04 May 2026 12:00:00 GMT

The acceleration of AI has rewritten many of the assumptions that once guided SaaS growth. Product categories are shifting, cost structures are no longer predictable, and customer expectations have risen sharply. In this environment, founders and GTM leaders must navigate a landscape where clarity, precision, and value alignment matter more than ever.

A panel discussion at The Usage Economy Summit 2025 moderated by Preethy Padmanabhan brought together three seasoned investors (Priya Saiprasad of Touring Capital, Ashmeet Sidana of Engineering Capital, and Anupam Rastogi of Emergent Ventures) to share what truly separates scalable startups from those that stall early.

Clarity Before Scale

Investors are prioritizing founders who show a disciplined understanding of their Ideal Customer Profile. This means specificity. The buyer, champion, evaluator, and user must be clearly defined long before aggressive sales hiring begins. Strong founders validate their thesis with fifty to one hundred conversations. They know who the product is for and equally important who it is not for.

Early stage companies are not expected to have a flawless GTM plan, but vague personas or competing narratives are immediate red flags. Precision of thinking matters more than polished slides.

Choosing the Right GTM Motion

Product led growth, sales led growth, and channel led growth all have their place. The early stage challenge is choosing one motion that aligns with how the target buyer discovers, evaluates, and purchases software.

Finance leaders do not download software on a whim. Customer service teams cannot self deploy tools that touch core operations. These functions demand a sales led approach. Developers and marketers, by contrast, might adopt software through usage. That makes PLG a credible option.

VCs consistently warn against splitting motions too early. Mixed GTM only works once a company has traction and repeatability. Until then, focus beats optionality.

Pricing in an AI Driven World

Seat based pricing is losing relevance when AI reduces the need for human interaction. If the product is successful, usage per user often goes down. That places pressure on traditional ARR models.

Founders are encouraged to start with usage based pricing that captures a fair share of value without creating friction. Once measurable ROI is proven, pricing can move toward outcome aligned models or a transparent take rate. Investors expect clarity on this evolution, not a static model.

AI also changes cost structures. Third party LLM fees can erode margins if usage surges. Pricing must protect unit economics or growth will work against the company.

Why Storytelling Still Wins

Founders often present one narrative to customers and a completely different narrative to investors. That disconnect is a warning sign. VCs want a single message rooted in the problem, the urgency behind the problem, and the ROI the product creates. The same story should resonate with users, buyers, and the board.

In crowded markets, the companies that win are those that articulate why they are different, why they matter now, and how they tie value creation directly to measurable outcomes.

The Importance of Trust and Transparency

AI era pricing introduces complexity. Customers expect visibility into meters, thresholds, and usage patterns. They want confidence that the bill is accurate and aligned to the value they receive. Companies that provide real time telemetry, easy to read usage statements, and clear audit trails build trust faster and renew more predictably.

Internally, pricing, finance, RevOps, and compensation need to stay in sync. Misalignment creates confusion in the field and revenue leakage in the back office.

What Differentiates Early Winners

Founders who deeply understand their customers outperform those who rely on assumptions. VCs consistently highlight the value of relentless customer interaction. Insight into user behavior, buying dynamics, and unspoken problems often creates the earliest differentiators in competitive categories.

Retention is also becoming a pressure point. AI pilots often start from innovation budgets. Renewal comes from business unit budgets. Value must be delivered in under twelve months to survive that transition.

Looking Toward 2026

Investors expect the next two years to be a turning point. AI applications will move from experimentation to production. New interaction models will reshape software categories. Vertical SaaS leaders will pursue acquisitions to fill AI gaps, creating meaningful exit opportunities.

This shift rewards founders who pair technical innovation with commercial excellence. A strong product is not enough. A strong GTM, clear pricing, and consistent storytelling will determine who breaks out.

The Telco Monetization Reset

Ryan Susanna — Mon, 27 Apr 2026 12:00:00 GMT

For decades, telecom monetization was a solved problem.
Build infrastructure. Sell access. Optimize scale.

That model no longer holds.

Today’s communications providers are delivering far more value than connectivity alone. AI-driven automation, real-time analytics, usage-based services, and industry-specific solutions are redefining what customers buy and how they experience value. Yet in many organizations, monetization has not evolved at the same pace.

This disconnect is now showing up at the executive level, not as a billing issue, but as a growth and retention risk.

Innovation Has Outpaced Monetization

Across UCaaS, CCaaS, VoIP, and regional telcos, the pattern is strikingly consistent.

Product teams ship AI-enabled features, smarter routing, automation, and analytics.
Sales teams bundle services creatively to win competitive deals.
Customers consume services dynamically, based on demand, seasonality, and outcomes.

Meanwhile, pricing and billing often remain anchored to static models designed for a different era. Per-seat pricing, rigid subscriptions, and manual adjustments struggle to reflect how value is actually delivered today.

The result is not a lack of demand. It is a monetization gap.

Providers are delivering more value than they can reliably price, bill, and enforce.

Why Retention Is Now a Monetization Question

Historically, churn was addressed through service quality, coverage, and price competitiveness. Those factors still matter, but they are no longer sufficient.

In consumption-driven environments, retention is increasingly shaped by how well customers understand and trust the relationship between usage and cost.

When billing is opaque, inconsistent, or difficult to reconcile, customer confidence erodes. Disputes increase. Finance teams intervene manually. Sales teams hesitate to introduce new services for fear of billing complexity.

Retention suffers not because customers lack value, but because value is not translated cleanly into revenue.

This is where the concept of a monetization reset becomes critical. Monetization is no longer a back-office function. It is a core driver of customer experience and lifetime value.

Agentic AI Changes the Equation

Agentic AI introduces a new operational reality for communications providers.

Rather than simply analyzing data, AI agents can take action across the service lifecycle. They can trigger changes in pricing, bundles, entitlements, and customer engagement based on real-time usage patterns and behaviors.

This shifts monetization from a static configuration exercise to a dynamic capability.

However, this only works if the underlying monetization platform can support it. AI does not eliminate complexity. It accelerates it.

Without flexible, usage-aware monetization systems, AI-driven services amplify existing constraints. Providers end up delivering advanced capabilities while billing as if nothing has changed.

The Executive Blind Spot

For many leadership teams, monetization challenges remain invisible until they surface as symptoms:

Revenue leakage that is hard to quantify
Manual billing work that scales with growth
Increasing contract disputes and exceptions
Slower time to market for new offerings

By the time these issues reach the executive agenda, they are no longer tactical. They affect margins, retention, and strategic agility.

The most forward-looking providers are addressing this earlier by treating monetization as a strategic capability rather than an operational afterthought.

What a Modern Monetization Strategy Looks Like

A future-proof monetization approach is not about choosing a single pricing model. It is about enabling optionality.

Leading providers are building the ability to:

Combine subscriptions, usage, events, and outcomes seamlessly
Enforce negotiated contracts automatically at scale
Support customer-specific pricing without operational overhead
Introduce AI-driven services without billing workarounds
Adapt pricing in response to consumption and engagement signals

This does not require abandoning existing business models. It requires an architecture that can evolve alongside them.

Monetization as a Leadership Responsibility

The telco monetization reset is ultimately an executive issue because it sits at the intersection of strategy, technology, and customer trust.

When monetization lags innovation, growth stalls quietly.
When monetization aligns with value, retention strengthens and expansion follows.

As AI and usage-driven services reshape communications, the winners will be those who recognize that billing is no longer just about charging accurately. It is about enabling the business to grow confidently.

The question for leadership teams is no longer whether monetization needs to change.
It is whether the organization is prepared to make monetization a core capability again.

AI Pricing Lessons from Telco

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.

Usage-Driven GTM Strategies

Ryan Susanna — Mon, 06 Apr 2026 12:00:00 GMT

Pricing expert Mark Stiving delivered a clear reminder at the Usage Economy Summit 2025 that revenue growth in subscription businesses is not accidental. It is the result of disciplined choices about value, segmentation, packaging, and pricing metrics. For companies operating in recurring revenue models, his message was both simple and demanding. Growth requires mastery of three revenue buckets: winning customers, keeping customers, and growing customers. Each one must be managed deliberately.

Buyers Pay for Value, Not Products

Mark began with the fundamental principle that drives all purchase decisions. Buyers trade money for value. In practice, this means that customers evaluate whether a product will help them make more money or reduce costs. Every pricing decision should reflect this reality.

In traditional perpetual license models, companies grew by increasing sales and marketing spend to acquire new customers. Recurring revenue businesses operate differently. The initial transaction is often smaller, and long term value is created only when customers stay, use the product, and increase their spend over time. Retention and expansion become powerful sources of revenue stability and growth.

The Three Revenue Buckets: Win, Keep, Grow

Mark’s framework for subscription success centers on three buckets that must be managed independently.

Win: Attracting and converting new customers.
Keep: Ensuring customers realize value so they renew.
Grow: Expanding revenue within the existing customer base through higher usage, upsells, cross-sells, or price increases.

Every business decision should support at least one of these buckets, and in a mature SaaS business the “grow” bucket becomes increasingly important. It may not feel urgent, but its impact on long term revenue is undeniable.

Three Levers That Shape Value

Mark identified three strategic levers that determine whether a company can capture the full value it creates.

1. Market Segmentation

Market segments should be defined by the problems customers are trying to solve rather than by industry, geography, or company size. Choosing the right segment sharpens messaging, improves product-market fit, and lays the foundation for differentiated pricing.

Platforms that serve many use cases risk being undervalued when sold as broad, undifferentiated offerings. Turning a platform into a segment-specific solution creates pricing power. He used LinkedIn as an example. Instead of a single offering, LinkedIn built distinct solutions for Recruiters, Sales professionals, Job Seekers, and general Professionals. Value becomes clearer. Willingness to pay rises.

2. Packaging

Packaging should be crafted inside each market segment. Good-Better-Best structures, when aligned with segment needs, offer customers a transparent and intuitive way to upgrade. Packaging also creates internal clarity. Teams know where new features belong and customers understand the trade-offs between tiers.

3. Pricing Metrics

The pricing metric defines what the customer is paying for. Strong metrics correlate closely with the customer’s own value metrics. In B2B, value is often measured in incremental profit. Companies must understand the KPIs that signal profit improvements and design pricing around those KPIs. When the metric aligns with value, customers accept higher usage, expansion becomes easier, and pricing grows more durable.

A misaligned metric, by contrast, leaves value uncaptured. Mark shared an example of a legal software provider that charged per user. Small firms paid more because lawyers used the tool directly, while large firms centralized usage with a few admins and paid less despite receiving far more value. The wrong metric limited revenue.

Why Companies Underinvest in Expansion

Despite its importance, expansion is often neglected. Mark explained why. Acquiring and retaining customers feel urgent. Expansion rarely does. No one is fired for a flat NRR number, but the long term impact of weak expansion is severe.

To expand effectively, companies must operationalize four levers:

Price increases when value delivered grows.
Upsells such as Good to Better or Better to Best.
Cross-sells for adjacent solutions.
Usage based growth driven by value aligned metrics.

Successful SaaS businesses plan for all four. None happen on their own.

A Value Architecture for Clearer Monetization

Stiving offered a structure for understanding how customers make decisions and how companies should align their pricing and packaging accordingly. It includes three layers.

Foundational Problem: Why customers buy the category at all. -> Guides segmentation and top level messaging.
Problem Scope: The customer’s specific needs and context. -> Guides packaging decisions and metric selection.
Situational Context: The conditions at the moment of purchase. -> Guides price segmentation and discounting.

Uber served as an illustration. Although the company handles many use cases, behind the scenes Uber organizes around three segments: people, food, and packages. Inside each segment are different packages, each optimized for a specific problem scope. Customers see a simple interface, while the underlying monetization structure is highly engineered. This is the blueprint for scalable pricing.

Aligning Sales With Value

The same value architecture applies to sales motions. Value-based prospecting identifies customers with strong problems. Consultative discovery quantifies the impact of solving those problems. Negotiation focuses on the value created, not arbitrary rates. Compensation, discount authority, and playbooks must reinforce this value centric approach.

Final Thought

Mark ended with a practical rule. If some customers extract significant value but pay very little, the pricing metric is almost certainly wrong. Companies that fix this problem unlock hidden expansion revenue and strengthen the integrity of their entire pricing strategy.

Watch Mark’s presentation to learn the practical pricing frameworks, value strategies, and growth levers that top recurring-revenue companies use to win, keep, and grow customers more effectively.

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

As the AI gold rush continues, only a few are turning hype into real revenue. This session with Marcos Rivera, CEO at Pricing I/O, cuts through the noise to explore what’s actually working for startups monetizing AI. From pricing models and product design to customer acquisition strategies, learn the patterns investors are watching and the tactics leading companies are using to turn AI capabilities into scalable businesses.

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.