Tech Transformed

SaaS companies moving toward usage-based and hybrid pricing models are discovering that revenue is no longer secured when the contract is signed.
Instead, revenue is earned continuously through product usage, introducing new challenges for finance teams around billing accuracy, revenue visibility, forecasting, and managing increasingly complex cost structures driven by AI-powered products.
In the latest episode of Tech Transformed, host Dana Gardner speaks with Lee Greene, Vice President of Sales at Vayu, about how AI and usage-based pricing are reshaping the economics of SaaS and why many companies are discovering that their pricing strategy is only as strong as the infrastructure behind it.
One idea from the conversation
“Pricing strategy is only as strong as the infrastructure behind it.”
What you will learn in this episode
Why usage-based pricing exposes hidden revenue leakage in many SaaS companies
• How AI-driven products introduce unpredictable cost structures and margin pressure
• Why disconnected CRM, product, and ERP systems break revenue visibility
• What finance and revenue teams need to support scalable usage-based billing and forecasting

Why SaaS Economics Are Breaking Away From Fixed Subscriptions
Greene argues that usage-based pricing isn’t simply an emerging trend. It is a response to assumptions that no longer hold true.
Traditional SaaS subscription models were built around predictable costs and relatively stable product usage. AI-driven products have fundamentally changed that equation. Each interaction with an AI-powered system can create variable cost, making static pricing models increasingly difficult to sustain.
This shift is also changing buyer expectations. Customers increasingly resist flat pricing structures and instead prefer models that reflect the value they actually receive. Usage-based pricing aligns economic benefit with real consumption, allowing buyers to justify spend internally while pushing vendors to be accountable for measurable outcomes rather than bundled feature sets.
AI’s Double Role
The conversation also highlights how AI is introducing a structural challenge for SaaS finance and revenue teams.
Usage-based pricing generates enormous volumes of data across product usage, customer behaviour, and cost inputs. Traditional billing systems were not designed to process this level of complexity.
At the same time, AI is also becoming the only scalable way to manage it. Automated usage tracking, dynamic pricing logic, and real-time billing reconciliation are increasingly necessary to maintain operational accuracy and financial control.
Treating AI solely as a product capability, rather than embedding it into revenue operations, can leave organizations exposed to billing errors, misaligned pricing models, and revenue leakage.
Revenue Management Shifts From Contracts to Operations
One of Greene’s key observations is that usage-based pricing does not necessarily create revenue leakage. Instead, it reveals problems that already existed.
The difference is visibility.
In traditional SaaS models, revenue was largely secured at the moment of contract signature. In usage-based models, revenue must be earned continuously through product consumption. This means billing accuracy, system integration, and data flow directly influence financial performance.
Disconnected CRM, product, and ERP systems can create gaps that lead to misbilling, delayed revenue recognition, and customer disputes. As a result, the infrastructure supporting revenue operations becomes inseparable from pricing strategy itself.
What SaaS Leaders Must Build to Stay Economically Viable
The discussion concludes with a broader perspective on how SaaS companies must evolve to support this new economic model.
The future belongs to organizations that design their pricing and revenue systems for variability. Pricing models must adapt to changing demand, and the systems behind them must support that flexibility without relying on heavy manual processes.
Automation and no-code AI tools are increasingly enabling finance and revenue teams to adjust pricing models as usage patterns evolve. This agility is not simply about speed. It is about maintaining control in an environment where AI-driven cost structures and product usage can shift rapidly.
Usage-based pricing is doing more than changing how SaaS products are sold. It is reshaping how companies think about value, risk, and revenue itself, making flexibility, intelligent automation, and data-driven decision making central to long-term success.
About Vayu
Vayu helps SaaS companies manage complex usage-based and hybrid revenue models by connecting product usage data, billing systems, and finance infrastructure.
Learn more at:https://www.withvayu.com/
Takeaways
The shift from fixed subscription models to usage-based pricing driven by AI
How AI is both creating and solving new pricing and billing challenges
Why revenue infrastructure plays a critical role in preventing revenue leakage
The importance of flexible pricing models that adapt to demand and usage patterns
The growing role of automation and AI in modern revenue operations

Chapters
00:00 – Introduction
02:30 – The economic shift in SaaS: Moving toward usage-based models
05:00 – The role of AI in transforming SaaS pricing and revenue streams
06:47 – Buyer preferences and evolving value quantification
08:38 – Infrastructure's role in supporting flexible billing models
11:49 – How finance teams can shape technology to control revenue
14:24 – Process reengineering and AI-driven automation
17:15 – Adaptable SaaS infrastructure and market signals
20:30 – Preparing for the unknown: sandboxing and scenario modeling
24:49 – Opportunities in connecting SaaS apps and managing data flow
28:54 – Building automated, scalable billing and integration flow

Managing product complexity has become increasingly critical as customers demand greater customisation. Manufacturers face the challenge of connecting disparate data systems effectively. In this episode of Tech Transformed, host Christina Stathopoulos and Laura Beckwith, Director of Product Management at Configit, discuss the complexities of managing product data in manufacturing, focusing on the concept of the digital thread. They explore the challenges manufacturers face in connecting disparate data systems, the importance of customisation, and how a Configuration Lifecycle Management (CLM) approach can provide a reliable foundation for digital threads.
Understanding the Digital Thread
The digital thread represents the traceability of all decisions and information regarding a product from its inception and throughout its lifecycle. According to Laura Beckwith, the digital thread allows manufacturers to trace decisions made during the requirements stage through to engineering and ultimately to manufacturing and service. This traceability is not just about having data; it’s also about ensuring that various teams and systems can access the right information to facilitate informed decision-making.
Challenges in Implementing the Digital Thread
Despite the promise that digital threads hold, manufacturers face significant challenges in connecting data from multiple systems. Beckwith highlights the example of a smartphone, which undergoes various phases from design to manufacturing. Each phase involves distinct software systems—like CAD for design and ERP for manufacturing—many of which do not communicate well with one another. This lack of integration often leads to inefficiencies, such as manual data entry and miscommunication between teams.
The Impact of Customisation on Complexity
As customisation becomes the norm, the complexity of managing product data increases exponentially. Beckwith notes that while smartphones may have limited customisations, products like cars offer vast configurability. For instance, when configuring a car, consumers can choose from an extensive array of options. Behind the scenes, however, manufacturers must manage numerous engineering constraints and compliance regulations. This is where the digital thread becomes essential, enabling manufacturers to track and manage these complex configurations effectively.
The Role of Configuration Lifecycle Management (CLM)
The upcoming CLM Summit 2026 will focus on mastering customisation complexity and building a reliable data foundation for configurable products. Beckwith explains that a scalable CLM approach is crucial for establishing a reliable digital thread. It ensures that all product configurations, such as the combination of seat heating and memory seats in a car, are tracked accurately. This not only aids in the manufacturing process but also enhances customer service by allowing manufacturers to address issues based on specific configurations.
More broadly, the digital thread provides manufacturers with a framework for managing the growing complexity of modern product development. By enabling seamless communication between data systems and implementing effective CLM practices, organisations can better align engineering, manufacturing, and service functions.
For more information visit: https://configit.com/
Takeaways
The digital thread provides traceability of product decisions.
Manufacturers face challenges due to siloed data systems.
Customisation complexity is increasing in manufacturing.
Digital threads are crucial for configurable products like cars.
CLM helps bridge the gap between engineering and marketing.
Starting small can lead to the successful implementation of digital threads.
Data alignment is essential for effective communication.
Real-world examples illustrate the benefits of digital threads.
A strong digital thread enhances customer experience.
AI can leverage data from digital threads for predictive maintenance.

Chapters
00:00 Introduction to Digital Threads in Manufacturing
02:14 Understanding the Digital Thread
06:47 Challenges in Connecting Data Systems
11:12 Customisation, Complexity, and Digital Threads
15:43 The Role of Configuration Lifecycle Management (CLM)
20:23 Real-World Use Case: Implementing Digital Threads
23:42 Guidance for Early Adopters of Digital Threads

As AI systems move rapidly from experimentation into production, organizations are discovering that adoption alone is not the hard part, understanding, governing, and trusting AI in live environments is.
In this episode of the Tech Transformed, Shubhangi Dua speaks with Camden Swita, Head of AI, New Relic, about why AI observability has become a critical requirement for modern enterprises, particularly as agentic AI and AI-driven operations take on increasingly autonomous roles.
The discussion explores how traditional observability models fall short when applied to probabilistic systems, why many AI ops initiatives stall at proof-of-concept, and what security and IT leaders must prioritize to safely scale AI in production.
Be the first to see how intelligent observability takes you beyond dashboards to agentic AI with business impact at New Relic Advance, February 24, 2026.
Why AI Adoption Is Outpacing Operational Readiness
While AI adoption is accelerating rapidly, most organizations still lack visibility into what their AI systems are actually doing once deployed. Generative AI is already widely used for natural language querying, coding assistants, customer support bots, and increasingly within IT operations and SRE workflows.
As these systems move into production, new challenges emerge around cost control, governance, performance quality, and trust. Leaders recognize AI’s potential value, but without deep observability, they struggle to determine whether AI-enabled systems are delivering consistent outcomes or introducing hidden operational and security risks.
How Observability Must Evolve for Agentic AI and AI Ops
The episode then examines how observability itself must evolve to support agentic and autonomous AI systems. While core observability principles still apply, AI introduces a new layer of complexity that requires visibility into model behavior, agent decision-making, and multi-step workflows.
Modern AI observability extends traditional application performance monitoring by capturing telemetry from LLM interactions, agent orchestration layers, and automated evaluations of output quality against intended use cases.
Without this visibility, teams are effectively operating blind, unable to diagnose failures, validate compliance, or confidently deploy AI at scale. At the same time, AI is increasingly being embedded into observability platforms to reduce noise, accelerate root cause analysis, and improve incident response.
Making Agentic AI Work in Practice
Successful adoption starts with low-risk, high-friction tasks such as incident triage, dashboard interpretation, and runbook summarization, rather than fully autonomous remediation. These use cases deliver immediate productivity gains while preserving human oversight. Over time, stronger feedback loops, better context management, and human-in-the-loop learning allow agents to become more reliable and useful. Looking ahead, Camden predicts that 2026 will be a turning point for agentic AI in production, driven by maturing AI observability platforms, richer semantic data, and knowledge graphs that connect technical telemetry to real business outcomes.
Listen to Are “Vibe-Coded” Systems the Next Big Risk to Enterprise Stability?
When Vibe Code Breaks Ops
AI-generated code is pushing prototypes into production faster than ops can cope. How observability becomes the gatekeeper for enterprise resilience.
Key Takeaways
AI adoption is accelerating in enterprise environments.
Organizations face complexities in productionizing AI features.
Natural language querying is a common AI application.
AI agents are increasingly used in IT operations.
Observability is crucial for understanding AI systems.
Traditional observability solutions are evolving to include AI monitoring.
Incident response teams struggle with alert noise and context gathering.
AI can assist in incident management and root cause analysis.
Future trends include more reliable AI agents and monitoring solutions.
Organizations need to invest in AI observability to succeed.

Chapters
01:20 The Current State of AI Adoption
02:28 Purposeful AI Usage in Organizations
04:40 Observability in the Age of AI
08:05 Evolving Observability Solutions
11:36 Challenges in Incident Response
16:04 Integrating AI in Operations
23:33 Future Trends in AI Monitoring
30:29 Investment Strategies for AI Solutions

#ArtificialIntelligence #EnterpriseAI #GenerativeAI #AgenticAI #AIAgents #AIObservability #AIInProduction #AIOps #AISecurity #AIGovernance #ModelMonitoring #LLMOps #ITOperations #SRE #DevOps #IncidentResponse #RootCauseAnalysis #DigitalTransformation #Automation #FutureOfAI

Did you know that on average, 35 per cent of calls to automotive dealerships go unanswered? In today’s competitive market, missed calls mean missed sales and dealerships are turning to AI and analytics to fix this.
In this episode of Tech Transformed, host Jon Arnold and Ben Chodor, Chief Executive Officer of CallRevu, about how AI is reshaping the way dealerships handle calls, manage repair orders, and engage with customers throughout their journey. They explore the role of real-time analytics in improving interactions, the importance of answering every incoming call, and why AI has become essential in modern dealership operations.
Customer Experience Has Changed
The customer journey is no longer a simple transaction. Today, it spans pre-purchase research, purchasing, and post-purchase support. Chodor highlights that every interaction matters; customers now expect engagement and guidance at every stage, not just information.
Competition in automotive sales is fierce, and customers expect fast responses. Chodor notes that dealerships leveraging AI can provide updates on service times, answer inquiries promptly, and ensure no customer engagement is lost. Real-time insights also empower managers to make better operational decisions and improve the overall customer experience.
AI in Automotive Dealerships
AI technology is changing the way dealerships operate. Chodor discusses how CallRevu’s technology listens to every sales and service call, providing real-time analytics to dealerships. This capability allows managers to intervene in calls, ensuring that customer concerns are addressed promptly. For instance, if a call goes unanswered, the system can alert management, enabling them to engage with the customer immediately, thus reducing missed opportunities.
The integration of AI and analytics in automotive dealerships is not just about improving sales; it's about transforming the entire customer experience. From ensuring every call is answered to providing real-time insights for better decision-making, technology is reshaping how dealerships engage with customers. As the automotive industry continues to evolve, those who prioritise customer experience through innovative solutions will undoubtedly lead the way.
If you would like to find out more information, go to https://www.callrevu.com/
Takeaways
AI enhances customer engagement in automotive dealerships.
Real-time analytics can significantly improve communication.
Every call to a dealership is crucial for sales.
AI helps reduce the number of calls going to voicemail.
Dealerships must adapt to a more competitive landscape.
Customer experience is more than just selling cars.
AI can provide instant responses to customer inquiries.
Training tools powered by AI can improve sales techniques.
The automotive industry is shifting towards data-driven decisions.
AI is essential for modern dealership operations.

Chapters
00:00 Introduction to Customer Experience in Automotive Dealerships
05:00 The Role of AI in Enhancing Communication
10:08 Transforming Customer Engagement with Real-Time Analytics
15:03 The Importance of Incoming Calls and Tracking
19:55 AI's Impact on the Automotive Industry
24:49 Future Trends in Automotive Technology

Podcast: Tech Transformed Podcast
Guest: Manesh Tailor, EMEA Field CTO, New Relic
Host: Shubhangi Dua, B2B Tech Journalist, EM360Tech
AI-driven development has become obsessive recently, with vibe-coding becoming more common and accelerating innovation at an unprecedented rate. This, however, is also leading to a substantial increase in costly outages. Many organisations do not fully grasp the repercussions until their customers are affected.
In this episode of the Tech Transformed Podcast, EM360Tech’s Podcast Producer and B2B Tech Journalist, Shubhangi Dua, spoke with Manesh Tailor, EMEA Field CTO at New Relic, about why AI-generated code, also called vibe-coding, rapid prototyping, and a focus on speed create dangerous gaps. They also talked about why full-stack observability is now crucial for operational resilience in 2026 and beyond.
AI Vibe Code Prioritising Speed over Stability
AI has changed how software is built. Problems are solved faster, prototypes are created in hours, and proofs-of-concept (POC) swiftly reach production. But this speed comes with drawbacks.
“These prototypes, these POCs, make it to production very readily,” Tailor explained. “Because they work—and they work very quickly.”
In the past, the time needed to design and implement a solution served as a natural filter. However, the barrier has now disappeared.
Tailor tells Dua: “The problem occurs, the solution is quick, and these things get out into production super, super fast. Now you’ve got something that wasn’t necessarily designed well.”
The outcome is that the new systems work but do not scale. They lack operational resilience and greatly increase the cognitive load on engineering teams.
New Relic's research indicates that in EMEA alone:
The annual median cost of high-impact IT outages for EMEA businesses is $102 million per year
Downtime costs EMEA businesses an average of $2 million per hour
More than a third (37%) of EMEA businesses experience high-impact outages weekly or more often.

Essentially, AI-driven development heightens risks and increases blind spots. “There are unrealised problems that take longer to solve—and they occur more often,” Tailor noted. This is because many AI-generated solutions overlook operability, scaling, or long-term maintenance.
Modern architectures were already complex before AI came along. Microservices, SaaS dependencies, and distributed systems scatter visibility across the stack.
“We’ve got more solutions, more technology, more unknowns, all moving faster,” he tells Dua. “That’s generated more data, more noise—and more blind spots.”
Traditional monitoring tools were built for known issues—predefined components, predictable dependencies, and static systems. “Monitoring was about what you already understood,” Tailor explained. “Observability is about the unknown unknowns.”
AI-generated code complicates the situation because teams often lack detailed knowledge of how that code was created, how components interact, or how dependencies change over time.
This is where full-stack observability becomes essential—not as a standalone tool, but as a coordinated capability that connects signals across applications, infrastructure, data, and AI systems in real time.
Also Watch: How Do AI and Observability Redefine Application Performance?
Reactive to Proactive: The Role of AI in Observability
Ironically, the same AI that increases complexity is also necessary to manage it. According to New Relic data, 96 per cent of organisations plan to adopt AI monitoring and 84 per cent plan to implement AIOps by 2028.
However, Tailor stresses that success relies on using AI to enhance—rather than replace—human expertise. “We have to leverage AI to establish baselines much faster,” he said. “But humans still bring experience and judgment that machines don’t have.”
AI allows teams to shift from responding to known patterns to proactively spotting anomalies before they turn into customer-facing incidents.
Beyond uptime and performance, observability is becoming a regulatory requirement. “If it’s not observed, then it’s rogue,” Tailor warned.
New regulations like the EU AI Act and ISO 42001 will require organisations to show visibility into AI systems, decision-making processes, and operational behaviour. “You won’t be allowed to operate AI solutions without the right level of observability,” he added.
The 2026 Takeaway: Observability is Essential for AI
As AI-driven development becomes the norm, Tailor’s message to CIOs, CTOs, and CDOs is: “Observability isn’t an option. Without it, your AI strategy simply won’t work.”
Organisations that neglect to invest in centralised, full-stack observability risk more than outages—they risk compliance failures, security issues, and rising operational costs.
“Otherwise,” Tailor stated, “you will limit the ability to benefit from your AI strategy.”
To learn more, visit NewRelic.com or listen to the full episode of the Tech Transformed podcast at EM360Tech.com.
Also Watch: How Can AI Bridge the Gap from Observability to Understandability?
Takeaways
If you don't get your observability house in order, all the grand plans with AI may be at risk.
Speed has been favoured over good governance and engineering standards.
Observability is about understanding the relationship between components, not just monitoring known issues.
AI can help establish baselines faster in a rapidly changing environment.
Without observability, you can't make your AI strategy work.

Chapters
00:00 Introduction to AI and Observability
01:11 The Risks of Rapid Software Development
04:21 Understanding the Cost of Outages
06:30 Blind Spots in AI-Driven Systems
11:29 Transitioning to Full-Stack Observability
13:58 Moving from Reactive to Proactive Monitoring
18:54 Real-World Applications of AI Monitoring
19:51 The Future of AI and Observability

#Observability #AIOps #AIDrivenDevelopment #FullStackObservability #ITOutages #VibeCoding #AIinProduction #DevOps #NewRelic #TechPodcast