TECHtonic: Trends in Technology and Services

What does it take to turn AI from an exciting innovation into a business model that actually delivers value? On this episode of TECHtonic, Thomas Lah walks through TSIA’s AI pricing ladder with Alon Talmor, founder and CEO of Mosaic AI, to unpack how companies can move beyond traditional seat-based pricing and start monetizing the work AI gets done. After selling his first startup to Salesforce and earning a PhD in natural language processing, Alon saw an opportunity to use AI to fundamentally transform technical support and how engineers tackle complex challenges.
Alon shares how Mosaic AI takes a different approach to AI adoption, starting with an in-depth look at how support engineers work to identify the highest-impact automation opportunities. Thomas then connects this approach to a bigger question facing technology companies: How do you price AI based on the value it creates? Together, they discuss why understanding the work, defining success with customers, and measuring the right productivity metrics are critical to building an AI business model that works.
But pricing is only part of the equation. They also tackle what it takes to drive adoption, bring experienced engineers along on the AI journey, and rethink the economics of software in a world where AI does more of the work. From outcome-based pricing to the future of technical support, this conversation challenges some long-standing assumptions about how technology companies sell and deliver value. The big takeaway? If AI is changing how work gets done, it’s time to rethink how you charge for it.

Technology providers have mastered the art of selling value, but too often struggle to prove it after the deal is signed. In this episode of TSIA’s TECHtonic, Thomas Lah sits down with Steve Zimba, founder of Nulia, and Sue Nabeth Moore, founder of Success Chain, to explore how their teams have come together to build Tivium, a platform designed to bridge the gap between technology adoption and measurable business outcomes. Through their work on value realization, they reveal why product usage alone isn't enough, how activity and performance measurements can connect everyday workflows to business results, and why understanding both realized and perceived value is critical to customer retention and growth.
Steve and Sue share how Tivium helps providers track progress, identify customers who may be falling short of expected results, and use those insights to strengthen retention and drive expansion. As AI raises customer expectations for tangible returns on technology investments, the ability to consistently deliver on value promises is becoming a business imperative. The episode challenges providers to rethink the role of customer success, embrace monetizable value-added services, and close the loop between what they build, what they sell, and the outcomes customers actually achieve.

AI is creating a new question for managed services: what happens when the work itself becomes software-driven? On this episode of TSIA’s TECHtonic, Thomas Lah talks with Scott McIsaac, CEO of Helios Core, about the shift from labor-driven IT support to AI-driven operations. Scott brings two decades of managed services experience to the conversation, including leadership roles at Secure-24 and NTT, and shares how customer demand led to the creation of Mira Resolve, an AI-powered platform he describes as an autopilot for IT.
The conversation looks at what happens when AI starts doing the work traditionally handled by IT support teams, including how agents can operate across an environment while bringing humans in for the moments that matter. Thomas and Scott also dig into the knowledge-management problem underneath AI adoption, the challenges of pricing AI-driven work, and why buying AI technology alone does not create efficiency. The bigger question is how services organizations adapt their technology, knowledge, and business models when labor is no longer the primary unit of work.

AI is forcing technology companies to rethink what customers are actually paying for, and how they prove the value they deliver.
On this episode of TSIA’s TECHtonic, Thomas Lah sits down with Michael Speranza, CEO of Kantata, to explore how AI is reshaping the economics of SaaS, professional services, and enterprise technology. As software becomes easier to build and AI accelerates automation, the code itself is becoming less defensible. The real value is shifting toward industry expertise, data, context, and the ability to turn insights into measurable business outcomes.
Thomas and Michael unpack why simple AI automation is quickly becoming table stakes, while predictive and agentic capabilities are opening the door to a new level of value creation. They also examine the growing pressure to move beyond seat-based and time-and-materials pricing toward consumption- and outcome-based models—and why both technology providers and their customers are still figuring out what that transition should look like.
The conversation also explores the rise of the hybrid workforce, the changing role of technology services, the importance of reducing time to value, and why the future of enterprise software may not be a choice between standardization and customization, but a new model that combines the two.
For technology and services leaders navigating AI, the message is clear: the question is no longer simply what your technology can do. It’s what becomes possible for your customer because of it, and whether you can prove it.

What happens when an AI agent fails, and how can enterprises prove they saw it coming? On this episode of TSIA’s TECHtonic, Thomas Lah takes on one of the most important questions facing organizations moving AI from experimentation into the enterprise: How do you prove that AI is actually delivering the outcomes you promised? Drawing on TSIA’s Five Proofs of Outcome-Based Revenue, Thomas and guest Sekhar Sarukkai unpack why proof of performance and telemetry are becoming essential to proving business value. Sarukkai, a serial entrepreneur who previously founded Skyhigh Networks and Securent, now leads Chatsee.ai, which recently raised $6.5 million to build what he calls a failure intelligence layer for AI agents.
The conversation takes a revealing look at what really goes wrong when AI agents enter the real world. After analyzing 10,000 enterprise agent failures, Chatsee identified 157 distinct failure categories, and found that hallucinations account for less than 10% of actual failures. Instead, enterprises are facing bigger and often invisible challenges around resolution, escalation, silent execution, and the growing gap between pre-deployment controls and runtime governance. Thomas and Sekhar also unpack the hidden economics of AI failure, including how a seemingly minor error can quietly spread through downstream systems for weeks. Sekhar introduces a framework for measuring direct loss, propagation, detection delay, and reversibility, while making the case for shared accountability across enterprises, AI platforms, and integrators.
If your organization is serious about moving AI agents into production, and proving the value they deliver, this is a conversation you’ll want to hear.