TECHtonic: Trends in Technology and Services

What does it take to build a successful forward deployed engineering (FDE) function as AI reshapes how technology companies deliver value? Thomas Lah kicks off the conversation with TSIA’s newly published FDE Maturity Model, revealing why this emerging function is still evolving across the industry. Joining him is Usman Nasir, VP of AI Forward Deployed Engineering at Salesforce, who shares an inside look at how Salesforce is building and scaling its FDE capability, from deploying production-ready AI agents to driving customer adoption and turning field insights into product innovation.

From building the right team and defining the FDE role to understanding the economics behind customer deployments, Usman shares what it takes to make this model work at enterprise scale. Thomas and Usman unpack Salesforce’s pod approach, the talent needed to succeed, and why testing AI agents demands an entirely new mindset. As organizations race to turn AI potential into production results, this episode offers a closer look at the people, operating models, and strategies shaping the future of forward deployed engineering.

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.

Thomas Lah opens the episode by describing TSIA's model-driven revenue engine framework: using AI to monitor every customer touchpoint in real time instead of running revenue off CRM fields and pipeline reviews. His guest, Stephen Messer, has spent three decades living that shift firsthand. Messer co-founded LinkShare in the 1990s, and later co-founded Collective[i], the AI sales intelligence network the Wall Street Journal has compared to Waze for sales.

Messer argues that most AI investment in sales, from chatbot-assisted CRM entry to faster email drafting, is being layered onto a system that was never built around the buyer. He explains how Collective[i] models buying committees, introduces sequencing, and maps the hidden relationships driving each deal.

The conversation also challenges one of sales' longest-standing practices: forecasting. Messer argues that traditional forecast calls are little more than weekly guesswork that consumes valuable selling time without improving accuracy. In its place, he outlines an AI-first approach that provides a dynamic, daily view of deal health, highlighting what's changed, why it changed, and where sales teams should focus next. Like a navigation app that constantly recalculates the fastest route, AI helps revenue leaders adapt to changing buyer behavior as it happens.

Thomas Lah welcomes social psychologist Sarah DiMuccio to talk about the human side of AI transformation. They dig into why AI adoption triggers anxiety across every role, seniority level, and demographic: it isn't resistance to a task, it's a threat to how people see themselves professionally. Sarah explains that companies are investing heavily in AI technology while investing almost nothing in enablement, treating adoption as something that happens automatically once the tool is switched on rather than a genuine redesign of how people work. That gap shows up as “quiet checkout,” a form of disengagement Sarah argues is more damaging than outright sabotage because it's invisible to leadership and never gets addressed.

The conversation turns to what actually builds trust and follow-through: naming the fear directly instead of talking around it, leaders modeling experimentation in front of their teams, and co-creating AI use cases with employees rather than imposing them from the top down. Sarah introduces her framework for future-ready leadership, a Venn diagram of AI fluency, strategic agility, and relational intelligence, arguing that leaders missing the relational piece may retain talent short-term through “golden handcuffs,” but they lose the honesty, experimentation, and judgment that AI-era competitiveness actually depends on.

In this episode of TECHtonic, Thomas Lah welcomes Deb Ashton, Founder of Certinia, for a conversation about the transformation of professional services in the age of AI. They explore why traditional utilization-based business models are giving way to outcome-driven engagements, how AI is changing pricing, delivery, and workforce strategies, and why customer value must become the foundation of every services organization.

Deb shares practical insights from working with technology companies that are embracing AI to streamline delivery while empowering consultants to focus on strategic guidance, governance, and customer relationships. Together, they discuss the rise of Professional Services 2.0, the importance of measuring time-to-value and business outcomes, and what leaders must do today to build more scalable, profitable, and customer-centric services organizations.

AI is changing everything—but there's one part of the conversation many organizations still aren't having: the economics. As enterprises race to deploy copilots, agents, and generative AI across every department, leaders are discovering that AI costs don't arrive as a single invoice. They show up across GPUs, token consumption, cloud infrastructure, data platforms, and idle compute resources, making it difficult to understand whether AI investments are actually delivering business value.

In this episode of TECHtonic, TSIA Executive Director Thomas Lah sits down with Kunal Agarwal, CEO and co-founder of Unravel Data, to discuss why AI FinOps has become one of the most important disciplines for enterprise technology leaders. Kunal explains how organizations can optimize prompts, right-size AI models, eliminate wasted GPU capacity, and gain real-time visibility into the full AI technology stack. Together, they explore why AI should no longer be treated as a science experiment, how leading organizations are creating headroom to fund continued innovation, and why the companies that combine AI ambition with financial discipline will become tomorrow's AI-native market leaders.

If you're responsible for AI strategy, cloud operations, infrastructure, finance, or technology investments, this episode offers a practical roadmap for balancing innovation with profitability—and ensuring your AI initiatives deliver measurable business outcomes.