TECHtonic: Trends in Technology and Services

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.

In this episode of TECHtonic, host Thomas Lah, EVP and Executive Director of TSIA, sits down with Agam Vasani, former SVP of Customer Experience at LeanData, to explore what it actually takes to build an AI-driven post-sale organization. Agam shares how his team was drowning in over 40 fragmented customer health signals, leaving CSMs spending more time assembling data than acting on it. He then reveals how they used AI to consolidate those signals into a single, coherent view that reps could actually use.He also breaks down the SIGNAL framework, a six-part filter he developed to cut through a crowded AI vendor market and evaluate tools on source of truth, intelligence quality, go-to action, workflow fit, team-wide adoption, and continuous learning.

Discover how peer-driven "AI jams" drove grassroots adoption where top-down mandates failed, and why most AI tools fall short because they're sold like SaaS when AI behaves nothing like it. Don't miss this candid conversation on what separates AI deployments that move the needle from ones that just add another tool to the stack.

Your CRM knows what happened. It doesn’t know why—or what’s about to happen next. That gap is costing revenue teams millions in preventable churn, missed expansion, and deals that slip away long before anyone saw it coming.

In this episode of TECHtonic, TSIA Executive Director Thomas Lah sits down with Alok Shukla, CEO and co-founder of Funnel Story, to explore a new category of technology: the AI-powered revenue intelligence layer. Unlike traditional CRM dashboards that report on structured activity data in a single point in time, Funnel Story’s patented composite model combines structured data (usage, revenue, activity), unstructured conversational data (calls, emails, notes), and third-party market signals—then reverse-engineers your full historical timeline to train itself from day one. Median deployment time: less than a day.

Alok introduces the concept of “needle movers”—AI-detected early warning patterns that surface months before churn or expansion become visible to any human. He shares a compelling real-world example where signals from three different organizational levels (an executive conversation, a support ticket, and a CSM interaction) were silently pointing to competitive risk—patterns that only emerged because of historical churn analysis. Without the intelligence layer connecting those dots, the account would have been marked “healthy” right up until it churned.

Drawing on his 20+ years in cybersecurity (McAfee, Intel Security, Imperva), Alok makes a powerful analogy: the Security Operations Center went from 80% people / 20% tech to nearly the inverse over 20 years—and that transformation is now coming for revenue and CS organizations. The leaders who will thrive are those who start thinking now about what it means to manage a fleet of agents rather than a team of reps.