Disambiguation

In this solo deep dive episode of the Disambiguation podcast, host Michael Fauscette walks through the new Arion Research report, Agentic AI Trends 2027: 16 trends organized into three tiers, a scorecard grading last year's predictions, and one big idea that ties everything together.

The episode opens with a report card on the 12 predictions from the 2026 trends report. 4 ran ahead of forecast, 7 landed on track, and 1 split. The pattern underneath the scores matters more than the scores themselves: vendor supply outran enterprise adoption. Every major platform shipped agent control planes, memory, governance tooling, and marketplaces faster than expected, while enterprises absorbed them more slowly. McKinsey's 2026 State of AI survey found that 40% of large organizations are now scaling AI agents, up from 27% a year earlier.

For 2027: agents are becoming a workforce that has to be managed like one. Can you identify every agent you run? Can you govern what it is allowed to do? Can you measure whether it is creating value? Are you paying for results, not seats? Those four questions, identified, governed, measured, and paid for by results, map to nearly every trend in the report.

Topics covered include why every agent needs its own identity, scope, permissions, and guardrails, and why the CISO becomes a required approver for any agent that touches production. How coding agents proved the model for knowledge work, and why the workspace agent becomes standard equipment in 2027. Why orchestration went cross-vendor and 26% of large U.S. companies cannot detect unauthorized agents running inside their walls. Why agent ops must shift from tooling to continuous production evaluation tied to business outcomes. Why the entry-level talent pipeline is now a board-level issue. How per-seat pricing breaks when agents do the work, and why hybrid pricing with cost-per-outcome metrics will dominate. Why systems of record are becoming headless enterprise applications where the assistant becomes the front door. How long-horizon agents can now work for 11-plus hours autonomously, but management practices haven't caught up. Why context engineering replaced enterprise memory as the harder problem. How agent skills became the reusable unit. And why, with regulation receding, assurance is being built by the market through certification, insurance, and contract requirements.

The episode closes with the concept of the Recomposable Enterprise, where governed infrastructure, AI builders, and operating discipline combine to let organizations assemble, use, retire, and rebuild software and agent teams around the work in front of them. Seven action items for leaders and a risk framework round out the discussion.

The full report, Agentic AI Trends 2027, available at arionresearch.com on the Research Reports page.

Timestamps:
00:00 - Introduction
02:08 - Method: three tiers, five dimensions per trend
02:15 - 2026 scorecard: 4 ran ahead, 7 on track, 1 split
03:06 - Tier 1 from 2025: what ran ahead and what surprised
05:08 - Agent identity shipped ahead of schedule
05:45 - 4 lessons carried into 2027
06:06 - The big idea: agents as a managed workforce
06:57 - Map of 16 trends
07:53 - Tier 1: agent identity and security
09:34 - Tier 1: agents in knowledge work
10:07 - Tier 1: agent orchestration and control planes
11:27 - Tier 1: agent ops 2.0
12:04 - Tier 1: workforce restructuring
12:51 - Tier 1: agent economics
13:14 - Tier 1: domain agents at scale
13:58 - Tier 1: headless enterprise apps
14:38 - Tier 2: long-horizon agents
15:59 - Tier 2: context engineering
16:22 - Tier 2: agent skills
16:55 - Tier 2: process redesign
17:08 - Tier 3: cross-company agent networks
17:38 - Tier 3: agentic commerce
18:05 - Tier 3: agent browsers
18:31 - Tier 3: regulation and assurance
19:07 - The Recomposable Enterprise
22:09 - 7 moves for leaders
22:48 - Risks
23:16 - Identified, governed, measured, paid for by results

Host: Michael Fauscette, CEO & Chief Analyst, Arion Research

In this episode of the Disambiguation podcast, host Michael Fauscette talks with John Arnold, Head of Product Marketing and Strategic Advisory at Creatio, about the findings from Creatio's State of Enterprise AI Adoption report, a survey of 608 IT and business decision-makers that reveals where enterprise AI actually stands, what separates the leaders from the rest, and why governance is the biggest gap between companies that scale AI and companies that stall.
John is a former Forrester analyst who now runs a strategic advisory practice at Creatio, where he helps customers navigate AI adoption and builds research to give the broader market a clear-eyed view of what is working and what is not. The 2026 report is the second annual edition and was conducted through an independent professional survey organization, not a customer poll. It scores organizations across two axes: depth of AI capabilities and breadth of deployment across departments, creating a rigorous classification that identified 17% of respondents as AI adoption leaders.
The conversation covers why 70% of organizations have moved past AI experimentation and 78% expect to scale AI agents across departments within 12 months, what separates the 17% of AI adoption leaders who have automated more than half their workflows from the 83% still working through it, why organizations with established AI governance are four times more likely to feel confident about scaling while only 29% have a full governance framework in place, how barriers to AI adoption shift from talent and skills gaps early on to governance, security, and integration challenges at maturity, why shadow AI and bring-your-own-AI trends are creating sprawl that IT teams must address through platform unification rather than lockdown, how the report links deeper AI adoption to revenue outcomes with organizations automating more than half their workflows being twice as likely to report revenue gains, why measurement has not kept pace with adoption as companies chase productivity gains without establishing KPIs for customer experience and business growth, how AI is creating new roles and fusion teams where IT and business users collaborate to build agents on shared platforms, why human-in-the-loop needs to evolve into risk-based autonomy where AI earns trust through demonstrated performance, and what CIOs still in experimentation should do first to close the gap with the leaders group.
Timestamps:00:00 - Introduction00:46 - John's background: Forrester analyst to Creatio advisory01:23 - Why Creatio built the State of Enterprise AI Adoption report02:56 - Headline finding: 70% past experimentation, 78% scaling within 12 months06:12 - What defines the 17% AI adoption leaders08:00 - Leaders scoring high on depth and breadth of AI deployment10:00 - Governance as the 4x confidence multiplier11:05 - Why only 29% have established governance12:33 - Governance by design, not governance after the fact13:19 - Barriers shifting from skills to integration and security16:21 - Shadow AI and the bring-your-own-AI trend19:08 - Platform unification: solving AI sprawl and AI stall20:10 - No-code enabling business users to build agents22:56 - IT and business alignment across four-plus functions29:05 - Measurement gap: productivity tracked, customer experience lagging30:05 - AI-specific KPIs vs. traditional satisfaction metrics32:07 - Human-in-the-loop vs. risk-based autonomy34:48 - Managing AI agents like managing a team39:11 - Advice for CIOs: fusion teams, growth mindset, AI as solution41:46 - Recommendation: Katherine Kostereva, CEO of Creatio
Guest: John Arnold, Head of Product Marketing and Strategic Advisory, CreatioHost: Michael Fauscette, CEO & Chief Analyst, Arion Research
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Download the Report: https://www.creatio.com/page/the-state-of-enterprise-ai-adoption?activity=banner

In this episode of the Disambiguation podcast, host Michael Fauscette talks with Julia Samoylenko, Founder and CEO of Asteri AI, about why deploying AI agents without redesigning the workforce around them is a missed opportunity, how her platform embeds workforce redesign directly into the product rather than leaving it as a customer problem, and what it actually takes to orchestrate humans and AI agents as a governed team inside highly regulated life sciences organizations.
Julia spent 11 years at Medtronic in cross-functional roles across multiple regions, most recently as Director of Transformation, before founding Asteri AI in Silicon Valley. The company builds what she calls an AI operating system for life sciences, a platform that enables enterprises to run humans and AI agents as one governed workforce. Asteri AI maps how work gets done across the enterprise, identifies where agents would perform best, and then orchestrates the resulting human-AI team with built-in governance, traceability, and continuous measurement.
The conversation covers why deploying agents without understanding the full picture of work leads to fragmented results, how Asteri AI starts by mapping work across the enterprise before proposing which agents to deploy and where, why workforce redesign should be built into the product rather than treated as a separate consulting engagement, what a governed human-AI workforce looks like in practice with full traceability of every agent action and human approval points at every step, how the platform automatically proposes agent deployment based on where inefficiencies exist and where AI would have the most impact, why measurement should focus on capacity increase rather than cost savings alone, how agent work differs across regulatory submissions, clinical operations, and quality and safety, why life sciences leaders need to think beyond single agents and take a systems view of their entire function, and why change management for AI is a continuous process rather than a one-time event.
Timestamps:00:00 - Introduction00:31 - Episode title and guest intro00:45 - Julia's background: 11 years at Medtronic01:19 - What led her to start Asteri AI03:06 - What "AI operating system for life sciences" means04:24 - Increasing capacity through human-AI orchestration05:02 - The holistic view: treating agents as team members05:52 - Company-wide deployment and the work intelligence module06:43 - Continuous value measurement as a differentiator07:57 - Shifting from technology focus to workflow focus08:28 - What a governed human-AI workforce looks like09:56 - Workforce redesign built into the product10:39 - Starting with how work gets done, not with the agents12:16 - Change management: still required but now data-driven13:27 - Deep vertical knowledge as competitive advantage13:44 - Governance and oversight in regulated industries15:40 - Scaling past pilots: capacity and measurement16:43 - Capacity increase vs. cost savings17:07 - Regulatory submissions: measuring throughput and time18:03 - Process simulation for decision makers18:58 - Growing capacity without proportional headcount growth19:32 - How enterprise experience shapes the product and earns trust22:13 - How AI work differs across clinical, regulatory, and quality25:11 - What life sciences leaders should know before deploying agents27:05 - Roles becoming obsolete and new work emerging28:37 - Workflow-first approach vs. technology-first29:00 - Change management as an ongoing process30:42 - Orchestration, governance, and workflow redesign as priorities31:01 - Recommendation: Thomas Otter and Ethan Mollick
Guest: Julia Samoylenko, Founder and CEO, Asteri AIHost: Michael Fauscette, CEO & Chief Analyst, Arion Research
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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Daniel Fallmann, Founder and CEO of Mindbreeze, about why the most capable AI models in the world are still guessing without an enterprise knowledge layer, how the consumer of enterprise knowledge has shifted from humans searching for documents to AI agents performing tasks, and what it actually takes to make twenty years of enterprise information usable by the next generation of AI.
Daniel founded Mindbreeze in 2005 at the age of 23, right after his computer science studies, and has spent two decades building the company into one of the leading enterprise search and knowledge management platforms. Mindbreeze is part of Fabasoft Group, headquartered in Linz, Austria, with a North American headquarters in Chicago and customers across Europe and the US. The platform connects to more than 500 enterprise data sources, understands permissions and context, and now serves as the knowledge layer for AI assistants, agents, and applications.
The conversation covers why the consumer of enterprise knowledge has changed (it is no longer just a person searching for a document but an AI assistant, an agent, or an application that needs context in real time), what the enterprise knowledge layer needs to look like before AI can work reliably (connectivity, permissions, semantic retrieval, and focused context rather than more context), why agents should not know everything but only what they need for a specific task and a specific user, how Mindbreeze InSight Touchpoints move organizations from search and retrieval to orchestrated workflows where agents select the right tools and get work done, why forward deployed engineers matter in enterprise AI (connecting SharePoint is a technology task but understanding which information matters for an engineering process and how it relates to SAP data is a business problem), why AI is displacing individual activities rather than entire professions (the question is not whether AI eliminates a role but which specific tasks no longer need to be done manually), why change management for AI should be continuous work redesign rather than a one-time event, what separates AI native organizations from AI augmented ones (adding a copilot to every application does not make a company AI native), and the three questions every C-level leader should ask before scaling AI.
Timestamps:00:00 - Introduction00:45 - Daniel's background: founding Mindbreeze at 2304:04 - Mindbreeze as the knowledge interface for AI agents and applications05:09 - Why models are no longer the biggest problem enterprises have06:48 - Enterprise search as the knowledge layer for AI07:46 - Your AI will change but your enterprise knowledge will not08:34 - The strategic role of the knowledge layer12:16 - Forward deployed engineers and the last mile of enterprise AI14:41 - Turning customer knowledge into productive AI native use cases15:36 - Adoption, trust, and getting people to use AI18:40 - More context is not better context19:08 - Build the knowledge layer first, then scale agents on top20:30 - Why the bottom-up approach works better than top-down22:41 - Where AI is displacing knowledge work fastest23:11 - Displacement means activities, not entire professions25:03 - Start with which tasks consume thousands of hours today27:33 - Winners will continuously redesign how humans and AI divide work28:51 - Change management is a process, not an event29:14 - US vs Europe: speed versus governance30:15 - Speed without governance creates risk31:17 - Governance without execution creates no value33:28 - AI native versus AI augmented organizations34:02 - Adding a copilot does not make you AI native38:33 - Models become replaceable when knowledge is your foundation39:43 - Enterprise knowledge is becoming infrastructure40:27 - Recommendation
Guest: Daniel Fallmann, Founder and CEO, MindbreezeHost: Michael Fauscette, CEO & Chief Analyst, Arion Research

In this episode of the Disambiguation podcast, host Michael Fauscette talks with Awais Rafeeq, Founder of AI Data House, about what actually breaks when companies try to move AI from pilot to production, why most small and mid-sized businesses are still running critical operations on disconnected spreadsheets, and the practical playbook for building automation that sticks.
Awais started his automation career before the AI wave. As a mechanical engineering graduate working as a data analyst, he built his first automation using Excel VBA to evaluate e-commerce products, a process that used to take two to three days per product. He sold that tool to classmates for $100 each and never looked back. That side project evolved into data analytics, reporting dashboards, and RPA-based process automation before AI entered the picture. Today, AI Data House has completed more than 500 business automation projects, primarily for small and mid-sized businesses in the US.
The conversation covers why companies are still running seven people on seven separate Excel files with no central system (and why the first step is not AI but centralized collaboration), the most practical starting points for automation (connecting Gmail leads to a CRM, adding chatbots, deploying calling agents for after-hours coverage), what breaks when a company tries to move from pilot to production (people hesitation, the plug-and-play myth, and the need for 15 to 20 days of monitored maturation), why you cannot automate a broken process (and what it takes to document processes, define gates, and map edge cases before any AI touches them), how to build trust with AI phase by phase (start a chatbot on FAQ only, add refund processing later, begin dashboards as read-only before enabling optimization), what healthcare AI taught him about compliance and data residency (you cannot give patient information to Gemini or Claude, everything stays in compliant containers), why small businesses move faster than enterprises (convince one or two people, get full context from one or two people, and start building), and the change management approach that actually works (automate one part of someone's job, show the results, and they will ask for more).
Timestamps:00:00 - Introduction00:33 - Episode title and guest intro01:07 - Awais's background: mechanical engineering to automation01:47 - The first automation: Excel VBA for e-commerce product evaluation03:06 - Patterns in SMBs: still running on disconnected spreadsheets04:23 - Starting points: Gmail to CRM, chatbots, calling agents05:19 - What breaks moving from pilot to production05:35 - People hesitation and the plug-and-play myth06:19 - Monitoring, maturation, and the importance of logging accuracy07:20 - Data problems when walking into a new client08:33 - Documented processes and process ownership09:21 - Trust building: deploying chatbots and agents phase by phase10:59 - Read-only dashboards before enabling AI optimization11:46 - Healthcare AI: compliance, data residency, and compliant containers13:17 - Automating referral processing and calling agents in healthcare14:36 - Why small businesses move faster than enterprises16:09 - Change management and employee resistance16:58 - Fear declining: adopt AI or be replaced by someone who does18:29 - Automate one part, show results, they ask for more19:26 - Finding high-value starting points for automation22:01 - You cannot automate a broken process24:25 - Recommendation
Guest: Awais Rafeeq, Founder, AI Data HouseHost: Michael Fauscette, CEO & Chief Analyst, Arion Research
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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Vasanth Mohan, Product Marketing Lead at SambaNova Systems, about why inference speed is the bottleneck holding back AI agents, how purpose-built silicon changes the economics of enterprise AI, and what business leaders need to understand about the infrastructure layer that sits beneath every AI application they use.
Vasanth's career has tracked the frontier of emerging technology. He studied computer science at Stanford, worked in VR during the Oculus era, moved into edge computing and telco networking, and landed in AI infrastructure as the bottleneck shifted from software to silicon. At SambaNova, he leads technical product marketing and developer relations for the company's reconfigurable dataflow unit (RDU), a chip designed specifically for AI inference.
The conversation covers which AI use cases are gaining real enterprise traction (coding is the base layer, coworker tools are actually coding under the hood, and the world is moving through three waves from chat to RAG to agents), what premium inference actually means for business leaders (not just faster tokens but the sweet spot of running large models fast enough for agents to work), why a coding agent that takes 25 hours could run in under one hour with the right infrastructure (and why that changes everything about productivity), how disaggregated inference splits the pipeline so that GPUs handle one phase while purpose-built chips handle another (prefill is compute-bound, decode is memory-bound, and they need different architectures), what the dataflow architecture does differently from GPUs (operator fusion that streams data through operations instead of bouncing back to memory), why speed is now priced as a distinct tier (and when paying double for fast tokens makes sense versus batching overnight), why JP Morgan Chase and sovereign AI deployments across Europe and Asia Pacific are choosing on-premises inference (regulatory requirements, audit concerns about closed providers, and the fact that 80% of data centers are air-cooled), what developers are building on SambaNova Cloud (planning agents on frontier models delegating to fast open source execution agents), and the one metric enterprise leaders should use to evaluate inference infrastructure (tokens per second per megawatt, normalized for the service levels their applications actually need).
Timestamps:00:00 - Introduction00:32 - Episode title and guest intro00:49 - Vasanth's background: Stanford, VR, edge computing to AI infrastructure05:00 - AI agent use cases gaining real traction05:27 - Coding as the base use case for enterprise AI06:20 - Coworker tools are actually coding under the hood07:00 - Three waves: chat era, RAG deployments, agent era09:18 - Premium inference: a distinct product tier, not just speed10:18 - Model size versus speed: the competing forces11:52 - Prompt caching is solved; decode speed is the unsolved bottleneck12:14 - The 25-hour coding agent: what 20x faster inference changes13:57 - Two components of agent workloads: CPU execution and inference15:14 - Disaggregated inference: splitting prefill from decode18:30 - Dataflow architecture and operator fusion on the RDU20:09 - Speed priced as a tier: when paying double makes sense22:41 - Voice agents need sub-500 millisecond response time25:00 - On-prem inference: JP Morgan Chase and regulatory requirements26:17 - Open source models are essential for secure environments27:36 - Sovereign AI and geopolitical data concerns31:58 - Right-sizing models to the task34:00 - Developer patterns: planning agents plus fast execution agents35:44 - Evaluating infrastructure: tokens per second per megawatt37:06 - Six-month ROI for on-prem NeoCloud customers38:08 - Recommendation: Semi Analysis and Dylan Patel
Guest: Vasanth Mohan, Product Marketing Lead, SambaNova SystemsHost: Michael Fauscette, CEO & Chief Analyst, Arion Research
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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Zoher Karu, Head of AI at Taelor, about why data governance has suddenly become a boardroom priority, how dirty data and missing business context cause AI projects to fail, and what practical steps leaders can take to build a data foundation that actually supports AI at scale.
Zoher has spent his career at the intersection of data, analytics, and business strategy. He holds a PhD in electrical engineering from MIT, started at McKinsey, then founded startups in retail analytics and call center intelligence before leading enterprise-wide data and analytics organizations at Sears Holdings, eBay, Citibank (across 17 markets in Asia and Europe), and Blue Shield of California.
The conversation covers why data governance is "sexy again" (AI amplifies data quality problems, so bad data now means bad decisions at machine speed), the blood-in-the-body analogy for enterprise data (every organ needs it, it should not be dirty or leaking), why a two-year data cleanup project is the wrong approach (clean as you go with a use-case-driven mindset), three reasons AI projects fail to deliver results (data quality and trust, missing business context that lives in people's heads not databases, and change management resistance), the "Susie knows how to do that" problem (business rules and institutional knowledge that AI agents cannot access), why pilot success does not predict production success (manually cleaned spreadsheets do not reflect real-world data), change management and the value exchange (people need to know what is in it for them), why productivity gains are not the same as business transformation (the real power of AI is reimagining processes entirely), cross-industry patterns in data challenges (siloed customer views, fractured definitions, departmental selfishness), how data teams are evolving toward full-stack roles with AI-assisted tools, governance as brakes that help you go faster (knowing the boundaries lets you push all the way to them), and practical first steps for business leaders (start with cost savings, ask employees what would make their job easier, build momentum through small wins).
Timestamps:00:00 - Introduction00:32 - Episode title and guest intro00:45 - Zoher's background: MIT, McKinsey, startups, Sears, eBay, Citibank, Blue Shield03:18 - Business-first mindset for data leadership05:03 - Why data governance is sexy again06:18 - Data is like blood: the enterprise body analogy07:33 - When "revenue" means different things to different teams08:02 - Clean as you go, not a two-year cleanup project09:30 - The shift from AI productivity to AI governance10:00 - Three reasons AI projects fail10:55 - Missing business context: the "Susie knows" problem13:10 - Pilot versus production: the cleaned spreadsheet trap13:38 - Change management and the value exchange15:07 - Making existing work easier: the call center notes example15:28 - The real power of AI: reimagining processes entirely17:34 - Board-driven AI mandates and the fear of being left behind18:44 - AI is a tool, not a solution looking for a problem19:32 - Cross-industry patterns in siloed customer data21:49 - Citibank example: loan data missing time of day23:40 - Full-stack data teams and AI-assisted tools26:17 - Building governance that protects without becoming a bottleneck27:07 - Traffic laws analogy: rules of the road for AI29:32 - Brakes help you go faster30:14 - Practical first steps: start with cost savings and productivity32:06 - Ask your employees what would make their job easier33:04 - Success builds on success33:31 - Recommendation: Factfulness by Hans Rosling
Guest: Zoher Karu, Head of AI, TaelorHost: Michael Fauscette, CEO & Chief Analyst, Arion Research
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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Krisztian Kiraly, International Partnership Manager at OptiMonk, about why the future of e-commerce is AI-powered conversion rate optimization, how smart personalization turns existing traffic into significantly more revenue, and where the line sits between a relevant experience and a creepy one.
Krisztian has spent more than a decade in digital marketing and e-commerce, with a deep focus on conversion rate optimization. He ran Conversion Masters, a CRO agency that worked with e-commerce brands to improve conversion rates and unit economics, before joining OptiMonk to lead international partnerships.
The conversation covers the double squeeze facing e-commerce operators (ad costs up 222% in eight years while giants like Amazon, Temu, and Shein compete with nearly unlimited marketing budgets), why conversion before acquisition is the smarter growth strategy (pouring more water into a leaky bucket does not fix the leak), how AI-powered CRO removes bottlenecks that used to take developer teams weeks to address, the behavioral signals AI can read the moment a visitor arrives on a site, the evolution of exit intent from annoying pop-ups to dynamic AI-driven interventions tailored to the exact product page a visitor is leaving, why the product page is the new landing page (40 to 70% of visitors now land directly on product pages through Performance Max campaigns), AI-generated embedded content that shows benefit badges directly on product images, the 10 to 40% conversion rate uplift OptiMonk is seeing from A/B testing AI-optimized product pages, the privacy balance between first-party and zero-party data versus over-personalization that feels invasive, a 70% revenue increase for a large e-commerce store achieved through 100-plus small optimizations over one year, and practical first steps for e-commerce leaders who have not yet started using AI for conversion optimization.
Timestamps:00:00 - Introduction00:32 - Episode title and guest intro00:48 - Krisztian's background: CRO agency to OptiMonk02:16 - The double squeeze: rising ad costs and giant competitors03:11 - Why increasing ad spend is not a long-term solution03:43 - AI-powered CRO: analyzing data and running tests at scale05:27 - How AI reads behavioral signals the moment a visitor arrives06:27 - The gap between AI-driven traffic and static landing pages07:25 - 67% of visitors now expect personalized experiences08:07 - Exit intent reimagined: dynamic headlines, images, and CTAs09:18 - Pop-ups are a tool, not inherently good or bad10:14 - Hyper-personalization powered by AI11:50 - Conversion before acquisition: the leaky bucket problem13:02 - CRO affects two profit factors versus one for ad spend14:34 - The product page is the new landing page15:57 - AI-generated embedded content and benefit badges17:23 - Answering "what is in it for me" in five seconds18:27 - 10 to 40% conversion rate uplift from A/B testing19:14 - Privacy and personalization: finding the golden balance21:39 - Zero-party data: the win-win of asking visitors directly23:03 - Where personalization crosses into creepy23:32 - 70% revenue increase: 100 small fixes, compounding results26:11 - Without data you are just another person with an opinion28:22 - First steps: know your data and start with product pages30:02 - Lifestyle images, benefit-focused headlines, and trust signals33:20 - Recommendation: Dan Martell on AI efficiency in business
Guest: Krisztian Kiraly, International Partnership Manager, OptiMonkHost: Michael Fauscette, CEO & Chief Analyst, Arion Research
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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Hemang Upadhyay, Senior Product and AI Leader, about why most AI pilots die the moment they connect to real enterprise infrastructure, what it actually takes to deploy adaptive AI agents in production commerce environments, and how the convergence of AI and quantum computing will reshape enterprise architecture.
Hemang has spent more than 16 years as a strategic product leader building AI-driven solutions for US commerce, e-commerce, and technology companies. His work across enterprise automation, AI-powered search, and recommendation engines has generated more than $450 million in combined business impact. He published a paper that won the IEEE Best Paper Award on quantum computing as a service, and has spoken at the AI Genetic Summit, Commerce Media Brand Summit, eTailer Boston, B2B Connect, and Identity Week America.
The conversation covers the distinction between conversational AI and agentic AI (chatbots produce language, agents produce consequences), the five-layer pattern for scaling agents from pilot to production (constrain, translate, authorize, observe, recover), why autonomy should live inside the workflow but authority should live outside the model, the shift in commerce from helping customers find products to helping them achieve outcomes, the agent passport concept for identity and trust in agent-to-agent interactions, how mid-market retailers should adopt AI (data readiness first, then journey focus, then capability composition), six security controls for building trust into agentic architectures, the honest framing on AI and jobs (task automation, role redesign, and workforce decisions are related but not identical), and the hybrid future of quantum computing and AI.
Timestamps:00:00 - Introduction00:32 - Episode title and guest intro00:48 - Hemang's background: 16+ years in product leadership and AI02:19 - Simplifying complexity across digital commerce04:45 - Research, speaking, and the IEEE best paper on quantum as a service07:05 - Conversational AI vs agentic AI: language vs consequences08:30 - Five components of an agent: goal, reasoning, memory, tools, authority10:00 - Connecting probabilistic decisions to deterministic systems12:42 - Scaling from pilot to production: the five-layer pattern13:45 - Constrain, translate, authorize, observe, recover17:36 - Autonomy inside the workflow, authority outside the model18:03 - Commerce agents: from product discovery to outcome achievement18:52 - Amazon Rufus to Alexa shopping transition20:06 - The apartment furnishing example: scattered work to one plan23:12 - Agent-to-agent identity: the agent passport concept25:18 - Authentication vs authorization at the moment of action28:00 - Mid-market retailers: start with focus, not scale29:25 - Data readiness: agents make bad decisions faster on bad data31:14 - Capability composition and graduated autonomy33:17 - Trust: does the system remain safe when the model is wrong?34:23 - Six practical controls for agentic security37:22 - Governance as executable policy inside the product39:00 - AI and jobs: three effects, not one narrative42:22 - Which parts of my work are becoming easier to automate?44:07 - Quantum computing and AI convergence: the hybrid future46:07 - Quantum as a service: orchestration across classical, AI, and quantum47:32 - Three actions for business leaders on quantum readiness50:03 - Recommendations: Ethan Mollick and Andrew Ng
Guest: Hemang Upadhyay, Senior Product & AI LeaderHost: Michael Fauscette, CEO & Chief Analyst, Arion Research
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In this episode of the Disambiguation podcast, host Michael Fauscette talks with Tim Lidman, Co-Founder and CEO of Clyde AI, about why the chat window is not the right interface for real human-AI collaboration, why most companies are stuck building Frankenstein workbenches of cobbled-together AI tools, and what it actually takes to design a product where humans and AI work together as a hybrid team.
Tim's career traces the entire evolution of enterprise collaboration. He started in tech sales at WebEx and Cisco selling audio conferencing, moved to SAP SuccessFactors, then founded Think Tank, a structured collaboration platform based on decades of research into behavioral science and group decision support systems. Think Tank was acquired by Accenture in 2021, where Tim operated as a partner for four years. He co-founded Clyde AI with Chris Bricker to build what he calls AI-native collaboration: a product where AI is a first-class citizen in the architecture and UX, not bolted on top of legacy workflows.
The conversation covers Tim's contrarian take on LLMs and the promise of natural language interfaces (prompt engineering and context engineering are just new paradigms users have to learn, not the elimination of paradigms), why single-threaded chat is the wrong model for complex problem solving, the AI advisor concept (multi-threaded AI with separate domain knowledge and personas working in parallel without polluting each other's context), the design blueprint of mapping what AI is good at against what humans are good at, why humans are still better at inventing new information and making judgment calls, the Frankenstein workbench problem (employees cobbling together tools just to check the AI box), fear-driven adoption as terrible leadership, the micro win approach (20 minutes to solve one small problem and build trust), why anyone using AI for more than 10% of their workflow is ahead of 99.99% of workers, why the term change management may not survive (change is now constant, not a project with a start and end date), the people-process-technology split that must blend into one unified experience, and the industrial revolution parallel compressed 100x.
Timestamps:00:00 - Introduction00:31 - Episode title and guest intro00:50 - Tim's background: heavy metal drummer to tech sales to collaboration to AI02:07 - The golden thread: evolution of collaboration from audio conferencing to AI03:15 - Enterprise social networking: Microsoft buys Yammer for $1.2 billion03:42 - Think Tank: structured collaboration and group decision support systems05:32 - The hypothesis for Clyde: AI removing expensive synthesis work06:41 - What AI cannot replace: tribal knowledge, human judgment, buy-in08:13 - Building Clyde: AI where AI excels, human UX where humans excel09:33 - AI advisors: multi-threaded thinking inside a collaborative workspace11:14 - The chat window problem: LLMs created a new paradigm, not eliminated one14:03 - Putting the onus on the tool to extract context and intent16:19 - Single-threaded chat vs multi-threaded problem solving18:37 - Example: business plan with parallel risk, strategy, and financial advisors20:29 - Legacy tools bolting AI on top of old architectures22:32 - Fear-driven adoption: use AI or you are fired29:10 - Building hybrid teams: human-to-human vs human-to-AI collaboration30:52 - Humans can invent new information; AI is pattern recognition34:38 - Change is now constant, not a project with start and end dates36:28 - The term change management may not survive38:32 - Continuous optimization: building and optimizing happen simultaneously41:42 - People-process-technology must blend into one unified experience44:11 - The industrial revolution parallel, compressed 100x46:27 - Recommendation: Sapiens by Yuval Harari47:20 - Recommendation: CEO of Lovable, 0 to $100M ARR in nine months
Guest: Tim Lidman, Co-Founder & CEO, Clyde AIHost: Michael Fauscette, CEO & Chief Analyst, Arion Research