Episodes
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SUMMARY: While we spend a lot of time discussing AI models, we don’t always spend enough time on the challenges of managing the unstructured data used to train, tune, and enable those models.
SHOW: 1043
SHOW TRANSCRIPT: The Enterprise AI Show #1043 Transcript
SHOW VIDEO: https://youtu.be/OAqnuhorMJ4
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
Topic 1 - Welcome to the show. Tell us a bit about your background and where you focus today at Nasuni
Topic 2 - We’ve spent two years talking about models. Are we finally entering the era where the biggest differentiator is data quality rather than model quality?
Topic 3 - When customers inventory their AI-ready data, what surprises them most?
Topic 4 - Where is the intersection of file data, metadata, and RAG systems that augment a company’s AI experience with their own data?
Topic 5 - People talk about AI governance, but isn’t most AI governance actually data governance?
Topic 6 - Are today’s enterprise file systems designed for machine consumers (AI Agents) instead of human consumers?
Topic 7 - What are the economics of data, in your world, as it relates to AI?
Topic 8 - What’s next for enterprise file platforms?
FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: Are CEO's frustrated with the lack of control, costs and sovereignty of their AI environments?
SHOW: 1042
SHOW TRANSCRIPT: The Enterprise AI Show #1042 Transcript
SHOW VIDEO: https://youtu.be/xgQv8WP-DNI
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:
Palantir and NVIDIA partnership (June 2026) - first 11 minutesPalantir CEO (Alex Karp) on CNBC“The VPC Privacy Illusion - Why Private LLMs still expose your data”The biggest mistake organizations make isn’t choosing the right model, it’s focusing on models at all (via LinkedIn)There’s a level of unhappiness and distrust of the frontier labs from CEOsThere needs to be an application layer on top of LLMs (e.g. “harness”, Palantir Ontology)This application layer prevents the LLMs from learning your business from your data“Alpha” is business differentiation (ability to outperform the market)He questions why the frontier model labs are charging by tokens and not outcomes (questions the entire AI business model)He questions “the true cost” of AI outputs He claims that CEOs are now concerned about frontier labs entering the business of the customers - brings up an interesting misunderstanding of how interacting with LLMs works (“we’re safe, it’s deployed in our VPC”)FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
Missing episodes?
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SUMMARY: Brian Gracely (@bgracely) and Aron Delp (@aarondelp) discuss the biggest AI news stories from the month of June, 2026.
SHOW: 1041
SHOW TRANSCRIPT: The Enterprise AI Show #1041 Transcript
SHOW VIDEO: https://youtu.be/SXmPOgE5jGk
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
Links to all the AI News covered in this month’s showFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: As AI within the Enterprise matures, we look at 10 concerns and challenges that are still causing Chief AI Officers to worry about success in the future.
SHOW: 1040
SHOW TRANSCRIPT: The Enterprise AI Show #1040 Transcript
SHOW VIDEO: https://youtu.be/RyB4m17YK_4
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
THESIS: After spending time with a number of Enterprise companies, what are a list of challenges and concerns they still have in implementing GenAI across a broad set of use-cases within the Financial Services industry?
Everybody started with what was available (e.g. CoPilot)Enterprise implementations (now) aren’t autonomousRising costs are the looming concernGovernance is a rising concernMeasurements of improvement are available, but variedExplaining measurements is complicatedExplaining trust is more complicatedUse-cases are fragmented, but there if you apply the technology, but not always obviousDe-centralized (shadow AI) to Centralized to De-centralized (semi-controlled) The learning curves are very asymmetrical across teamsNot everyone has access to Mythos or GPT-5.5-Cyber (yet)FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: What does a Forward-Deployed Engineer actually do? And what about deploying AI Harness? Let’s dig into the real-world with these evolving AI concepts and technologies.
SHOW: 1039
SHOW TRANSCRIPT: The Enterprise AI Show #1039 Transcript
SHOW VIDEO: https://youtu.be/QY0fqu2O84M
SHOW SPONSORS:
OutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:
Mozilla Thunderbolt launched Mozilla Thunderbolt (homepage)Topic 1 - Welcome to the show, tell us a bit about your background and what you focus on these days.
Topic 2 - Let’s talk about the role of Forward Deployed Engineer, it’s being talked about a lot, but you’re living in that world now. What problems are FDEs usually tasked with trying to solve, or new things to implement?
Topic 3 - We’ve seen other roles (DevOps, PlatformEng, etc.) that evolved from other roles or skills. What type of background lends itself to success in FDE? What skills are needed going forward?
Topic 4 - You’re also working on some AI harness implementations. What can you tell us about those challenges and the technologies behind the harness?
Topic 5 - At what point does an AI harness make sense for a company? What types of AI challenges typically require those next steps?
Topic 6 - Working in the middle of this evolving AI space, what are some perspectives you’ve gained over the last 6-12 months? What do you wish you knew ahead of time?
FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: On Father's Day, how would you explain some of the volatility of the AI market to your father? What advice might he give you to navigate the ups and downs and uncertainties?
SHOW: 1038
SHOW TRANSCRIPT: The Enterprise AI Show #1038 Transcript
SHOW VIDEO: https://youtu.be/T2ZIYLpl_cE
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureSHOW NOTES:
Leaked documents show OpenAI is losing billionsAnthropic’s Fable and Mythos models banned from non-US foreign nationalsThe AI layoff wave is becoming a powder kegProfessors says AI-related job losses are inevitableTHESIS: On this Father’s Day, with an AI market that often times doesn’t make any sense, I thought about the type of advice that my father gave me over the years and how it would apply to this time of significant change.
Show up, keep up and shut upMake yourself invaluableFocus on what you can controlBe an expert in somethingWhen in doubt, get closer to people and how money is madeWhen things don’t make sense, focus on fundamentalsMarkets can be irrational way longer than you can be solventTry and think a couple steps aheadFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: As tools like Mythos create new AI-cybersecurity concerns, CIOs and CISOs need to be prepared for two challenges: Security Remediation and Patch to Production acceleration.
SHOW: 1037
SHOW TRANSCRIPT: The Enterprise AI Show #1037 Transcript
SHOW VIDEO: https://youtu.be/H5KxoiEIfUo
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
Project Lightwell (Red Hat and IBM)Athena (Chainguard)Anthropic Project GlasswingOpenAI GPT 5.5-CyberTHESIS: Major initiatives are forming to help enterprise organizations combat security vulnerability threats found or created using new AI-cyber tools such as Anthropic Mythos. What are the key considerations, and what additional steps do organizations need to take to be advantaged by these capabilities?
Part 1
The Breaking Point and the Mythos MomentThe scope of open source security and supportPatches, disclosures and upstream open sourceClearinghouses, EOs, Laws and CommunitiesRemediation - Build vs. BuyPart 2
How fast can you get from Patch to Production?Mitigation before patchingFast path and stable patch pipelines?Automation in patching vs. automation in deploymentFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: How can CIOs balance innovation and control as they roll our AI capabilities across their organization. How can they balance onboarding, experience, security and flexibility?
SHOW: 1036
SHOW TRANSCRIPT: The Enterprise AI Show #1036 Transcript
SHOW VIDEO: https://youtu.be/ZgkMF7G3Yfo
SHOW SPONSORS:
OutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:
Andy Weir (The Martian) on Eps. 193Systems of Record Won the SaaS Era - Clearinghouses Will Win the Agents EraHarness Engineering is where Enterprise AI becomes realTHESIS: It comes up as different control points, but CIOs are ultimately trying to figure out how to get the value from Enterprise AI while delivering a set of consistency across different teams and use-cases. Let’s explore what this “Enterprise Harness” is starting to look like.
Enterprise Clearinghouse Enterprise Intelligence (a.k.a. Middleware)Enterprise Catalog - Models as a Service, Agents as a ServiceEnterprise Skills or Shareable Prompt HarnessesSymantec Routing to ModelsAI Gateway ControlsFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: If the cost of public AI continues to rise, because of various market shortages, should CIOs start looking at backup plans to better own their AI journeys and futures?
SHOW: 1035
SHOW TRANSCRIPT: The Enterprise AI Show #1035 Transcript
SHOW VIDEO: https://youtu.be/ngBBpP2Lgdo
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoOutShift by Cisco - “Scaling Out Superintelligence” The Internet of Cognition architectureSHOW NOTES:
THESIS: Between pending IPOs (Wall St. demands), high user-demand, GPU/TPU shortages, Data Center shortages, Model prices increasing (open models fading away), the cost of using AI is going to get more expensive over time. Should CIOs start thinking about a Backup plan to their current AI adoption that has lower cost alternatives?
Topic 1 - Assuming you could get access to GPUs/TPUs/Accelerators, and suitable data center space to host them, what would be your thinking as a CIO if you felt like you needed to own some aspect of your AI roadmap/journey?
Topic 2 - Assuming the normal “Shadow AI” backlash that you’d receive for offering something that wasn’t “frontier” level, how would you go about trying to communicate that within your organization?
Topic 3 - What metrics or KPIs would you initially target to try and get buy-in that your approach was acceptable and moving towards the company goals?
FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: When we get to the end of 2026, how will enterprise companies be measuring the success of their AI projects? And how well will their teams be sharing their AI learning curves?
SHOW: 1034
SHOW TRANSCRIPT: The Enterprise AI Show #1034 Transcript
SHOW VIDEO: https://youtu.be/TvIFwNN-6ck
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoOutShift - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
Why AI Economics are changingHow will team collaboration evolve with Enterprise AI?Topic 1 - How do we measure AI-adoption success?
Number of workloads?Financial metrics (Spend, ROI, Costs-Saved, etc.)?Speed improvements?People-level?Topic 2 Right now the AI tools are very individual-centric
The machinery to share, even at the basic enterprise-level, is very difficultThe experience to share is non-deterministic, just as everyone’s working style is different.Topic 3 - The motivation to share is still unknown.
How do you encourage collaboration when so many companies are laying off people, or the specter of that happening is growing?What was the motivation before (team goals?) and how does that change now? People don’t want to be monitored, so how does a manager have visibility?What happens when companies remove the managers (“the counters”)?FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: After the first successful AI IPO of 2026, we dig into what makes the Cerebras WSE architecture unique in the market for fast inference.
GUEST: Andy Hock, Chief Strategy Officer at Cerebras AI
SHOW: 1033
SHOW TRANSCRIPT: The Enterprise AI Show #1033 Transcript
SHOW VIDEO: https://youtu.be/ed2nVbOtZiA
SHOW SPONSORS:
OutShift - “Scaling Out Superintelligence” The Internet of Cognition architectureShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:
OpenAI announces 750MW partnership with CerebrasCerebras and AWS partnershipCerebras announces IPOTopic 1 - Welcome to the show. Tell us about your background, and what you focus on today.
Topic 2 - For anyone that’s not familiar with Cerebras, give us an overview of the company, and especially an overview on the Cerebras technologies (e.g. Wafer-Scale Engine).
Topic 3 - Cerebras’ WSE architecture is different from many of the GPU or GPU-like architectures in the market today. Centralized vs. distributed architectures always have their tradeoffs. Walk us through the technical and economic value of the Cerebras architecture.
Topic 4 - Congratulations on the recent IPO (raised $5.55B). Let’s use that as a point in time vs the previous planned IPO. How has the market changed in that timeframe, and how has the Cerebras position changed?
Topic 5 - Cerebras (today) offer both WSE hardware, and Cerebras Cloud (API) - very different GTM paths. Can we expect both of those to stay top priorities, or have the market dynamics shifted such that the priorities shift more towards the WSE business - as we’re seeing OpenAI, AWS and other engagements announced?
Topic 6 - Is Cerebras a training and inference company, or are the economics of inference significantly different enough that it needs to be the sole focus of the company (for now)?
Topic 7 - How much effort is it for any company to add support for the Cerebras chips if they have previously been using other architectures?
Topic 8 - An IPO is a major milestone for any company, but the markets will now look for your future story. How do you see the AI market evolving over the next 2-5 years, and what are some things that people aren’t understanding yet about how it will evolve?
FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: The biggest enterprise AI question is which organization can most effectively operationalize, govern, and economically scale AI agents across the business.
SHOW: 1032
SHOW TRANSCRIPT: The Enterprise AI Show #1032 Transcript
SHOW VIDEO: https://youtu.be/GsK_RUnYroI
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:
Opening Thesis - How will team collaboration evolve within Enterprise AI?
Question: Any suggestions on how to introduce enterprise-level governance and standardisation for agentic coding? Like skills, rules, plugins, context etc
Key Topics
1. This isn’t a Coding-specific problem. Every team has this issue.
If your processes weren’t well defined and enforced before, they will be worse nowNot it’s not just process standardization, but “buy-in” standardization2. Everything moves so fast, so managers don’t have the answers (yet)
AI value is being created bottom-up, but paid for (and mandated) top-downThe current measurements aren’t useful (tokenmaxxing, all-or-nothing, etc.)3. The governance tools don’t exist yet.
And it’s not clear that anyone wants them. They didn’t want them before. How do you even define governance? What’s the baby step before that, reuse and basic sharing?4. Are we ready to invest in “Centers of Excellence” again?
5. We under-estimate the “creativity” element in human buy-in.
Is success measured in improvement or replacement?How much of that did “you” do? We don’t know how to measure that.We haven’t lived through an AI-centric promotion cycle yet6. Bottom-up and Top-down need to find some common language and middle ground.
Have they walked a mile in each other’s shoes yet (or lately)?How to bring a reality to the hype vs. demands vs. learning curve?How long is an AI-centric cycle vs. a pre-AI-centric cycle?FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, Software Defined Talk and Failover Media) discuss the biggest AI news stories from the month of May, 2026.
SHOW: 1031
SHOW TRANSCRIPT: The Reasoning Show #1031 Transcript
SHOW VIDEO: https://youtu.be/MNihDdBSteI
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
Links to all the AI News covered in this month’s showFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: The biggest enterprise AI question may no longer be
Which model is smartest? Instead, which organization can most effectively operationalize, govern, and economically scale AI agents across the business?’SHOW: 1030
SHOW TRANSCRIPT: The Enterprise AI Show #1030 Transcript
SHOW VIDEO: https://youtu.be/acOBfRI0P3U
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:
Opening Thesis - Was the first wave of AI adoption artificially cheap? - The industry may be transitioning from subsidized growth to usage-based economics.
Key Topics
1. Evidence AI Was Subsidized
Massive CAPEX vs low end-user pricing Generous enterprise bundles Frontier model access for $20/month2. The Hidden Economics of AI Agents -
Agents consume exponentially more inference Tool orchestration, retries, memory, verification3. Why Frontier Labs Are Shifting Focus
From benchmark supremacy to orchestration Governance, memory, connectors, MCP, workflows4. Forecasting AI Pricing
12 Months: Commodity inference gets cheaper - Frontier reasoning remains premium 24 Months: AI billing resembles AWS-style infrastructure billing Runtime, memory, latency and orchestration become billable 36 Months: Outcome-based pricing emerges AI spending shifts from IT budgets to labor budgets Final Takeaways Commodity AI becomes utility-priced Frontier reasoning becomes premium Agents reshape enterprise economicsKey Conclusions
1. AI probably was subsidized
The economics strongly suggest adoption-first pricing.2. The subsidy era may be ending
Premium tiers and metered pricing are emerging.3. AI agents fundamentally alter economics
Usage scales exponentially with autonomy.4. Commodity AI and frontier reasoning are separating
One becomes cheap.
One becomes premium.5. The real battle is moving upward in the stack
orchestrationgovernanceworkflowsenterprise contextoperational tooling
The future moat may be:
Final Closing Thought
“The biggest enterprise AI question may no longer be:
‘Which model is smartest?’Instead:
‘Which organization can most effectively operationalize, govern, and economically scale AI agents across the business?’”FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: As AI Agents are being brought into complex, regulated workflows, we explore the importance of accountability and accuracy, and how platforms and harnesses accomplish that goal. Can the CFO really fall in love with AI?
GUEST: Ram Venkatesh, Co-Founder/CTO of Sema4.ai
SHOW: 1029
SHOW TRANSCRIPT: The Enterprise AI Show #1029 Transcript
SHOW VIDEO: https://youtu.be/Lc3XS44Ixg4
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.SHOW NOTES:
Topic 1 - Welcome to the show. Tell us about your background, and what led you to create Sema4.ai?.
Topic 2 - AI Agents vs. Automation 2.0. What Actually Changed. Tell us about the Sema4.ai platform and capabilities. What challenges does it solve today?
Topic 3 - You’re initially focused on solving challenges for the CFO, which means there is a ROI-focus all the time. Why did you target that segment of the business first?
Topic 3a - What are the biggest hidden costs in enterprise AI deployments today?
Topic 4 - Sema4.ai emphasizes “your LLM, your VPC, your data.” What are the biggest considerations for companies looking to create these private/sovereign AI solutions? What typically gets overlooked?
Topic 5 - How do you tend to frame the conversation about AI trustworthiness, and the role of humans vs. agents for enterprise work?
Topic 6 - It feels like so much has changed or evolved with AI in the last 2-3 years. How does an Enterprise think about this much change for something that will be core to many critical applications? What will the Enterprise Architecture look like in 2 years?
Topic 7 - Sema4.ai emerged partly from the acquisition of Robocorp and has roots in open-source automation. Do you have a perspective on the role open-source will play in AI going forward?
FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: As AI agents become embedded in everyday work, Microsoft 365 governance is no longer a back-office compliance exercise. it’s the “traction control” that lets enterprises innovate faster without losing control of their data, identities, and workflows.
GUEST: Richard Harbridge, Principal Industry Advisor, Microsoft 365 at ShareGate
SHOW: 1028
SHOW TRANSCRIPT: The Enterprise AI Show #1028 Transcript
SHOW VIDEO: https://youtu.be/sgqg7uqErA0
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance. We got this.Nasuni - Activate your data for AI and request a demoSHOW NOTES:
Nearly 1 in 3 Organizations Report AI-Driven Data Exposure IncidentsOther Resources:A complete checklist for Microsoft 365 governance https://sharegate.com/guides/checklist-for-microsoft-365-governance Request a demo of ShareGate: Get a 1:1 ShareGate demo tailored to your Microsoft 365 use case Article around that divide of confidence vs reality of data exposure sharegate.com/blog/93-of-it-leaders-are-confident-in-their-ai-governance-but-nearly-1-in-3-report-data-exposure-incidents The State of Microsoft 365 industry report with more stats and insights - State of Microsoft 365 2025 | Free survey report – ShareGate | Sharegate (new one coming SOON)Topic 1 - Welcome to the show. Tell us about your background, and what you focus on today. Tell us about Sharegate.
Topic 2 - How has generative AI changed the definition of “governance” inside Microsoft 365 environments?
Topic 3 - What are organizations underestimating about AI readiness in M365?
Topic 4 - What do you think about “oversharing risk” in the era of AI assistants?
Topic 5 - What patterns are you seeing around shadow AI and unsanctioned SaaS usage?
Topic 6 - How should organizations rethink identity and access management for AI-driven workflows?
Topic 7 - What does good AI governance look like operationally—not just as a policy document?
FEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: RIP Reasoning, hello The Enterprise AI Show. We do a point-in-time analysis of the AI market for May 2026, across 11 major categories.
SHOW: 1027
SHOW TRANSCRIPT: The Enterprise AI Show #1027 Transcript
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
Reviewing the Major AI VendorsFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: We explore one of the most overlooked bottlenecks in the AI boom: energy and infrastructure and why power availability is becoming the limiting factor.
GUEST: Wannie Park, Founder/CEO of PADO AI
SHOW: 1026
SHOW TRANSCRIPT: The Reasoning Show #1026 Transcript
SHOW VIDEO: https://youtu.be/satMQRxKQC8
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:
1. AI’s Hidden Constraint: Power
AI growth is no longer limited only by GPUs and computePower generation, cooling, and grid interconnects are emerging as major bottlenecksData centers could account for 10–12% of North American power demand in coming years2. Why Data Centers Are Being Reimagined
Traditional data centers were built for enterprise IT, not AI-scale workloadsAI infrastructure introduces:Massive power density needsAdvanced cooling challenges3. The Grid Wasn’t Built for AI
Utilities are designed around peak demand scenariosMost grids run well below peak capacity most of the timeAI workloads create volatile and unpredictable consumption patternsLong interconnection timelines are pushing companies toward alternative infrastructure models
GPU clusters are often underutilized because of:Scheduling inefficiencies, Cooling limitations, SLA constraintsEffective GPU utilization may be as low as 12–13% in some environments
4. GPU Utilization Is Surprisingly Low5. Cooling as a Major Optimization Layer
Legacy data centers often cool entire zones inefficientlyPado AI alignsAI workloads, Cooling systems, Power allocationWorkload-aware orchestration helps optimize cooling and compute efficiency
Pado forecasts compute demand instead of energy demandThe platform models:GPU workloads, Power consumption, Cooling requirements, SLA prioritiesGoal: maximize “compute per megawatt”
6. The Rise of “Compute Forecasting”7. AI Workloads Become Time-Aware
AI providers may increasingly:Shift workloads to off-peak periodsIncentivize delayed non-urgent jobsDynamically balance compute demandUsers are already seeing variable inference latency in real-world AI systems8. Sustainability vs Reliability vs Profitability
Operators must balance:Uptime expectations, Infrastructure costs, Sustainability goalsRenewable adoption is growing, but reliability still drives investment in natural gas and battery-backed systems9. Brownfield vs Greenfield Opportunities
Pado AI is focused primarily on existing (“brownfield”) data centersExisting enterprise infrastructure can often be extended and optimized instead of rebuiltEnterprises may gain significant AI capability without hyperscale GPU deploymentsFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: Brian Gracely (@bgracely) and Brandon Whichard (@bwhichard, Software Defined Talk and Failover Media) discuss the biggest AI news stories from the month of April, 2026.
SHOW: 1025
SHOW TRANSCRIPT: The Reasoning Show #1025 Transcript
SHOW VIDEO: https://youtu.be/Gl-49dmAgBs
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:
Links to all the AI News covered in this months showFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow -
SUMMARY: Draft guru Brandon Whichard (Software Defined Talk) joins us for the inaugural AI Draft, where we predict the next year of AI winners, losers, trends, and headlines.
GUEST: Brandon Whichard, Software Defined Talk
SHOW: 1024
SHOW TRANSCRIPT: The Reasoning Show #1024 Transcript
SHOW VIDEO: https://youtu.be/BjT_HKhOcRE
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!Nasuni - Activate your data for AI and request a demoSHOW NOTES:
Brian’s Picks
GoogleMajor AI-centric IPO in 2026 ($1T valuation)Amazon (cloud)Company has more Agents that EmployeesTSMC (hardware)AMD (hardware)Family asks about AI at the holidaysData center issue causes a significant change to human existenceBrandon’s Picks
AnthropicNVIDIABroadcomOpenAI (frontier model)AI Consumption-based pricing (end of subsidies)AI Energy DemandThe end of “vibe-coding”Sam Altman out at CEO of OpenAIFEEDBACK?
Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow - Show more
