The Enterprise AI Show

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Enabling AI Governance for M365 May 17, 2026

Show notes

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 demo

SHOW NOTES:

Nearly 1 in 3 Organizations Report AI-Driven Data Exposure Incidents
Other 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 com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

An AI Market Analysis, May 2026 May 13, 2026

Show notes

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 demo
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!

SHOW NOTES:

Reviewing the Major AI Vendors

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
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AI, Data Centers, and the Power Crunch May 10, 2026

Show notes

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 demo

SHOW NOTES:

1. AI’s Hidden Constraint: Power

AI growth is no longer limited only by GPUs and compute
Power generation, cooling, and grid interconnects are emerging as major bottlenecks
Data centers could account for 10–12% of North American power demand in coming years

2. Why Data Centers Are Being Reimagined

Traditional data centers were built for enterprise IT, not AI-scale workloads
AI infrastructure introduces:
- Massive power density needs
- Advanced cooling challenges

3. The Grid Wasn’t Built for AI

Utilities are designed around peak demand scenarios
Most grids run well below peak capacity most of the time
AI workloads create volatile and unpredictable consumption patterns
Long interconnection timelines are pushing companies toward alternative infrastructure models

4. GPU Utilization Is Surprisingly Low

GPU clusters are often underutilized because of:
- Scheduling inefficiencies, Cooling limitations, SLA constraints
- Effective GPU utilization may be as low as 12–13% in some environments

5. Cooling as a Major Optimization Layer

Legacy data centers often cool entire zones inefficiently
Pado AI aligns
AI workloads, Cooling systems, Power allocation
Workload-aware orchestration helps optimize cooling and compute efficiency

6. The Rise of “Compute Forecasting”

Pado forecasts compute demand instead of energy demand
The platform models:
- GPU workloads, Power consumption, Cooling requirements, SLA priorities
- Goal: maximize “compute per megawatt”

7. AI Workloads Become Time-Aware

AI providers may increasingly:
- Shift workloads to off-peak periods
- Incentivize delayed non-urgent jobs
- Dynamically balance compute demand
- Users are already seeing variable inference latency in real-world AI systems

8. Sustainability vs Reliability vs Profitability

Operators must balance:
- Uptime expectations, Infrastructure costs, Sustainability goals
- Renewable adoption is growing, but reliability still drives investment in natural gas and battery-backed systems

9. Brownfield vs Greenfield Opportunities

Pado AI is focused primarily on existing (“brownfield”) data centers
Existing enterprise infrastructure can often be extended and optimized instead of rebuilt
Enterprises may gain significant AI capability without hyperscale GPU deployments

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

AI News of the Month for April 2026 May 06, 2026

Show notes

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 demo
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!

SHOW NOTES:

Links to all the AI News covered in this months show

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

The 2026 AI Draft May 03, 2026

Show notes

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 demo

SHOW NOTES:

Brian’s Picks

Google
Major AI-centric IPO in 2026 ($1T valuation)
Amazon (cloud)
Company has more Agents that Employees
TSMC (hardware)
AMD (hardware)
Family asks about AI at the holidays
Data center issue causes a significant change to human existence

Brandon’s Picks

Anthropic
NVIDIA
Broadcom
OpenAI (frontier model)
AI Consumption-based pricing (end of subsidies)
AI Energy Demand
The end of “vibe-coding”
Sam Altman out at CEO of OpenAI

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

Halt & Retool: Rewriting Software Development in the Age of AI Agents Apr 29, 2026

Show notes

SUMMARY: Exploring how to fully embrace AI-driven, agent-based software development, resulting in dramatically increased productivity and faster feature delivery. It highlights a broader shift in engineering—from writing code to orchestrating AI agents.

GUEST: Sam Ramji, CEO/Co-founder at Sailplane

SHOW: 1023

SHOW TRANSCRIPT: The Reasoning Show #1023 Transcript

SHOW VIDEO: https://youtu.be/q50s0oL37pQ

SHOW SPONSORS:

Nasuni - Activate your data for AI and request a demo
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!

SHOW NOTES:

1. The “Halt and Retool” Moment

A single-day build and deployment of a production feature triggered a company-wide realization
Paused all development to reassess how AI fundamentally changes engineering workflows
Creating “shock moments” (like stopping work) is key to driving mindset shifts

2. From Coding to Agent Orchestration

Developers are shifting from writing code → managing AI agents
Work resembles “multi-boxing” or conducting an orchestra of parallel agents
Success depends on coordinating tasks, not executing them directly

3. The Rise of Harness Engineering

Defined as everything between raw AI prompts and production-ready output
Focus: eliminating friction across the software development lifecycle
Key practices:
- Logging agent errors and friction points
- Continuously refining workflows and tooling
- Letting AI reflect on and improve its own mistakes

4. Spec-Driven Development Becomes Critical

Poor specifications lead to exponential inefficiencies
Teams now spend significantly more time on design and specs than coding

5. Measuring the Impact

~3x increase in code velocity
Near-zero “bit rot”
Faster feature delivery—sometimes within 24 hours

6. Token Maxing & Developer Fitness

Higher token usage often signals better workflows and deeper integration with AI
Performance becomes about system design, not efficiency constraints

7. New Tools & Interfaces

Increased use of voice interfaces over typing
Terminal-first workflows replacing traditional IDE-centric approaches
AI-accessible knowledge bases becoming standard

8. The Future of Software Engineering

Within ~6 months: developers may stop writing code
Within ~12 months: developers may stop reading code
Focus shifts to:
- Intent, design, and orchestration. Domain expertise and problem modeling

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

The Zero-CVE Mirage: Hardening Software in the Age of AI Attacks Apr 26, 2026

Show notes

SUMMARY: How software development is rapidly evolving in the age of AI and automation. Matt Moore shares how his team is rethinking secure software supply chains, scaling infrastructure, and safely integrating AI agents into development workflows.

GUEST: Matt Moore, CTO at Chainguard

SHOW: 1022

SHOW TRANSCRIPT: The Reasoning Show #1022 Transcript

SHOW VIDEO: https://youtu.be/9Q0kWkTYRs8

SHOW SPONSORS:

ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
Nasuni - Activate your data for AI and request a demo

SHOW NOTES:

Scaling Challenges & “Factory” Evolution

Early automation relied on tools like GitHub Actions
At scale, simple systems broke due to:
- Massive event volumes
- API rate limits (e.g., GitHub quotas)
- Exponential fan-out effects
Key innovation: custom work queue + reconciliation model
- ~90% event deduplication
- Controlled throughput and backpressure
- Improved reliability and system stability
Introduced Driftless
Built on reconciliation principles (inspired by Kubernetes):
- Compare desired vs. actual state
- Continuously reconcile differences
Benefits:
- Resilience to missed events
- Automatic retries and recovery
- Scales better than purely event-driven systems

AI Agents in Software Development

AI is dramatically accelerating development workflows
Chainguard uses agents to:
- Remediate vulnerabilities (CVEs)
- Update dependencies
- Fix failing tests and adapt to upstream changes

Key Design Philosophy

Least privilege → “least tool call”
- Avoid giving agents full system access
- Provide narrowly scoped tools for specific tasks
Delegate execution to sandboxed systems (e.g., CI pipelines)
Focus on safe, controlled automation

Industry Shift: Velocity vs. Security

Explosion of AI-driven tools (e.g., autonomous PR generation)
Massive increase in development velocity
New risks:
- Poorly secured agent frameworks
- Malicious or unsafe automation patterns

Key Takeaways

Scale changes everything
- Simple systems break under massive workloads
- Purpose-built infrastructure becomes necessary
Reconciliation > pure event-driven systems at scale
- More resilient, predictable, and controllable
AI is a force multiplier—but requires guardrails
- Unrestricted agents introduce serious risk
- Constrained, purpose-built agents are safer and more effective
Continuous learning is mandatory
- AI tooling is evolving too fast for static skillsets
- Teams must actively experiment and adapt

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

The Grid’s Breaking Point: Can AI Save the Infrastructure It’s About to Crash? Apr 22, 2026

Show notes

SUMMARY: How real-time power flow optimization at the edge is helping data centers and the electrical grid handle surging AI energy demands more efficiently. By unlocking hidden capacity and dynamically managing power systems, we explain how existing infrastructure can support significantly more compute without massive new buildouts.

GUEST: Marissa Hummon, CTO Utilidata

SHOW: 1021

SHOW TRANSCRIPT: The Reasoning Show #1021 Transcript

SHOW VIDEO: https://youtu.be/ItcpU8UjOFE

SHOW SPONSORS:

Nasuni - Activate your data for AI and request a demo
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!

SHOW NOTES:

KEY TOPICS:

Differences between grid power dynamics vs. AI workloads
Edge AI for real-time power flow optimization
Unlocking stranded capacity in existing infrastructure
“4-to-make-3” vs. “4-to-make-4” data center design
AI training vs. inference power consumption patterns
Role of NVIDIA-powered edge compute modules
Grid modernization and coordination with utilities
Security and resilience in critical infrastructure

KEY MOMENTS:

From centralized AI models to edge-based decision-making
Defining efficiency: utilization vs. thermal performance
Why AI workloads aren’t as constant as they seem
NVIDIA partnership and edge compute in power systems
Using redundancy to increase usable capacity
Increasing density of AI compute and hidden capacity
Data center vs. utility responsibilities
Addressing data center bottlenecks and scaling challenges
Customer landscape: hyperscalers to enterprise
Security, resilience, and critical infrastructure

KEY INSIGHTS:

AI workloads are dynamic, not constant: Training and inference create fluctuating power demands that can be optimized.
Edge intelligence is critical: Real-time sensing and decision-making at the edge unlock efficiency gains not possible with centralized models.
Hidden capacity exists: Many data centers have up to 2x unused power capacity due to lack of visibility and control.
Software-defined power is the future: Faster control loops allow systems to safely exceed traditional design limits.
Efficiency = utilization: The biggest gains come from better use of existing infrastructure, not just improving hardware efficiency.

TAKEAWAYS:

AI infrastructure growth is as much an energy challenge as a compute challenge
Real-time, edge-based control systems are key to scaling sustainably
Existing grid and data center investments can go further with smarter orchestration
The future of AI scaling depends on aligning compute innovation with energy intelligence

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

Shadow AI is Faster Than Your Governance: Why Guardrails are Failing Apr 19, 2026

Show notes

SUMMARY: Shadow AI is growing much faster than known AI adoption across businesses. How can IT teams get Shadow AI under control?

GUEST: Uri Haramati, CEO at Torii

SHOW: 1020

SHOW TRANSCRIPT: The Reasoning Show #1020 Transcript

SHOW VIDEO: https://youtu.be/AUrh_xICPzM

SHOW SPONSORS:

ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
Nasuni - Activate your data for AI and request a demo

SHOW NOTES:

Torii (homepage)

Topic 1 - Welcome to the show. Tell us about your background and your focus at Torii.

Topic 2 - Is Shadow AI really a security problem—or is it a product-market fit problem inside the enterprise?

Topic 3 - Why does Shadow AI spread faster—and become more dangerous—than traditional Shadow IT?

Topic 4 - What’s the first signal a company should look for to know Shadow AI is already happening?

Topic 5 - How do you balance visibility vs. control without killing the productivity gains that drove Shadow AI in the first place?

Topic 6 - How should organizations rethink ‘data loss prevention’ in a world where the leak is a prompt, not a file?

Topic 7 - What does a ‘well-governed’ AI environment actually look like in practice—day-to-day for an employee?

Topic 8 - “Do you think Shadow AI ever fully goes away—or does it become a permanent operating model that companies need to design around?”

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

The Junior Dev Crisis: Who Inherits the Code When AI Does the Work? Apr 15, 2026

Show notes

SUMMARY: Have we reached a point where coding is a solved problem? And if so, what are the downstream effects on companies that need software to differentiate their business?

GUEST: Brandon Whichard, Co-Host of Software Defined Talk

SHOW: 1019

SHOW TRANSCRIPT: The Reasoning Show #1019 Transcript

SHOW VIDEO: https://youtu.be/q0mksIKcBzk

SHOW SPONSORS:

ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
Nasuni - Activate your data for AI and request a demo

SHOW NOTES:

The New Kingmakers (Stephen O’Grady - 2014)
Developer Growth Rates

[Via ChatGPT] A useful way to think about it:

Typing code → mostly commoditized
Designing systems → partially assisted
Owning outcomes → still very human

Topic 1 - How many years into Public Cloud did we assume that Cloud had solved the IT problem?

Topic 2 - Developers - what are we solving for?

10% of time coding, mostly on the last 10-15%
Lots of time in planning meetings (decoding requirements, resource planning, updates, etc.)
Decent amount of time fixing, troubleshooting, technical debt reduction

Topic 2a - Business people have unlimited ideas, and most ideas are money + tech

What would be their interface to problem solving without developers? (is this just a shift to consultants)
Is this a massive opportunity for a great PaaS 3.0 company (e.g. is Vercel an example?)

Topic 3 - [Hypothetical] Let’s assume a fairly normal company fired all their software developers tomorrow. How long before they could get a moderately complex new application of integration into production?

Topic 4 - Nobody likes to work on legacy code - missing source, missing engineers, etc. What do we call any code written by AI that was abandoned within the last 6-12 months?

FEEDBACK?

Email: show @ the enterprise ai show dot com
Bluesky: @TheEntAIShow.bsky.social
Twitter/X: @TheEntAIShow
Instagram: @TheEntAIShow

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