This article began with a spark.

A colleague recently shared a thoughtful LinkedIn post exploring how to allocate the costs of AI agent activity across departments. The post raised a deceptively simple question:

“If multiple teams use the same AI agent, how do we fairly distribute the cost?”

On the surface, it looks easy. In reality, it’s one of the most common–and most misunderstood–challenges organizations face as they ramp up AI adoption.

AI agents built with Microsoft 365 Copilot, SharePoint Agents, and Copilot Studio increasingly serve multiple departments. Finance wants transparency. IT needs governance. Business units want frictionless access. And no one wants a surprise Azure invoice at month‑end.

As multi‑agent orchestration grows and agents become embedded in every business process, the need for cost management becomes as important as model quality, security, and adoption.

This article provides a practical, enterprise‑ready framework for building a transparent, defensible charged‑back model using the latest capabilities across Microsoft 365 Admin Center, Power Platform Admin Center, and the emerging Agent 365 control plane.

The scenario: A shared, Enterprise agent

Imagine you’ve built a powerful Copilot Studio agent used daily across Finance, HR, Operations, and Customer Service:

• It’s deployed in production.
• Hundreds of employees rely on it.
• It’s quickly becoming mission‑critical.

Then Finance asks the inevitable:

“How do we allocate Copilot costs back to the departments that use it?”

This is where complexity begins. Because:

• Microsoft 365 Copilot Chat uses Department‑level Billing Policies
• SharePoint and M365-surface agents use the same billing foundation
• Copilot Studio uses credit‑based billing through prepaid capacity packs or PAYG
• Power Platform PAYG is tied to environments, not departments
• Shared agents don’t align cleanly to a single cost center
• Usage varies by user population, feature usage, and agent design

And until recently, both cost visibility and cost control were limited.

Without a strategy, all costs flow into a single opaque pool – leaving Finance with no insight and business units with no accountability.

The core problem: Shared agents, shared costs, no native split

Copilot Studio (PPAC) does not natively support:

• Department-level billing
• User-level caps
• Automatic cost-center attribution

Microsoft 365 Copilot supports departmental billing,

but only for:

• Copilot Chat
• SharePoint Agents
• Agents running within M365 surfaces

Agents deployed outside M365 surfaces rely on PPAC.

This creates three enterprise risks:

1. No transparency – Finance cannot determine which department is driving consumption.
2. No accountability – Business units cannot forecast or self-govern their AI usage.
3. No cost protection – A single agent–or a single department– can accidentally consume all available capacity.

To solve this, we need a layered model approach using the tools available today.

The cost controls landscape: Track, Manage, Forecast

Microsoft’s cost‑control model spans three pillars of the broader Copilot Control System:

1. Track Costs

Consumption analytics, agent usage reporting, and message-level insights.

2. Manage Costs

Departmental billing, prepaid capacity allocation, overage enforcement, and agent-level limits.

3. Forecast Costs

Copilot Studio Agent Consumption Estimator. Let’s break them down.

1. Track costs: Visibility and Analytics

Microsoft 365 Admin Center (M365 Copilot & M365 Agents)

Copilot Credit Consumption Reports

• Track message usage in near real-time
• Alert on high-consumption users (e.g., >2,000 credits in 30 days)
• Drilldowns by user, agent, and user–agent pair
• Identify which agents drive the most usage
• Support decisions to assign full Copilot licenses to heavy users

Agent Usage Report

• Unified view across all M365 Copilot users (licensed + unlicensed)
• Active agents, active users per agent, publisher type segmentation
• User and agent-level response counts
• Low-latency insights

Power Platform Admin Center (Copilot Studio Agents)

Within the Licensing Hub, admins can now see:

• All billing plans
• Total prepaid capacity purchased vs. consumed
• Month-to-date usage vs. historical usage
• Top consuming environments
• Per-agent consumption within each environment
• Whether consumption flowed through prepaid or PAYG

This is the foundation for internal cost attribution.

2. Manage costs: Billing, Isolation, and Enforcement

A. Departmental & Agent-level Billing Policies (M365 Admin Center)

Department-level billing policies allow organizations to:

• Assign PAYG access to specific departments or Entra security groups
• Map consumption to Azure subscriptions & resource groups
• Set budget limits with email alerts and enforcement
• Control which departments can use AI agents
• Track usage per group and per agent

Agent-level billing policies

Admins are able to:

• Associate specific agents with billing policies
• Enforce governance per agent
• Separate costs for high-impact or high-cost agents

This is a major change and belongs in your chargeback model.

B. Prepaid Credit capacity packs (Copilot Studio)

Packs deliver:

• $200 / 25,000 credits per month
• Purchasable in M365 Admin Center
• Assignable per environment
• Hard boundaries (Marketing cannot consume HR’s pack)
• Rebalance capability (shift unused capacity between environments)
• Isolation that supports departmental chargeback

This is the most predictable cost model for Copilot Studio.

C. Power Platform PAYG Billing Plans

Linking an environment to Azure provides:

• Metered consumption at $0.01 per message
• No upfront commitment
• Billing visibility via Azure Cost Management
• Ability to configure budgets and alerts

PAYG Setup Requirements

• Azure subscription
• Owner / Contributor permissions
• Registered resource providers
• Production or sandbox environment (no trials, no Dataverse for Teams, no default)

This is essential as a fallback mechanism.

D. Overage Enforcement Mechanisms

When a prepaid environment exceeds allocation:

• New agent publications are blocked
• Agent executions may fail
• Admins must either:
  • Pull from tenant-level unassigned capacity
  • Or burst into PAYG

Proactive monitoring and enabling PAYG as a safety net is critical.

E. Agent-Level Message Limits (PPAC)

One of the most important new governance tools:

Admins can now set monthly consumption caps per agent:

• Soft guardrail: email warnings before hitting limit
• Hard guardrail: auto-disable agent upon reaching limit
• Per-agent status (within limit / near limit / over limit)
• Ability to manually turn an agent off

This directly solves runaway consumption.

3. Forecast costs: The agent consumption estimator

Forecasting is finally first-class.

The Copilot Studio Agent Consumption Estimator enables teams to:

• Estimate monthly credit volume
• Model usage trends based on:
  • User population & interaction frequency
  • Orchestration mode (Generative vs Classic)
  • % of knowledge-based vs task flows
  • External triggers and automation
  • Use of Prompt Tools
• Compare licensing models (PAYG, prepaid packs, M365 Copilot licenses)

Accessible via:

• PPAC Licensing Hub
• Copilot Studio homepage
• Marketing site
• here

This tool should become part of every agent governance process.

Solution path: Microsoft´s current cost-control mechanisms

Now that we do understand the pillars, let´s taka a closer look into building a practical, enterprise‑ready view of the actual mechanisms available across Copilot Studio, Microsoft 365 Copilot, Power Platform, and the emerging Agent 365 ecosystem. Each solves a different part of the problem.

1. Microsoft 365 Copilot Billing Policies (PAYG, Department- OR agent-level)

Best for: Native chargeback & departmental governance

Billing Policies let admins:

• Map security groups → Azure subscription/resource group
• Set department budget limits
• Assign PAYG access only to selected user groups
• Track message consumption per department
• Enforce alerts and spending guardrails

This is the cleanest chargeback model Microsoft offers today, but applies only to M365 Copilot Chat, SharePoint Agents, and other M365 Copilot agent surfaces – not Copilot Studio standalone agents.

However, recent updates also introduced:

• Agent‑level billing policy assignment (governance per agent)
• Agent usage analytics (licensed vs. unlicensed usage)
• High‑usage alerts for heavy consumers
• Improved agent control via Agent 365 capabilities

2. Copilot prepaid credit capacity packs

Best for: Predictable, isolated departmental budgets

Copilot Credit capacity packs are offered through the Copilot Studio subscription license. Copilot
Credit capacity packs should be purchased for the anticipated number of Copilot Credits your
tenant is expected to consume each month. Unused credits do not roll over to the next month.

Packs can be allocated to specific environments.

This gives:

• Strong budget isolation
• No inter‑department “spillover”
• Easy reallocation across environments
• Zero risk of unwanted cross‑charging

If Marketing gets 50,000 credits capacity, HR cannot consume them – even if HR runs out.

This is essential for predictable budgets and chargeback.

3. Power Platform PAYG (Environment-level)

Best for: Scalable consumption with clear billing boundaries

With environment‑level billing plans:

• Each environment connects to a specific Azure subscription + resource group.
• All usage in that environment is billed to that subscription.
• Admins can track consumption via Azure Cost Management.
• Alerts, budgets, tags, and RBAC can be applied at the subscription/RG level.

What it doesn’t do:

• No per‑user or per‑department separation
• No per‑agent billing split (yet)

But it’s the foundational billing boundary for Copilot Studio..

4. Agent-level credit capacity limits

Best for: Preventing runaway consumption and enforcing governance

The new agent‑level limit feature gives admins:

• Monthly consumption caps for individual agents
• Soft guardrail (notifications)
• Hard guardrail (agent auto‑disable)
• Visibility into usage per agent per environment
• Alerts when approaching limits

This directly solves the classic “single agent consumes the entire pool” problem.

5. Azure Cost Management & Budgets

Best for: Protecting enterprise‑wide budgets

Azure Cost Management allows:

• Spend alerts
• Budget limits
• Automated actions
• Meter‑level tracking
• Cross‑subscription views

This does not split costs by department – but is critical for financial governance and protection.

6. Dataverse usage logging (via a custom table)

Best for: DIY granular departmental chargeback

Create a custom table (e.g., cs_AgentUsage) storing:

• User
• Cost center
• Agent
• Message estimate
• Timestamp
• Scenario metadata

This becomes your internal ledger for cost attribution.

It is the only way to achieve true, granular departmental chargeback for Copilot Studio today.

7. Entra ID (Azure AD) attributes: Department & cost center

Best for: Automating attribution logic

Entra ID attributes enable automatic mapping:

• User → Department
• User → Cost Center
• User → Business Unit

Combined with usage logs or agent telemetry, this gives you frictionless, zero‑touch attribution.

8. Tenant‑Wide agent inventory (Power Platform Admin Center)

Best for: Governance & shadow agent detection

The new tenant‑wide agent inventory provides:

• A complete list of all agents
• Ownership metadata
• Environment & region
• Status and usage patterns

While not a billing tool, it’s critical for:

• Determining who should pay for an agent
• Detecting “hidden” agents
• Centralizing governance

Use it as your metadata catalog for joining cost and usage data.

9. Microsoft Agent 365

Best for: Long‑term unified agent governance (not billing)

Agent 365 introduces:

• Entra Agent ID (first‑class identity for every agent)
• Unified registry across M365, Copilot Studio, and custom agents
• Observability hooks
• Security & compliance integration
• Lifecycle management

While not a chargeback engine – yet – Agent 365 provides:

• A consistent identity to join cost data
• A unified catalog
• A cross‑platform governance layer

It is the strategic control plane for enterprise AI agents. Agent 365 is not the chargeback solution – but it is the strategic backbone for building one.

10. Planning & Forecasting

Microsoft recently introduced the Copilot Studio Agent Consumption Estimator.

It helps estimate:

• Monthly credit volume
• Effects of features (flows, triggers, knowledge, prompt tools)
• Internal vs external users
• Licensed vs unlicensed users
• Generative vs classic orchestration

It’s not a pricing calculator – but it gives Finance and IT a reliable forecasting baseline.

Comparison Tables

Let´s take a look on above shared insights consolidated in a comparison table:

M365 Copilot vs Copilot Studio vs Agent 365

A practical Enterprise chargeback framework

Additional thoughts: One production environment hosting many agents

Most enterprises don’t want one environment per agent. They want:

• A dedicated Production environment
• Hosting dozens of Copilot Studio agents
• Serving multiple departments
• With shared connectors, shared governance, and shared data policies

This is the real world – and it introduces new layers of complexity. Tenant‑Wide agent inventory and Agent 365 become essential here.

Guidance for multi‑agent production environments

Based on today´s capabilities, a robust enterprise-ready model looks like this:

1. Distinguish deployment surfaces
   • M365 surfaces → use Billing Policies
   • External deployments → use PPAC environments
     
2. Allocate prepaid packs to departments
   • Predictable budgets with strict isolation.
     
3. Enable PAYG as a fallback for each environment
   • Avoid outages when hitting prepaid limits.
     
4. Enforce agent-level consumption limits
   • Protect shared capacity pools.
     
5. Log actual usage to Dataverse for internal chargeback
   • Join with Entra attributes for cost-center attribution.
     
6. Provide Finance with monthly reporting
   • Use M365 Consumption Reports + PPAC consumption analytics.
     
7. Forecast proactively using the Agent Estimator
   • Supports budgeting, governance boards, and internal approvals.

Conclusion: The enterprise chargeback framework

AI agents are rapidly becoming core to business processes. Without a strategy, shared agents become a financial black box.

The good news:
Microsoft now provides end-to-end cost management capabilities across both admin centers–and a roadmap that makes cost attribution increasingly precise.

For Microsoft 365 Copilot PAYG
Use Billing Policies for native cost‑center attribution.

For Copilot Studio (single or multi‑agent environments)
Use Azure Budgets to protect total spend, Dataverse logging for attribution, Entra ID for cost center mapping, Managed Environments for governance, and Agent Limits as safety valves.

For multi‑agent production environments
Use Tenant‑Wide agent inventory to understand ownership and scope, and Agent 365 as the strategic control plane for identity, governance, and future cost alignment.

This is the model that aligns Finance, IT, and the business – and ensures your AI strategy scales sustainably.

With the layered approach above, organizations can deliver:

• Transparent consumption
• Predictable budgeting
• Fair chargeback
• Strong governance

Agent 365 will unify this further with consistent agent identity, observability, and governance across the ecosystem–but today, the tools to build a cost-transparent agent strategy already exist they just need to become orchestrated to fit your strategy. Until then,…

The enterprise is crossing a threshold unlike any it has faced before.
For decades, technology entered the organization as a set of tools – systems to automate tasks, store data, and streamline workflows. But the arrival of AI as a learning system has changed the nature of work itself. It has introduced something fundamentally new into the enterprise:
a workforce of agents that learn, adapt, and act alongside us.

This shift is not theoretical. It is already unfolding across every industry. Right now.

Satya Nadella described it as the moment when AI stops being a tool and becomes a team – researchers, analysts, creators, developers, and decision‑making councils of models working in parallel during his recent AI Tour keynote. Satish Thomas showed how business applications are transforming from passive systems of record into agentic systems of action, where intelligence flows across finance, supply chain, sales, service, and operations during his keynote at recent Convergence. Judson Althoff calls this the hallmark of the frontier firm – organizations that put AI in the flow of human ambition, empower every employee to innovate, and reshape processes from the ground up during his keynote at Ignite. Ryan Cunningham reminds us that the magic‑wand moment is not transformation. The real barrier is the operating system of the company – the humans, the software, the processes, the decades of decisions that created today’s fragmented landscape. And Charles Lamanna brings the final piece into focus: with billions of agents emerging across the enterprise, we need a new control plane to govern, secure, and orchestrate them at scale.

Together, these perspectives reveal a profound truth:

• AI transformation is no longer about deploying technology.
• It is about redesigning the enterprise for a world where intelligence is everywhere.

Agents will not sit at the edges of the business. They will live inside every workflow, every decision, every customer interaction, every operational process. They will be created by developers, analysts, domain experts, and frontline and backoffice employees. They will reason over the organization’s data, collaborate with each other, and act with increasing autonomy.

Charles describes agents as the connective tissue between intelligence and execution.

They link AI to tools, APIs, data, and organizational knowledge — and they operate autonomously inside critical processes. They run continuously, escalate to people when needed, and deliver outcomes at a pace no human‑only workflow can match. In many organizations, agents are already becoming a dependable layer of the enterprise, shaping how work gets done and how leaders lead.

And with this new capability comes a new responsibility. So customers asking how an action framework could look like for them. The answer?

A closer look into nature could bring us some insights on how to approach these new challenges.

• Caterpillars transform into butterflies through a process called metamorphosis, which allows them togrow, reproduce, and adapt to their environment.
• Monkeys learned to use a tool. Other monkeys learn from it by copying successful patterns.
• Ants collaborate as a team to cross the impossible.
• Bees tend to their hives, ensuring growth and success in a protected and controlled environment.

The enterprise must now answer questions it has never faced before:

• How do we govern a workforce of digital agents
• How do we ensure they act safely, ethically, and in alignment with business goals
• How do we observe, secure, and optimize intelligence that is distributed across every system
• How do we design processes for learning systems rather than static ones
• How do we turn ubiquitous intelligence into competitive advantage

What follows should become the operating model for the AI‑first enterprise – a blueprint for how organizations can structure their data, orchestrate intelligence, govern agents, and build the conditions under which human ambition and machine capability reinforce each other. Need a little guidance? Here´s a more detailed strategy paper that helps you get started.

Because the organizations that lead in this new era will not be the ones that simply adopt AI.
They will be the ones that embrace the agentic enterprise, where intelligence is democratized, innovation is ubiquitous, and governance becomes the foundation for scale.

Upgrading the Operating System of the Enterprise

True transformation requires flipping the model: start with bold ambitions, assemble the right mix of humans and agents, and iterate rapidly on a platform that democratizes creation and collapses the distance between the person who feels the pain and the person who can fix it.

But even ambition and capability are not enough.

As Charles Lamanna made it clear during his speech at Ignite, the next era will be defined by scale.
By 2028, the world will run on more than a billion agents – built by developers, analysts, domain experts, and frontline employees.

This is why the enterprise now needs an agent control plane – a way to see every agent, understand what it touches, manage how it behaves, secure how it accesses data, and ensure it acts in alignment with business goals. Because intelligence without governance is chaos. And governance without intelligence is stagnation.

An operating system where:

• Intelligence is everywhere
• Agents are teammates
• Data becomes organizational IQ
• Every employee becomes a maker
• Every process becomes adaptive
• Every application becomes a system of action
• Every agent is governed, observable, and secure by design

How to turn AI Theory into practice in 2026

Every enterprise leader today feels the same tension: AI is advancing faster than any technology in history – yet meaningful transformation inside organizations remains painfully slow.

• We’ve all seen the magic‑wand moments.
• We’ve all watched demos that feel like science fiction.
• We’ve all run hackathons, brainstormed use cases, and experimented with copilots.

And yet, the real question remains:

How do we turn AI from impressive prototypes into a new operating system for the enterprise?

Watching recent talks from five leaders – Satya Nadella, Judson Althoff, Satish Thomas, Ryan Cunningham, and Charles Lamanna – offered a remarkably aligned answer. Each approached the problem from a different altitude, but together they revealed a clear, actionable blueprint for how organizations can move from theory to practice to reshape their business in 2026.

Start with the hard truth: Your current Operating System can’t get you there

Most AI projects fail not because the technology is weak – but because the enterprise is fragmented.

• Decades of SaaS sprawl
• Siloed data
• Human‑heavy processes
• Legacy workflows
• Governance models built for static systems

As Ryan Cunningham put it:

“The operating system of the company – the humans, the software, the processes – is what holds us back.”

Judson Althoff reinforced this with data:

“There’s never been a technology adopted faster than AI, but success rates remain low.”

Why?
Because organizations treat AI as a technology project, not a business transformation.

Practical takeaway:
Before you deploy another pilot, ask: What business constraint or ambition are we trying to change?
If you can’t answer that, you’re not ready to build.

Redefine the Enterprise: You’re becoming an agentic organization

Satya Nadella describes the shift bluntly:

“AI is no longer a tool. It is a team.”

Agents – digital workers that can perceive, reason, and act – are becoming embedded in every workflow. They take on roles:

• Researcher
• Analyst
• Creator
• Developer
• Decision‑making council

Satish Thomas extends this into the business application layer:

“Systems of record are becoming systems of action.”

CRM, ERP, HR, supply chain – all become agentic surfaces where intelligence initiates work, not just records it.

And Charles Lamanna brings the operational reality into focus:

„…agents are already moving into the workflows that matter most.“

Across finance, operations, supply chain, and customer support, agents are:

• Improving accuracy
• Reducing manual effort
• Accelerating cycle times
• Enhancing customer experience

They connect AI to tools, APIs, data, and organizational knowledge – and they operate autonomously inside critical processes. They escalate to people when needed, run continuously, and deliver outcomes at a speed and scale we’ve never seen before.

Practical takeaway:
Stop thinking about “adding AI” to your processes. Start thinking about which roles agents will play in your processes.

Build your Enterprise IQ: Intelligence is now your competitive advantage

Satya introduced the concept of the IQ layer:

• Work IQ – relationships between people, projects, files, communications
• Fabric IQ – operational + analytical data
• Foundry IQ – unstructured + structured search

Judson added the missing piece:

“We need to put the ‘I’ back in AI.”

Developers today “sip data through a thousand tiny straws.” Agents need context, relationships, and signals – not isolated datasets.

Charles reinforces this with operational clarity:

„agents must interoperate safely with your data, your apps, your communications, across Microsoft and partner ecosystems.“

Practical takeaway:
Your data governance is your agent governance. If your data is fragmented, your agents will be too.

Empower every process expert: Democratize creation

This is where theory becomes practice. Ryan explains that the old “citizen developer” model was built around limitations. AI collapses those limitations.

• Process experts become process builders
• Developers become accelerators and enablers
• Copilot, App Builder, Vibe, and Copilot Studio become the new productivity suite

Judson calls this ubiquitous innovation:

“There is a maker in every room of the house.”

Charles adds the scale dimension:

„agents will be built by everyone in your organization, not just developers.“

Practical takeaway:
If you want to upgrade every process, you must empower every process expert. This is no longer optional.

Govern the new workforce: Agents need a Control Plane

By 2028, IDC predicts 1.3 billion agents will be deployed. Charles Lamanna introduced the missing piece:
Agent 365 – the agent control plane.

It provides:

• A single agent registry
• Access control
• Usage and risk visibility
• Secure interop across apps, data, and clouds
• Threat detection and data loss prevention

This is the governance model for the agentic enterprise.

Practical takeaway:
You cannot scale what you cannot govern. Before you build agents, build the control plane.

Put it all together: How to turn theory into practice

Here’s the practical blueprint distilled from all five leaders:

Step 1 – Start with ambition, not ideas

Define the business outcome you want to change.
Then design the process around that ambition.

Step 2 – Identify the agent roles

Ask:

• What would a researcher do here
• What would an analyst do
• What would a coordinator do
• What would a creator do

Design the agentic workflow.

Step 3 – Build your enterprise IQ

Unify your data.
Connect your signals.
Establish Work IQ + Fabric IQ + Foundry IQ.

Step 4 – Empower the makers

Give process experts:

• Copilot
• App Builder
• Vibe.PowerApps
• Copilot Studio
• Dataverse

Let them build the first version.
Let developers harden and scale it.

Step 5 – Deploy the control plane

Use Agent 365 to:

• Register agents
• Govern access
• Monitor behavior
• Secure interactions
• Observe outcomes

Step 6 – Iterate like a frontier firm

Small loops.
Fast cycles.
Bold goals.
Continuous learning.

The bottom line: You’re not adopting AI – You’re upgrading your Operating System

The organizations that win in 2026 won’t be the ones that deploy the most AI pilots.
They’ll be the ones that:

• Redesign processes for learning systems
• Empower every employee to build
• Govern agents like a new workforce
• Treat intelligence as a core capability
• Operate with ambition, not caution

This is the moment to move from theory to practice. Not by adding AI to the enterprise – but by rebuilding the enterprise around AI. I look forward to assessing the responsibilities and outcomes that will be addressed in the upcoming discussions and events scheduled for early 2026. Until then, …

Wishing you joyful holidays with loved ones and a wonderful 2026.

The Scene:
The CIO and CISO of a large enterprise company returned from Microsoft Ignite, energized and a little unsettled. The keynote’s vision of an AI-first, agentic Power Platform is no longer a distant future – it’s here. They gather the Power Platform Governance team for a workshop, posing a single, provocative question:
“Are we agent-ready to become frontier with our Power Platform governance strategy?”

The Challenge: From Apps & workflows to Copilots and Autonomous Agents

The company’s Power Platform footprint is massive: hundreds of Power Apps, thousands of monthly active flows, and a culture of business-led innovation. But Ignite’s announcements – multi-agent orchestration, Copilot Studio, advanced pipelines – signal a new era. The CIO frames the challenge:

“We’ve mastered low-code at scale. But are we ready for agents that reason, plan, act, and learn? Can our governance keep up with the speed and autonomy of AI-driven automation?”

We thought we were fine. Ignite proved otherwise. The governance and security team walked into the room confident.

• They had a solid zone model.
• They had ALM with pipelines.
• They had connector policies, DLP, and Dataverse carefully fenced.

Then the CIO and CISO came back from Microsoft Ignite. They had spent two days in sessions about AI agents, multi‑agent orchestration, CUA, MCP, and the idea of “human‑led, agent‑operated” enterprises. On the flight home, one question kept coming up between them:

“Our Power Platform is governed for apps and flows. But is it governed for agents?”

By Monday morning, that question had become a mandate:
Run a workshop. Review our governance and environment strategy. Show us how we will handle agentic AI.

The Debate: Governance in the Agentic AI Era

The room buzzes with debate. The CISO raises concerns about risk boundaries and compliance, especially with the EU AI Act’s new requirements for agentic systems. The governance lead points to the four foundational pillars

Managed Security, Managed Governance, Managed Operations, and Managed Availability

as the backbone, but acknowledges that agentic AI stretches every boundary

The CIO & CISO’s challenge: are we agent‑ready?

The brief for the workshop was simple and brutal:

• Don’t just show us our current state.
• Show us what has to change to safely adopt multi‑agents, CUA, MCP, and agent‑to‑agent (A2A) patterns.
• Align it with the Managed Platform capabilities we just saw at Ignite.
• And don’t come back with an academic vision; come back with something we can actually enforce in the Power Platform Admin Center.

The governance, security, and platform team did what most mature organizations do first: they pulled out their existing Power Platform governance deck.

On paper, it looked good:

• Environment zones from personal to mission‑critical
• ALM enforcement via Power Platform Pipelines
• Advanced connector policies and DLP
• Dataverse as system of record in enterprise environments
• Monitoring, analytics, and audit logs

The CIO looked at it, nodded, and asked a single question:

“Where, in this entire model, do we govern agents?”

Silence.

Inside the workshop: Discovering the blind spot

We started the workshop by mapping out what had changed since this governance model was first written.

Key topics ignite intense discussion:

Multi-Agent Orchestration:
The architects are excited about multi-agent systems (MAS) for complex workflows, but the CISO warns: “Without clear escalation boundaries and telemetry, MAS could become a black box.” The group agrees on the need for transparent agent-to-agent (A2A) protocols, auditable logs, and strict depth limits on delegation.

Zone-Based Governance:
How do we segment environments so that high-risk agents never run in low-governance zones? The team maps workloads to the new zone model – personal, innovation, team, departmental, and enterprise/mission-critical – debating where agents should live and how to enforce connector policies at scale.

Agent Evaluation and Lifecycle:
With Copilot Studio, anyone can build an agent. But how do we ensure every agent is evaluated for safety, compliance, and business alignment before deployment? The team explores embedding agent evaluation gates into Power Platform Pipelines, leveraging new Ignite features for multi-stage approvals and automated quality checks.

Memory and Knowledge Governance:
Agent memory is powerful – enabling agents to learn and personalize – but risky if not governed. The team debates retention policies, auditability, and the importance of grounding agents in validated, up-to-date knowledge sources.

Compliance and the EU AI Act:
The compliance officer outlines how Microsoft Purview and Compliance Manager can be used to assess agentic systems, classify risk, and document controls. The group commits to integrating continuous compliance monitoring into their pipelines.

From Ignite and the latest product updates, we listed the new realities:

• Agents can be multi‑agent systems, not just single copilots.
• Agents can call other agents using A2A.
• Agents can use CUA as a tool to drive UI automation across apps.
• Agents can invoke MCP tools to talk to external systems.
• Agents can hold memory over time.
• Agents can make decisions, not just respond.

Then we overlaid this on our current governance model and asked:

• Where do we define which agents are allowed to call which other agents?
• Where do we govern CUA usage as a tool with risk comparable to RPA or privileged automation?
• Where do we treat MCP endpoints as governed, versioned connectors instead of ad‑hoc integrations?
• Where do we set policies for what agents are allowed to remember – and for how long?
• Where in ALM do we evaluate agents, not just deploy them?

The answer was uncomfortable:
We didn’t. Not explicitly.

We had governed platform primitives (environments, connectors, solutions) but not agentic behavior. Ignite had just made that gap impossible to ignore.

Agents are not just apps with better prompts

The turning point in the workshop came when someone from security said:

“We’re still talking about agents like fancy apps. But the Ignite message is clear: Agents are digital workers.”

That one line shifted the conversation.

If agents are digital workers, then:

• Environment = where they’re allowed to work
• Connectors & tools (including CUA & MCP) = what they’re allowed to use
• A2A = who they’re allowed to delegate to
• Memory = what they’re allowed to remember
• Pipelines & ALM = how they’re hired, promoted, or retired
• Telemetry & KPIs = how we know if they’re performing or causing harm

Suddenly, our existing model looked incomplete, not wrong.

We didn’t need to throw it away. We needed to extend it to treat agents as first‑class citizens.

Five uncomfortable questions we had to answer in the room

To move from theory to action, we forced ourselves through five uncomfortable questions:

1. Zone strategy:
   In which zones are agents even allowed – and under what conditions?
   Is Zone 0 (personal dev) allowed to host agents, or is that restricted to sandbox and above?
   
2. Multi‑agent & A2A:
   What guardrails do we set for agents calling other agents?
   Do we allow high‑risk agents to call low‑risk agents?
   Do we define depth limits for delegation chains?
   
3. CUA as a tool:
   Do we treat CUA like just another capability, or like a privileged automation tool with stricter oversight?
   How do we monitor and restrict CUA‑driven UI actions?
   
4. MCP governance:
   Who owns MCP endpoints?
   How are they versioned, tested, and deployed?
   Are they treated like managed connectors, or like custom plugins we must deeply review?
   
5. Agent memory & lifecycle:
   What is the retention policy for agent memory?
   Who approves it?
   How do we evaluate agents before they reach production – and how do we roll them back if they drift?

By the time we’d argued through those five, it was clear: we needed an agent‑ready governance model.

How we rebuilt the governance model in one workshop day

We didn’t start from scratch. We started from strength.

The company already had:

• A zone‑based environment strategy
• Admin Center–only governance
• Pipelines and solutions for ALM
• Connector policies and DLP
• Clear RACI between IT, security, and makers

Our task was to upgrade this model to speak the language of agents.

The Breakthrough: New Features, New Mindset

As the workshop progresses, the mood shifts from anxiety to action. The team identifies quick wins:

• Embed Telemetry and KPI Alignment to measure agent impact and detect drift in real time.
• Adopt Environment Groups and Rules to standardize governance across all environments.
• Implement Advanced Connector Policies for action-level control, especially for agents and flows.
• Leverage Ignite’s Enhanced Pipelines for automated ALM, agent evaluation, and deployment gates.
• Establish an Enterprise Agent Catalog to track, approve, and reuse agents and skills.

We made five deliberate moves.

1. We refreshed the zone model through an “agent lens”

We kept the familiar zones, but explicitly defined what agents are allowed to do in each:

Zone 0 – Personal / Playground

• No sensitive production data
• No persistent memory
• No multi‑agent chains
• CUA disabled

Zone I – Innovation / Hackathon

• Sandboxed agents only
• Synthetic or masked data
• Multi‑agent allowed, but with strict depth limits
• Clear time‑boxing and auto‑cleanup

Zone 1 – Team / Sandbox

• Early departmental agents
• Limited A2A (within same owner/team)
• Memory allowed, but non‑sensitive and short‑lived

Zone 2 – Departmental / LOB

• Scoped agents tied to documented workflows
• Multi‑agent and A2A allowed with governance
• MCP and CUA allowed under policy
• Mandatory ALM via pipelines

Zone 3 – Enterprise / Mission‑Critical

• Fully governed, high‑impact agents
• Strict A2A and tool whitelisting
• Full telemetry, SIEM integration, and safety evaluation
• Clear rollback and retirement paths

We wrote these rules down as environment group rules and policies the Admin Center can enforce.

2. We brought multi‑agent orchestration and A2A under governance

Ignite made it clear that multi‑agent systems and A2A are not edge cases – they’re the future.

We defined:

• Which roles of agents may call which other roles (e.g., “research” agents can call “summarizer” agents, but not “approval” agents).
• A2A trust boundaries, so an agent can never use another agent to side‑step connector policies or data boundaries.
• Lifecycle alignment, ensuring any agent in an A2A chain is versioned and deployed via the same pipeline set.
• Observability, including logging agent‑to‑agent calls to detect loops, abuse, or unexpected patterns.

Suddenly, multi‑agents stopped being scary “black box swarms” and became just another governed pattern in the platform.

3. We treated CUA as a privileged tool, not a toy

CUA was one of the biggest wake‑up calls. Used well, CUA is an incredible accelerator. Used badly, it’s a compliance incident waiting to happen.

We positioned CUA as:

• A high‑risk tool whose usage is allowed only in specific zones and environments.
• Subject to the same scrutiny as high‑privilege automation (think RPA against critical systems).
• Observable: CUA‑driven actions must be logged and attributable.

In practice, that meant:

• CUA is disabled by default in lower‑risk zones, enabled selectively in higher‑governed ones.
• Any agent using CUA must pass stricter pre‑production evaluation.
• CUA usage is part of the security and AI Red Team review.

4. We put MCP on the same footing as connectors

The team realized something important: If MCP is how agents talk to external systems, then MCP is effectively a connector framework.

So we treated MCP as such:

• MCP servers must be registered, owned, and documented.
• MCP tools are versioned and deployed via solutions and pipelines.
• MCP endpoints are subject to the same review and approval as custom connectors.
• MCP is logged and monitored, so we can see what agents are doing with external tools.

This gives the company a way to adopt MCP with confidence instead of fear.

5. We extended ALM to include agent evaluation, memory, and retirement

Finally, we acknowledged the obvious: Our ALM model was great at shipping solutions. It was silent on evaluating agent behavior.

We fixed that.

The updated ALM model now requires:

• Pre‑prod evaluation for any agent touching business‑critical workflows:
  • Safety testing, prompt injection tests, tool misuse checks.
• A clear owner signing off on acceptable risk.
• Memory governance:
  • What agents are allowed to remember.
• How long they retain it.
• How to reset or wipe memory when policies or context change.
• Retirement and rollback:
  • Agents must be designed with a lifecycle.
• There must be a clear, pipeline‑driven rollback path.
• Deprecated agents must be removed from catalogs and A2A chains.

This turned agents from “just another artifact” into living systems with a managed lifecycle.

The CIO closes with a rallying call:

“Agent-readiness isn’t just about technology – it’s about mindset. We need to operate faster, more agile, and with relentless focus on governance. Let’s lead, not follow.”

The frontier firm test: Where we landed on the Agent readiness spectrum

At the end of the workshop, we overlaid our new model on the Agent Readiness Framework across its five pillars:

• Business & AI Strategy:
  Clearer linkage between zones, agents, and business‑critical workflows.
• Business Process Mapping:
  Agents tied to documented workflows, KPIs, and success metrics, not just “let’s see what it does.”
• Technology & Data:
  MCP, CUA, A2A, and multi‑agent orchestration all brought under the same platform governance umbrella.
• Organizational Readiness & Culture:
  Updated RACI, clear ownership, and a shared language between IT, security, and business around “digital workers.”
• Security & Governance:
  Agent evaluation, memory governance, A2A controls, and telemetry now considered non‑negotiable.

The CIO’s verdict at the end was simple:

“We’re not a frontier firm yet. But for the first time, I can see the path there – and it’s built on the platform we already have.”

Conclusion: The Spirit of the Workshop

This workshop wasn’t just a technical deep-dive – it was a cultural inflection point. The debates were intense, the stakes high, but the outcome clear: Agentic AI is here, and governance must evolve.
With the right strategy – zone-based governance, advanced pipelines, continuous compliance, and a culture of transparency – this enterprise is not just agent-ready. It’s frontier.

What your governance and security team should do next

If you recognize yourself in this story – solid Power Platform governance, but no explicit answer for agents – you don’t need to start from zero.

You can:

1. Run your own workshop with the same central question:
   “Where, in our current model, do we explicitly govern agents?”
2. Overlay the Agent Readiness Framework on your existing governance to find gaps across strategy, processes, tech, culture, and governance.
3. Extend your existing model, don’t replace it:
   • Add an “agent lens” to your zone strategy.
   • Bring multi‑agent, A2A, CUA, MCP, and memory into scope.
   • Evolve ALM from “deploy solutions” to “evaluate, monitor, and retire agents.”
   • Improve experience by implementing latest capabilities.
4. Anchor everything in Admin Center capabilities so policies are enforceable, not just aspirational.

Because in an AI‑first world, the question isn’t whether you’re using agents. It’s whether you’re ready to govern them like the digital workforce they’re becoming.

Hope, you like´d my short little story about a workshop with an enterprise customer – aiming to become frontier and review their strategy for it. Until then,…

But the story is changing. Not because low‑code failed, but because it has been absorbed into something bigger. The canvas hasn’t disappeared — it has simply faded into the background, becoming the invisible infrastructure that now powers an AI‑first era.

Competitive Landscape: Agentforce, and Niche Innovators

As Microsoft Power Platform pivots toward an AI-first future with its 2025 Wave 2 Plan, the competitive landscape is heating up. Rivals—both established and emerging—are rapidly evolving their strategies to capitalize on the shift toward agentic automation, conversational app creation, and “vibe coding.

Salesforce has rebranded and reimagined its AI strategy with Agentforce, a full-fledged agentic platform designed to bring autonomous digital labor to every business process. Agentforce is not just a CRM enhancement—it’s a strategic bid to redefine enterprise automation. By integrating deeply with Slack, Data Cloud, and vertical-specific workflows, Salesforce is positioning Agentforce as the go-to platform for intelligent agents that can plan, execute, and optimize tasks across departments.

Startups like Lovable and open-source platforms like n8n are redefining what it means to build software. Lovable’s chat-based app builder lets users create full-stack applications by simply describing their needs, while n8n offers flexible, self-hostable workflow automation. These tools embody the “vibe coding” ethos—software creation driven by intuition, conversation, and AI assistance.

The Canvas Fades, the Fabric Emerges

The entry point is no longer a blank screen or a data grid. It’s a conversation.

With Copilot embedded across Power Apps, Power Automate, Power Pages, and Power BI, you don’t start by dragging controls. You start by describing intent: “Build me an app to track customer onboarding” or “Automate invoice approvals with escalation rules.”

Copilot scaffolds the solution. Low‑code remains — but it’s the refinement layer, the governance substrate, the runtime fabric. The toolkit hasn’t died; it’s been abstracted.

Agents as the New Workforce

The next leap is agentic AI.

Instead of humans manually wiring flows, AI agents orchestrate across systems, monitor events, and take action. They draft contracts, triage service cases, enrich leads, and coordinate across ERP and CRM.

And they don’t just assist — they act. With guardian patterns like pre‑flight checks, rollback, and audit logs, enterprises can safely grant autonomy where ROI is proven.

MCP: The Universal Connector

One of the biggest questions is legacy: “What about our systems of record?”

This is where the Model Context Protocol (MCP) comes in. Think of it as the USB‑C of AI connectivity. A universal standard that lets agents plug into any system — modern SaaS or decades‑old mainframe — through a consistent interface.

With MCP, Power Platform becomes not just a toolkit, but the operating fabric of enterprise AI.

Multi-Model: The Next Big Leap in AI for Work

The future of AI in the workplace isn’t about a single model—it’s about choice and flexibility. Multi-model capabilities are emerging as the next big thing, empowering organizations to select the right model for the right task. This approach recognizes that no single model can excel at everything. Some models are optimized for reasoning, others for creativity, and others for speed and cost efficiency.

By enabling multiple models within Microsoft 365 Copilot & Copilot Studio, businesses gain:

• Flexibility: Choose models tailored to specific workflows—whether drafting content, analyzing data, or generating code.
• Resilience: Reduce dependency on a single provider and ensure continuity even as technology evolves.
• Innovation: Leverage specialized models for unique industry needs, unlocking new possibilities for productivity and creativity.

This multi-model strategy isn’t just a technical upgrade—it’s a paradigm shift. It puts control in the hands of organizations, allowing them to align AI capabilities with their goals, compliance requirements, and budget. In short, multi-model is the foundation for a more adaptable, intelligent, and future-ready workplace.

Governance and Sovereignty: The Differentiator

Speed without trust is a dead end. Especially for EU‑based enterprises, the Data Act and sovereignty concerns are real.

Microsoft’s approach addresses this head‑on:

• EU data residency in Dataverse.
• Customer‑managed keys and Lockbox‑style access approvals.
• Sovereign cloud options for restricted workloads.

The message: you don’t have to choose between moving fast and staying compliant.

Platform Evolution Timeline

• 2023: Low-code platforms dominate (Power Apps, AppSheet, Honeycode, Salesforce Flow).
• 2024: Agentic AI emerges (Agentforce, Copilot agents, Duet AI, SnapLogic Agent Creator).
• 2025: Vibe coding and open automation surge (Lovable, n8n, Bolt, Replit).
• 2026: Unified AI-first platforms (Power Platform Wave 1, Agentforce 3.0) become the new standard.

The New Reality

So here’s the truth: Power Platform is no longer just a low‑code toolkit. It has evolved into an AI‑first fabric where:

• Copilot is the new entry point.
• Agents are the new workforce.
• MCP is the universal connector.
• Governance is the trust layer.

This isn’t the end of low‑code. It’s the next chapter — one where low‑code becomes the invisible foundation for an AI‑first enterprise.

Closing Thought

The companies that cling to the old narrative of “low‑code democratization” will miss the bigger story. The ones that embrace Power Platform as an AI‑first operating fabric will become the Frontier Firms of the agentic era.

The canvas may fade, but the fabric is stronger than ever.

The airport evolution metaphor

As business demands evolved, passenger volumes and flight patterns outgrew the old gates. New planes caused your old runways to their maximum. You were forced to make a decision. Close the airport for an unpredictable time or start modernizing on the ground next door. Avoid major disruptions while working on the foundation is not an easy task – though signs have been clear: Modern AI workloads, agentic automation, and complex orchestration stretched capacity beyond its limits. The blueprint grid reveals runway queues and bustling tarmac—signs that a new aviation hub is needed.

Rather than halting operations, Power Platform expand in mid-flight. A second terminal rises alongside the legacy structure, seamlessly connected by the Hub: Power Platform. Transforming tools into invisible foundation of AI-first thought services. This new MCP-ready terminal accommodates AI tools and systems of record, ensuring smooth transitions for Copilot Studio, Agentic AI, and Agent Flows.

Until then,…

…you don’t get quiet agreement. You get sparks. And that’s exactly what happened recently in a workshop. I walked into the room knowing exactly what to expect: a group of seasoned RPA developers, a few API specialists, and the quiet skepticism that always surfaces when the conversation turns to AI-driven automation.

The Skeptics Speak Up

The RPA veterans were the first to challenge:

• “AI is too error-prone. Why would we replace stable bots with something that needs constant human babysitting?”
• “We’ve spent years hardening our automations. Why gamble on something that might hallucinate?”

Their concerns weren’t dismissive — they were grounded in experience. RPA has earned its place by thriving in stable, rule-based, legacy environments. Once trained, bots can run for years with minimal change. That reliability is hard to argue against.

We started taking a look at the following visual to see where things could get us started.

When presenting I said:

“Notice today is not about replacing RPA. We came together to protect your RPA investment, double down on the APIs transition to MCPs trend we discussed last time and discover possible piloting of CUA where gaps are identified.”

The API Counterpoint

Then the API developers weighed in after a while of talking about the initiatives currently taken care of in terms of „translating“ internal APIs into MCP Server and therefore simplify automation and integration in a future scope:

• “Instead of teaching an AI to click buttons, why not invest in turning our APIs into MCPs? That’s a cleaner, more scalable strategy.”
• “If APIs are the backbone, extending them into Copilot tools gives us long-term ROI without the unpredictability of AI-driven UI automation.”

They had a point. APIs are the cleanest integration path — fast, reliable, and low-maintenance. And MCPs (being used as Agentic AI Tools) represent a way to expose those APIs directly into Copilot agent experiences, effectively future-proofing the investment.

The room warmed up quickly, so I shifted the discussion to assess how familiar this audience was with RPA and API shifting. My goal was to encourage them to adopt a growth mindset and take a closer look at CUA.

The CUA Vision: Filling the Gaps

„This is where Microsoft’s CUA vision comes into focus. CUAs aren’t meant to replace RPA or APIs. They’re designed to fill the messy middle

• Dynamic, UI-heavy workflows where APIs don’t exist.
• Exception-rich processes where RPA breaks too easily.
• Customer-facing journeys where variability is the norm.

The insight is this: CUAs extend automation into territories that were previously “too messy” to touch. They don’t compete with APIs or RPA — they complement them.“

Above timeline presents analysts‘ predictions for the technology mix in automation by the end of 2030. The comparison below aims to provide a clearer understanding of these projections.

Lessons Learned

From the heated discussion, a few lessons crystallized:

1. RPA is still the workhorse. Protect it where it delivers ROI, especially in legacy systems.
2. APIs are the backbone. And yes, turning them into MCPs is a smart long-term play — it extends your value into the Copilot ecosystem.
3. CUAs are the frontier. They unlock automation in places neither RPA nor APIs can reach.
4. The future is orchestration. The winners won’t be those who pick one tool, but those who master the hybrid stack: RPA + APIs + CUAs.

Closing Reflection

By the end of the workshop, the skeptics weren’t fully convinced — but they weren’t dismissive either. The RPA developers began to see CUAs not as a threat, but as a way to tackle the “ugly processes” they’ve always avoided. The API specialists recognized that MCPs and CUAs aren’t competing strategies, but parallel ones: MCPs strengthen the backbone, CUAs expand the frontier.

The overall lesson? Automation isn’t a zero-sum game. It’s a portfolio. And Microsoft’s CUA vision isn’t about replacing what works — it’s about expanding what’s possible. Until then,…

A game‑changing moment in the boardroom

“Don’t fund another isolated AI project. Rebuild your business as an intelligent‑apps platform.”

At a recent C‑level leadership summit, one Microsoft executive reframed the AI conversation with a deceptively simple challenge:

It was a sharp pivot from the endless cycle of “yet another Copilot” to a bold vision for deploying a digital workforce at scale. The idea wasn’t to sprinkle chatbots into workflows, but to modernize the entire software estate so every application becomes smarter, more adaptive, and more valuable over time.

From 10% optimisation to 10x transformation

What stood out was the insistence on scale economics: when intelligent capabilities are designed once, reused everywhere, and governed centrally, their value compounds. That is the difference between a 10% incremental improvement and a 10x leap in enterprise performance.

• From pilots to portfolios — Shift from experimenting in silos to operating a platform of reusable capabilities.
• From automation to orchestration — Move beyond individual task automation into AI‑led processes that coordinate people, systems, and decisions in real time.
• From speed to scale — Balance the rapid build capabilities of the Power Platform with the governance and lifecycle rigour of Azure AI Foundry.

The intelligent‑apps stack

Microsoft’s approach unites key pillars:

Why this resonates at the C‑suite level

The business cases cut across sectors:

• Customer operations: Containment rates rise, handle times drop, customer satisfaction surges.
• Finance & compliance: Days‑to‑close shrink while audit exceptions decline.
• Supply chain: Exceptions are resolved faster with fewer expedite costs.
• Field service: First‑time fix rates improve, cutting downtime and costs.

For each, Microsoft advocates measuring outcomes with KPIs that matter to boards and investors — not vanity metrics.

The operating model shift — Frontier Firm vs. Intelligent-apps fabric

Technology is only half the story. This approach demands:

• Fusion teams that blend domain expertise with platform mastery.
• Shared guardrails — prompt libraries, data connectors, safety policies.
• Safe iteration in production with telemetry and rollback paths.
• Relentless measurement via the Business Value Toolkit to prove ROI.

If you hold the Intelligent‑Apps Fabric vision we’ve been shaping up against Microsoft’s Frontier Firm perspective, the through‑line is striking — they’re essentially two lenses on the same strategic shift, but aimed at different altitudes of the conversation.

From the Frontier Firm guide, the core idea is that the next generation of market leaders will:

• Put AI at the operational core — not as a bolt‑on, but as the engine that runs decision‑making, execution, and adaptation.
• Scale digital labor — moving beyond isolated automation to a workforce model where AI agents and human talent are orchestrated together.
• Continuously reimagine work — treating processes, roles, and even business models as fluid, evolving assets.

When you map that to the intelligent‑apps narrative:

My takeaway: the Frontier Firm vision is the why — a macro‑level call to action for CEOs to re‑architect their companies around AI‑driven operating models. The intelligent‑apps framework is the how — a concrete, technology‑anchored playbook for making that shift real, with specific tools, governance patterns, and business cases.

Why the timing is critical

With budget planning underway, CFOs and CEOs face a choice:
Fund “safe” point solutions that will be obsolete in two years, or invest in a platform that compounds value across the enterprise.

The message from this summit was clear: Incrementalism is the risk. Platforms are the opportunity.

Key questions for leaders before approving the next transformation budget

1. Are we funding another project, or are we investing in a platform?
2. Can our apps and workflows adapt in production — or are they frozen the day they go live?
3. Do we have the governance model to scale AI safely across our business?
4. What’s our 10x scenario — and how soon can we prove it?

Final word: The organisations that will lead in the 2030s are already building their intelligent‑apps fabric today. They’re not chasing the latest AI tool; they’re industrialising intelligence as a core business capability. The only question is whether you’ll be one of them. Until then,…

The room was full. A handful of Power Platform CoE leads, a couple of makers who had built scrappy Copilot Studio prototypes, and the CIO—arms folded, curious but cautious. “We’ve got demos,” he said, “but I don’t have something I’m ready to bet the business on.” That was the moment to introduce the missing piece: a Red Team for AI agents—not as bureaucracy, but as the engine that converts promise into production.

Inside the workshop

We started with what they’d already done: a few Copilot Studio agents answering internal FAQs and sketching data lookups. Demos that looked great in a controlled setting, but brittle in the wild. The CoE was strong on governance and enablement; the gap was assurance. The question we explored together: how do we prevent being another PoC that never scales, and instead build the muscle to launch agents users trust?

I drew three columns on the board—Risk, Readiness, ROI—and we mapped their agents against each. That’s where the Red Team clicked. It wasn’t “security theater.” It was the method to break things on purpose so they don’t break in production—and to prove business value with evidence, not optimism.

The Red Team, explained simply

A Red Team for Copilot Studio agents is a specialized assurance function that stress-tests agents before your customers do. It treats prompts, actions, and data like software—with CI/CD, tests, and release gates—and it brings adversarial thinking into your delivery flow.

• Core mission: Turn unknown risks into managed risks by hacking your own agents—prompt injection, tool misuse, data exfiltration, hallucinations under pressure—and then hardening them.
• Scope of work: Threat modeling: System prompts, grounding sources, actions/plugins, memory, and connectors.
• Adversarial testing: Jailbreaks, role confusion, refusal bypasses, and privacy attacks—automated in a repeatable harness.
• Safety controls: Content filters, PII redaction, DLP guardrails, purpose limitation.
• Reliability checks: Grounded answer rates, refusal accuracy, degradation behavior under load or outages.
• Action hardening: Least-privilege identities, parameter allowlists, approvals for destructive operations.
• Monitoring & response: Canaries, feature flags, kill-switches, and incident runbooks.
• What “good” looks like: Release gates wired to evals. If grounded accuracy drops or toxicity rises, the build fails. Canary releases catch regressions. Every fix becomes a reusable template or wrapper.

The CIO’s posture shifted. “So this is our safety net—and our speed booster.” Exactly.

Why it lives inside your Power Platform CoE

Putting the Red Team inside the CoE gives it reach and leverage: one intake, one standards library, one dashboard for risk and value. It stays independent on release decisions but deeply integrated with how makers build.

• Benefits of integration: Shared guardrails: DLP, environment strategy, and identity policies applied consistently to agents.
• Reusable assets: Hardened prompt patterns, action wrappers, test datasets, and pipelines available to every maker.
• Faster approvals: Evidence packs (threat model, eval results, rollback plans) streamline Legal/Compliance sign-off.
• Shift-left enablement: Office hours and templates reduce “mystery work” and prevent shadow IT patterns.
• Decision rights:
  • High‑risk agents: Red Team holds the release gate; business owners accept any residual risk explicitly.
  • Low/medium risk: Advisory sign-off with automated gates in CI/CD; no bottlenecking.
• Feedback loop: Findings feed directly into CoE standards so the whole platform gets safer.

This is how you scale governance without killing agility—and why projects stop dying after PoC.

Industry wins you can steal

Financial services (internal servicing copilot):

• Problem: Early agent could read/write sensitive customer data; trust and compliance concerns blocked rollout.

• Red Team findings: Prompt injection via case notes; overly broad action scopes.
• Fixes: Server-side sanitation, retrieval filters, least-privilege service accounts, and confirmation flows for high-impact actions.
• Outcome: Cleared compliance quickly; launched to a pilot group with zero data leakage incidents and rapid adoption by service reps who saved hours weekly on lookup and case prep.

Pharma R&D (SOP and trial assistant):

• Problem: Hallucinations when grounding faltered; auditors needed traceability.

• Red Team findings: Low-confidence answers without citations; fragile under multi-turn clarifications.
• Fixes: Mandatory citations with refusal on low-confidence, offline cache for critical SOPs, and multi-turn evals in CI.
• Outcome: Researchers trusted the agent; time-to-answer dropped; auditability improved, enabling broader rollout.

Manufacturing (maintenance work-order copilot):

• Problem: Risk of mass ticket creation from ambiguous prompts; fear of operational disruption.

• Red Team findings: Tool-abuse vectors; missing rate limits and guardrails.
• Fixes: Parameter allowlists, rate limiting, supervisor approvals for bulk actions, and context-echo confirmations.
• Outcome: No spurious work orders; meantime-to-repair improved as technicians adopted the agent confidently.

Each story shares a pattern: the Red Team uncovered a blocker that would have killed trust post-launch, converted it into a control, and documented evidence that unlocked executive approval and user adoption.

A 60‑day play you can run next Monday

You don’t need a big-bang program to get value. Start thin, automate fast, and prove the loop from “we broke it” to “we fixed it” to “we shipped it.”

Week 1–2

• Stand up the basics:
  • Charter: Define scope, risk tiers, and release gates for Copilot Studio agents.
• Harness: Create a small eval set—50–100 “gold” prompts plus refusal cases and a dozen adversarial attacks.
• Guardrails: Enable content filtering, PII redaction, and DLP; set least‑privilege identities for actions.

Week 3–4

• Wire into CI/CD:
  • Automation: Every prompt or action change triggers evals; block deploys on regression.
• Quick wins: Harden one high-impact agent (e.g., service knowledge, maintenance assistant) with action wrappers and confirmation flows.
• Evidence pack: Threat model, test results, telemetry and rollback plans—ready for compliance.

Week 5–8

• Prove scale and value:
  • Canary and monitor: 5–10% user rollout with live safety metrics and a kill-switch.
• Chaos drills: Simulate grounding outages and rate limiting; validate graceful degradation.
• Publish impact: Show grounded answer rate, refusal accuracy, incident rate, and hours saved by the pilot team; decommission a legacy workaround the agent replaces.
• What to measure:
  • Quality: Grounded answer rate, refusal accuracy, tool misuse rate.
• Risk: Policy violations prevented, critical incidents, time-to-rollback.
• Flow: Approval lead time, change failure rate, cycle time from idea to stable release.
• Value: Verified hours saved, error reduction, increased throughput or satisfaction.

Tip: Treat prompts and action configs like code. Version them, test them, and promote them through environments just like your best software projects.

The moment that sold the room

At the end, the CIO asked, “If we had this Red Team three months ago, would we be live?” The CoE lead answered before I could: “We’d have caught the exact issues that scared everyone—and we’d have the evidence to prove we were ready.” That’s the shift. From flashy demos to durable products. From fragile optimism to measured, repeatable wins.

If this resonates, start with one agent, one Red Team sprint, and one measurable outcome. I’m happy to help you tailor the harness, gates, and playbook—and to stand with your CoE when you make the case to leadership. Until then,…

This week in Vienna, I had the privilege of attending the European Power Platform Conference (#EPPC25)—a gathering that not only showcased the latest innovations from Microsoft but also sparked deep conversations about the future of work, the role of AI, and how we as professionals can prepare for what’s next.
A powerful message was delivered throughout the sessions: the future isn’t coming—it’s already here. Through visionary keynotes, live demos, critical frameworks, breakouts, and real customer stories, we glimpsed how AI—and specifically autonomous agents—are redefining work by an order of magnitude.

The 10× Vision: Reinventing Productivity

Ryan Cunningham kicked off the conference with a powerful message: we are no longer just building apps—we are building teams of digital coworkers. The Power Platform is evolving into an agentic platform, where every user can be supported by a network of autonomous agents that collaborate, reason, and act on our behalf. He challenged us to shift from incremental thinking (10% better) to order-of-magnitude thinking (10x better). This isn’t just about productivity—it’s about reimagining how entire industries operate. And it’s already happening: from Heineken’s agent-powered logistics to EY’s AI-driven general ledger operations, the future is here:

• Incremental gains (10%) come from working harder in existing structures.
• True exponential gains (10×) require reimagining how work is structured, embedding digital coworkers (agents) alongside humans.
• IDC’s forecast of 1.3 billion agents over the next few years underscores the scale of the opportunity and the urgency.

But with this shift comes a new kind of complexity. Clay introduced the concept of the Agent Feed—a centralized place where users can supervise, trust, and collaborate with their agents. It’s a familiar pattern (think LinkedIn or Instagram feeds), but applied to digital work.

Data as Knowledge: The Foundation of Smart Agents

On Day 2 (morning), Nirav Shah followed with a deep dive into the agentic data platform. His message was clear: AI agents are only as good as the data they operate on. That means we need to evolve our data strategies—not just to store data, but to enrich it with knowledge, context, and organizational language.

He introduced the concept of knowledge as a first-class citizen in Copilot Studio, powered by enhanced connectors (like SharePoint, Snowflake, and Dataverse) and the emerging Model Context Protocol (MCP). This allows agents to reason over data in real time, collaborate with each other, and trigger actions across systems.

• Integrate Dataverse, SharePoint, Snowflake, and more into a unified knowledge layer.
• Enrich data with organizational language, policies, and real-time signals so agents can reason like trusted teammates.
• Balance ambition with efficiency—oversized models for simple tasks become “burning down a forest to toast a marshmallow.”

Autonomous Agents in Action

Later that afternoon, Vik Singh took us even deeper into the world of autonomous agents. He outlined a spectrum—from assistive copilots to fully autonomous agents—and emphasized that the real power lies in delegation. Agents that can reason, act, and improve over time are no longer science fiction—they’re being deployed today in sales, finance, and operations, moving from assistive copilots to fully autonomous teammates:

1. Researcher Agent—digests dense academic papers (and citations) to produce annotated code and deep insights.
2. Sales Agent—acts as an AI SDR, autonomously engaging leads, logging CRM updates, and ensuring no inquiry goes unanswered.
3. Finance Agent—converts a six-hour reconciliation into a one-minute task, freeing experts for strategic analysis.

But Vik also acknowledged the skepticism in the room. What about job displacement? What about the environmental cost of running large models? What about the ethical implications of AI making decisions on our behalf? These demos proved that teams of agents can coordinate, execute, and learn—paving the way to 10× impact.

Critical Reflections from the Audience

Throughout the conference, I heard thoughtful, sometimes skeptical voices from attendees:

• “What if I’ve already missed the AI train?”
  Many professionals feel overwhelmed by the pace of change. A recent Stanford study on the future of work highlights this anxiety and the urgent need for upskilling.
• “If agents work 24/7, what happens to work-life balance?”
  The idea of waking up to hundreds of agent-generated tasks sparked both excitement and concern. How do we avoid digital burnout?
• “AI is reducing traffic accidents—but also organ donations.”
  A chilling example of unintended consequences. As we automate more, we must consider the ripple effects on society.
• “Using the wrong large language model is like burning down a forest to toast a marshmallow. How do we balance innovation with environmental responsibility?”
  The energy consumption of AI models is real. Choosing the right model isn’t just a technical decision—it’s an ethical one. Efficiency must be balanced with sustainability.

Rethinking Automation: Critical Insights

A recent study (arXiv:2506.06576) introduces the Human Agency Scale (HAS), urging us to classify tasks into:

• Green Zone—safe for full automation.
• Red Zone—requires human judgment and creativity.
• R&D Zone—emerging areas for AI augmentation.
• Low-Priority Zone—minimal productivity gain from automation.

This framework reminds us that not every task should be automated—human strengths must guide autonomous systems design.

Democratizing Innovation: Customer Success Stories

On Day 3, Leon Welicki closed the keynotes with a passionate call to action: democratize business innovation. He shared inspiring stories from companies like Suffolk Construction and Adidas, who used Power Platform to replace outdated, paper-based processes with intelligent, secure, and beautifully branded solutions. But more than that, Leon emphasized that these solutions are bridges to the AI-first future. They prepare organizations to embrace agents—not just technically, but culturally. And he reminded us that apps still matter. In fact, apps and agents need each other: apps provide structure, while agents provide intelligence.

• Suffolk Construction saved $1 million annually by automating site-access processes for 60,000 contractors, replacing 10 disparate SaaS tools.
• Adidas cut 16,000 work hours per year by digitizing prototype testing forms into branded Power Apps.
• Network Rail UK improved incident response for 1.6 billion passenger journeys by deploying an agent-driven safety platform.

Leon’s key message: “Apps need agents. Agents need apps.” Together, they unlock unprecedented agility and scale.

Scaling Safely: The Managed Platform Imperative

As Ryan and Pria demonstrated, scaling thousands of apps and agents safely requires a Managed Platform:

• Environment Groups & Routing—auto-provision personal dev-test sandboxes linked to governed production tiers.
• Governance Controls—DLP policies, sharing limits, advanced connector rules, and in-product deployment pipelines.
• Advisor & Monitoring—AI-driven recommendations for security, compliance, and operational health.
• Centralized Management—Copilot Hub and admin center for CI/CD, backup/DR drills, and cost monitoring.

This foundation lets organizations dream big—while staying secure, compliant, and resilient.

Real Talk from the Trenches: HEINEKEN’s “David vs. Goliath” Story

In a breakout #EPPC25 session, HEINEKEN’s Power Platform team framed governance as a battle against the “default environment beast.” Their takeaways:

1. Default ≠ Disposable: Guided makers away from the unmanaged default into managed spaces, preserving creativity.
2. Zoned Governance: Classified projects into Heineken 0.0, Silver, Original, each with tailored policies and welcome-kit guides.
3. Structured Migration: A five-step playbook (Scope → Plan → Migrate → Communicate → Support) converted 400+ unmanaged apps into governed environments.
4. Empathy & Humor: From “Circles of Hell” migration charts to region-specific Teams channels, they acknowledged change fatigue and met makers where they worked.
5. Tangible Results: They flipped from 75 % of apps in the default tenant to 67 % in personal dev sandboxes—and reclaimed 4,000+ hours of administrative effort per year.

Their journey proves that robust governance can be empathetic, delivering security without stifling innovation.

High Code Meets Low Code: The Generative AI Frontier

Another breakout with Jussi Roine and Donald Hessing delivered a powerful framing: Generative AI is blurring the lines between low-code and high-code development:

• Agentic SDLC: Development life cycles will revolve around design agents, build agents, test agents, deploy agents, and more—each orchestrated around business goals.
• Code-as-Behavior: We’re shifting from static code-artifacts to dynamic, adaptive behaviors powered by AI.
• End-to-End AI Integration: From story writing to incident resolution, GenAI touches every phase—business analysis, coding, testing, observability, and incident management.
• MCP Orchestration: The Model Context Protocol emerges as the essential glue, enabling secure, standardized interactions among LLMs, APIs, and human prompts.

Their vision wasn’t about tools—it was about collaboration. In the future, the highest-value skill won’t be syntax mastery, but crafting clear intents, coordinating AI teammates, and supervising agentic workflows.

“Over the next five years, we’ll move from code-as-artifact to code-as-behavior.”
— Jussi Roine & Donald Hessing

Your 10× Call to Action: Homework for the AI Era

As I left Vienna, I found myself both inspired and challenged. Here are a few action items I’m taking—and I encourage you to consider them too:

1. Start small, but start now: Pick one process in your team and explore how agents could improve it. Start small with agent pilots. Measure synergy, not just efficiency.
2. Upskill continuously: Tools like Copilot Studio, M365 Researcher, and Power Platform Plan Designer are your new best friends.
3. Think critically: Don’t just adopt AI—question it. Understand its impact on people, processes, and the planet.
4. Build your agent team: Whether you’re a developer or a business user, you can be an Agent Boss. Start assembling your digital coworkers today.
5. Build Your Knowledge Backbone: Integrate, enrich, and govern data so agents can reason like trusted coworkers.
6. Embrace the Managed Platform: Scale securely with robust governance, environment routing, and AI-driven monitoring.
7. Lead with Empathy: Communicate changes clearly, provide simple guides, and meet makers where they collaborate (Teams, SharePoint, one-pagers).
8. Measure & Refine: Track outcomes in productivity, cost, compliance, and sustainability—and continuously refine your approach.
9. Join the conversation: The Power Platform community is one of the most vibrant and supportive in tech. Share your learnings, your doubts, and your dreams.

Conclusion

If EPPC25 was any indication, the future of Power Platform is bright—and deeply human. EPPC25 was not just a conference—it was a blueprint for the future of work. We saw how 10× thinking, contextualized data, agentic workflows, robust governance, and the fusion of low-code and high-code will converge to empower truly exponential innovation.

The future is not a choice between human and machine. It’s about forging powerful partnerships where every employee is extended by a team of digital collaborators. The time to become an Agent Boss is now—let’s seize this moment, partner with our digital coworkers, and reshape what’s possible for our organizations and ourselves. Until then,…

While curiosity propels learning and creativity, without a framework for scrutinizing how content is tailored and understanding the psychological impact of constant digital engagement, both children and adults risk losing their grip on a broader, more measured reality.

This tension lies at the heart of today’s AI debates—a discussion that echoes the transformative struggles experienced during earlier technological revolutions, such as the advent of electricity.

The Four AI Camps: Lessons from Past and Present

Today’s AI landscape is characterized by a spectrum of attitudes reminiscent of historic technological shifts. Much like the chaos and dialogue surrounding early electrification—including the fierce „War of Currents“ between direct and alternating current—modern society is seeing the emergence of distinct “AI camps”. Take this LinkedIn post by Steve Mordue as an example, him outlining his perspectives on distinct „AI camps“. I thought of the following while reflecting on his post:

• The Deniers:
  Often driven by fear and an instinctual resistance to change, this group refuses to acknowledge the disruptive potential of AI. Their skepticism, while understandable, may ultimately leave them unprepared in a world swiftly reshaped by technology.
• The Complacents („We’ve Got Ten Years“):
  Believing that there’s ample time to adapt, this camp risks being overtaken as competitors who embrace AI’s potential progress at a faster pace. Their assumption may inadvertently foster a dangerous sense of security.
• The Experimentalists („Poking it with a Long Stick“):
  Representing a balanced approach, these individuals test the waters through small proofs-of-concept. Their methodical exploration facilitates a learning environment that values both innovation and caution.
• The Early Adopters („Hold My Beer“):
  Fueled by FOMO and an unyielding belief in rapid transformation, this group dives headfirst into AI’s possibilities. Their boldness can spark breakthroughs, but it also carries the risk of premature moves that might lead to unforeseen setbacks.

These diverse approaches reflect an ongoing dialogue about how best to harness transformative technologies. Just as the electrification era challenged society to reconcile rapid innovation with public safety, today’s debate over AI ownership, regulation, and application calls for open discussion and thoughtful strategy.

Leadership in the Digital Age: Catalyzing Open, Inclusive Dialogue

Modern leaders stand at a critical juncture. They must inspire their teams to identify where they fall within these “AI camps” and, more importantly, encourage a mindset that prioritizes informed experimentation over blind adherence or perpetual denial. By fostering an environment of open dialogue among technical experts, policymakers, educators, and everyday users, leaders can galvanize efforts that not only drive innovation but also embed a culture of ethical responsibility.

I recently watched a keynote (Topic: How is artificial intelligence reshaping our working world—and what do leaders need to know, understand, and do?) that delved into these crucial questions. The speaker, Christoph Magnussen broke down the fundamentals of AI, clearly outlining the difference between all the hype and real-world, practical applications. He explored concepts such as agents, reasoning, and prompting, and emphasized the heavy responsibility we carry as decision-makers in this transformative era. It was a wake-up call for anyone in leadership: to truly grasp the impact of AI, we must move beyond buzzwords and take decisive, informed action.

This keynote has left me reflecting on how critical it is for leaders to adapt, educate themselves, and steer their organizations responsibly through this digital revolution.

And this responsibility extends beyond the workplace into the realm of education. In a world where platforms like TikTok craft deeply personalized algorithms that capture constant attention, there is an urgent need for digital literacy programs that teach children—and adults—to balance unbridled curiosity with rigorous critical thought. By integrating these skills into everyday learning and workplace practices, leaders can help ensure that innovation serves a broad, inclusive societal benefit.

Some Hypotheses for a Balanced AI Future

Brad Smith, Microsoft’s Vice Chair and President, recently underscored AI’s transformational potential by drawing parallels between AI and electricity—both as general-purpose technologies that redefine entire industries. His remarks offer valuable hypotheses that can guide us through this period of uncertainty:

• AI as the New Electricity:
  Smith posits that, just as electricity transformed industries and daily life over a century ago, AI is set to revolutionize the way we work, create, and interact. This technological shift presents unprecedented opportunities—if harnessed wisely.
• A Three-Part Vision for Success:
  His strategy involves advancing technology, investing in robust AI infrastructure, and implementing widespread skilling programs. The aim is to democratize AI, ensuring that everyone from industry specialists to later adopters can participate in this digital economy.
• Balanced Regulation Without Innovation Stifling:
  Smith emphasizes that initiatives like the EU AI Act must safeguard society without curtailing the entrepreneurial spirit that fuels innovation. Crafting regulation that protects public safety and ethical standards while leaving room for breakthrough advancements is a delicate—but essential—balance.
• Global Collaboration and Accessibility:
  Recognizing the risks of concentrating AI expertise and power in the hands of a few tech giants or dominant countries, Smith advocates for accessible, open research initiatives. Such efforts are key to ensuring that the benefits of AI are shared globally and serve the broader public interest.

Bridging the Digital Literacy Gap: From Workplaces to Classrooms

The challenges of this digital revolution extend well beyond corporate boardrooms. Many adults were never equipped with the digital literacy necessary to navigate the complexities of modern AI—and our children are even more vulnerable. Initiatives such as Microsoft’s AI education series in North Rhine-Westphalia serve as a clarion call to reinvigorate education. These programs go beyond basic technical instruction, incorporating ethical considerations, critical media analysis, and hands-on learning to empower both teachers and students to engage with AI thoughtfully and safely.

By integrating these educational reforms, we can create a lifelong learning culture. This not only prepares the next generation to leverage AI responsibly but also ensures that current workers are able to adapt to, rather than be overwhelmed by, the rapid technological changes reshaping our world.

A Call to Action for Leaders, Educators, and Communities

As we stand at this crossroads—where digital curiosity meets the demand for critical thought—the future is ours to shape. Leaders and educators must work hand in hand to foster open, inclusive dialogues that bridge the gap between innovative enthusiasm and cautious deliberation.

Encouraging a healthy self-reflection about which “AI camp” we occupy can drive strategic choices that empower employees and safeguard digital well-being for future generations.

Ultimately, the choices made today will determine whether AI becomes a tool for shared progress or a source of deepening divides. The evolving dialogue—mirroring historical debates from the dawn of electricity to today’s digital revolutions—calls upon all of us to engage, learn, and govern wisely. It’s time to ask: How will you, as a leader or educator, ensure that your community not only embraces AI’s potentials but also challenges its implications, fostering a future that balances bold innovation with thoughtful critical oversight?

Reflecting on above AI Camp: Self-Assessment for Leaders

As we stand at the crossroads where digital curiosity meets the demand for critical thought, it’s important not only to understand the broader landscape but also to examine where you personally—and your organization—fit within it. Are you among those who dismiss AI’s potential, content to wait it out, cautiously experiment, or plunge in headfirst?

Challenger Questions for Self-Assessment

To sharpen your strategic vision and ensure that your organization is prepared to navigate the AI revolution responsibly, consider these questions:

Curiosity & Adoption:

• When a new AI tool or innovation emerges, is my first reaction one of eager experimentation, cautious observation, or outright skepticism?
• Do I actively encourage my team to explore and test AI solutions, or do we often wait for competitors to set the pace?

Balanced Risk:

• How do we balance the desire to innovate with the need to protect digital well-being and manage risk?
• Have we established mechanisms to evaluate both the opportunities and potential pitfalls of new AI deployments?

Cultivating Dialogue:

• In what ways do I foster open and inclusive dialogues within my team and with stakeholders about AI’s ethical, operational, and strategic implications?
• Are there regular forums or initiatives in place that encourage a healthy exchange of ideas regarding our digital transformation journey?

Educational Commitment:

• Am I investing in digital literacy and continuous learning—not just for myself but also for my team—to ensure we understand AI on a deeper level?
• How are we equipping our employees to critically assess where our AI innovations fit within the broader societal context?

Future Readiness:

• If I recognize that I or my organization lean too heavily toward a particular camp (for instance, the complacent “we’ve got ten years” mindset), what concrete steps can be taken to shift toward a more dynamic, experimental approach?
• How can I ensure that this balance between innovation and deliberation is maintained as our digital landscape continues to evolve?

By reflecting on these questions, you can gain valuable insight into whether your current strategic approach is driving meaningful innovation while also safeguarding your team’s and community’s long-term well-being. Whether you’re a denier, complacent, experimentalist, or early adopter, remember that the journey is not fixed—you have the power to pivot, learn, and lead toward a future that harmonizes digital curiosity with critical thought.

Where Do I Stand and Why?

Personally, I lean toward the Experimentalist camp. There’s immense value in taking measured steps—running pilot projects, engaging in rigorous evaluation, and iterating based on feedback. You might think, this sounds biased, due to my current employer or my job role. This approach though not only nurtures learning and innovation but also ensures that risks are managed and insights are gathered with care. By proceeding incrementally, you build both competence and confidence, allowing for informed decision-making as well as agile adaptation in the face of unforeseen challenges. This is what I’ve seen organizations being successful with running workshops or being in conversation with them. Let this be the beginning of a conversation that transcends boardrooms and classrooms—a journey toward an AI-driven future that upholds the principles of openness, inclusion, and ethical responsibility. Until then,…

Revisiting the Traditional Role

When Power Apps and Microsoft Flow first burst onto the scene, business technologists quickly became indispensable by:

• Empowering Domain Experts: Converting frontline business insights into tangible applications that replaced cumbersome manual processes with agile digital solutions.
• Bridging IT and Business: Acting as fluent translators, they ensured that low-code innovations were both technically robust and closely aligned with strategic objectives.
• Co-Piloting Digital Initiatives: Collaborating closely with professional developers to iterate, prototype, and scale digital innovations—overcoming traditional IT resource constraints.

Their pioneering work laid the foundation for today’s hybrid roles, demonstrating that blending business acumen with technical agility can drive transformative change.

Embracing a New Dimension: Agentic AI and the Digital Ecosystem

Today’s landscape is evolving beyond low-code solutions. With the emergence of the open agentic web and insights from Microsoft’s recent Work Trend Index, we now face a paradigm shift toward digital labor and fluid organizational structures. Key trends include:

• Human-Agent Hybrid Teams: AI agents have evolved from passive tools to proactive digital colleagues that fill capacity gaps and empower teams to overcome traditional limitations.
• The Rise of Frontier Firms: New organizational models are dismantling outdated hierarchies. In these agile, outcome-driven firms, human intuition blends seamlessly with autonomous digital processes.
• Reimagined Governance: As autonomous systems assume greater roles, business technologists now carry responsibilities for ethical oversight, risk management, and orchestrating interconnected digital ecosystems.

This expanded role challenges Business Technologists to evolve from simple solution builders into architects and stewards of holistic, AI-driven systems.

Rethinking Cognitive Filters and AI Augmentation

I had the chance to listen-in at re:publica 25, where Björn Ommer reshaped our understanding of data and decision-making. He argued that while data is the “oil” of our digital age, the methods used to refine it are inherently biased. Drawing on Daniel Kahneman’s framework, Ommer highlighted two modes of processing:

• System 1 (The Instinctive, Fast Thinker): Operates automatically to handle routine decisions with minimal effort, yet it is vulnerable to manipulation and oversimplification.
• System 2 (The Reflective, Deliberate Thinker): Requires significant cognitive effort but provides more reliable, measured decision-making.

This perspective challenges us to view generative AI not just as a speed tool but as a personalized cognitive augment—a way to boost our reflective capacities. For me, it initiated a revisit of one of my previous article on Business Technologists and reflect on the definitions of this role. A key learning I took out of the keynote from Ommer—for Business Technologists, the goal is to design systems that automate routine tasks while elevating the quality of strategic decisions.

From Static Information to Living Knowledge: A Paradigm Shift

Ommer also illustrated the dramatic evolution in our interaction with data:

• Elimination of Manual Data Feeding: Modern AI now processes raw, unstructured data in real time, eliminating the old need for labor-intensive inputs.
• Transition from Static to Dynamic Knowledge: Where the printing press delivered static content, today’s digital tools and AI provide live, personalized knowledge, converting data streams into actionable insights.
• Building a True Knowledge Society: With AI at the helm of personalization, we are moving beyond a mere information society into one where deep, dynamic knowledge shapes decision-making.
• Essential Mindset Transformation: Just as historical leaps (from horse-drawn carriages to air travel) required a new way of thinking, we too must adopt a multidimensional perspective to fully leverage AI’s potential.
• A Catalyst for New Business Models: Generative AI is not just about cost savings or speed; it opens up opportunities for entirely new industries and value propositions, challenging us to rethink traditional business models.

Redefining the Role for a Hybrid Future

Drawing on lessons from the Power Platform era, Microsoft’s Work Trend Index, and the insights from re:publica 25, the Modern Business Technologist is evolving along three key dimensions:

1. Digital Ecosystem Orchestration:
   Designing interconnected systems where autonomous AI agents, low-code solutions, and data analytics work in concert to drive strategic business outcomes.
2. Strategic Digital Labor Integration:
   Harnessing AI as an on-demand digital colleague to build and manage human-AI hybrid teams that overcome capacity limitations and spark innovation.
3. Ethical Governance and Cognitive Enhancement:
   Addressing cognitive biases by developing personalized tools and advanced AI systems that bridge the gap between our instinctive (System 1) and reflective (System 2) thinking—ensuring transparent operations and high-quality decision-making.

Emerging Analyst Insights: New Frontiers in Digital Transformation

Recent research from leading analysts such as Gartner and Forrester adds further depth to the evolving role of Business Technologists:

• Dynamic, Composable Architectures:
  The future of digital transformation hinges on building systems that are modular and interchangeable, allowing for rapid assembly of digital ecosystems where AI models, low-code solutions, and traditional development integrate seamlessly.
• Digital Labor Integration and the New Builder Mindset:
  The next phase of innovation involves nurturing environments where human intelligence and digital labor work as integrated teams. Business Technologists must foster agile, hybrid teams that leverage AI as a true digital colleague to push beyond existing capacity boundaries.
• Protecting Investments and Future-Proofing Strategies:
  Long-term success requires not only rapid innovation but also the safeguarding of intellectual property and strategic investments. Business Technologists need to ensure that new solutions are resilient, sustainable, and designed with future challenges in mind.
• Emergence of New Business Models:
  Generative AI is poised to redefine entire industries by creating novel value propositions and competitive advantages. This shift transforms Business Technologists from mere implementers to strategic architects who envision and drive innovative, AI-driven business models.

A Modern Business Technologist is evolving into a visionary orchestrator of dynamic, integrated ecosystems—one who not only implements technology but also steers broader strategic transformation.

Concluding Reflections

The evolution from citizen developer to agentic orchestrator encapsulates a profound transformation in how technology and business interact. The work on low-code platforms, such as the Power Platform, has matured into an era defined by AI-driven digital labor, cognitive augmentation, and personalized knowledge. At this critical juncture, Business Technologists are uniquely positioned to reprogram our reality—merging human intuition with the limitless potential of artificial intelligence.

Call to Action: Decide to Transform

To summarize the essence of these insights: generative AI is creating entirely new possibilities. However, realizing these opportunities is up to us. The potential is there, but nothing will change automatically—it requires our deliberate decision and commitment to innovate. Now is the time to act, reprogram reality, and harness generative AI as the catalyst for a transformative future.

Next chance to enrich our perspective is upcoming with the European Power Platform Conference, Vienna. I am looking forward to meet with known and new Business Technologists and discuss their definition of their role in this new Era of AI. Until then,…