Welcome to this week’s edition! 🍂 We’re kicking off autumn with our bi-weekly look at what’s changing in enterprise AI and one issue is becoming harder to ignore: Shadow AI doesn’t always begin with bad governance. Sometimes, it starts with unclear discovery.

As AI moves from answering questions to taking actions, understanding where a solution fits, what it can access, and how much autonomy it has is becoming just as important as understanding what features it offers.

Take a short break, settle in, and ✨ enjoy this edition

What’s happening in AI this week

Shadow AI is becoming harder to see: Veeam’s recent EMEA research found that 70% of organizations admit that AI workflows are interacting with sensitive corporate data without full oversight, while 67% reported employees creating autonomous AI workflows that IT could not fully track. Veeam Software. Companies increasingly need visibility into which workflows those providers are part of, what data they touch, and who is responsible for the outcome.

Autonomy makes workflow boundaries more important: Recent agent testing by the UK AI Security Institute showed what can happen when AI systems are given broad permissions. The incident stemmed from a single evaluation where agents were given a task of solving a cyber security challenge. The institute emphasized that these testing conditions do not represent normal public deployment, but the findings illustrate why autonomy and permissions need clear boundaries. AI Security Institute

The practical lesson is straightforward: the more an AI system can access and act on, the clearer its job, limits, monitoring, and ownership need to be.

The deeper look, when shadow AI starts as a discovery problem: Shadow AI is often treated as a governance problem, but the issue can begin much earlier when teams adopt a solution provider without a clear way to evaluate fit. Marketing chooses one assistant, Sales tests another, Operations adds an agent, and soon nobody has a complete view of where each solution sits, what data it touches, or how much autonomy it has. A better starting point is the business process itself. Instead of asking which provider has the most features, teams can ask which solution fits the task, data environment, human handoffs, and business objective. That makes provider comparison clearer and gives companies a stronger foundation before implementation begins.

FEATURED WEEK’S POST 🛎️

AI Shopping bag full of random Features? A 6-Part workflow brief for guided Discovery

This post looks at a practical alternative: define the job before comparing providers. The 6-part workflow brief asks:

1. Trigger: What exactly starts the work?
2. Inputs: What information or data enters the process?
3. Task: What specific job should the AI perform?
4. Boundaries: Where should AI stop and human judgment begin?
5. Surrounding systems: Which existing a solution provider and systems are part of the workflow?
6. Outcome: What measurable business result should improve?

The idea is simple: every provider gets evaluated against the same operational requirement, that makes comparisons clearer, and it becomes especially useful when AI can take actions across systems rather than simply produce an answer.

Quick tip 🧠

Let´s take some process as an example: When a supplier invoice arrives, the AI should extract the invoice data, check it against the purchase order and payment rules, flag exceptions for human review, work with our ERP and accounting system, and help reduce manual processing time.

Then ask every provider to show you how their solution would handle that scenario. If the sentence is still difficult to complete, the process probably needs a little more definition before software comparison begins.

🎉 New AI solution providers added this week you should check out!👀

A warm welcome to four new AI providers joining. Thank you for your valuable contribution!

Fuse – AI Learning Platform for Employee Development Workflow: Learning Need → Learning & Knowledge Access → Practice / AI Coaching → Ongoing Development Combines structured learning, social and peer learning, knowledge access, and Lyra AI coaching to support upskilling, onboarding, and learning in the flow of work. Explore Fuse

Brand24 AI Social Listening Software for Reputation Monitoring Workflow: Public Mention → Monitoring → Sentiment & Context Analysis → Insight → Response Helps teams monitor public conversations about brands, competitors, campaigns, and market topics and identify sentiment changes, recurring themes, and issues requiring attention. Explore Brand24

SentiSum – AI CX Intelligence for Voice of Customer, Root-Cause Analysis and Quality Assurance. Workflow: Customer Signals → AI Analysis → Issue & Root-Cause Identification → Prioritization → Action Analyzes customer conversations and feedback to help teams identify recurring problems, understand root causes, monitor emerging issues, and support quality analysis. Explore SentiSum

Tiledesk -No-code, multi-agent AI orchestration platform. Workflow: Incoming Request → AI Agent Workflow → Resolution / Action → Human Handoff Supports AI-agent workflows for customer support, lead qualification, and internal processes using company knowledge, defined workflow logic, and human handoffs when needed. Explore Tiledesk

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