Here is a rewritten version of the text in a more natural and conversational tone, while maintaining the original content:
**Enterprise AI World 2025: Notes from the Field – Evolving AI from Chatbots to Colleagues That Make an Impact**
As I reflect on Enterprise AI World 2025, I’m struck by the shift in tone from “drop a chatbot on the intranet and call it transformation” to a more nuanced understanding of AI’s potential. The conversations have evolved to focus on AI that’s deeply integrated into actual work, capturing tacit knowledge, restructuring workflows, and reframing what people are actually good for.
Throughout the keynotes and sessions, three patterns emerged:
1. AI is shifting from content generator to decision partner and team member.
2. Data, structured and contextual, is becoming the platform.
3. Coverage and adoption, including management and team design, are now the rate-limiting steps, not models.
Each speaker brought their own angle, from conversational surrogate brokers and quantum-inspired succession planning to graph-powered RAG and agent ecosystems spanning Amazon, Google, and Microsoft.
**Sample 1: AI as Collective Intelligence, Not Just Automation**
David Baltaxe from Unanimous AI started his talk with a simple comment: organizations still treat people like data points, rather than information processors. Polls, surveys, and forms strip away the very thing that makes a team valuable: its ability to think collectively in real-time.
Unanimous AI’s Thinkscape product employs its Hyperchat AI and Swarm AI technologies, using “conversational surrogate brokers” embedded in small teams to scale dialogue. Brokers listen to breakout conversations, extract arguments and rationales, and share them with their colleagues in other teams. The brokers deliberately seek out conflict and opposing views, and then feed those back into the rooms to keep thinking sharp.
**Sample 2: From LLMs to Brokers**
Several sessions made a point to distinguish between large language models and brokers. The panel with leaders from AWS, Legion, and Feith Systems hammered this home. An LLM is one part of an agent, responsible for language and reasoning. The agent itself wraps that model with memory, tools, instruments, insurance, and audit trails.
This distinction matters because organizations keep buying “chatbots” and wondering why they don’t see value. Generic Q&A interfaces without a specific job often become just another SaaS value center. Real wins come from tightly scoped agentic workflows aligned to hard costs, such as shortening a 27-day process to 9 hours, cutting time beyond regulation, or eliminating backlogs, not from generic assistants floating in a browser tab.
**Sample 3: Data as Infrastructure – Graphs, RAG, and Tacit Capture**
As brokers begin to impinge on working systems and workflows, it’s essential to refocus on the core components of data management, which many organizations have given short shrift over time. For AI to work in enterprises, it requires enterprise data to eat and incorporate into its models. Many failures in AI don’t come from flaws in how AI works, but from the messiness of the enterprise content uncovered as ingestion pipelines that return poor results that make it hard for end-users to build trust.
Zorina Alliata, Principal AI Strategist at Amazon, and Theresa Minton-Eversole, Project Manager, Internet Influence, positioned data graphs as organizational memory, a way to encode entities and relationships so AI can converse with context, not just text strings. Their framework distinguished the following data types:
* Persistent data: Manuals, slide decks, videos – relatively simple to ingest.
* Transient data: Conferences, chats, emails – captured increasingly by assistants.
* Tacit data: The instinct and shortcuts of specialists – still the hardest part.
The strongest undercurrent throughout Enterprise AI World wasn’t model talk – it was anxiety and opportunity around the workforce.
What should organizations really do? Here’s a set of converging practices that wise organizations can adopt now:
1. Stop treating people as rows in a dataset. Make use of techniques like thinkscape that use AI to scale deliberation, not just data collection. Build in mechanisms that surface disagreement, not just aggregate it away.
2. Treat brokers as long-lived products, not experiments. Use frameworks like the 6Ds, clear OKRs, and strong monitoring. Start with one high-value workflow, run it to manufacturing scale, collect scar tissue, and then replicate.
3. Invest in a semantic spine. Taxonomies, ontologies, and data graphs are not elective for serious AI. They’re the substrate that allows Graph RAG, cross-silo retrieval, and governance. Rent or develop taxologists and data scientists who can sit between data science and business execution.
4. Use AI to inexpensively capture tacit data. Use multimodal models to turn real work like video, display recordings, and conversations into structured insights. Let specialists do the work while AI observes and drafts. Reserve scarce human time for validation, not authorship.
5. Differentiate generic AI from “alpha-generating” AI. Accept that generic solutions will be bundled into productivity suites and SaaS. Focus customized investments where proprietary data and workflows create enduring benefit.
6. Design for the emergent meritocracy. Explicitly plan for new roles around brokers, from orchestration and monitoring to ethics and governance. Build learning paths and incentives so the people closest to the work develop into AI-literate co-designers, not passive recipients.
7. Plan for intimacy and dependency. As brokers become ever more embedded in daily life, actively defend essential thinking, metacognition, and ethical judgment. Measure them. Prepare for them. Don’t assume they survive by default.
Enterprise AI World 2025 didn’t resolve the open questions on jobs, company, or the long arc of automation. It did something more pragmatic: it showed how quickly AI is moving from novelty to infrastructure, from chatbots on the edge to brokers in the middle of every important workflow.
Organizations now face a choice. They can keep adding bots to websites and running small, disconnected pilots. Or they can acknowledge that AI is becoming a part of the fabric of information, work, and management, and start redesigning that fabric with intent, before someone asks an agent to do it for them.
