Smart AI, Goldfish Memory

I recently showed up to a meeting, puffed up from a week of speaking engagements—sharing insights and knowledge.

I joined a Zoom call like a bull in a china shop: a busy week behind me, one last meeting to knock out. I started talking and slowly watched faces turn sour.

I caught myself—but not soon enough. I had failed to gather context for the meeting.

I spewed out unhelpful, redundant information and missed the mark entirely. I insulted some very nice and helpful people, all because I lacked context.

I acted like a jackass because I didn't bother to understand the stakeholders or offer information that would actually help them.

LLMs can act the same way. They have incredible general knowledge, but without context, they have an incomplete picture—sophisticated systems that approach every conversation like I approached that meeting.

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Why enterprise AI systems reset every conversation—and what contextual memory changes

Think about how much time you spend getting people on the same page before any productive conversation can happen. The average executive spends 23% of their time providing context—briefing team members on project history, explaining stakeholder relationships, summarizing previous decisions. McKinsey research shows that knowledge workers waste 1.8 hours daily searching for information or recreating work that already exists elsewhere in the organization.

We accept this “context tax” because humans eventually learn. They build institutional memory. They connect dots across conversations separated by weeks or months. But enterprise AI systems reset their memory every interaction like digital goldfish, forcing us to pay the context tax over and over again.

The promise of digital assistants was supposed to solve this. Siri and Alexa arrived with the vision of hands-free intelligence, but they operate on impossibly limited context. They know your name and location, maybe your calendar, but they have no idea what you discussed in yesterday's client meeting or why the Peterson account matters to your Q4 numbers. They're glorified voice search engines—not true assistants.

What we need is fundamentally different: AI that captures context continuously and uses it intelligently. Some companies are experimenting with wearable solutions—Meta's smart glasses record conversations, Limitless pins promise always-on memory. But I believe the real opportunity lies in our existing devices. Our phones already know our location, contacts, and communication patterns. Our desktop environments contain our work history, project files, and collaboration threads.

The tightrope act is building digital assistants that augment human intelligence while respecting privacy boundaries. This means AI that remembers your preferences without storing unnecessary personal details, understands your work relationships without compromising confidential communications, and learns from your patterns without becoming surveillance technology.

The next breakthrough in business AI won't come from better models or faster processing. It will come from memory—persistent, searchable, actionable context that transforms AI from a reactive tool into a proactive partner.

Today AI models are like brilliant college graduates—full of facts but lacking institutional knowledge. Customer-support bots forget earlier refund requests and make you re-enter order details each time because they lack access to all the systems that provide context. Planning assistants remember your trip itinerary but lose track of your seat preferences by the next session. Healthcare assistants may fail to recall allergies and could give dangerous guidance. This isn't a technology problem—it's an experience problem.

Today, I use memory in ChatGPT to share the history of my chats across AI conversations. If I provide incorrect information, I update it by deleting “incorrect memories.” It knows a lot about me including my bio and business.

Current AI operates with impressive theoretical knowledge but no practical context about your business, relationships, or ongoing projects. The gap between AI capability and business utility comes down to this: sophisticated systems with no institutional memory.

Contextual AI combines persistent memory with real-time data access to maintain ongoing awareness of user preferences, project status, and relationship dynamics. Unlike traditional AI systems that treat each interaction independently, contextual AI builds cumulative understanding that improves decision-making over time.

Consider a sales AI agent handling enterprise accounts. Today's systems might help draft a proposal, but they don’t know that this client prefers quarterly reviews over monthly check-ins, or that their CFO always asks about ROI timelines in Q3 budget meetings. A contextual AI agent would track these patterns across months of interactions, automatically adjusting its approach and proactively surfacing relevant insights.

The technology relies on three core components: memory systems that store conversation history and user preferences, integration platforms that access live business data, and inference engines that can reason across temporal and relational contexts to provide relevant insights.

The biggest implementation risk is treating contextual AI as a search enhancement rather than an agent capability system. Today, deep research from OpenAI ChatGPT and Google Gemini is just that. Instead of a list of blue links, it goes through the links and summarizes the research. But that’s typically just someone else's thoughts. Imagine if the search could act on your stored preferences. You think Ars Technica and the Economist are valid sources while you question the neutrality of Fox News. You have a thesis and you want to take that into account either proving or disproving that research.

Organizations that focus solely on information retrieval miss the strategic value of proactive intelligence and automated workflow optimization. Context isn't just about finding old emails—it's about understanding patterns that inform future actions.

Another common mistake is underestimating the agent orchestration complexity. Contextual AI agents need secure access to email, calendar, document management, and communication platforms while maintaining awareness of user permissions and data boundaries. A marketing agent shouldn't access HR files, even if both systems contain employee data. This level of integration demands careful architecture planning and robust security controls.

It’s likely we’ll see this delivered by the USB-C of AI, the model context protocol. We’ll be able to provide this data to agents through this emerging standard. Plugging data from our systems into this protocol will standardize how AI agents understand and act on context.

Privacy concerns often derail implementations when they're addressed reactively rather than proactively. Successful deployments establish clear policies for data access, retention, and user control before beginning technical development. Users need to understand what their AI agents remember, how long that memory persists, and how to modify or delete specific memories when circumstances change.

Organizations should begin preparing for contextual AI by auditing their current data architecture and identifying integration points where memory-enabled AI could deliver immediate value. The highest-impact applications typically involve executive support, project management, and client relationship management—areas where context switching currently creates the most friction.

The privacy and security implications require immediate attention. Contextual AI systems will need access to sensitive business communications, which means security architectures must evolve to support granular permission models and comprehensive audit capabilities.

As AI tools become more personalized, the ability to remember your work, preferences, and daily context is no longer a futuristic idea—it’s available now. Whether you’re writing reports, attending meetings, or juggling multiple projects, AI memory can help reduce repetition, improve focus, and make your digital assistant truly useful. Here are five simple ways to start using memory in your everyday workflow.

Turn on and Use ChatGPT’s Memory

ChatGPT’s memory feature keeps track of your preferences—like your profession or communication style—and uses them in future conversations. To enable it, go to Settings → Personalization → Memory. You can also manage or delete what it remembers there.

Keep Work Notes in One Central Place

Use an app like Notion, Mem, or Obsidian to store meeting notes, ideas, and research in a single spot. Tagging and linking within these tools helps you quickly find past work without searching across folders or platforms.

Let Your Desktop Remember What You Do

Apps like Rewind and Raycast Pro track your activity—including open files, meetings, and browser tabs—and make that history searchable. You can ask questions like “What did I work on last Tuesday afternoon?”

Summarize and Save Daily Highlights Automatically

Tools like Otter.ai can capture and summarize your meeting transcripts, while Readwise Reader gathers and highlights articles and PDFs. Both apps help build a searchable record of your day without requiring extra work.

Regularly Review and Clean Your AI Memory

Review what your AI tools remember to keep them accurate and relevant. In ChatGPT, go to Settings → Memory → Manage to delete outdated or sensitive info. Do the same in other tools like Rewind or your note-taking app to stay organized and secure.

While personal AI memory helps individuals work more efficiently, enterprise memory is about building shared understanding across the organization. Contextual AI at the enterprise level requires more than just remembering user preferences—it must track projects, decisions, and relationships across teams, departments, and systems. That means encoding institutional knowledge: what’s been said, agreed upon, or delayed—so AI agents can collaborate with the same awareness a seasoned team member would bring.

Build Persistent Memory into AI Systems

The foundation of Contextual AI is persistent memory—structured, searchable, and accessible across sessions. Start by implementing long-term memory storage using vector databases like Pinecone, Weaviate, or ChromaDB. These systems store and retrieve semantic embeddings (representations of prior conversations, preferences, documents) that allow AI to “remember” what matters.

For working memory, technologies like Redis or LlamaIndex's memory constructs help simulate short-term recall during a session. Combined, these form a hybrid memory system—think of it as attention span plus long-term knowledge.

Enterprises can use these memory stacks to encode things like user preferences, stakeholder personas, project timelines, or recurring decisions—so the AI doesn’t need to be reminded every time.

Connect to Real-Time Business Systems

Contextual AI becomes exponentially more useful when it’s wired into your live enterprise data sources. This includes CRMs like Salesforce, messaging platforms like Slack and Microsoft Teams, and project management tools such as Asana, Jira, or ClickUp. These integrations can be handled through APIs or platforms like:

The key is to transform live events (e.g., a deal moves to Q3 forecast, a customer opens a ticket, or a CFO replies to an ROI email) into structured context the AI can act on.

Add a Reasoning Layer That Understands Time and Relationships

To move from memory storage to memory intelligence, enterprises need an inference layer that reasons across time and context.

This is where frameworks like LangGraph or CrewAI come in. They allow for multi-agent orchestration, where different AI roles (sales assistant, researcher, operations coordinator) can work in sync, sharing context through a common memory bus.

Advances in temporal reasoning models, such as Letta (formerly MemGPT) enhance AI's ability to “think with memory”—recalling past behavior to make current decisions more accurate.

This reasoning layer should also include relationship logic: understanding roles (e.g., CFO vs. VP of Product), preferences, and organizational cadences (e.g., quarterly reviews vs. monthly syncs).

Govern Memory with Policy and Compliance in Mind

As memory expands, so do the risks. AI memory needs to be as secure and compliant as any enterprise data store.

Tools like Monitaur or Credo AI offer governance solutions to audit memory usage, track model decisions, and enforce policy compliance. Some agent platforms like OpenDevin or Adept are also adding granular memory permissions and memory TTL (time-to-live) logic.

Organizations should define clear memory zones:

Access should be controlled by user role, project scope, and regulatory frameworks like SOC 2 or GDPR.

Give Users Transparency and Control

For memory to be trusted, users must see it and shape it.

Modern AI systems need memory dashboards that show what the system knows, where that knowledge came from, and how it’s being used. Projects like Reka, Cognition, and Harvey are experimenting with this kind of memory transparency in legal and enterprise settings.

For end-users, this means features like:

It’s the difference between a black box and a useful assistant.

By weaving together structured memory storage, real-time sync with enterprise systems, intelligent reasoning across time and stakeholders, and robust governance + transparency, you create AI that evolves from interaction to institutional awareness. It shifts from tool to teammate—adding strategic value along the way.

Curated tools for building contextual awareness in your AI workflows

Executives waste significant time gathering context before important meetings or decisions. They need to synthesize information from multiple sources quickly, but that can require extensive prompting to understand relationships and priorities. What if you used AI to create a brief that ensures you have the data you need at your fingertips?

If you have added your files from Google Drive, Microsoft OneDrive Personal including SharePoint to ChatGPT you are adding knowledge to help provide that context. Though you have to be explicit in your prompts to access that information.

But imagine this simple use case you use an AI notetaker (Fireflies, Granola, or your Limitless pendant) and all your conversations from your weekly staff meeting are in a folder on Google or Microsoft. You could use this prompt to chat with that folder along with sharing your latest data to create a report that gets you ready for a meeting.

You could upload meeting notes from last week that were taken by a recorder, or you could connect a folder to ChatGPT and chat with it directly to extract insights.

Pro Tip: You could take it a step farther and use this prompt with an agent framework like Obot.ai to search your Google Drive every week before a meeting and send you an update.

I appreciate your support.