--- Title: "What I’m learning about enterprise AI: the next advantage is memory" Url: "https://devrev.ai/blog/advantage-shared-memory" Published: "2026-05-20" Last Updated: "2026-08-10" Author: "Malcolm Koh" Category: "AI Quality & Trust" Excerpt: "Enterprise AI does not fail because models are not smart enough. It fails because systems forget the customer, the context, and the work already done. Malcolm Koh explores why shared memory is the missing layer for precise, efficient, and safe AI." Reading Time: 13 --- # What I’m learning about enterprise AI: the next advantage is memory For the past two years, enterprise AI conversations have been dominated by a simple question: _Should we be using AI?_ That question has now passed. Most leaders I speak with have already made their first move. They have deployed chatbots, copilots, enterprise search, workflow automations, or some combination of all four. Their teams are using AI every day – sometimes officially, sometimes not. The budget has been allocated. The pilots have been run. The demos have impressed. Now comes the harder question: **Is it working?** Not: Is it producing answers? Not: Is it reducing a few clicks? Not: Is it consuming a lot of tokens? But: Is it resolving customer problems? Is it helping employees make better decisions? Is it making the business faster _and_ safer? And can leaders show a clear return for the investment? What I am learning is that the gap between AI that looks impressive and AI that works in production is rarely about the intelligence of the model. It is about whether the AI can remember. ## **We have moved past the model question** Today’s frontier models are already remarkably capable. They can reason, summarize, write, search, analyze, and converse in ways that would have felt extraordinary only a few years ago. Yet many organizations still experience AI as a series of disconnected moments. A customer asks a question through chat. An agent responds. The conversation ends. The customer calls the next day and has to explain everything again. A support representative receives a ticket and opens the CRM, the help center, the billing system, the issue tracker, internal chat, and a dozen browser tabs. The information exists, but it is spread across systems that do not share context. An AI assistant provides a polished answer that sounds right – but misses the product issue, the billing history, the internal escalation, or the account-specific policy that would have changed the answer entirely. This is the challenge I keep coming back to: **most AI is still forgetful by design.** It starts each session from zero. It sees the prompt, then tries to retrieve enough information to answer it. But the customer’s story, the team’s past actions, the state of the product, and the relationships between all those things are rarely available in one complete view. So the system guesses from fragments. That might be acceptable for drafting an email. It is not acceptable when AI is expected to resolve a billing dispute, guide a customer through an outage, support a regulated workflow, or make a recommendation that affects a commercial relationship. The problem is not that AI needs a better brain. **The problem is that it needs a better memory.** ## **Memory is what turns information into context** ![image](https://cdn.sanity.io/images/umrbtih2/production/fea3247de409ae7a81ee35649ebc036c0a5b3856-3840x2160.png) There is a meaningful difference between a system that can retrieve documents and one that understands how a business works. A document can tell an AI what a refund policy says. Context tells it whether this customer has an open dispute, whether an exception was previously approved, whether there is an active incident affecting their account, and whether the agent is authorized to issue the refund. That is why we built Computer, by DevRev around [**Shared Memory**.](https://devrev.ai/blog/shared-memory) Computer brings structured information – such as customer records, tickets, product data, and account history – together with unstructured information, including conversations, documents, emails, and internal knowledge. This creates a living, permission-aware view of the relationships between customers, teams, products, issues, and work underway. It is not about putting more data in front of a model. It is about organizing the right context before the model is asked to reason. That creates three kinds of memory that matter in real work: - **Personal memory.** A customer interaction can continue from where it left off, rather than beginning from a blank page every time. - **Team memory.** When one person or AI teammate learns how to solve a recurring problem, that learning can be shared rather than remaining trapped in someone’s inbox or notes. - **Organizational memory.** Critical knowledge remains available as people change roles, teams evolve, and systems change. The result is a different kind of AI experience. Instead of asking, “What information can I find for this prompt?” Computer can begin with, “What do we already know about this customer, this issue, this product, and this moment?” That is a small shift in language, but a profound shift in outcome. ## **The three standards enterprise AI has to meet** In my view, enterprise AI must meet three standards to earn the right to scale: **Precision, Efficiency, and Safety.** These are not separate features to layer on later. They are the operating principles that determine whether AI becomes a dependable teammate or just another tool people have to supervise. ### **Precision: not more answers, better answers** The first job of AI is not to answer every question. It is to answer the right question correctly. Precision comes from context. When AI can see a ticket, the customer’s account history, a related engineering issue, the latest internal discussion, and the relevant policy, it can move beyond generic responses and toward an answer grounded in the reality of the business. Computer provides answers based on connected business data, with sources that users can check. It also respects user permissions, so the answer is informed by the right context without exposing information the person is not authorized to see. That matters because a confident answer is not the same as a trustworthy answer. In customer operations, the cost of an incorrect answer is not just a bad interaction. It can mean a lost customer, an unnecessary escalation, a compliance issue, or a frontline team that loses faith in the technology. Precision is what lets teams move from “the AI suggested this” to “we know why this is the right next step.” ### **Efficiency: resolve the work, not just the conversation** The second standard is efficiency. But efficiency has been misunderstood. Too much of the conversation still centers on deflection: did a customer avoid speaking with a human? Did an agent finish a response faster? Did the team close more tickets? Those are useful signals, but they do not tell us whether the customer’s problem was actually solved. A customer who gives up after a chatbot fails has been deflected. They have not been helped. The better measure is [**resolution**](https://devrev.ai/blog/ai-support-ticket-triaging). Computer is designed to help teams move through the full arc of work: search for the right context, understand the problem, recommend or take an approved action, update the relevant systems, and bring in a human when judgment is needed.4 Consider a customer who calls about a duplicate charge. A conventional voice bot may authenticate the customer, answer a basic question, and route the call to an agent. A memory-first system can look across the order, payment record, prior support history, product signals, and any known incident. It can identify the likely cause and take an approved action while the customer is still on the line. That is the difference between a conversation and a resolution. The outcomes can be material. [BILL](https://devrev.ai/customers/bill) reports $4.5 million in savings and 70% of tickets resolved without human intervention after deploying Computer; Bolt reports 40% faster resolution, four times team productivity, and a 25% increase in retention. The important point is not the headline metric. It is the design principle behind it: AI becomes valuable when it eliminates unnecessary work rather than simply accelerating it. ### **Safety: AI should be able to act – within boundaries** The third standard is safety. This is especially important in industries where privacy, auditability, regulated processes, or customer trust cannot be compromised. But it matters everywhere. The question is not whether AI should take action. It should. The question is whether it can take the _right_ action, with the right permissions, under the right controls. Computer is designed for safe actions: it operates within permission boundaries, can require human approval for sensitive work, records actions for auditability, and supports reversibility when an action needs to be undone. That changes the dynamic for teams. Rather than asking people to trust a black box, it gives them a way to supervise, approve, and improve an AI teammate. The goal is not autonomy without oversight. The goal is useful autonomy with accountability. ## **What this makes possible now** The promise of shared memory becomes tangible when we look at what Computer can now do across customer and employee experiences. ### **Customer Agent: built from the way your team already works** ![image](https://cdn.sanity.io/images/umrbtih2/production/00e95c17c971cd728880bad8d830d9caac8f4730-2400x2400.png) A customer-facing AI agent should not meet your business for the first time on launch day. [Customer Agent ](https://devrev.ai/use-cases/customer-agent)is designed to learn from an organization’s support history, identify recurring patterns, draft workflows, propose skills, and suggest guardrails based on how the team has actually resolved cases. Teams review, edit, and approve what goes live. That is important because “build versus buy” is often framed as a false choice. Organizations should be able to shape an agent around their business without spending months rebuilding the undifferentiated infrastructure underneath it. Customer Agent is also designed to improve over time. It reviews customer interactions and proposes changes for the questions it missed, with human review before those improvements are deployed. This is how AI starts to become an extension of the team’s operating knowledge – not just another channel for generic answers. ### **Voice AI: continuity across the channel where issues get harder** Voice has long been the place where customer-service automation breaks down. It is relatively easy to automate simple questions over chat. But when a customer calls, it is often because the problem is complex, urgent, emotional, or spread across more than one system. [Voice AI](https://devrev.ai/blog/voice-ai) extends the same Shared Memory that powers chat and email into live customer calls. It can draw on business context, reason across connected systems, take actions during the call, and hand off to a human with the full context intact when needed. That matters because a voice agent should not be a sophisticated IVR menu. It should be able to say: “I can see the order, the payment status, the previous conversation, and the issue affecting your account. Here is what happened, and here is what I can do next.” Voice AI is designed to work with existing telephony systems and supports call recording, transcription, post-call analysis, and controlled agent versioning.9 The channel changes. The memory should not. ### **Agent Assist, Knowledge Health, and real-time signals** AI should help the people doing the work, not only the customers asking for help. Computer’s [agent-assist ](https://devrev.ai/blog/agent-assist)capabilities investigate tickets from within the support workflow, assemble evidence and root-cause context, and propose next actions for a team member to approve. At the same time, Computer can identify unanswered questions and help fill knowledge gaps by drafting new Q&A content for review or publishing. It can also surface sentiment signals across conversations and roll them up to the customer account, giving teams a chance to intervene before frustration turns into churn. This is an important shift. AI should not merely react to the ticket that arrives. It should help teams see the issues that are emerging, the knowledge that is missing, and the customers who may need attention. ### **Multiplayer AI: because support is never a single-player task** The most complicated customer issues are rarely solved by one person. A support agent may need a product specialist. A product specialist may need engineering. An account manager may need to understand commercial context. A leader may need a concise view of risk and customer impact. The old way of working requires people to forward messages, open a new thread, rewrite the history, and hope the context survives the handoff. [Multiplayer AI ](https://devrev.ai/blog/multiplayer-ai)is built for a different model: teammates can join the same live session with Computer, work from the same history and context, and continue the work without forcing anyone to catch up from scratch. This is where the idea of AI as a teammate becomes real. It is not a one-to-one chat interface isolated from the team. It is a shared working environment where humans and AI can collaborate on the same problem. One person can build a useful workflow or skill. The wider team can benefit from it. That is how intelligence compounds. ### **Agent Studio and hardened skills: turning expertise into repeatable capability** Every organization has valuable expertise: the support lead who knows how to diagnose a particular product issue, the operations manager who understands an exception workflow, the finance team that knows the approval process, or the account executive who can spot a renewal risk. The challenge is making that expertise repeatable without forcing people to recreate it manually every time. With [Agent Studio](https://devrev.ai/agent-studio), teams can build, test, refine, and deploy custom agents and reusable skills. These can be evaluated before production use, governed with defined boundaries, versioned, and rolled back when needed.10 This is how AI moves from a general-purpose assistant to a set of business capabilities that can be shared, improved, and governed across the organization. ## **The real opportunity is not more AI. It is better continuity.** I do not believe the future belongs to the company that buys the most AI tools. It belongs to the company that builds the best continuity between people, data, systems, and actions. That continuity means a customer does not have to repeat themselves. It means an agent does not have to hunt across ten systems to understand a case. It means AI can give a precise answer because it has the relevant context, not because it happened to retrieve a plausible document. It means a sensitive action can be taken safely, with boundaries and accountability. And it means the organization gets smarter with every resolved issue, every improved workflow, and every interaction shared across the team. That is the standard we should hold enterprise AI to. Not whether it can talk. Whether it can remember, reason, act, and work alongside people in a way that makes the whole organization better. **Ready to move beyond forgetful AI? Let’s [chat](https://devrev.ai/request-a-demo) about how Computer brings your customer context, teams, and actions into one shared memory.** ## FAQ ### What is AI agent memory? AI agent memory is the durable information an AI system preserves across conversations, sessions, tasks, and workflows. In enterprise settings, it includes prior interactions and the governed relationships between customers, systems, decisions, products, teams, and permissions. ### What is persistent memory in AI agents? Persistent memory lets an AI agent retain useful knowledge after a prompt or session ends. It prevents the system from starting cold whenever a user asks a related question, while allowing the underlying information to update as the business changes. ### How is shared memory different from RAG? RAG retrieves relevant document passages at query time. Shared memory preserves the relationships and organizational state that make those passages meaningful — including whether they are current, authorized, and connected to the customer, product, or decision in question. ### What is the difference between AI memory and context? AI memory is what persists over time: facts, preferences, decisions, relationships, and prior events. Context is the selected working set the model receives for a task. Effective systems use memory to assemble only the context relevant to the question being answered. ### Can multiple AI agents share memory securely? Yes, if the memory layer enforces source-system permissions and scopes access by user, team, role, and task. Multiple agents can reuse the same organizational memory without receiving blanket access to every record, document, or conversation. ### How do you govern AI agents with persistent memory? Governed AI memory requires permissions to be checked before information reaches the model, provenance for every answer, and auditability for every action. A shared memory layer should make it possible to see what an agent knew, where that knowledge came from, and whether it was authorized to use it.