The Future of CRM: Will AI Agents Make CRM Systems Obsolete?

What Does the Future of CRM Look Like?

When people talk about a CRM system today, they usually mean the application itself: logging in, opening accounts, reviewing opportunities and checking dashboards. This approach has been taken for granted for decades. AI agents are now fundamentally changing it. Increasingly, they access data and business processes, complete tasks and move between different applications.

This raises an uncomfortable question: in a few years, will businesses still need a CRM system as we know it today?

The good news first: companies will continue to use CRM systems. Just differently from today. CRM systems are here to stay because organisations need a layer that enables them to map processes consistently and with clear governance, particularly in an international context. What is changing is how people interact with this layer.

Why Does the Process Layer Remain Relevant Even with AI Agents?

AI opens up new channels and integrates unstructured topics into existing processes. However, it does not replace the workflow layer itself, as process governance is too important. Process governance refers to the binding rules that determine how business processes operate, who may change them and who makes decisions in which cases.

A comparison with the introduction of ERP and CRM systems in the 1980s and 1990s helps put this development into context. At that time, previously manual, paper-based processes were systematically mapped digitally for the first time. This created a reliable process layer on which business operations continue to build today.

AI agents now enable these processes to be executed more flexibly and autonomously. They do not replace the underlying structure. On the contrary, the more independently agents act, the more important it becomes to have a clearly defined process layer they can access.

Where Will Teams Work in Future: in the CRM System or Other Channels?

In future, teams will no longer work in one fixed location, but rather in the CRM system, Slack, AI assistants such as Claude, or other channels – depending on the task at hand. The most suitable channel depends on the specific situation.

Anyone who wants to understand a structure tends to work at the workflow level and look directly in the CRM system. Those working from a broader context, developing ideas or clarifying questions through dialogue are more likely to use additional channels such as Claude or Slack, which provide a different perspective from the workflow level alone. Work becomes more situational and personalised, and better embedded in the relevant environment.

Data management is changing, too. Team members no longer need to transfer information into input forms. They can, for example, summarise customer conversations in a voice message or photograph a business card. An AI agent then transfers the content into the CRM system in a structured format via Voice to CRM. Knowledge that previously often existed only in individual team members’ minds is captured in the system with significantly less effort.

This changes what team members need to know. In future, they no longer need to know where information belongs in the system. The AI agent assigns it based on defined structures and rules. However, teams still need to know which information a complete data record requires, as an agent can only capture what is available to it. For this reason, the rules in the CRM system must be clearly defined so that people and agents can apply them consistently.

What Remains of the Traditional CRM System?

As the user interface becomes less important, the role of the CRM system shifts. It evolves from the place where team members complete every task into the central layer for data, processes and governance.

It defines the information required for a process, the rules that apply, who is responsible for what, and which data people and agents use. These definitions and the governance of the processes remain embedded in the CRM system.

Precisely because CRM systems now evolve faster than ever, this also presents the greatest risk. Organisations that embed too many processes into a system too quickly, without carefully assessing what truly adds value, can easily lose control of how operations are managed across the business. Establishing and maintaining clear processes will therefore remain essential.

A CRM system reflects how an organisation manages its customer relationships, which processes apply and who is responsible for what. What organisations also need – such as a 360-degree view of customers with all relevant data – becomes even more important in the Agentic Enterprise. Without a solid data foundation, agents lack the tools they need to take intelligent action.

How Is Decision-Making Evolving Between Humans and AI Agents?

Today, AI primarily supports people with clearly defined tasks such as creating a summary or preparing for a meeting. In future, AI agents in CRM can identify at-risk opportunities, recommend actions and carry them out independently. Organisations therefore need to define which decisions AI agents may make and which they may not.

The specific use case is key. Situations that require human empathy, such as negotiations, are difficult to delegate to agents. Traditional automation often handles standardised, repetitive tasks more easily. Agents demonstrate their strengths in context-dependent tasks, such as data maintenance.

In most processes, AI agents and people therefore share the work. The agent prepares, the person decides. There are exceptions: within clearly defined boundaries, agents can also act independently.

The principle is familiar from rule-based automation, where, for example, opportunities below a certain threshold are processed automatically. AI agents apply this principle to decisions that cannot be captured in a single rule. For instance, an agent can approve a standard quote with the usual discount independently by considering purchase history and open service cases. If a quote differs significantly from the norm – for example, due to a substantial discount or specific contractual terms – it refers the decision to the responsible person.

However, organisations do not always determine the boundary between an AI agent and a person on their own. The European AI Act classifies AI applications according to their level of risk. For high-risk applications, such as those used in HR, it requires data provenance and decisions to be traceable, among other things, and for people to oversee the systems.

Do More AI Agents Automatically Lead to Greater Productivity?

As AI agents take on an increasing number of tasks related to CRM data and processes, it is reasonable to assume that greater agentification automatically leads to higher productivity.

Gartner forecasts a significant rise in the number of AI agents. By 2028, AI agents are expected to outnumber sales team members tenfold. At the same time, fewer than 40% of sales team members are likely to say that agents have genuinely improved their productivity.

More AI agents do not automatically create more value. As their use increases, so does the complexity: agents need access to the right data, must be integrated meaningfully into existing processes and aligned with one another. The effort therefore shifts rather than disappears.

What matters is not the number of agents, but the foundation. Gartner reaches the same conclusion: according to its forecast, sales organisations that fundamentally redesign data, automation and user experience are five times more likely to achieve a return on investment (ROI) from AI by 2028 than those that rely on isolated quick fixes. A single use case can be implemented relatively quickly. The real challenge lies in scaling that use case across the entire organisation.

The future of CRM therefore does not depend solely on new AI capabilities, but on whether data, processes and governance are designed to enable agents to work with them increasingly independently.

How Are Companies Preparing Now?

The CRM of the future will become more situational, channel-agnostic and increasingly supported by agents. Four steps help organisations prepare for this development:

1. Define Process Governance Before Technology:
   Before embedding new use cases into a system, clearly define how a process should run, who makes decisions and which exceptions apply.
2. Consolidate the Data Foundation:
   A robust, consistent data foundation is essential for AI agents to operate reliably. This includes removing duplicates and synchronising master data across all connected systems.
3. Deliberately Allocate Roles Between People and AI Agents:
   Not every process is suitable for an agent. Clear criteria for when a person makes decisions and when an agent prepares or executes tasks prevent unnecessary risks.
4. Consider Channels for External Agents:
   In the long term, not only company-owned agents will access CRM data and processes. AI agents used by customers or partners may also retrieve information or submit requests. Organisations should therefore assess now which data and processes they intend to open up for this type of access in future and how they will secure them.

Conclusion: The CRM System Remains

AI agents do not make the CRM system obsolete, but they do change its role. The user interface becomes less important, while the underlying layer gains significance. This is where the data resides, where processes are defined and where decision-making responsibilities are assigned. And this is precisely the layer that AI agents access independently.

This also shifts the central task for organisations. They no longer only need to ensure that team members can work efficiently with the CRM. Going forward, data, processes and governance must be designed to enable AI agents to act reliably within defined boundaries.

The next step is already taking shape. In addition to their own team members and AI agents, organisations will soon also see external agents accessing their systems. Without the right channels in place, an organisation is as invisible to these agents as a business without a website.

Investing in process governance, data quality and integration now lays the foundation for individual AI use cases and for a CRM where people and AI agents can work together in the future.