Unlike specialized or supporting IT tools, CRM is at the center of business processes that directly affect revenue, customer retention and quality of service. Integration of AI into such a circuit allows immediately starting working with applied tasks, where the effect is measurable and understandable to business: from prioritizing leads and predicting demand to personalizing offers and supporting employees in their daily work.
Use cases
CRM, as a rule, has already accumulated historical data, built processes and defined user roles, that allows to run AI scenarios iteratively, without radical restructuring of the IT landscape. One of the most notable uses of AI in CRM is analytics and forecasting.
Algorithms allow working not only with retrospective reporting, but also with probabilistic scenarios for the development of events. Forecasting demand, assessing the likelihood of closing deals, identifying outflow risks – all this shifts focus from intuitive solutions to solutions based on historical data. It is important that such models do not require perfect data purity from the first day: they develop gradually, clarifying their conclusions as new signals appear.
AI in this context includes both classic predictive models and generative AI (GenAI) tools that help, for example, analyze the “kilometers” of correspondence with customers.
A significant effect is also provided by a deeper segmentation of the client base that AI allows. Instead of formal criteria, such as industry, region or volume of purchases, the system begins to take into account patterns of behavior, to find non-obvious relationships by which segmentation can be carried out. This allows to determine priorities, and predict successful transactions with greater accuracy, concentrating your efforts on truly promising customers or leads.
Personalization becomes a logical continuation of this approach. If earlier the main challenge was to increase the speed of processing calls and the number of contacts, today the priority is shifted towards the quality and appropriateness of interaction. In conditions of high information load, accurate and relevant communications become a key condition that builds trust and customer loyalty.
AI helps form suggestions and recommendations based on previous experience of interacting with a specific client, and not on average scenarios. For managers and operators, this means a qualitatively new level of support. CRM with AI elements can prepare an employee for a conversation in advance by structuring all relevant information about the client and prompting possible dialogue scenarios. As a result, communication becomes more meaningful and personal, and communications are perceived not as intrusive sales, but as appropriate and timely prompts that increase the value of the service and the level of trust.
Automation of daily operations deserves special attention. Preparation of brief summaries of meetings, fixing agreements, filling out customer cards, forming reminders and draft letters are increasingly carried out in a semi-automatic mode. For a business, this means not so much saving time as reducing operational risks: fewer lost agreements, less information distortion and higher data discipline.
Together, these scenarios create a new quality of CRM use: the system ceases to be an accounting and control tool and begins to support employees in making decisions based on analysis, not assumptions.
Harmful illusions
One of the main misconceptions about AI in CRM is related to the idea that it is able to instantly solve accumulated problems. In practice, the use of any algorithms requires data, time for training and constant adjustment. AI does not arise “out of the box” in a ready-made form – it develops along with the system and the quality of management decisions.
For example, the model will not be able to make qualitative predictions if the company lacks a representative history of customer interactions or the data for training is scattered and contradictory.
Do not strive to robotize all processes at the same time. The key to successful integration of AI is a strategic approach: first, you need to identify specific “pain points” of the business, and then choose the so-called “quick-win” – a solution that, with minimal investment, can bring maximum and measurable effect. It is on this pilot project that the use of AI should be honed, clearly demonstrating its value to the company.
Another overestimated request is related to the expectation of full autonomy. AI is often perceived as a potential replacement for humans, especially in analytics and sales. However, in real scenarios, it rather strengthens employees, removing the routine from them and helping to navigate complex arrays of information. Decisions, especially in situations with high uncertainty or strategic implications, remain in the area of human responsibility.
In parallel with this, there is an opposite problem – underestimation of the AI potential. Many companies limit themselves to automating individual operations, considering AI exclusively as an auxiliary tool.
For example, the analysis of unstructured data is often underestimated. Correspondence, telephone records, reviews and comments contain a huge amount of information about real expectations and customer problems, but are traditionally used in fragments. Intelligent models allow to extract meaning from these sources systematically, forming generalized conclusions and using them to adjust the interaction strategy.
This opens up opportunities for more accurate personalization and proactive work with outflow, when the system does not just record the client’s departure, but indicates in advance the risk and possible reasons.
Market without rose glasses
The development of AI functionality in CRM is largely determined by the maturity of the platforms themselves and the strategy of vendors. Large international players began to integrate intelligent mechanisms into their solutions earlier than others, relying on the deep integration of AI directly into the platform core.
For example, market leader Salesforce originally designed the Einstein intelligent module as part of the ecosystem and it is today used to predict sales, rank and prioritize leads, analyze correspondence, auto-generate responses, and process telephone conversations and unstructured data. An important point: AI here does not work in isolation, but relies on a single array of data and processes, that makes its conclusions applicable in operational activities.
The Russian market is developing in the same direction, but with an emphasis on application and process scenarios. Domestic platforms more often consider AI as a tool for optimizing specific business processes, rather than as a universal analytical layer. At the same time, for most customers, the key factor is not the list of available AI functions, but the degree of their integration into existing processes and the quality of support from the vendor.
For example, BPMSoft integrates ML and LLM models directly into the business process designer, allowing predictive lead scoring, outflow prediction, case classification, and intelligent segmentation of the client base within existing operational logic – without creating a separate IT loop. The platform “out of the box” supports cloud LLM models of the Yandex Cloud provider, as well as local open-source models through the Ollama API or OpenAI API. Users can add AI agents on their own without recycling the system core.
Other vendors also have various “features” using AI: writing scripts for sales managers and call center specialists, speech analytics, setting up sales funnels, automating work with documents, and much more.
The on-board CRM system with AI takes on the role of a link between data, processes and people. It helps businesses act more consciously, adapt to changes faster and build more sustainable relationships with customers. Expanding its capabilities using AI turns CRM into an attentive, always remembering assistant who knows exactly what a particular client needs – but only on condition that we constantly provide the models with high-quality data.
At the same time, responsibility for the strategy and the choice of development directions remains with the human: this gives the necessary balance of automation and control.
By Tatiana Kirillova, Head of CRM Practice, Axenix