ChatGPT persistent memory: what changes for B2B SMEs

The Dreaming system: what OpenAI changed in ChatGPT's memory

On June 4, 2026, OpenAI released a official update on ChatGPT's memory system , called Dreaming . This is a substantial evolution compared to the manual memory previously available. In fact, the new approach automatically consolidates user preferences and context across different sessions, without requiring explicit intervention.

Technically speaking, the model periodically processes past conversations to extract relevant patterns. Therefore, when a session starts again, ChatGPT already has an updated profile of the interlocutor. This process happens in the background, transparently for the end-user.

Furthermore, the system distinguishes between stable preferences — such as preferred communication tone or industry — and temporary contextual information. Consequently, the memory is more selective and useful than a simple conversation log.

Immediate impact on business workflows

For B2B SMEs, the most direct impact concerns the continuity of interactions. Before this update, each ChatGPT session started from scratch. Therefore, the operator had to re-contextualize their sector, type of clientele, and operational instructions each time. Now, this step is largely eliminated.

In particular, three operational areas immediately benefit from this novelty:

• Automated customer service: an AI assistant that remembers previous interactions with a customer can respond more relevantly, reducing ticket handling times.
• Lead nurturing: ChatGPT can keep track of the funnel stage a prospect is in, adapting the proposed content accordingly.
• Internal support for sales teams: salespeople can query the assistant with contextualized questions, without having to repeat the customer briefing each time.

However, it's important to emphasize that these benefits are fully realized only with proper configuration. Therefore, it's not enough to activate the feature: interaction flows need to be structured so that memory is fed with useful and consistent data.

Why contextual memory changes the logic of AI automation

Until today, most conversational AI implementations in SMEs relied on static prompts and predefined knowledge bases. This approach works, but it has a structural limitation: it doesn't scale with the increasing complexity of business relationships.

Conversely, a system with persistent memory approaches the behavior of an expert collaborator. Similar to how an account manager remembers the preferences of a long-term client, ChatGPT can now maintain a dynamic profile of the interlocutor. According to analyses by McKinsey on the economic potential of generative AI , contextual personalization is among the factors that determine the greatest increase in productivity in sales and customer care functions.

In addition to this, persistent memory reduces the so-called prompt overhead : the time spent instructing the model in each session. For a team that uses ChatGPT dozens of times a day, the cumulative savings are far from negligible.

What to do now: three action steps for SMEs

We at SHM Studio we suggest a structured three-phase approach to immediately capitalize on this new feature.

First step — Audit of existing interactions. First of all, it's a good idea to map out the use cases where ChatGPT is already being used internally. Then, identify those where the lack of memory has led to inefficiencies or inconsistent responses. This audit typically requires one or two work sessions with the teams involved.

Second step — Structuring the initial context. Furthermore, it is useful to define a set of information that the assistant must memorize from the very first interaction: sector, type of clients, communication tone, main products or services. This information should be explicitly entered in the first sessions to correctly feed the Dreaming system.

Third step — Integration with the processes of Digital marketing and CRM. Finally, the maximum value is achieved when ChatGPT's memory is aligned with the data present in the company's CRM. This way, the assistant can contextualize responses not only based on conversational preferences but also on the client's sales history.

Still a work in progress: limits and cautions to keep in mind

Nevertheless, it would be incorrect to present this novelty as a definitive solution. There are at least three areas of attention that SMEs should keep in mind.

Firstly, ChatGPT's memory is tied to the user account, not the end customer. Therefore, in customer service scenarios where multiple operators manage the same customer, memory consistency depends on the discipline with which the team uses the shared account or APIs. This requires internal governance that doesn't always exist in small SMEs.

Secondly, open questions remain regarding data processing. According to the indications of GDPR.eu , any system that stores information about individuals must comply with the principles of minimization and storage limitation. Therefore, before implementing flows based on ChatGPT's persistent memory, a check with your DPO or legal advisor is recommended.

Thirdly, the quality of the memory depends on the quality of the conversations that feed it. So, if the initial prompts are vague or contradictory, the system will consolidate unhelpful information. Careful structuring of interactions remains an essential prerequisite.

Outlook: where AI memory is heading in the coming quarters

Looking ahead to 2027-2028, the direction is clear. Language models are evolving towards increasingly granular and persistent user profiles. Gartner predicts that by 2027, over 40% of customer service interactions in medium-sized enterprises will be handled by AI assistants with advanced contextual memory.

For Italian SMEs, this means that those who start structuring flows based on persistent memory today will have a measurable competitive advantage over those who wait. In fact, the system's learning curve — both for AI and for internal teams — requires time and iterations.

In this scenario, the areas of AI consulting , Digital marketing and SEO integrate ever more closely. Therefore, a coherent strategy must consider how AI memory interacts with published content, with the google ads campaigns and with the activities of lead generation on LinkedIn .

SHM Studio's take: a paradigm shift, not just a feature

From SHM Studio , we observe this novelty with strategic, not just technical, interest. ChatGPT's persistent memory is not simply an additional feature. It's a paradigm shift in how companies can relate to their AI tools.

Until today, AI was a reactive tool: it responded to specific inputs. From today, it can become a collaborator with memory, capable of building an understanding of the business context over time. This shifts the value from single interactions to ongoing relationships.

For Italian B2B and retail SMEs, which often operate with small teams and limited resources, this evolution is particularly relevant. In fact, an AI assistant with memory reduces the cognitive load on employees and improves the consistency of customer communications. Consequently, the investment in a correct initial configuration pays off quickly.

Those who want to learn more about how to integrate these features into their processes can explore our services , read insights on the Blog or contact us directly for an initial evaluation. Also, for those working on digital content, the service of SEO copywriting and that of web development can be integrated with personalized AI flows based on the new contextual memory.