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In social housing, certain periods are known to generate a large number of tenant requests. This happens in particular during APL (housing allowance) update campaigns, charge adjustments, ahead of the winter truce, or in the event of elevator or heating breakdowns in a residence. These moments create a sudden surge in requests, which is difficult for teams to handle. Even well-organized, they quickly find themselves overwhelmed. The consequences appear quickly: calls pile up, waiting times increase, agents become overloaded, and tenants have to wait longer for a response. Faced with these activity peaks, artificial intelligence (AI) provides valuable support: it automates simple requests, streamlines multichannel management, and anticipates peaks to maintain service quality.
Seasonal peaks are linked to predictable times of the year. For example, at the start of the school year, many tenants request a housing change to be closer to their workplace or their children’s school, or they ask questions about their rights and the procedures to follow. The same happens during housing benefit renewal campaigns (such as APL) or during annual rent review periods.
Event-driven peaks are caused by exceptional and often unpredictable situations. This may include storms or floods requiring rapid rehousing, a regulatory reform that must be applied immediately, or even a health crisis leading to the temporary closure of physical agencies.
Operational peaks concern periods when several internal tasks overlap. This is the case during major renovation campaigns (energy upgrades, façade restoration), the annual adjustment of service charges, or when many contracts expire at the same time. These peaks generate significant pressure on both technical and administrative services.
These activity peaks represent a real opportunity for social housing providers to optimize their operations, strengthen their agility, and accelerate their digital transformation. When requests rise sharply, these periods highlight the need for more agile and efficient tools to better organize responses, speed up request processing, and ensure optimal service continuity.
These situations are also an opportunity to identify several areas for improvement, such as smoother coordination between departments, faster processing of simple requests, and stronger involvement of human teams in cases requiring personalized support. Rather than merely enduring these peaks, they can pave the way for concrete improvements in internal processes, better support for teams, and a smoother, fairer tenant experience.
When facing activity peaks, artificial intelligence is not meant to replace human staff. Instead, it acts as operational support, able to take over certain tasks to streamline workflows and optimize available resources.
Beyond automation, AI also enables a better understanding of requests through natural language processing (NLP), which can recognize phrasing that may be approximate or incomplete.
It also integrates predictive analytics capabilities, able to cross-reference different sources and databases: request histories, seasonality, external events such as weather or regulatory changes, to anticipate demand surges and adjust resources accordingly.
Far from being just a technical tool, AI becomes a true organizational lever: it enables better planning, improves task distribution between humans and machines, and ensures consistent service quality, even during periods of high pressure.
During high-demand periods, AI plays a key role in guaranteeing seamless service continuity. It allows tenants to easily access practical information without having to wait on hold or repeat their request multiple times.
By leveraging the information already available in the tenant’s profile, AI can adapt its suggestions in real time. It directs the person to the channel best suited to their situation, whether step-by-step guidance, faster connection with an advisor, or direct access to a useful resource. This personalization avoids unnecessary detours and makes the interaction smoother and more efficient. The journey is therefore better structured, and the tenant’s procedures become simpler and faster.
This proactive assistance strengthens the feeling of being heard and supported, even when teams are under heavy pressure. Tenants feel taken care of at every step. They remain autonomous in their processes (self-care), while still benefiting from guidance tailored to their needs, which enhances their overall experience and highlights the professionalism of the housing provider.
Automation and artificial intelligence are not intended to replace humans, but to support them by distributing tasks more intelligently. By delegating the handling of simple, repetitive requests to AI agents, customer service advisors can refocus on complex situations that require judgment, empathy, and human interaction.
This new way of organizing work helps reduce frequent interruptions and makes the daily life of call center agents smoother. It lessens the burden of repetitive tasks and improves their ability to concentrate on more valuable and engaging missions. In this way, the advisor becomes “augmented” by AI tools, allowing them to dedicate more time to sensitive requests, such as debt situations, conflicts within residences, or needs for personalized social support.
In the longer term, this contributes to creating a more balanced and serene working environment, even during peak activity periods. The team remains focused on what truly matters, which limits chronic fatigue, reduces the risk of disengagement, and fosters a more sustainable and motivating dynamic within the organization.
The implementation of artificial intelligence in a social housing organization relies on a progressive, structured approach adapted to real-world conditions. A first step consists in identifying simple, repetitive, and frequent tenant-facing tasks. This may include, for example, helping tenants understand a scheme, supporting them in completing a procedure, or directing them to the right department.
AI can then be integrated into the existing ecosystem, leveraging current business tools such as property management software, maintenance platforms, CRMs, or tenant portals. The objective is to streamline processes without destabilizing teams. However, this requires reliable and well-structured data. An audit of information systems and harmonization of databases may be necessary to ensure smooth exchanges between AI and business tools.
AI solutions must demonstrate interoperability to interact with the entire information system of the housing organization, which sometimes requires technical adjustments or specific partnerships. Some AI solutions include so-called “trust components.” These ensure that the responses provided comply with the organization’s internal rules, while keeping a clear record of exchanges. These tools operate in a secure environment and limit the risk of errors or misuse. A good example is "LLM as a judge" where a second AI reviews the responses of the first. This review step relies on internal rules or legal obligations and provides an additional layer of control, helping to avoid errors while ensuring that interactions remain reliable and compliant.
On the organizational side, personal data protection remains a fundamental pillar. AI must comply with GDPR, particularly when accessing sensitive tenant data. This requires the implementation of robust technical and organizational measures: access control, logging, auditability, and the right to an explanation in the event of an automated decision. The ethical approach is also central. Algorithms must be transparent, regularly audited, and designed to avoid any bias or unintentional discrimination.
To determine whether the actions implemented are effective, it is useful to track a few key performance indicators. It is relevant here to monitor the number of requests handled automatically, the share of requests handled by teams, changes in response times, as well as the level of satisfaction expressed by tenants. These measures make it possible to track progress over time and adjust the strategy based on results, while involving teams and partners in the process.
By moving forward step by step, with reliable tools, good data management, and compliance with regulations, artificial intelligence can become a real asset for improving tenant relations and making the organization more responsive and efficient.
Far from being just a technological tool, artificial intelligence is establishing itself as a true strategic support for social housing organizations, especially when they face activity peaks. It helps streamline the management of requests without compromising service quality, while refocusing human teams on their essential mission: supporting tenants with care and responsiveness.
And what if these periods of high pressure became an opportunity to rethink the organization, experiment with new tools, and embed a lasting new dynamic within your services?
