In response to the rapid development of artificial intelligence, governments are enacting laws to regulate it, such as the European AI Act.

There are high expectations for this legislation, which will gradually set guidelines for the ethical use of AI within the European Union.

Artificial intelligence is no longer a distant promise but an operational reality that is profoundly transforming the economic and social fabric. However, this technological acceleration is accompanied by a growing need for ethical and regulatory guidelines. It is in this context that Europe has drafted the AI Act, an ambitious regulation aimed at governing the use of AI.

To understand its practical implications, we spoke with Mathieu Changeat, co-founder of Dydu and now chief operating officer of the Zaion Dydu Group. His analysis, grounded in practical expertise and a sociological perspective, reveals a complex landscape where sovereignty, trust, and business pragmatism vie for center stage.

The AI Act: An Essential Legislative Framework, Not a Panacea

The AI Act, discussions of which predated the ChatGPT boom, is a European response to the need to clarify the use of AI. It will therefore impose obligations regarding model training and protect intellectual property.

Its implementation was initially scheduled for 2026. It will now be phased in gradually through 2028, but this delay should not be interpreted as a sign of weakness. According to Mathieu Changeat, Europe’s real challenge lies more in a massive lack of funding compared to the American and Chinese giants.

“The AI Act was supposed to be the big bang of August 2026, but there’s already talk of delays until 2028 for certain aspects. But I think the problem lies elsewhere. It’s funding,” he explains.

Indeed, despite the emergence of European players valued at billions—notably Mistral AI—the financial firepower is still lacking. It will be necessary for the development and democratization of generative AI models.

However, the AI Act plays a crucial role as a certification label. The “AI Act” compliance label will strengthen users’ perception of trust—even though it does not guarantee the total absence of algorithmic hallucinations. These are a result of the algorithms’ technical architecture. The label certifies that the models have been trained on ethical data, thereby enhancing reassurance and transparency.

Sovereignty and the Digital Divide: The European Challenge

One of the most significant impacts of the AI Act is evident in the quest for digital sovereignty. Mathieu Changeat observes a shift in mindset, particularly among large corporations and the public sector. He notes that “for the public sector and large enterprises, digital sovereignty has become a priority, sometimes at the expense of pure performance. They now want European solutions in light of the current geopolitical situation.” ”

Digital sovereignty is becoming a major reassurance factor for large enterprises and the European public sector. As a result, they are favoring local solutions even in the face of initial performance gaps. This trend toward self-regulation, a precursor to the AI Act, means that the Act will not disrupt their established practices.

However, a technological divide is emerging. While large corporations have the resources to invest in customized, sovereign solutions, questions remain for SMEs. They risk becoming more dependent on off-the-shelf solutions, often from the U.S. The reason: a lack of resources and time to keep pace with regulatory changes.

Risk Management and Ethics in the AI Act

The advent of autonomous AI agents, capable of collaborating on complex tasks, raises new governance issues. Mathieu Changeat emphasizes the need to strictly define the agents’ scope of action and to implement technical safeguards.

The goal is to prevent unexpected behavior or problematic interactions. Similarly, this approach remains more accessible to large corporations capable of financing these custom developments.

Furthermore, user mistrust regarding their personal data is rising sharply. Studies indicate that the cookie refusal rate will reach nearly 40% by 2026. This heightened vigilance is pushing companies to anticipate transparency requirements, even before the AI Act is fully implemented.

The main “red flag” for a company remains the “black box” nature of certain AI models. As projects evolve, systems that were initially stable may become incompatible, leading to contradictory results that could be penalized under the AI Act. For customer service, the use of AI is not “high-risk” as long as transparency is maintained and access to data is limited to what is strictly necessary.

Humans Augmented, Not Replaced: The Customer Service Paradox

Forecasts warn daily of the risk of humans being massively replaced by machines as AI becomes more widespread. However,contrary to alarmist predictions of massive automation in customer service, Mathieu Changeat advocates for a more nuanced view.

“90%: that’s alarmist; I don’t really believe it based on my experience. Ultimately, it’s people’s choice: if we give users the option to be connected with a human agent or a chatbot, the majority choose the human. ”

Indeed, users still prefer human interaction for complex situations. These are the situations that require empathy and a nuanced understanding of the context. AI is more likely to enhance the capabilities of human agents.

It will therefore relieve them of tedious tasks, allowing them to focus on high-value-added interactions. However, it’s important to note that since the AI Act is not a labor code, it will have no direct impact on protecting employees from automation.

Outlook for 2028: The AI Act as a Reassurance Label, Not a Constraint

Looking ahead to 2028, Mathieu Changeat estimates that AI will be standardized. The AI Act will not represent a restrictive “big bang” for companies already familiar with best practices in data management (GDPR). Rather, it will be part of a continuum of regulation, serving as a seal of reassurance for users.

Mathieu’s ultimate strategic advice is clear. We must focus on real business needs rather than succumbing to the AI fad. Many use cases do not require complex tools to be solved, and a pragmatic, value-added approach remains the key to success.

The AI Act, therefore, does not present itself as an insurmountable barrier to innovation. Rather, it will act as a catalystfor more ethical, transparent, and sovereign AI. While funding challenges and the technological divide between large corporations and SMEs remain significant, awareness and self-regulation are already underway.

Will the future of AI in Europe thus take shape as a model of technological development that serves people and fosters trust, balancing performance with social responsibility? Only time—and the ability of stakeholders to incorporate these imperatives—will tell.

Article published by lebigdata.fr

Behind the power of these models, one reality remains: the technologies most widely used today are, for the most part, developed and operated outside Europe. For French companies, the stakes go beyond the question of performance. They touch on economic sovereignty, the law, and the ability to maintain control over components that have become fundamental to their operations.

Data and Dependency: The Risk of Technological Lock-in

Entrusting sensitive data flows to non-European platforms is never without risk. This can lead to transfers outside the European Union, expose companies to foreign laws, and increase dependence on actors whose priorities do not always align with European data protection requirements.

In many projects, the issue of data hosting and control comes up too late. This is due to a lack of time, but also because certain solutions have become established as standards. Yet generative AI relies on business, customer, or internal data that has become a strategic asset.

As these technologies become integrated into critical processes, another challenge emerges: technological lock-in. The more central a model becomes, the harder it is to move away from it. Companies rely on specific APIs, adapt their processes, train their teams on an ecosystem, and integrate these tools into their customer journeys.

This dependency can, over time, reduce their bargaining power and their technical or budgetary flexibility. For a long time, the market prioritized performance over control. But as AI becomes an operational infrastructure, data governance and freedom of technological choice are once again becoming decisive criteria.

Maintaining control is not just about choosing one provider over another, but also involves building expertise. Many companies still perceive AI as a complex black box, difficult to manage, oversee, or audit. Strengthening internal expertise is therefore becoming a strategic priority to build teams capable of governing data, evaluating models, and structuring robust use cases. Autonomy begins with the ability to understand what is being deployed, not just to consume tools.

Rebalancing with French and European solutions

Dependence on major international models is not inevitable. The French and European ecosystem is advancing rapidly, with solutions capable of meeting concrete needs while offering better regulatory and cultural alignment.

Players like Mistral AI embody a new generation of models developed in Europe, with a more open approach designed for industrial use. In Germany, Aleph Alpha is working on systems tailored to sensitive environments, where traceability and transparency are essential. And on the infrastructure side, groups like OVHcloud remind us that there are also credible alternatives for hosting and scaling AI applications without relying exclusively on major international cloud providers.

These initiatives do not replace GPT-5. Rather, they allow for the reintroduction of choice, customization, and above all, greater control over data and terms of use. Technological sovereignty rests on the ability to diversify and manage dependencies.

Building a collective, industrial, and European response

With the gradual implementation of the AI Act, companies must now better structure their artificial intelligence projects: document systems, clarify responsibilities, and strengthen governance. This framework comes after a phase of rapid experimentation, where innovation often preceded organization.

But these challenges extend beyond the scope of any single company. France, like Europe, has often approached major technological transformations in a fragmented manner. AI, on the contrary, demands a collective response: industrial alliances, public-private partnerships, support for European players, and the development of infrastructure capable of supporting its industrialization.

The talent exists, the research is solid, and many companies are gaining momentum. The challenge now is to transform these assets into sustainable industrial capacity. The question is not about choosing between GPT-5 and European alternatives, nor about pitting openness against sovereignty.
The challenge for French companies is to retain the ability to decide: where their data goes, which technological building blocks they rely on, and to what extent they are willing to delegate functions that have become essential.

Advances in reasoning, multimodality, and automation are opening up new perspectives and marking a turning point. The transformation of uses, organizations, and operational performance is now being built into the way economic players integrate models into their processes and systems. The plateau observed is not technological; it is strategic.

When power is no longer enough

GPT-5 highlights a profound shift in the issues at stake. The debate is no longer about the intrinsic performance of models, but about the ability of organizations to leverage them to create concrete and sustainable value. In many projects, the same pattern emerges: convincing demonstrations, followed by complex production rollouts, mixed results across different business lines, and increased dependence on generalist models whose behavior remains difficult to control.

Despite their level of maturity, models remain difficult to integrate on a large scale within organizations. Nearly four out of five AI projects, or about 80%¹, fail to meet their objectives or are abandoned along the way. This failure rate is almost twice that of traditional IT projects.² Generative AI, long perceived as a natural accelerator of transformation, is becoming a demanding tool to manage. Senior management is therefore shifting its attention to more structural issues: operating costs, operational risks, robustness of responses, and the ability to demonstrate a tangible return on investment.

For companies, value creation is no longer about choosing the “best” model or optimizing prompts. It depends above all on the structuring of uses. This evolution is redefining expectations of Large Language Model (LLM) providers, who are now expected to be able to serve concrete and operational cases. The example of Claude, from Anthropic, which specializes in IT development, illustrates this transition towards models designed to integrate effectively into specific business contexts.

Moving away from the illusion of the central model

This plateau marks a stage of maturity. It invites companies to move beyond their fascination with raw performance and refocus on the essentials: concrete applications, robust systems, and a lasting relationship of trust.

In many organizations, however, AI strategy remains centered around a central model that is presented as capable of understanding, deciding, and producing everything. Today, this vision is showing its limitations and hindering long-term value creation. Value is now built into the architecture that frames AI. Knowledge structuring, clear delineation of uses, specialization by business case, and orchestration of rules, data, and generation are becoming key performance factors. Generative AI is thus a building block within a larger system designed to ensure consistency, control, and reliability.

The most successful companies will be those that know how to use AI systems that are useful before they are spectacular, controlled before they are autonomous, and sustainable before they are simply innovative.

2026: AI enters the era of results

By 2026, AI will have fully entered a phase of economic and operational maturity. The gradual implementation of the AI Act ³ will accompany and accelerate this movement. By imposing increased requirements in terms of understanding, control, and explainability, it will push companies to structure systems that are more transparent, better governed, and closely aligned with business challenges.

In this context, organizations are gradually moving away from experimentation and focusing their investments on uses that can produce measurable and industrializable results. AI is becoming integrated into core processes: it automates task chains, supports decision-making, and works closely with human teams. It is becoming a key driver of operational transformation.

To meet these new performance and control requirements, technological strategies are diversifying. Organizations are combining large models with more specialized models to optimize costs, energy impact, and business alignment. This approach requires careful orchestration, in which human expertise plays a central role in framing uses, supervising decisions, and ensuring the reliability of the systems deployed.

The future of AI will depend on the ability of organizations to transform these technologies into sustainable levers of performance and trust. This value will be based on a balance between companies that are able to structure specific business uses and model publishers that are able to evolve their models towards truly operational specialization.

1 Source: RAND think tank report

2 Source: Gartner study

3 AI Act: European regulation on artificial intelligence that sets out obligations for suppliers of general-purpose AI systems (GPAI) and prohibits certain AI systems that infringe on fundamental rights.

Opinion piece by Samir Dilmi published on économie matin.

OpenAI refers to a technological leap forward, confirmed by certain benchmarks in which GPT-5 outperforms GPT-4o in coding, mathematics, and image analysis tasks. However, despite these advances, the model remains unfinished. Powerful, but unstable; more refined, yet still fallible. GPT-5 marks a step forward, not a breakthrough.

It is a more sophisticated model, but not a revolutionary one.

The main change is in its architecture: an internal router selects the most appropriate sub-model in real time, depending on the request.

This logic improves the accuracy of reasoning and the ability to follow complex instructions. Multimodality—text, image, audio—is also becoming more fluid. However, the gap with GPT-4 remains less spectacular than expected. In symbolic logic and general reasoning tests, some competitors still outperform it. The GAIA benchmark, designed to evaluate the performance of complex tasks, ranks GPT-5 Medium behind other models. Above all, beyond the scores, the limitations appear in concrete uses: in operational productions such as the creation of infographics, presentations, or the development of structured code, GPT-5’s outputs often lag behind certain competitors, notably Gemini. Generative AI is now progressing more through successive adjustments than through real qualitative leaps.

Power that also amplifies risks.

Because it can generate text, images, audio, or code, GPT-5 mechanically increases the risks associated with misinformation. Deepfakes are becoming more realistic, while detection methods are becoming less reliable. This has led the European Union to impose, via the AI Act, a transparency requirement on synthetic content, and France to strengthen the fight against manipulation with the SREN¹ law. But these measures alone will not be enough. Education on how to use AI, source verification, and a culture of reasonable doubt remain the first lines of defense. More powerful AI requires more vigilant users.

Technical limitations remind us that AI is not automatically true.

Despite the announced improvements, GPT-5 continues to produce factual errors, inconsistent deductions, and approximations. Its performance is superior, but its reliability is not guaranteed. Independent analyses point to persistent difficulties: fragile reasoning without step-by-step instructions, simple mathematical errors, and poorly executed complex code projects. Even its hallucination rate, although declining according to Vectara², remains incompatible with total delegation to the machine. These weaknesses fuel a broader debate: has generative AI reached a plateau? Several researchers believe that current architectures are showing their limitations and that a real technological breakthrough will be necessary to reach a new milestone.

A model that is still very Anglo-centric.

Like all major American and Chinese models, GPT-5 has been trained primarily on English-language data. The result is clear to see: cultural biases, misunderstood French nuances, and fluctuating tone. Since its launch, GPT-5 has been criticized for having a colder style than GPT-4, a direct consequence of internal optimizations. For less represented languages, performance remains mechanical. Nuances, subtext, local culture: all of this remains difficult to reproduce. European models are progressing, but American and Chinese dominance in terms of data and computing power remains overwhelming.

GPT-5 confirms that generative AI has become an indispensable tool, while remaining far from truly autonomous. The more mixed reception of GPT-5.2 illustrates the current tensions in the sector: in a context of increased competition, particularly in the face of rapid advances by Gemini, some observers refer to a release that is perceived as rushed, accompanied by uneven performance depending on the use case. This climate fuels expectations that are sometimes disappointed and reminds us that the race to make announcements does not, on its own, guarantee a lasting qualitative leap.

Its limitations show that no model can replace human analysis, discernment, or responsibility. Companies would therefore be wise to integrate these technologies with a clear head: AI serves to augment teams, not replace them. The future of the sector will not be determined solely by the size or speed of model deployment, but by the ability to combine innovation, transparency, and controlled use. GPT-5 marks a new stage, and human vigilance remains the determining factor.

1The SREN law (Securing and Regulating the Digital Space), enacted in May 2024, strengthens the framework for large platforms in terms of protecting the public and combating the manipulation of information, particularly content generated or altered by AI. The text expands ARCOM’s powers to identify, report, and sanction deepfakes, and imposes new transparency obligations on digital players. Sources: Légifrance (Law No. 2024-449 of May 21, 2024), National Assembly.

2Vectara is a platform specializing in assessing the factual reliability of large language models. It has developed a hallucination evaluation model (Hughes Hallucination Evaluation Model, HHEM) and a Hallucination Leaderboard, which are used to measure and compare the rate of “hallucinations,” i.e., factually incorrect or invented information, of different LLMs when they summarize or generate content.

Written by Mathieu Changeat, Co-founder of Dydu, published on économie matin.

In 2025, 76% of HR professionals will express frustration with the burden of administrative tasks. Today, the employee experience is becoming as strategic as the customer experience!

At a time when employee experience is becoming as strategic as customer satisfaction, HR departments are facing a double challenge: meeting growing expectations with often limited resources. In 2025, 76% of HR professionals express frustration with the burden of administrative tasks, which continue to dominate their daily lives, to the detriment of human support and talent development¹. In this context, HR chatbots are no longer just a technological gadget. They are emerging as powerful levers for automating recurring requests, streamlining internal communications, and refocusing HR teams on what is at the heart of their business: people.

Artificial intelligence at the service of employees’ daily lives

Faced with rapidly changing expectations and work environments, human resources departments are now at a turning point. Between growing employee expectations, budget constraints, CSR, digital transformation of tools, and the war for talent, the HR function must reinvent itself. At the heart of this change is the employee experience, now considered a strategic lever for retention and performance.

In this context, automation through conversational agents—or chatbots—is no longer a luxury or a technological curiosity, but a concrete and operational solution. Responding in real time to employees’ most common needs—vacation requests, internal regulations, teleworking policy, meal vouchers, training, health insurance, expense reports, etc.—is part of what chatbots do. These questions, which are often simple but numerous, represent an invisible but very real burden for HR teams.

A well-configured chatbot is capable of automatically responding to more than 70% of level 1 HR requests within the first few weeks of deployment². These virtual assistants are available 24/7, on mobile and desktop devices, via the usual communication channels (Teams, Slack, intranet, etc.), making it easier for all employees to access information, including those working irregular hours, on the move, or at sites without internet access. In fact, 64% of field employees prefer to use a virtual assistant rather than a traditional HR portal³.

The benefits are immediate: employees find answers faster, HR saves time and can refocus on people, talent management, and quality of life at work. Like any technological solution, the effectiveness of chatbots also depends on how they are implemented: if they are poorly populated or not promoted internally, their impact will be limited.

A strategic role in a rapidly changing environment

By 2025, HR chatbots will no longer be simple automation tools. They will become partners in organizational transformation. Thanks to advances in AI, particularly generative AI, they will no longer be limited to answering closed questions: they will be able to rephrase vague requests, understand intentions, suggest additional content, and even guide employees through administrative procedures.

Their ability to centralize and redistribute HR information also makes them valuable allies for internal communication. In a multi-site group or a company with partial teleworking, HR chatbots can ensure consistency and uniformity of messages, while personalizing the experience according to the user’s profile. 65% of HR directors believe that artificial intelligence will play a decisive role in transforming their function within the next two years⁴. At the same time, 77% of HR professionals cite time savings and smoother communication as the main advantages of conversational agents.⁵

These tools are increasingly integrated into existing HR ecosystems: interconnected with HRIS, intranets, knowledge bases, and training platforms, they are becoming “single interfaces” between employees and the various departments of the company. In this way, they promote more efficient, fluid, and inclusive HR management.

And candidates are not left out. During the recruitment phase, some chatbots already assist applicants by answering frequently asked questions, simulating interviews, and helping them write their resumes or cover letters. In 2024, 58% of applicants said they were in favor of using a virtual assistant in the application process, provided that the company was transparent about its use of artificial intelligence. This requirement will become a legal obligation by 2026, with the entry into force of European regulations on AI. However, humans remain essential: 51% of applicants want to be able to interact with a recruiter throughout the process, reminding us that efficiency depends on the right balance between automation and human contact.⁷

Chatbots: facilitators rather than substitutes

It would be simplistic to view HR chatbots as a replacement tool. They do not eliminate the HR function, but rather relieve it of repetitive tasks. They do not dehumanize interactions, but rather improve their fluidity. In reality, chatbots are a lever for engagement and simplification at a time when employees demand immediate, accessible, and personalized responses.

By 2025, deploying an HR chatbot will no longer be a technological gamble, but a genuine strategic choice in favor of agility, inclusion, and trust. In a world of work that is constantly reinventing itself, this is precisely what HR needs to stay aligned with employee expectations and the challenges of tomorrow.

Because beyond the tool itself, it is the promise of fluid and responsive dialogue between the company and its employees that is emerging. A dialogue where technology, far from replacing humans, supports, amplifies, and liberates them. A well-designed and well-integrated HR chatbot is therefore not a gadget: it is a new interface for social connection within the company.

Sources:

• 1: According to the 2025 HR Barometer
• 2: According to Emerton Data (2024)
• 3: According to Gartner (2023)
• 4: According to PwC France “Augmented HR” (2024)
• 5: According to myRHline (2024)
• 6: According to Workable
• 7: According to Workable

Generative AI models have transformed our relationship with digital technology, but at what cost? As companies seek to exploit their full potential, several major challenges are emerging: growing energy consumption, costly infrastructure, and the reliability of responses. The environmental impact of these technologies is becoming a central concern, especially as AI’s energy consumption could increase four- to ninefold by 2050.

Faced with these challenges, a new generation of models is emerging: Small Language Models (SLMs) and Medium Language Models (MLMs). More compact, more specialized, and less energy-intensive, they offer a more efficient and sustainable alternative that is better suited to the specific needs of businesses and individuals.

Smaller models, bigger impact: the efficiency of SLMs and MLMs

Small and Medium Language Models (SLMs and MLMs) stand out for their ability to respond in a targeted manner to user needs, while remaining much lighter than large models. Unlike LLMs, which require massive infrastructure for training and operation, they are designed to be lighter while maintaining high performance. An SLM has fewer than 30 billion parameters, such as Microsoft’s Phi-3, which has 3.8 billion. In contrast, an LLM such as GPT-4o is based on a much more massive architecture, with 1.8 trillion parameters. Thanks to their optimized design, SLMs and MLMs can perform complex tasks, such as text generation and semantic analysis, with significantly fewer resources.

This smaller size also allows for greater customization, giving companies the ability to tailor models to their specific needs. For example, Meditron, developed by Meta, assists healthcare professionals in clinical decision-making. Florence2 specializes in image recognition, while Spreadsheet is designed for data processing in spreadsheets. These specialized models are not only better suited to business needs, but also more accessible, allowing organizations to leverage AI without requiring massive investments in expensive infrastructure.

In addition, SLMs and MLMs benefit from increased flexibility thanks to multimodal models, which are capable of processing text, images, and sound simultaneously. This paves the way for richer applications. For example, in medical image analysis, these models can not only process visuals, but also generate textual descriptions to provide a more comprehensive diagnosis. Applications such as GPT-4o, which integrate audio capabilities, enable the development of innovative solutions for areas such as security and advanced voice assistance.

The ecological footprint of AI: towards more sustainable models

One of the great promises of Small and Medium Language Models is their ability to address environmental concerns related to AI. Due to their size and constant need for powerful computing, LLMs have a significant ecological footprint. Their training requires not only massive servers but also colossal energy consumption. For example, training large-scale models such as GPT-3 requires approximately 1,287 megawatt hours (MWh) of electricity, equivalent to the annual consumption of 120 US households. According to forecasts, global demand for AI could lead to the withdrawal of 4.2 to 6.6 billion cubic meters of water by 2027, an amount equivalent to 4 to 6 times Denmark’s annual consumption.

By adopting smaller models, companies can not only reduce their costs, but also lessen their impact on the environment. For example, companies that choose to use more specialized models for specific tasks can minimize the use of servers and therefore the energy required to run them. An SLM can run on a local computer without an internet connection, provided it has a GPU (Graphics Processing Unit) chip. For example, Microsoft’s Phi-3 can run on a MacBook M3, while a more massive model such as GPT requires hundreds of GPUs to operate. This approach contributes to a more responsible use of computing resources and is part of a general trend toward sustainability in the technology sector.

Trends to watch in 2025: between promise and challenge

Artificial intelligence is entering a phase of maturation where efficiency and specialization are taking precedence over the race for ever larger models. Thanks to specialized, compact, and multimodal models, AI is becoming more accessible, powerful, and adaptable, addressing the challenges of personalization, sustainability, and optimization for businesses.

However, its adoption remains hampered by human, technical, environmental, and legal constraints. Despite significant advances in understanding and processing context, AI remains limited by the quality of the data on which it is trained. Biased, incomplete, or obsolete data sets can lead to errors and compromise its reliability. Furthermore, the more complex a context is or the more specialized knowledge it requires, the more difficult it becomes for AI to provide accurate answers. In addition, it still struggles to interpret certain subtleties such as humor, irony, or specific cultural references.

To date, existing models do not yet allow for a transition to general artificial intelligence. It is therefore essential to move forward with caution and discernment. AI should be seen as a tool for assistance and optimization, rather than a substitute for human intelligence. The future of AI lies in a balanced collaboration between humans and technology, where intelligent and user-friendly tools, tailored to users’ needs, will become powerful levers for supporting innovation and business performance.

1- Source: Deloitte study, October 2024

2- Source: Sciences et Avenir No. 935, dated January 2025.

3- Source: Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models, October 29, 2023

In France, call centers occupy a central place in customer relations, with around 3,500 sites spread throughout the country. True showcases for the company, they make it possible to respond to customer requests. In 2023, 78% of French people had contacted customer service at least once, and 55% had done so by telephone, which remains the preferred channel for this type of request¹.

However, the average wait often exceeds their expectations. 45% of customers have a negative memory of their interaction with customer service², and 75.5% say they have switched brands or companies after a bad experience. In this context, while dissatisfaction is mainly due to response times, how can call center practices be adjusted to achieve the long-awaited zero wait time?

Instantaneity and personalization: polarized customer expectations

In a world where everything is accelerating, consumers demand customer service that combines speed and personalization. For 96.5% of them, getting an immediate response is a priority. However, the reality of waiting times remains a major source of frustration: 80% of customers wait more than six minutes before they are taken care of³, while 60% would like assistance in less than a minute⁴. This gap between expectations and reality is widening in the face of increasing call flows and the growing complexity of the cases to be handled. The advisors often struggle to answer all the calls, with an average of 2.8 attempts needed to get an answer⁵.

This tension between the demand for immediacy and the need for a human connection based on empathy and expertise puts the call centers and their teams under pressure. Customer tolerance for waiting is limited: a third of them start to get annoyed after 20 minutes of waiting, and 64% say they would no longer contact the company if the wait exceeded 30 minutes. Poor expectation management can quickly result in a loss of loyalty and a deterioration of the brand image.

To meet these high standards, companies must rethink their approach by integrating tools and strategies that not only reduce waiting times, but also preserve the quality and personalization of interactions.

Zero waiting time: when technology transforms the customer experience

The integration of new technologies is transforming the customer experience. Connected to CRM (Customer Relationship Management) tools, the implementation of chatbots has already made it possible to reduce some of the telephone traffic and provide personalized responses. However, the voice part remains sub-optimized in many companies. Despite the use of Interactive Voice Servers (IVS) for several years, call centers often remain overloaded, resulting in long waiting times, frequent failures and growing frustration among customers.

The integration of callbots in call centers is a real opportunity to improve operational efficiency and customer satisfaction. Available by telephone 24/7, they handle customer calls automatically, in the context of fluid interactions in natural language. They are able to automate certain processes 100% and perform simple first-level tasks such as checking bank overdrafts, providing store addresses and opening hours, etc. They can pre-qualify customer requests, handle calls outside business hours, and make outgoing calls (callbacks, appointment scheduling). By drastically reducing wait times, these tools enable an average productivity gain estimated at 2 hours 20 minutes per day for processing requests.

Companies that adopt these technologies see positive effects: a 20% increase in customer satisfaction⁷, as well as a 40% reduction in the time spent on repetitive tasks⁸. This automation also benefits advisors, whose role is upgraded, strengthening their professional commitment. Freed from these tedious tasks, they can focus on more complex cases that require listening, empathy and personalization, thus improving the customer experience and satisfaction. 60% of employees say they feel more involved in their work when automation frees them from monotonous tasks⁹.

This hybrid model, combining new technologies, artificial intelligence and human intervention, not only relieves the pressure on call centers, but also absorbs requests outside opening hours. It offers a complementary approach where advisors can focus on strategic and rewarding interactions, and free up time for high value-added tasks.

The global customer service market, valued at $16.91 billion in 2023, is expected to grow steadily to $52.54 billion by 2030. With this in mind, call centers need to be transformed to cope with the increase in incoming calls and growing consumer expectations. These centers must evolve gradually to offer a more fluid, fast and customized customer experience.

Sources:

1. Customer Services Observatory of the Year / BVA – 2023
2. Kiamo/Odoxa Study 2023
3. “Vite ma Hotline” Study, En-contact.com
4. Customer Services Observatory of the Year / BVA – 2023
5. Customer Services Observatory of the Year / BVA – 2023
6. An IVR, or interactive voice response, is a computer system that handles incoming calls using recorded voice messages or voice synthesis.
7. Gartner
8. McKinsey study
9. Deloitte

The tourism industry has entered a new era thanks to technological advances, particularly with the integration of generative artificial intelligence. These AI-powered tools are transforming travel experiences and meeting the increasingly demanding expectations of tourists, before, during and after their stays. Digitization has become indispensable for all players in the sector, whatever their field of activity or size. From finding hotel reviews to the best itineraries and activities, to comparing rates, artificial intelligence is involved at every stage of the customer journey. 73% of travelers say they use AI tools for travel-related tasks, a proportion that rises to 87% among 18-24 year olds. Today, it benefits travel agencies and hotel groups looking to improve their targeting. Optimizing operations, forecasting demand, filling: AI accompanies companies on all fronts. It can even help them with predictive analysis, anticipating both the risk of cancellation and market trends. While AI does not generate demand, it is necessary to seize opportunities when they arise. In this context, what are travelers’ expectations?

A frictionless experience and flexible booking management

Organizing a trip can be a time-consuming and complex task, especially as 60% of French people don’t have a specific destination in mind when they start booking their trip. For inspiration, they spend time online, on blogs, social networks, airline websites or watching advertisements. To encourage them to take the plunge, travelers want a seamless experience on online sites, from booking to check-in to itinerary management. The integration of artificial intelligence simplifies this process, offering intuitive navigation and automating numerous tasks such as online check-in or travel document management. In their quest for flexibility when planning their vacations, 41% of travellers want to be able to cancel their reservation free of charge, and 40% want to be able to modify it free of charge. Thanks to advanced technologies such as chatbots and AI systems, capable of handling these requests quickly and efficiently, e-tourism players need to offer flexible booking policies to build trust and customer loyalty in an increasingly competitive market.

Real-time information

Reactivity is a key expectation of travelers. 54% of French people want to be able to book their vacations at the last minute, leading to peaks in connections, bookings and conversations with advisors as the vacations approach. To meet these fluctuations, travel companies need to find new solutions. The integration of AI and, more specifically, chatbots and callbots on websites makes it possible to classify requests, handle those that don’t require the intervention of an advisor, and make others wait for a connection. In the tourism sector, a virtual assistant can respond 24/7 to a variety of requests, such as general information and logistics (check-in, services, rates and conditions, loyalty programs), reservations and purchases (bookings, modifications or cancellations of accommodation, car rentals, plane or train tickets), after-sales service (follow-up information, reservation modifications or cancellations, refund requests), as well as opinions and feedback (satisfaction surveys, suggestions). This permanent availability reassures passengers and enhances their overall experience.

Increased personalization for memorable experiences

Today’s travelers are no longer simply looking for exotic destinations or luxurious hotels. When customers enjoy a personalized experience, they are 44% more likely to become repeat buyers. They look for vacations that provide unique and unforgettable experiences, and prioritize activities and accommodations that allow them to discover local culture, gastronomy or traditions tailored to their specific preferences and needs.

Bots, thanks to data analysis and artificial intelligence, are now able to offer tailor-made itineraries, restaurant recommendations based on food tastes, and local activities that match travelers’ interests. This ability to respond individually to travelers’ expectations not only enhances their experience, but also boosts loyalty.

This summer, travelers expect tourism companies to take full advantage of artificial intelligence to deliver a seamless, personalized and secure travel experience. 41% of French people say they are ready to chat with a chatbot rather than an agent to get recommendations, organize their trip or suggest contingency plans in case of the unexpected. This proportion rises to 58% among 25-34 year-olds and 62% among 18-24 year-olds, demonstrating the enthusiasm of the new generations for these technologies. The integration of these technologies reduces friction during the booking process, cuts the time needed to complete a transaction and improves the overall user experience, making the booking process more pleasant and efficient. Players in the tourism sector who are able to meet these expectations will benefit from a clear competitive advantage, helping to redefine travel standards.

To learn more, discover our bots now through a demo!

1 – Source: Edreams Odigeo
2 – Source: Expedia platform study conducted with Luth Research
3 – Source: Travel Predictions 2022, booking
4 – Source: national federation of institutional tourism organizations
5 – Source: Econsultancy
6 – Source: Zendesk and Yougov study

The human relationship between patient and caregiver ensures well-being and trust in the healthcare system. With the advent of new technologies in the medical landscape, notably Artificial Intelligence (AI), this relationship is gradually being transformed, giving rise to a duality. On the one hand, there is the hope that the integration of AI will free up time for healthcare professionals, thereby strengthening patient support, and on the other, the fear of dehumanizing care. With the launch of the “Digital Health” scheme in 2021, an initiative promoting the emergence of innovative digital health solutions, and the growing adoption of AI by caregivers (54% in 2024)_⁽¹), France aims to position itself as a world leader in e-health. Against this backdrop, how can we ensure that AI serves the interests of both professionals and patients, while preserving the quality of the patient-caregiver relationship?

Integrating artificial intelligence into the healthcare sector

AI offers numerous opportunities for medical teams and care centers. Despite concerns about the possible dehumanization of care, statistics show significant adoption of AI by healthcare professionals. Among its most widespread uses, it facilitates access to medical information (46%), offers specialized training to healthcare professionals (37%) and contributes to the prescription of appropriate treatments (28%)._⁽²)

Its growing integration into medical practices and hospital systems, thanks to technologies such as conversational agents and health monitoring applications, means that patients benefit from access to comprehensive medical information, as well as faster advice. According to Salesforce, 86% of customers prefer to get a response from a chatbot rather than fill in a form on a website, a preference also observed in the healthcare sector. Chatbots offer a personal and anonymous alternative for interacting with healthcare services.

Automate administrative tasks to optimize patient care.

Chatbots are particularly effective in automating and simplifying administrative formalities, as well as providing access to medical information. Thanks to their ability to handle multiple requests simultaneously, bots reduce waiting times, improving the patient experience and boosting satisfaction rates. Accessible 24/7, they offer an immediate, automated response to recurring requests, such as booking appointments, opening hours, issuing medical documents or administrative procedures. It’s also a way of redirecting patients to the right specialty, providing preventive care, and offering initial advice on a wide range of subjects, such as smoking cessation, stress management or healthy eating.

These benefits are not limited to patients, but also benefit healthcare professionals. By considerably reducing the time spent on administrative tasks, and freeing them from these repetitive responsibilities, chatbots enable caregivers to devote more time to listening to and accompanying patients, thus reinforcing the human relationship at the heart of care. In fact, 71% of them would like AI to help them manage administrative tasks⁽³⁾.

However, adopting this technology is not without its challenges and risks. Chatbots are not designed to make diagnoses, nor are they intended to replace healthcare professionals. On a technical level, healthcare chatbots need to be rigorously designed to ensure the accuracy and reliability of the information provided. In addition, there are concerns about data security and the confidentiality of patient information, requiring special attention to ensure transparency and user trust.

The need to protect medical confidentiality

Ensuring the protection of medical secrecy and its ethical use is essential if AI is to benefit both professionals and patients. At present, the deterioration of the doctor-patient relationship is one of the main concerns of 49% of doctors. Familiarization with AI is necessary for its effective use, while making them aware of the associated ethical, legal and social issues. Furthermore, it is vital to ensure that these AIs are used responsibly, by establishing clear standards and regulations to protect patient privacy and guarantee the transparency, reliability and confidentiality of the systems used. To this end, HDS (Health Data Hosting) certification has been introduced to reinforce the protection of personal health data and build a climate of trust around e-health and patient monitoring.

Rather than fearing the dehumanization of medicine, AI should be seen as essential tools for tackling the major challenges facing the healthcare sector. Medical chatbots are a concrete example of the positive impact of artificial intelligence in the medical field. By relieving professionals of time-consuming and repetitive tasks linked to administrative management, medical chatbots complement traditional services, enabling caregivers to focus on their primary mission: offering personalized, quality care to every patient.

Source : AI in healthcare: ally or threat to healthcare professionals? PulseLife (ex 360 medics / Interaction Healthcare) – January 2024

Contributed by Samir Dilmi, Chief Revenue Officer at Dydu

To meet this recurring demand for information, the automation of the most frequently asked questions is gradually becoming the most appropriate response for human resources departments. Synonymous with more efficient management, enhanced business value and an improved employee experience, the use of conversational robots is emerging as a solution to relieve departmental bottlenecks.

More globally, Artificial Intelligence is revolutionizing the HR field. 60% of business leaders plan to enhance their HR departments with more AI over the next 5 years (1). By the end of the year, 69% of routine managerial tasks should already be fully automated (2). With change already underway, how can we continue to maximize the effectiveness of AI to strengthen support for HR departments and simplify recruitment processes?

Conversational robots: the fully-fledged virtual assistant for HR teams

AI and bots are revolutionizing HR processes by automating repetitive and administrative tasks. Leave, health insurance, catering, training, professional expenses, healthcare, teleworking, retirement, employee savings, company life – they support employees from the moment they join the company until they leave. Accessible at all times, on all devices (computers, mobiles or tablets) and via numerous channels (websites, applications, messaging, etc.), they facilitate the employee experience. However, these technologies are not limited to simple optimization. They play an important role in internal communication by directing employees to the appropriate resources, through the creation of virtual collaborators via Teams, for example. Chatbots ensure the effective dissemination of key messages within organizations, particularly those with multiple sites, several business families, or full-remote companies (3). It helps maintain overall harmony in corporate communications, standardizing the company’s discourse and ensuring consistency in the information transmitted to all staff.

Interconnected with the HRIS and the company’s intranet, the bot gives employees easy access to their personal data, to information linked to the convention and framework agreements, and to forms (badge request, canteen registration, etc.). Together, they become facilitators in the promotion of corporate culture.

Recruitment 3.0: the impact of Large Language Models in the talent acquisition process

At least 79% of companies are already using some form of AI or automation in their recruitment and hiring process (4) to make it more automated and interactive. By rapidly analyzing vast volumes of data and CVs to identify the best-qualified candidates, it optimizes and accelerates pre-selection. It predicts candidates’ compatibility with positions, conducts virtual interviews and assesses relevant skills, through the use of algorithms. Employers who have automated parts of their talent acquisition and management processes are very satisfied with the results. Some 2 out of 3 HR professionals report improved recruitment times for vacant positions (5) and resource savings thanks to these technological tools.

Conversational robots offer candidates a more fluid, confidential, accessible and personalized experience throughout the recruitment process. This improves communication with potential employers and understanding of company expectations. In 2023, 58% of candidates said they were happy to interact with recruitment bots during the early stages of the application process (6). They are able to provide information on vacancies, processes and company policies at any time of day, thanks to 24/7 availability. They answer the most frequently asked questions, and personalize advice according to experience and skills, enhancing the overall experience and building trust. Some are even designed to help candidates prepare for interviews by providing sample questions.

The human resources and recruitment market is constantly evolving, requiring employees to adapt. The rapid development of AI and conversational agents in this field reflects a profound transformation in the way companies manage human relations. They bring intelligent automation, increased personalization and operational efficiency, freeing up time for strategic activities and enhancing the overall employee experience. While current figures and projections paint a promising picture, it’s essential to remain aware of the ethical challenges and ensure harmonious collaboration between artificial intelligence, chatbots and professionals to guarantee a balanced and innovative future of work.

If you’d like to find out more, check out our article on the various steps involved in setting up an HR chatbot!

Contributed by Samir Dilmi, Chief Revenue Officer at Dydu

To meet this recurring demand for information, the automation of the most frequently asked questions is gradually becoming the most appropriate response for human resources departments. Synonymous with more efficient management, enhanced business value and improved employee experience, the introduction of conversational robots is emerging as a solution to relieve departmental bottlenecks.

More generally, Artificial Intelligence is revolutionizing the HR field. 60% of business leaders plan to enhance their HR departments with more AI over the next 5 years (1). By the end of the year, 69% of routine managerial tasks should already be fully automated (2). With change already underway, how can we continue to maximize the effectiveness of AI to strengthen HR department support and simplify recruitment processes?

Conversational bots: the fully-fledged virtual assistant for HR teams

AI and bots are revolutionizing HR processes by automating repetitive and administrative tasks. Leave, health insurance, catering, training, professional expenses, health, teleworking, retirement, employee savings, company life – they support employees from the moment they join the company until they leave. Accessible at all times, on all devices (computers, mobiles or tablets) and via numerous channels (websites, applications, messaging, etc.), they facilitate the employee experience. However, these technologies are not limited to simple optimization. They play an important role in internal communication by directing employees to the appropriate resources, through the creation of virtual collaborators via Teams, for example. Chatbots ensure the effective dissemination of key messages within organizations, particularly those with multiple sites, several business families, or full-remote companies (3). It helps maintain overall harmony in corporate communications, standardizing the company’s discourse and ensuring consistency in the information transmitted to all staff.

Interconnected with the company’s HRIS and intranet, the bot gives employees easy access to their personal data, information on collective bargaining agreements and framework agreements, as well as access to forms (badge request, canteen registration, etc.). Together, they become facilitators in the promotion of corporate culture.

Recruitment 3.0: the impact of Wide Language Models in the talent acquisition process

At least 79% of companies already use some form of AI or automation in their recruitment and hiring process (4) to automate it and make it interactive. By rapidly analyzing vast volumes of data and CVs to identify the best-qualified candidates, it optimizes and accelerates pre-selection. It predicts candidates’ compatibility with positions, conducts virtual interviews and assesses relevant skills, through the use of algorithms. Employers who have automated parts of their talent acquisition and management processes are very satisfied with the results. Around 2 out of 3 HR professionals report improved recruitment times for vacant positions (5) and resource savings thanks to these technological tools.

Conversational bots offer candidates a smoother, more confidential, accessible and personalized experience throughout the recruitment process. This improves communication with potential employers and understanding of company expectations. In 2023, 58% of candidates said they were happy to interact with recruitment bots during the early stages of the application process (6). They are able to provide information on vacancies, processes and company policies at any time of day, thanks to 24/7 availability. They answer the most frequently asked questions, and personalize advice according to experience and skills, enhancing the overall experience and building confidence. Some are even designed to help candidates prepare for interviews by providing sample questions.

The human resources and recruitment market is constantly evolving, requiring employees to adapt. The rapid development of AI and conversational agents in this field reflects a profound transformation in the way companies manage human relationships. They bring intelligent automation, increased personalization and operational efficiency, freeing up time for strategic activities and enhancing the overall employee experience. While current figures and projections paint a promising picture, remaining aware of the ethical challenges and ensuring harmonious collaboration between artificial intelligence, chatbots and professionals is essential to ensure a balanced and innovative future of work.

If you’d like to find out more, check out our article on the various steps involved in setting up an HR chatbot!

1 : According to SHRM (Society for Human Resource Management)
2 : According to Gartner
3 : Full remote: telecommuting for all employees
4: According to SHRM (Society for Human Resource Management)
5 : According to SHRM
6: According to Allegis

As generative AI opens up new possibilities in the development of conversational tools, a return to grace for chatbots is yet to come.

Interview given by Samir Dilmi, Chief Revenue Officer at Dydu, for Siècle Digital.

Ah… Chatbots, those services behind which users were promised to converse with some form of intelligence to ask it what they wanted at any time, any day, with a response close to that of a human. What a disappointment this was for millions of Internet users around the world, who quickly lost interest! Will the emergence of ChatGPT and, more broadly, large language models (LLMs) bring brands and consumers back into contact with chatbots?

It’s 2016, and chatbots are invading the internet in leaps and bounds. They can be found on many websites, and especially in Facebook’s Messenger application, which has given them a prominent place. After-sales service, recruitment, human resources, fast food, banking, press, university… In a period when “digital transformation” is hammered into every meeting, chatbots present themselves as a godsend to show that we know how to innovate.

“ Most chatbots have been rather disappointing for end-users, and costly for companies, ” recalls Ghislain de Pierrefeu, Partner Artificial Intelligence and Data at Wavestone. Although some had already put natural language processing into practice, enabling agents to understand multiple intentions, they proved limited in their responses. For those brands who persevered, with the promise of decongesting their customer relations centers, the price was high. They had to mobilize resources to train their chatbots, to provide them with quality and quantity data so that they could correctly read a user’s requests, understand and use specialized vocabulary… ” The players have never managed to process the chain all the way to the end to fetch and integrate personal data. (…)

There was a phenomenon of disaffection. People got fed up and preferred to call an advisor or send an e-mail, ” he adds.

An observation shared by Samir Dilmi, Chief Revenue Officer at Dydu, a French company that pioneered the creation of chatbots. ” This big boom was followed by a big letdown. Many companies jumped on a business challenge without taking care of the product, and without always understanding that it’s not a replacement solution, but a helping solution. “

Faced with a notable lack of interest from the general public, companies have turned to in-house applications, notably in IT support, human resources or sales assistance. “This is where the first HR chatbots began to appear, ” continues Samir Dilmi. In bank branches, advisors were able to access interfaces providing information on all products, and cross-reference them with other data such as account status.

The development of these uses has led to advances in natural language processing (NLP): “today, we’re able to understand a context, a question, and go and find the answer in the right place “. Even if the risk of in-house use is lower, brands have learned to devote time to training their chatbots. However, there are still shortcomings, such as the use of sector-specific vocabulary (banking, energy, healthcare…), or writing at a level of fluency close to human. These shortcomings could eventually be remedied by the integration of LLMs, which would bring additional benefits.

While the first major language models appeared in 2018, it was not until four years later that we discovered the extent of their capabilities, notably through generative AI. For businesses, OpenAI’s GPT, Meta’s LLaMA, or Google’s Bard offer skills that are both ergonomic, because you can ask them anything you like, and you’ll get an answer; and linguistic, because you have to admit they express themselves in near-perfect French.

An obvious advantage of using LLM is the time it takes to train your chatbot. ” The learning curve has been completely overhauled, ” says Samir Dilmi, who draws a parallel with a supervisory method in which a human validates responses, which is still widely used. ” We can retrieve service archives, study conversations, and train the robot before it’s even launched. We save precious time in setting up the bot and absorbing knowledge “.

But be careful not to leave everything in the hands of an LLM, to avoid a well-known but inexplicable phenomenon: hallucinations. These all-purpose AIs can invent answers, and for brands this represents an obvious risk. We should therefore expect to see LLM exploited primarily for simple needs, such as comparing products, creating cooking recipes… Rather than for confirming international insurance cover, or monitoring medical treatment. ” Before, we could be disappointed by a chatbot’s inability to provide an answer, today we could be disappointed by a wrong answer, ” analyzes Ghislain de Pierrefeu.

Castorama lent itself to the game with the launch of a chatbot on its site in November:

Hello Casto. The aim is to ” compensate online for what an in-store expert offers our customers, ” explains Romain Roulleau, Castorama’s Marketing and Digital Director. It operates in a closed environment, in an orchestrator called Athena designed at group level to develop artificial intelligence projects. It deals with issues relating to security, the relevance of responses, and the moderation of exchanges. Hello Casto is based on the three main LLMs on the market, Bard, GPT and Claude (Anthropic), who intervene according to the question and context. Despite encouraging feedback, the company is cautious. ” We make corrections several times a day. (.) We know that hallucinations do exist, and that’s why Athéna is here, ” adds Romain Roulleau.

As chatbots mature and technological solutions become available, a return to favor is definitely on the cards.

Artificial Intelligence, popularized by ChatGPT, is playing a growing role in many fields, and the market is expected to reach $1,300 billion by 2032. AI uses models such as GPT-4 to generate written, artistic, and musical content, as well as computer code.

In 2023, 72% of Internet users used generative AI to search for a product. It has also become a valuable resource for content creation and is revolutionizing how companies interact with their customers, manage operations, and create value. But how can we control the content produced and ensure it is accurate? Or measure its environmental impact? What major challenges can we expect in the coming months?

Generative AI: an essential partner in the modern world of work 

Virtual assistants and AI have revolutionized our ways of working and become essential partners in boosting efficiency and productivity. They simplify tasks and produce rich, varied content (images, videos, texts, translations). Companies are adopting conversational agents, or chatbots, on a massive scale to provide quick and accurate answers, reduce wait times, and increase customer satisfaction.

However, it’s important to remember that these algorithms can only create from what already exists. Employee expertise is essential to enrich information databases and create emotions. AI should be able to adapt to changing consumer preferences in real-time. Companies must, therefore, implement systems that learn and evolve with customer needs, which requires greater agility and responsiveness.

The real potential of these new technologies lies in human-machine collaboration. This synergy will be key in future years. However, it will only open the door to new professional opportunities with continuous employee training and constant adaptation.

New 2024 challenges to harness the power of this technological revolution

The rise of AI raises several financial, environmental, regulatory, and security issues. New artificial intelligence systems require a responsible framework to regulate the sector and prevent misuse.

The security of AI systems is a major concern. Chatbots can be vulnerable to adversarial attacks, and model errors can lead to the dissemination of false information. In 2023, 4 out of 10 consumers cited data privacy as the main reason preventing them from using AI for online shopping advice, followed by concerns about the reliability and quality of the answers. The only way to overcome this challenge and guarantee accurate information is with cybersecurity research and robust models.
There are also financial challenges. Large Language Models generate significant costs, requiring the creation of new business models. Investment in research and development and the acquisition of advanced technology is imperative. Companies must consider the cost of data storage and management, customized products and services, maintenance, updates, ethics, and compliance to develop effective, sustainable strategies in this new, constantly evolving economic landscape.

Greenhouse gas emissions (server powering), creation and management of electronic waste, water consumption, hardware replacement… Companies looking to use these systems face many challenges. AI models, in particular deep neural networks, require massive computing capacity. The servers and data centers powering these systems consume a great deal of energy, significantly contributing to a company’s carbon footprint. In an article that should be published later this year, Professor Ren’s team estimates that ChatGPT ‘drinks’ 500 milliliters of water, the equivalent of a small 50cl bottle of water, for every 5 to 50 questions asked.

In 2024, the challenge will be a more in-depth and sustainable integration of these technologies into businesses’ day-to-day operations. This will boost efficiency while limiting misuse. Globally, only 12% of companies are capitalizing on AI to improve their performance, and they are growing up to 50% faster than competitors. Adapting to these new technologies and overcoming the subsequent issues will enable companies to offer tailored experiences, as well as optimize their operations, and guarantee data protection. Awareness, innovation, regulation, and data measurement and analysis will be key to overcoming these challenges.

In the constantly evolving digital age, customer relations are being redefined through the dynamic integration of chatbots. These intelligent conversational agents, powered by artificial intelligence, are transforming customer relationship management. Join us as we explore the significant role chatbots are playing in this changing landscape, as well as the benefits, evolutions, and prospects of this digital revolution.

Using Chatbots to Boost Customer Relations

The Evolving Customer Relationship in the Age of Digital Transformation

Digital transformation has reshaped the way companies interact with their customers, opening up a wide range of possibilities. Customer relations chatbots  have become key assets to support this change. From simple information searches to order tracking, claim management, or after-sales issues, chatbots provide instant 24/7 support on websites and mobile apps. Their constant availability is perfectly in line with the age of instant gratification, meeting customers’ expectations for immediate and personalized answers about products and services. 

Companies can identify the most frequently asked questions and delegate them to their chatbot, enabling customers and prospects to get quick answers that meet their needs. Chatbots can also be interconnected with internal software such as CRM, to create a fully personalized experience. What’s more, integration with livechat technology enables to hand-off to a human operator if necessary, while retaining the dialog history. Phone (callbots) or voice (voicebots) versions enhance accessibility, ensuring an optimal customer experience. 

The Central Role of Chatbots in Customer Satisfaction

Chatbots play a key role in meeting new customer expectations. An estimated 67%* of consumers in the world have interacted with a chatbot over the last 12 months. Chatbots provide instant answers by using customers’ favorite communication channels, such as instant messaging systems. Artificial intelligence also enables chatbots to understand customers’ natural language, analyze their needs, and provide personalized responses. 

This kind of customization has a significant impact. It makes customers feel understood and cared for as individuals, boosting satisfaction. Chatbots are precious allies in the quest for more efficient customer relationship management. They reduce customer waiting times, improve the quality of responses, and optimize the handling of requests. Adopting a chatbot helps build a smoother customer journey, improves satisfaction, and fosters loyalty. Chatbots are particularly well suited to sectors such as e-commerce or banking and insurance, among others. These tools have become a must-have for companies seeking to optimize their customer relationship management.

The Benefits of Chatbots for Businesses and Customers

Benefits for Companies

Chatbots offer customers a wide range of benefits that enhance their experience. But what exactly are these benefits?

• Increased responsiveness: chatbots are available 24/7 and respond instantly to customer questions, eliminating waiting times.
• Personalization: API connections and CRM tools enable chatbots to provide tailored answers, integrating specific data. This combination creates more personalized interactions, for a one-to-one customer experience.
• Easy to use: chatbots use natural language which simplifies interactions.

Benefits for Companies

Chatbots also offer companies significant benefits:

• Task automation: chatbots automate repetitive tasks, freeing up employee time.
• Cost reduction: by automating frequent requests, companies can reduce operating costs
• Improved productivity: chatbots help human agents by providing contextual information, making them more efficient.

A Combined Future for Chatbots and Customer Relations?

The Evolution of Chatbots Towards More Human and Personalized Interactions

Chatbots are constantly evolving to offer ever more human and personalized interactions. The integration of artificial intelligence into these systems enables a deeper understanding of natural language, providing more authentic exchanges with customers. Some chatbots can even detect emotional nuances in conversations and adapt their responses to the customer’s emotional state. The aim is to create interactions where customers feel genuinely cared for in an empathetic way, thus strengthening customer relationship management.

Integrating Chatbots with Other Communication Channels and Advances in Artificial Intelligence 

The future of chatbots lies in their ability to integrate seamlessly with other communication channels. For example, a customer can start a conversation with a chatbot on a website, then continue their discussion on a messaging app. Advances in natural language understanding, personalized recommendations, and the anticipation of customer needs enable chatbots to manage the customer relationship more effectively. This combination of seamless integration and continuous improvement in artificial intelligence makes chatbots a key part of the customer relationship. 

To find out more about how chatbots and artificial intelligence are revolutionizing the customer relationship, download Kiamo’s complete white paper, where you’ll also find an interview with Samir Dilmi, Chief Revenue Officer at Dydu, as he shares key insights on this topic. 

¹Chatbot recruitment: the benefits of conversing with applicants 

Since the arrival of ChatGPT by OpenAI at the end of 2022, advances in AI have brought language models to the fore and accelerated their development. Large Language Models (LLM), through the use of Deep Learning, have radically transformed the way companies tackle language-related problems and offer a wide range of benefits. How do these generative artificial intelligence tools help companies modernize and improve their performance ?

Facilitating In-house Processing and Analysis of Textual Data

Companies today are undergoing a digital transformation and are looking for the best in terms of efficiency and performance. Businesses receive a constant flow of unstructured data from multiple sources such as emails, reports, customer reviews, or social media. Transforming all of these documents into knowledge bases is a particularly time-consuming task. One of the main strengths of Large Language Models lies in their ability to process, understand, and analyze large volumes of textual data. Large Language Models convert this data into actionable information, facilitating informed decision-making.

Automating Repetitive Tasks to Save Time and Energy

Large Language Models can be used to automate many repetitive language-related tasks, such as writing reports, classifying documents, responding to customer queries, and even translating text into foreign languages. By relieving employees of these time-consuming tasks, companies can optimize efficiency and focus resources on higher added-value activities.

Enhanced Customer Experience Through Better Understanding of the Human Language ?

Generative artificial intelligence processes huge volumes of data, providing more efficient analysis and task precision. The algorithm understands, synthesizes, and classifies information based on several parameters in order to provide the most accurate answers. By understanding the nuances of human language, it can analyze customer requests, needs, and feedback in detail, carefully identifying the services required and offering better customer service.

LLMs also enable companies to create or enhance chatbots and intelligent virtual assistants capable of delivering precise and personalized responses to customers. These systems provide 24/7 customer service which improves the customer experience and boosts overall consumer satisfaction.

Promoting Innovation and Creativity in the Workplace

Large Language Models are also powerful tools for encouraging innovation within companies. They can be used to drive new business approaches and even help with product design. They can spark inspiration and help employees be even more creative.

Fostering Strategic Decision-Making

In an ever-changing world, companies are faced with complex strategic decisions. Large Language Models provide in-depth analysis and predictive insights based on the analysis of large text datasets. This enables executives to make more informed decisions and better anticipate market trends.

Large Language Models are a strategic asset for businesses, offering them a better understanding of textual data, efficient automation, an improved customer experience, opportunities for innovation, and more informed decision-making. Companies that embrace these emerging technologies will be better equipped to succeed in a competitive and ever-changing business environment.

Feel free to reach out if you want to find out more about the integration of generative AI into our solution !

Over the last ten years, conversational agents (also known as bots) have gradually become a key part of the customer experience.

Automating customer interactions presents several benefits for companies: time savings, efficiency, cost optimization, etc.

Although self-care tools are particularly popular with consumers, the image of robots vs. humans persists in some people’s minds. 

At a time when brands need to connect with customers to build loyalty, how can they implement a winning strategy that leverages technology while maintaining a human touch?

Automated tools: the answer to changing consumer behaviour

Digital tools have brought about big changes in how customers consume and relate to brands.

The rise in multichannel platforms has led to a surge in incoming requests.

Consumer habits have also shifted, with people now shopping at any time of day and night, on public holidays, etc. 

As customer service budgets get smaller, companies must provide users with a 24/7 service that can assist them and provide immediate answers.

Customer service professionals admit that responding to all requests adequately is difficult. In fact, 63%* say that they struggle to strike a balance between speed and quality¹. 

72%* of consumers also prefer to solve problems themselves rather than speak directly to a salesperson or customer service rep².

With this in mind, automating the customer journey seems the best solution for satisfying users and customer service.

Technology and people: the ideal combo to ensure optimized customer service?

Contrary to popular belief, companies don’t implement bots to replace people but to add value for end-users and lighten the load for support teams.

Automating the processing of recurring requests, or pre-filtering them, saves advisors precious time, which they can use to reduce waiting times and personally handle complex requests.

Employees can then enjoy helping customers again while customers get the answers they need, without having to wait.

Bots, a bridge for businesses?

Conversational agents are designed to complement customer relations and take over from operators. They are available on various platforms, including social media, instant messaging or e-commerce sites.

Gone are the days of the little window at the bottom of your computer screen. Today, consumers interact with chatbots via Messenger or WhatsApp.

Combining artificial intelligence and human intervention enables customer services to provide a personalized and seamless experience.

This alliance boosts customer satisfaction and helps to handle incoming requests without overloading the support teams.

Let’s take an example. A car insurance bot can provide clear and quick answers to questions about policies or deals. 

But if a policyholder needs immediate assistance after an accident, the bot can connect them to an operator.

For a garage, an automated solution provides a contact point outside opening hours.

Customers can, therefore, make an appointment to have their car serviced or their windshield repaired at any time of day or night. In this case, the bot serves as a bridge for business.

The challenge is to strike the right balance between the automation of certain tasks and human support.

A solution equipped with natural language processing (NLP) will provide customers with a seamless experience by quickly understanding their requests, no matter the phrasing.

Finally, we highly recommend regularly collecting reviews and customer satisfaction scores to adapt to their expectations.

Natural language processing and a solution that can hand requests over to an operator when they become too complex are therefore key to automating the customer relationship without dehumanizing it.

Providing relevant answers in the shortest possible time will make customers feel valued and important.

¹ Salesforce: Focus on customer service 2020 https://www.salesforce.com/content/dam/web/fr_fr/www/PDF/guides/state-of-service-fr-2021.pdf

² According to a Forrester study

Since the Covid-19 health crisis, we’ve entered a new era of consumption, where more and more purchases are made online.
Consumer habits are changing, and customers are becoming increasingly demanding.
They want to consume better, everywhere, all the time.
Brands must be more mindful of the customer journey to increase satisfaction and their conversion rate.

Find out how a chatbot can improve your users’ experience.

E-Commerce Chatbots as Customer Relations Allies

A Seamless Customer Journey

• An e-commerce chatbot is available 24/7 to assist users throughout their buying journey
• It provides automatic, quick, relevant, and personalized answers
• It removes user barriers and buying blocks 
• It boosts sales 

Less Pressure on Customer Service Teams

Chatbots are a customer service agent’s best friend because they:

• Manage recurring questions and tier 1 problems
• Free up time so that agents can focus on more complex issues
• Reduce customer service waiting times

To find out more about the benefits of our e-commerce chatbot solution, read this article by our Head of Marketing and Communications, published by JDN.

Workplace inclusion is at the heart of CSR policies and increasingly held to high standards. Inclusion should be a key part of any business’ global strategy in order to be better valued longer term by employees and customers alike. People often think of bots’ skills as being diametrically opposed to human qualities. However in this particular case, implementing a bot can actually help companies move towards a more egalitarian and unprejudiced workplace.

Developing a neutral approach to requests

The notions of equal opportunities and accessibility are regularly called into question at work. A study* revealed that 57% of French people strongly believe that equality in the workplace does not exist, which comes as an alarming assessment. To overcome this challenge, some companies have chosen to include conversational bots in their strategy to act as neutral agents. These selfcare tools – callbots, chatbots, and voicebots – handle requests from employees, customers or applicants in the most objective way possible. They are instantly available, regardless of the user’s request, vocabulary, appearance or background. Bots are never judgmental. They are programmed to understand natural language (including colloquialisms, SMS, or spelling and grammar mistakes) and answer each request without bias or value judgement. The fact that they handle each request equally does not compromise the quality of their service. For example, recruitment bots enable companies to manage CVs in a completely impartial manner, based only on the skills required for the job.

Promoting digital accessibility for everyone

Bots are visible, easy to use and compliant with French accessibility RGAA guidelines. They can even go one step further with a voice feature, which is particularly inclusive for people with disabilities or anyone who is not at ease with digital tools.

Bots are available 24/7 and provide customers and employees with direct, consistent and standardised support. If the bot doesn’t understand a request, they can suggest an alternative or rephrase the question and, if necessary, transfer the user to a live agent. Bots handle the bulk of incoming requests, so physical agents can spend more time on cases that require specific treatment and provide personalised and more inclusive support.

Making users more autonomous

In addition to handling information in an inclusive way, bots also encourage users to be autonomous. For example, a recruitment bot can help applicants manage and monitor part of the process themselves, enabling recruiters to focus on processing their application.

Another example is after-sales bots. They can use fault codes to help customers solve their problems quickly without an operator’s intervention. These tools make an employee’s day-to-day less repetitive, which increases satisfaction and well-being at work. In many cases, bots also improve the company’s image in employees and customers’ eyes. According to a study**, 73% of HR professionals believe that implementing a company chatbot is an asset for the employer brand.

Bots help employees focus on higher added-value tasks, which in turn makes them feel more valued. This continually improving and inclusive technology focuses on each individual and treats everyone equally. 

*Study conducted by Qapa (Adecco) for Equal Opportunity Day
**4th edition of the barometer of the use of chatbots in HR

People often underestimate robots, yet they play an increasingly large role in our lives, especially at work. For example, more and more customer service departments use callbots to improve their performance.

Orders, refunds, bookings, insurance… Lots of queries require an immediate customer service response. However, there aren’t always readily available agents, which can be uncomfortable for customers as well as employees, who have to manage dissatisfied calls and messages on a daily basis. This situation can soon have a negative impact on the company. Could the solution be… a robot? Callbots are available immediately, all day and night, and can prove to be a key customer service asset.

A Robot that Adapts to Customer Needs

Call centres are rarely open all day, every day. Which is why some companies provide a complementary service in the form of a callbot. In a study* on the most important things in customer service, 60% of those surveyed highlighted quick problem solving, while 40% valued 24/7 assistance.

Callbots handle 100% of calls, significantly reducing the number of answered queries and unavailable agents. The tool therefore enables to capture all leads and respond to all existing customers. Everyone benefits: companies can seize new business opportunities, and customers don’t have to adapt to specific opening hours.

More Efficient and Happy Customer Service Reps

Customers and employees alike appreciate callbots, especially customer service agents who receive a lot of incoming calls. The robot handles customer requests first and then transfers those requiring expertise to an employee. Reps therefore only deal with part of the call and don’t get as many. The callbot authenticates the caller, verifies the order number etc., before transferring the call, thus ensuring quality interactions and better request management.

This process makes the employee’s work more rewarding and adds value, which helps develop their skills. The customers they deal with are also happier because the bot directs them to the right person and has already identified them. Callbots simplify employees’ day-to-day work and improve its quality, which makes the job more appealing. This in turn helps reduce staff turnover, which is often very high in customer service.

A Positive Financial Impact on Companies

A callbot creates value for customers in a modern and adaptable environment. The benefits of implementing a robot can soon be seen through the company’s data. Their recruitment needs will look different since the bot can handle certain calls by itself, requiring less, but more qualified, employees. They can also calculate the callbot’s ROI, almost in real time, based on the number, duration and resolution rate of calls received, the number of calls handled fully/partially, etc. 

Finally, this tool can provide businesses with a competitive edge over those without one. Depending on the context, callbots can manage 100% of customer requests outside working hours. This enables companies to place customer needs at the heart of their strategy and improve satisfaction, while building loyalty. 

* Source: Vocal Com

We live in a highly digitalised world, in which modern technologies have become a way of life, and the collaboration between humans and robots a must. This is increasingly true in the workplace. Behind every user/consumer, there is often an employee who would like to benefit from the same technology at work. Employees, managers, HR heads… Everyone has something to gain from implementing robots in the workplace, particularly a HR chatbot. 

Combining Immediacy and Personalisation

Implementing an internal company chatbot improves employees’ quality of work life. It provides instant access to information 24/7 wherever they are and from any device (computer or mobile, website, app, desktop, company messaging system, etc.) on a wide range of topics: HR, IT, purchasing or even general services. 

A chatbot treats everyone equally, whatever their status or request. It understands natural language and helps tackle inclusion issues that are an increasing focus for companies. A chatbot can be customised with tailored answers through the use of standard variables or by connecting it to the company’s HRIS.

The tool therefore frees up time for employees/managers and HR to listen and talk to each other. This is all the more important during health or economic crises. According to a study, 70% of HR professionals believe that chatbots are even more useful in times of crisis. By performing time-consuming tasks, the chatbot frees up time for each department to focus and think about the new and engaging issues that arise during such periods.  

Improving Employee Wellbeing and Productivity

A chatbot can play a key role in a company’s global strategy. It is a great support for businesses with a wide range of profiles, remote sites, a high turnover and/or travelling employees. PwC (specialised in consulting, auditing and legal and fiscal expertise), for example, has a dense intranet and wanted to facilitate the onboarding of new employees (nearly 2,000/year). They therefore implemented an internal chatbot to create a connection and ensure consistency. We often come across this need in decentralised companies, who use chatbots as a unifying internal communication tool.

The benefits of implementing this technology, which is more and more widespread, are immediate. A good level of service has a positive impact on employee wellbeing as well as productivity.

Adapting to the Challenges of Workforce Transformation

Some employees may be wary of the implementation of selfcare tools, particularly robots, for fear that they will dehumanise interactions in the workplace.

Yet bots are increasingly “close” to their users. For example, they can chat with employees on internal messaging systems (Teams) like any other user. When the human touch is required, the chatbot redirects users to the right department or person, meaning that everyone has more time and energy to build a good relationship.

Contrary to popular belief, not all robots take people’s jobs. In fact, the opposite can be said of chatbots. The disappearance of certain tasks allows new ones to emerge. Indeed, while 59% of HR professionals believe that chatbots will replace certain functions, 69% say that they will create new jobs.

* Source: Barometer of HR chatbot use by dydu 

* Source: Client story – dydu