Enterprise conversational systems: when AI becomes a colleague

Over the past two years, Generative AI has captured the imagination of companies and people alike: spectacular demos, rapid prototypes, promises of instant productivity. But between enthusiasm and real impact there is a gap that those working in the field know well. Turning an idea into a reliable, integrated system that is used every day requires a journey shaped by architectural choices, a strong focus on adoption, and governance capable of supporting the complexity and challenges of the enterprise context.

Within Mashfrog Solutions, the AI Solutions area has developed solid, hands-on experience in building conversational systems designed not just to "impress", but above all to support day-to-day operations.

A digital assistant that truly becomes a colleague, where it really matters

When an employee looks for an answer and can’t find it right away, the cost isn’t just lost time. It’s fragmented attention, disrupted workflows, the need to "ask around," waiting for a ticket, or a handoff. And often, paradoxically, the information already exists—it’s just scattered across procedures, technical documentation, knowledge bases, and internal portals.

The value of the conversational systems we build lies in giving employees a natural point of access, using clear and straightforward language, that acts as a digital assistant capable of becoming a true colleague: it helps users navigate procedural and technical documentation, reduces the friction caused by recurring questions that slow down work, and makes it easier to interact with enterprise systems and information.

From knowledge to action: dialogue and integration with the application ecosystem

A conversational system becomes truly useful when it doesn’t just "answer," but actively supports users in getting things done. That’s why the evolution of our solutions goes beyond the ability to interpret increasingly complex documents: it also involves the progressive expansion of scope through new integrations with enterprise systems, data sources, services, and operational applications.

In the enterprise context, AI doesn’t operate in isolation—it is embedded in processes, rules, roles, permissions, sensitive data, and responsibilities. Design must therefore balance response quality, contextualization based on who is asking what and in which scenario, reliability and control in critical cases, and alignment with real operating models and the tools people actually use. This is where the difference between an effective prototype and a production-ready system is made.

Proof of maturity: everyday use and continuous improvement

The distance between "it works" and "it’s useful" is measured over time. For more than a year, our solutions have been supporting thousands of people at work every day across a wide range of industries, evolving alongside our clients’ needs by expanding their scope—both through new integrations and through enhanced capabilities to understand and synthesize increasingly complex content. This trajectory is an important signal: moving beyond necessary experimentation to deliver tools that are adopted because they solve real problems, while respecting the pace of everyday work.

A market context that makes the result even more meaningful

This level of concreteness becomes even more significant when viewed within the broader context of GenAI adoption. According to the MIT report "The GenAI Divide: State of AI in Business 2025," 95% of enterprise GenAI initiatives fail to reach the production stage or to deliver tangible value.

In this scenario, user satisfaction and our clients’ willingness to extend the solutions over time—by increasing investments and expanding their capabilities—are a concrete indicator of effectiveness for us. It means that the approach adopted has made it possible to overcome the typical barriers of enterprise adoption and to strengthen trust in the value delivered.

A cross-functional impact: every function can benefit, the HR case

A key takeaway from experience is the cross-functional nature of these solutions. A digital assistant that truly becomes a colleague can support virtually every business function, because it operates at the most universal point of all: fast access to information and actions exactly when they’re needed.

The HR function is a particularly effective example we have already worked with. In many organizational contexts, HR manages a large volume of high-frequency information that directly impacts people’s productivity and the overall employee experience. A digital assistant can make it easier to access information about org charts and colleagues, roles and responsibilities within processes, as well as company policies, reducing search time and operational uncertainty. It can also enable guided support requests, helping users clearly articulate their needs and classifying requests more accurately, speeding up resolution and reducing unnecessary handoffs. Finally, it can provide summaries of the latest updates from corporate portals, making them more accessible and targeted, with tailored content for different user groups—thereby increasing relevance and reducing information noise.

The decisive factor: technology and "human touch" together

Behind these results lies work that goes beyond engineering. The team working on these systems has a multidisciplinary makeup: it includes AI, software, and data engineering specialists, data scientists, as well as professionals with a classical background, such as anthropologists and humanistic computing experts. This combination makes it possible not only to tackle challenges from a technological standpoint, but also to keep human beings—along with their needs and ways of relating—at the center, something that cannot be overlooked in conversational systems.

Following our motto "Tech human touch," the experience we’ve gained and the challenges that emerge every day in an ever-evolving world lead us toward an approach that starts from both ends—the human and the technological—and, through a process of synthesis, builds systems that are truly integrated into the enterprise workforce and capable of making a real difference.

Looking ahead: strength today, evolution tomorrow

The pace of change in AI will continue to accelerate, and with it, expectations. The direction, however, remains clear: conversational systems that are increasingly capable of understanding complex contexts, deeply integrating with processes, and supporting people in a natural, reliable, and measurable way.

The most important lesson we take with us is simple: when attention to technology and people comes together in design, AI stops being a promise and becomes a concrete accelerator.