AI agents in business workflows: three real-world case studies

The context: when a workflow becomes a capability

There's a big difference between using AI to automate a task and building a operational capability based on AI agents. In the first case, an existing activity is optimized. In the second, the way an organization produces value is systematically and replicably redesigned.

OpenAI has documented this shift through an analysis dedicated to AI-native companies , published in September 2026. The cases examined — Basis, Clay, and Exa Labs — are not tech giants. They are relatively lean organizations that chose to build their processes around AI agents from the start. Therefore, their approach is particularly interesting for those who need to integrate AI into existing structures.

In Italy, the question that marketing and digital managers always ask themselves is the same: where do you start? The answer, in all three cases analyzed, is identical — you start with high-frequency, low-variability processes, the ones that eat up time without producing differentiation.

Basis: onboarding as an autonomous system

Basis is a financial platform that re-engineered its customer onboarding process using AI agents. Traditionally, onboarding in fintech requires document collection, identity verification, account setup, and sequential communications. Every step involves at least one human.

The model adopted by Basis instead features an agent that orchestrates the entire workflow. The agent collects documents, checks compliance, generates personalized communications, and escalates to a human operator only in exceptional cases. As a result, the average onboarding time has dropped significantly, but most importantly, the quality of the experience has become much more consistent.

The critical point, however, is not speed. It is the scalability without degradation . A human team managing onboarding produces variable quality depending on workload. An AI agent-based system maintains the same standard regardless of volume. This is exactly the kind of competitive advantage that Italian companies in the professional services and B2B retail sectors can replicate.

To learn more about how to set up this type of automation, it's helpful to explore the SHM Studio's AI solutions , also designed for mid-market businesses that want to build scalable processes without upending their existing organization.

Clay: AI agents in sales account management

Clay is a well-known data enrichment and outbound automation tool in the sales tech landscape. However, the case documented by OpenAI involves internal use: Clay applied its AI agents to the management of existing accounts, not just the acquisition of new contacts.

Basically, the agents monitor signals of current customer activity—platform usage, behavioral shifts, external triggers like role changes or company news. Based on these signals, they generate action suggestions for customer success managers, or directly trigger low-priority personalized communications.

This approach solves one of the most common problems in account management: attention scatter. In fact, an account manager handling dozens of clients cannot monitor all relevant signals with the same intensity. AI agents act as an intelligent filter, bringing to the surface only what requires real human attention.

The implications for Italian B2B marketing are straightforward. Many mid-market companies manage large customer portfolios with understaffed sales teams. Moreover, CRM data quality is often insufficient to support proactive decisions. A well-configured agent system can compensate for both shortcomings. We at SHM Studio we frequently work on this type of integration, starting from the audit of available data all the way to defining activation triggers.

For those managing commercial nurturing campaigns, it is also worth considering how these agents integrate with the activities of LinkedIn campaigns and of Digital marketing broader.

Exa Labs: developer integrations and the documentation problem

Exa Labs is a company specializing in semantic search for developers. Their case is perhaps the most technical of the three, but it offers an insight that applies to non-tech contexts as well. The problem Exa tackled involves integrations: every new developer customer has to understand the APIs, set up the environment, and troubleshoot implementation issues. This process creates a high volume of support requests.

The chosen solution features an AI agent that guides the developer through the integration. The agent understands the project context, suggests the best code patterns, and answers technical questions by pulling from the latest documentation. Unlike a traditional chatbot, the agent keeps track of the session context and tailors its answers based on what the developer has already done.

The result, according to data reported by OpenAI, is a reduction in time-to-first-integration and a decrease in human support requests. Therefore, Exa's technical team can focus on truly complex cases, delegating standard support to the agent.

This model applies directly to any company offering products or services with a technical learning curve: management software, SaaS platforms, data integration services. In Italy, many software house SMEs and system integrators could benefit from a similar approach to cut down post-sales support costs.

Common patterns: what unites the three cases

Analyzing the three cases comparatively, three recurring patterns emerge. First of all, all three identified a high-frequency process as an entry point. They didn't try to automate everything at once. They chose a specific area, mapped it precisely, and built the agent around that perimeter.

Secondly, in all cases, the agent does not completely replace human intervention. It handles standard cases autonomously and escalates to a human only in exceptional situations. This design — often called Human-in-the-loop — it is essential to maintain quality and end-customer trust.

Thirdly, measurement is an integral part of the system. Each agent is configured to produce data on what it does, where it gets stuck, and on which cases it requires escalation. This data feeds the continuous improvement of the system itself. Therefore, the AI agent is not a finished product, but an evolving system.

What the numbers don't say yet

The case studies published by OpenAI show positive results. However, it is important to look at this data with a healthy dose of critical distance. AI-native companies like Basis, Clay, and Exa Labs have a structural advantage: they were born in a cultural environment that embraces automation. Their teams are used to working with agentic systems and trusting their outputs.

On the contrary, many Italian organizations — even digitally mature ones — have to deal with a problem of change management that case studies don't measure. Adopting AI agents requires reviewing processes, redefining roles, and often doing some non-trivial internal communication work. Even so, this isn't a reason to put it off. It's a reason to plan more carefully.

Also, the quality of the starting data is crucial. An AI agent that works on incomplete CRM data or outdated documentation produces unreliable output. Therefore, any project of this type must start with an audit of the quality of the available data, not with the choice of the technological tool.

Operational implications for Italian marketing managers

For marketing managers and digital leads who want to turn these cases into concrete actions, SHM Studio suggests a three-step approach. The first phase involves mapping: identifying the three or four highest-frequency processes in your funnel—onboarding, nurturing, post-sales support, lead qualification—and estimating the current cost in terms of person-hours.

The second phase is all about prototyping. Pick just one of these processes and build a pilot agent with a well-defined scope. Don't try to cover every use case right from the start. Set your success metrics before launching the pilot: average completion time, escalation rate, and end-user satisfaction.

The third phase is about scalability. Only after validating the pilot with real data should you expand the system to other processes or customer segments. This step-by-step approach cuts down on risk and helps build internal know-how along the way.

For those who want to dive deeper into the topic of SEO Strategy in a context where content is increasingly generated or optimized by AI agents, or for anyone evaluating how to integrate these systems into their own google ads campaigns , the starting point remains always the same: clearly define the business goal before choosing the technology.

Furthermore, it is useful to consider how these agents integrate with the content strategy. An agent that manages onboarding produces valuable data on what questions new customers ask most often. This data can feed the copywriting strategy and the production of high-value informational content. Similarly, signals gathered in account management can guide the choices of web development and user experience.

SHM Studio's perspective: capability, not feature

The most important takeaway from the three case studies isn't technical. It's conceptual. Basis, Clay, and Exa Labs didn't just buy an AI tool. They built a capability. That's a huge difference: a tool gets used, but a capability is something you own and protect over time.

For Italian marketing leaders, this distinction has direct implications for how AI investments are evaluated. It is not about picking the best chatbot or the top automation tool on the market. It is about deciding which business processes we want to make structurally more efficient and defensible against competitors.

We at SHM Studio We believe that 2026 is the year when this distinction becomes operational for Italian mid-market companies. It is no longer just a conference topic. It is a choice that impacts operating costs, service quality, and the ability to grow without increasing labor costs proportionally. To learn more about how to structure this journey, the team is available via the contact page or by exploring the resources of Blog .