AI omnichannel: how the Customer Experience is changing
Omnichannel AI means using artificial intelligence to orchestrate all customer contact channels in an integrated manner. Omnichannel AI isn't just a technology, but a true stress test of the organizational maturity and customer experience strategy of Italian companies.
In recent years, AI has accelerated Omnichannel Customer Experience projects, but has highlighted their fragility and structural limitations. Research by the Omnichannel Customer Experience (OCX) Observatory at the Polytechnic University of Milan shows that, without a solid foundation, AI risks remaining a tactical, fragmented innovation with little impact on the value generated for customers and organizations.
In this article, we explore why AI must be embedded within a well-defined governance, skills, and process framework, what the main enabling factors are, and how to transform use cases from simple automation into levers for redesigning end-to-end processes.
Omnichannel AI as a stress test of business maturity
The adoption of AI omnichannel In the Omnichannel Customer Experience, it's acting as a true organizational stress test. AI is accelerating experiments and pilot projects, but at the same time, it's highlighting critical issues that existed long before the technology.
The OCX Observatory data shows that the main barriers are not technical, but strategic and operational. The 33% of companies indicates the lack of a clear AI strategy as the primary obstacle to structural adoption. Next in line are difficulties integrating with existing systems (32%), which makes it difficult to connect AI to CRM, marketing, and customer service platforms.
Other significant obstacles include unmapped or non-standardized internal processes (25%) and cost or budget constraints (24%). In short, the technology is available, but the organizational and process architecture to support it is often lacking. Also significant is the still marginal weight of issues such as ethics, risk management, model hallucinations, or customer acceptance.
These aspects, currently secondary in the Italian sample, are instead central in the international debate and in emerging regulations such as the European AI Act (official EU website). It is reasonable to expect that, as AI enters more critical and customer-facing contexts, Italian companies will also have to systematically address these dimensions.
The research highlights a key point: AI is not the starting point for omnichannel transformation, but rather an accelerator that puts pressure on existing foundations. Where direction, integration, and a unified vision are lacking, the introduction of AI can amplify inefficiencies and information silos, rather than generating value for the customer experience.
Enabling factors for a structural omnichannel AI
To transform the’AI omnichannel A true Customer Experience enabler requires certain enabling conditions. The OCX research highlights four interconnected pillars that distinguish more mature companies from those still in the experimental phase.
1. Clear strategy and shared roadmap
The first element is strategy. Without a clear direction for AI, projects remain isolated and difficult to scale. Initiatives that begin as local experiments, if not incorporated into a shared roadmap, risk producing only "islands of innovation" poorly aligned with overall customer experience objectives.
An effective omnichannel AI strategy must define priorities, use cases, success metrics, and expected impacts along the customer journey. Only then can AI have a structural impact on processes, service models, and business outcomes, rather than remaining an afterthought.
2. Centralized governance and silo reduction
The second factor concerns governance. The research shows a still fragmented market: only about a quarter of companies coordinate AI projects through a central management team, while nearly half grant broad autonomy to individual business units.
This autonomy fosters experimentation, but exposes the risk of duplication, information silos, and non-scalable solutions. In more advanced clusters, however, centralized governance of omnichannel AI emerges, capable of aligning priorities, budgets, responsibilities, and impacts, especially on the OCX and customer journey fronts.
3. Skills and partner ecosystem
The third element is skills. AI adoption is progressing faster than the ability to develop appropriate skills. Only 11% of companies report having specialized and advanced skills, while the majority rank at intermediate or low levels.
Even among the most mature organizations, a significant gap remains, making it necessary to build an ecosystem of technology and consulting partners. In this context, it is useful to combine internal expertise, centers of excellence, technology vendors, and cloud platforms specialized in AI and data, such as the machine learning services offered by major hyperscalers (Google Cloud AI, Microsoft Azure AI).
4. Change management and the centrality of the human factor
Finally, change management remains a critical cross-functional factor. Internal training, pilot projects, dedicated task forces, and front-line involvement are the most common actions to support people and processes in AI adoption.
The centrality of the human factor is recognized regardless of the OCX maturity level. Without a structured support path, even the most technologically advanced initiatives struggle to generate value. Omnichannel AI requires hybrid models, in which AI empowers people, supports decisions, and reduces complexity, without completely replacing human intervention.
From tactical use to process redesign with omnichannel AI
The OCX Observatory's analysis reveals a clear gap between the potential of AI and the way it is currently used in Italian companies. In most cases, AI is used as a support tool or local automation, without redesigning customer experience models.
33% of companies use AI for empowerment activities, supporting people in existing operations. 30% use it as a tactical addition to automate individual operational steps, often in specific channels or touchpoints. A significant portion, equal to 16%, declares that it has not generated any significant changes in processes.
Only a minority uses AI as a lever for partial or strategic process redesign, with overall percentages of just over a fifth of the sample. This is where the real quantum leap lies: "Champion" companies are able to integrate omnichannel AI into the organization's overall operating logic, rethinking flows, responsibilities, and interaction methods throughout the entire customer journey.
However, the research warns of a concrete risk: the complexity trap. Introducing AI solutions in a fragmented manner, without building the basics of customer experience and a coherent data strategy, can actually worsen the customer experience. Silos increase, inconsistent touchpoints multiply, and it becomes more difficult to redesign end-to-end processes for omnichannel adoption.
From Research to Experience: Omnichannel AI in Business Case Studies
The interventions of the companies involved in the OCX conference confirm and operationalize the Research's messages. Despite the different contexts, a common vision emerges:’AI omnichannel It generates value only if grafted onto already structured processes, governance and skills, avoiding purely technological approaches.
Gian Luca Gallo, Chief Commercial Officer of TP Italia, emphasizes how AI is acting as an amplifier of existing organizational characteristics. If introduced into companies that aren't ready, AI risks simply making them more complex and expensive, without improving the customer experience. The starting point, therefore, remains a centrally managed understanding of corporate processes and knowledge.
Vincenzo D'Arienzo, Account Executive – Collaboration at Cisco, focuses on the evolution of customer service towards intelligent agent-based models. The adoption of autonomous agents and multi-agent architectures opens up new opportunities for scalability and specialization, but also introduces greater complexity.
In this scenario, orchestration becomes crucial to avoid fragmentation, improve the reliability of responses, and maintain a consistent omnichannel experience. A complementary perspective comes from Josef Novak, Chief Innovation Officer & Co-Founder of Spitch, who highlights the need to overcome the contradiction between automation and the human factor.
According to Novak, the future of the Contact Center and Customer Experience lies in models where AI and people collaborate: artificial intelligence helps mediate complexity, supports decisions, and enhances the professionalism of agents, without replacing them. Overall, the contributions converge on one key point: the adoption of AI is not a binary choice between technology and people, but a transformational journey that requires balance, thoughtful design, and strong integration with service models.
Omnichannel AI: Impact on Marketing and Business
The impact of the’AI omnichannel The impact on marketing and business is profound. In digital marketing, AI enables advanced content personalization, dynamic audience segmentation, and automated journey management across multiple channels, including WhatsApp, email, and social media.
For companies, this translates into more effective campaigns, greater message relevance, and better alignment between marketing communications and customer service interactions. The ability to integrate data from different channels—chat, apps, websites, and physical stores—allows them to build a 360° view of the customer and orchestrate more seamless and consistent journeys.
From a business perspective, omnichannel AI enables new relationship models: proactive virtual assistants, personalized notifications throughout the customer lifecycle, and dynamic offers based on actual behavior. Intelligent automation frees up agents' time, allowing them to focus on higher-value cases and complex relationships.
In terms of customer experience, AI supports faster responses, advanced self-service, and seamless cross-channel integration, reducing friction and wait times. This directly impacts KPIs like NPS, retention, and average customer value, generating tangible returns on investments in technology and data.
How SendApp Can Help with Omnichannel AI
In this scenario, platforms like SendApp become practical enablers of AI omnichannel Business-oriented, with a specific focus on WhatsApp Business and instant messaging. The goal is to transform the chat channel into the operational heart of omnichannel strategies.
With SendApp Official (WhatsApp Business API) Companies can securely and scalably integrate WhatsApp into their CRM, marketing automation, and customer service systems. This allows them to orchestrate automated and hybrid conversations, connect AI to omnichannel flows, and centralize data and interactions in a single place.
SendApp Agent It allows sales and support teams to collaboratively manage WhatsApp conversations, assign tickets, monitor performance, and combine bots and human agents in a hybrid model. This is the approach recommended by the Observatory: AI augments people, not replaces them.
For those who want to push advanced automation, SendApp Cloud It enables complex workflows, event-based triggers, integrations with external systems, and centralized orchestration of campaigns, notifications, and automated journeys via WhatsApp. In this way, omnichannel AI becomes a concrete lever for redesigning end-to-end processes, from marketing to customer care.
SendApp helps companies move from tactical experimentation to a truly omnichannel WhatsApp communications strategy, aligned with the principles emerged from OCX Research: clear governance, integration with existing systems, attention to the human factor and intelligent use of data.
For businesses that want to bring the’AI omnichannel Within your daily communication flows, the next step is a dedicated consultation on WhatsApp Business and automation. Discover all the solutions on the website. SendApp and request a free trial to start designing more effective, measurable, and results-oriented omnichannel journeys.
