Agentic Marketing: Definizione, trend ed esempi

Agentic marketing is the discipline that integrates artificial intelligence agents, AI systems capable of planning, deciding and acting autonomously across multi-step sequences, into an organisation’s marketing processes. It is a genuine evolutionary leap. Automation executes predefined rules; an AI agent assesses the context, selects the optimal strategy, coordinates tools and resources, and completes the task without human oversight at every single step. The word comes from the Latin agere, to act, to do, to carry forward, the same root as “agency”: the capacity to act in the world. Applied to artificial intelligence, it means the system does not respond to a prompt: it pursues a goal. In marketing, this translates into agents that analyse data in real time, optimise campaigns, personalise messages, manage the acquisition funnel and interact with customers, all in a coordinated, continuous and scalable way, without requiring human intervention for every operational decision.

In Italian, “agentic marketing” is still an emerging term, seen as the territory of big tech research labs or heavily funded start-ups. The 2026 data tell a different and urgent story. The Salesforce State of Marketing 2026, the tenth edition of the report, based on 4,450 marketers across 26 countries, captures a transformation already under way: 60% of Italian marketers already use AI in their work; top performers using AI agents report +20% marketing ROI, +20% customer satisfaction and a 19% reduction in costs; the same marketers save an average of 8 hours a week through agentic automation (Salesforce, State of Marketing 2026). Yet only 13% of marketers worldwide currently use agentic AI operationally. We are in the window in which those who put the right infrastructure in place build a structural competitive advantage; those who wait will be catching up from a position of disadvantage.

Our in-depth piece on how AI agents work in marketing examines the technical architecture that makes these processes possible.

In this guide, written by the strategy team at Bliss and Corallo AI, our operational artificial intelligence division, drawing on the leading international 2025–2026 reports on agentic AI and on direct experience implementing AI systems in clients’ marketing and brand processes, you will find:

• a precise definition of agentic marketing, and how it differs from generative AI;
• the 2025–2026 market data that quantify the transformation under way;
• the four levels of autonomy of AI agents in marketing;
• the five main applications of agentic marketing, with operational examples;
• the paradox of agentic marketing without brand governance;
• the 2026 trends and the roadmap for Italian SMEs;
• an FAQ covering the questions marketing managers and entrepreneurs ask most often.

From generative AI to agentic AI: the distinction that changes everything

To understand agentic marketing, one must start from the distinction between two modes of artificial intelligence that the market tends to conflate.

Generative AI responds to an input: you ask it to write a marketing email, it generates the email. You ask it to analyse a dataset, it produces the analysis. Control stays with the human who writes the prompt, evaluates the output and decides what to do. Generative AI amplifies human productivity but does not act autonomously.

Agentic AI pursues a goal: you tell it “optimise the Google Ads campaign to maximise ROAS this week” and the agent analyses current performance, identifies the variables to change, makes the necessary adjustments, monitors the results, corrects course and delivers a report, without you stepping in for every single decision. As McKinsey puts it in its classification of 2025 technology trends: agentic AI moves AI systems towards “virtual coworkers that plan and execute multi-step workflows, not just respond to prompts.” It is not an assistant: it is an operational collaborator.

The 2026 data: the agentic marketing market is already here

The transformation is not a future prospect. The 2026 figures mark a discontinuity that is already under way. The global agentic AI market is growing at a CAGR of 44% to 2030 (TechCrunch, cited by Tready, 2026). In Italy, the artificial intelligence market reached €1.8 billion in 2025, with growth accelerating in 2026 (Everest Innovation, 2026). Globally, during the last holiday season, AI and agents generated 20% of global orders, equal to $262 billion in sales, an unequivocal sign of change in the way people discover and buy (Salesforce, State of Marketing 2026).

On the professional adoption front, the Salesforce State of Marketing 2026 (4,450 marketers in 26 countries) identifies mounting pressure: 85% of marketers believe customer expectations are higher than ever; 69% report that acquiring new customers has become harder; 64% struggle to keep pace with constantly changing behaviour. Against this pressure, structural execution gaps emerge: 51% admit their campaigns are sometimes generic and 37% cite inconsistent messaging. Agentic AI is the technical answer to these problems, yet only 13% currently use it. Of those using or planning to use agents, 82% expect major or moderate improvements in ROI (Salesforce, State of Marketing 2026). McKinsey (State of AI 2025) confirms this: 62% of organisations are experimenting with AI agents, but only 23% are scaling them systematically.

The four levels of AI agent autonomy in marketing

Not all AI agents are equal. Autonomy sits on a spectrum ranging from the passive assistant to the fully autonomous agent. Understanding these levels is essential to defining a realistic implementation roadmap and to communicating clearly to the organisation what is expected of each system.

1. Level 1, rule-based chatbots. They follow predefined scripts. They answer FAQs, capture leads and handle standard requests. They are not agents in the proper sense: they are automations with a conversational interface. 90% of companies that “have an AI agent” are in fact at this level.
2. Level 2, LLM-based AI assistants. They can handle complex single tasks (writing copy, analysing data, answering open questions), but require a prompt for each task. They do not plan sequences of actions autonomously. This is the territory in which most AI tools currently adopted by marketing teams operate.
3. Level 3, Specialised agent. Can execute multi-step workflows within a specific domain (for example: autonomously optimising a Google Ads campaign, analysing performance, identifying gaps, adjusting bids, testing variants, reporting). Requires periodic supervision but not intervention for every single action. This is the level at which the top performers in the Salesforce 2026 report operate today.
4. Level 4, Multi-agent system. Several specialised agents coordinated by an orchestrator agent, managing the entire marketing funnel end to end: acquisition, nurturing, conversion, retention, reporting. By 2028, agentic AI will be present in around a third of enterprise software (Gartner, cited by Datamasters, 2025). Level 4 is the frontier the market is moving towards.

The five main applications of agentic marketing in 2026

1. Autonomous campaign management

AI agents for campaign management monitor performance on Google Ads, Meta Ads and other channels in real time, optimise bids, reallocate budget towards the best-performing channels and formats, test creative variants and deliver interpretive reports. All without human intervention for each individual optimisation. The result documented among top performers: +20% ROI with -19% operating costs (Salesforce, State of Marketing 2026). Bliss implements this approach through Corallo AI in its own performance marketing processes.

2. Personalisation at scale

84% of Italian marketers recognise that customers expect two-way conversations rather than simple broadcast communications, yet 52% say they struggle to respond in real time (Salesforce, 2026). Personalisation agents solve this problem structurally: they analyse each user’s behaviour in real time, select the message most relevant to the context and deliver it through the appropriate channel at the optimal moment. Not segmentation into 10 clusters, but a conversation with every single customer, at industrial scale.

3. Agentic lead generation and qualification

67% of B2B companies are piloting AI agents for sales management (a16z, Big Ideas 2026). In B2B marketing, AI agents can manage the entire lead qualification cycle: from first contact to handover to the sales team, including gathering the necessary information, nurturing with relevant content and assessing the buying stage. All without the salesperson having to step in until real negotiation begins. Bliss integrates this approach into its CRM consultancy service and into the intelligent CRM system developed with Corallo AI.

4. Content distribution and agentic SEO

85% of marketers are reshaping their organic strategy and 88% are optimising for AI-driven search experiences such as ChatGPT and Google AI Overview (Salesforce, State of Marketing 2026). Half of all Google searches already include AI-generated summaries that bypass brand websites entirely. In this scenario, AI agents for content distribution do not merely publish articles: they optimise content to be cited in AI system responses (GEO Strategy), monitor brand visibility in AI answers and update content in response to algorithm changes in real time.

5. Agentic customer service and retention

The boundary between marketing and customer service is dissolving in the era of AI agents. An agent handling a post-purchase interaction can simultaneously resolve a customer’s problem, gather product feedback, propose a contextual upsell and alert the marketing team to recurring patterns in complaints, all within a single interaction, with no handovers between functions. This is the operational convergence that marketing has always theorised and that agentic AI finally makes practicable at scale.

The paradox of agentic marketing without brand governance

There is a structural risk in agentic marketing that adoption reports tend to play down: an AI agent acting autonomously amplifies the brand’s identity, for better and for worse. If the brand has a codified identity, documented values and a defined tone of voice, the agent works from that foundation and scales it. If the brand has an implicit identity, one that “is understood” but is not written down in any document, the agent interprets on its own, producing messages that may be technically correct but inconsistent with the perception the company wants to build over time.

The Salesforce 2026 report confirms this indirectly: 51% of marketers admit their campaigns are “sometimes generic” and 37% cite “inconsistent messaging”. In many cases this genericness and inconsistency is not the result of faulty AI; it is the result of AI working without sound brand governance. The agent optimises for the metric: clicks, conversion, ROAS. It does not optimise for long-term brand equity if nobody has told it what that means for that specific brand.

At Bliss, the Corallo AI operating model responds precisely to this tension: AI accelerates and scales; the brand advisor ensures consistency with the brand’s identity. They are not two separate systems but two layers of the same process. The brand audit is always the prerequisite for agentic implementation: first the brand’s identity, values and tone of voice are documented; then the knowledge base on which the agent operates is built; then it scales. Without that foundation, agentic marketing is an accelerator of genericness.

Real examples of agentic marketing in 2026

IBM: $4.5 billion in productivity gained with AI agents

IBM has documented $4.5 billion in productivity gains through the implementation of AI agents in its own processes, including marketing (IBM, “IBM as Client Zero”, May 2025). IBM used itself as an implementation laboratory before taking the solutions to clients, an approach that provides real data rather than theoretical forecasts and that made it possible to identify the conditions required for effective agentic implementation: connected data, clear governance, integration with existing systems.

Salesforce Agentforce: marketing that stops “broadcasting”

Salesforce Agentforce is the multi-agent marketing system that enables teams to build specialised agents, for acquisition, nurturing and retention, coordinated by a central orchestrator with access to unified CRM, commerce and service data. The principle is the one Martina Pietrobon (Salesforce Italia) summed up precisely: “The old ‘broadcast and pray’ approach no longer works. Customers are already elsewhere. They no longer click through a series of blue links on Google: they search and get answers directly from AI.” (Salesforce, State of Marketing 2026). Agentic marketing is the operational response to this shift: not broadcast campaigns aimed at passive audiences, but conversations scaled to individuals with specific intent.

Corallo AI and the Bliss model: agentic marketing with brand oversight

At Bliss, agentic marketing implementation follows a four-phase framework: Brand Audit (documenting the brand’s identity and knowledge base), Data Infrastructure (connecting CRM, analytics and platforms into a unified system), Agent Deployment (implementing specialised agents for priority areas), Monitoring and Governance (overseeing performance and brand consistency). The documented result: the Doreca case, 2.5 million TikTok views and a 13.36% Google Ads CTR across 51,599 clicks, shows how an integrated marketing system with AI oversight delivers exceptional performance when brand identity and algorithmic optimisation work in step. For details: Bliss case studies.

The 5 Ws of agentic marketing

• Who: Any organisation with repetitive marketing processes, structured data and measurable objectives, not just large companies. Italian SMEs that integrate AI agents into specific bottlenecks in their funnel (lead qualification, campaign management, content distribution) gain the benefits without having to build dedicated in-house teams.
• What: The integration of AI agents able to plan, decide and act autonomously across marketing processes, from campaign management to message personalisation, from lead qualification to content distribution.
• When: 2026 is described as “the breakthrough year” for agentic AI (Gartner, McKinsey and Salesforce agree on this point). The time to build the data and brand governance infrastructure that enables effective agentic implementation is now, not once the market is saturated.
• Where: Within existing systems, CRM, advertising platforms, analytics, CMS, through API integrations that allow agents to access data and act on channels without replacing the existing infrastructure.
• Why: Because top performers using AI agents achieve +20% ROI, +20% customer satisfaction and -19% costs (Salesforce, 2026). And because the alternative, carrying on with generic campaigns aimed at audiences seeking personalised conversations, yields increasingly marginal results in a market moving towards AI.

8. 2026 trends: where agentic marketing is heading

AEO (Answer Engine Optimization) as the new imperative

Half of all Google searches already include AI summaries that bypass brand websites (Salesforce, 2026). 88% of marketers are optimising for AI-driven search. The traditional organic channel is fragmenting: visibility is no longer measured solely by Google ranking, but by presence in the answers given by ChatGPT, Perplexity and Google AI Overview. AEO (Answer Engine Optimization), optimising to appear in AI engine answers, is the new frontier of organic positioning. Bliss covers this territory through its GEO Strategy and its AI optimisation service.

“Agent washing”: the risk of terminological confusion

As interest in agentic AI grows, so does the phenomenon of “agent washing“: many companies are starting to call simple chatbots or rigid automations “agents”, with no real autonomy or capacity for multi-step decision-making, blurring the line between marketing and concrete value (Datamasters, 2025). The operational distinction is precise: a system that follows a predefined flowchart is not an AI agent, it is automation with a conversational interface. A genuine AI agent can modify its own plan in response to unexpected information, coordinate different tools and achieve complex objectives without every step having been predefined. Choosing partners and suppliers on the basis of this distinction is the first quality filter in implementing agentic marketing.

The agentic roadmap for Italian SMEs: infrastructure before use cases

The main mistake SMEs make when implementing agentic marketing is the reverse approach: starting from the use case (I want a chatbot on the website) instead of the infrastructure (do I have the connected data and the documented brand identity that allow the agent to act correctly?). As the Salesforce 2026 report puts it: “Agentic AI only works when it is fed by connected data. An agent cannot resolve a customer’s problem or suggest a product if it lacks real-time access to inventory, purchase history and preferences.” The right starting point is data, and before that, the brand. Bliss’s strategic marketing consultancy includes an assessment of data and brand infrastructure as a prerequisite for any agentic implementation.