16 agentic AI trends shaping marketing, data, and ops in 2027

Zach leads solutions engineering for Supermetrics in North America, helping marketing teams connect their data to AI agents in Claude, ChatGPT and Supermetrics Agents.

If someone in your organization asks what you’re doing with AI agents, answering well means knowing which developments are real, which are still forecasts, and which actually apply to you. It also means deeply understanding the blockers that often stall full-scale deployments, from poor data quality and a lack of governance to leadership misalignment.

The 16 agentic AI trends below draw on 2026 research from Supermetrics and recent publications from other authoritative organizations such as Google, Adobe, IDC, McKinsey, Anthropic, and Microsoft. They’re the ones worth watching going into 2027, especially if you’re in marketing, data, or ops.

What is agentic AI?

Agentic AI is a type of artificial intelligence that can take a goal, plan the steps, use tools and data, and carry out multi-step tasks with limited human input. Unlike generative AI, which produces content when prompted, agentic AI acts. A marketing agent can pull campaign data, spot a drop in ROAS and draft the budget change for a person to approve.

1. Marketers are under pressure to adopt AI, but very few have it fully embedded

Marketing teams are absorbing heavy expectations around AI adoption. In the 2026 Marketing Data Report, Supermetrics found that over 80% of the 435 marketers surveyed feel pressure to adopt AI." The respondents point to broader leadership teams (61%) and investor boards (28%) as the primary sources of the pressure.

Despite those pressures, the Supermetrics report found that only 6% have it fully embedded in their workflows. Several factors at play could help explain this gap. For example, nearly 4 in 10 (37%) of marketers lack a clear AI strategy or vision, and over 3 in 10 (35%) say that they don’t have proper training.

The deeper issue here, though, is that over half (52%) of marketing leaders say a data team outside of marketing defines their data strategy and measurement for their own data.

So, in essence, marketers are expected to own accountability for implementing AI, while another team controls the data that determines whether an agentic solution would actually work. Fully implementing AI agents in marketing is easier when marketers themselves have visibility into and control over their data.

2. AI agents are being deployed across functions for autonomous execution

Organizations are deploying AI agents across more functions, but most are still stuck between pilot and production. AWS’ 2025 IDC study, titled The Rise of Agentic AI, identified the following:

• 6 in 10 organizations are deploying agents in operational functions
• 4 in 10 organizations are deploying agents across marketing, sales, customer service, and IT

Google Cloud’s 2026 AI Agent Trends report, drawing on insights from almost 3,500 enterprise decision-makers, backs this up. They found that over half (52%) of executives at organizations that use generative AI already have agents in production. AI agents are clearly becoming a staple across functions and verticals.

3. Data quality and governance are the top barriers to agentic AI adoption

Data readiness outranks talent and budget as the primary obstacle to agentic AI adoption. Adobe’s 2026 AI and Digital Trends report found that 75% of organizations cite data integration and quality as the top challenge for implementing agentic AI solutions, even ahead of talent and upskilling limits (71%) and unclear ROI (68%).

Supermetrics data shows what this looks like inside marketing teams. Its research on the AI readiness gap found that only 6% of organizations strongly agreed that their marketing data is high quality, accurate, and accessible across teams.

Twenty-nine percent didn’t agree or disagree, and 24% outright disagreed. Without high-quality data, even the best agentic AI can’t produce outputs that are anything more than dubious at best, and flat-out wrong at worst.

The hype around adopting agentic AI is strong, and that’s because the potential value is absolutely real. However, AI agents need a strong foundation of accurate, unified data to deliver the value they’re meant to provide.

4. Vertical-specific AI agents are in demand

As agentic AI grows in popularity, organizations are seeking out new types of agents. Many now want to use vertical-specific platforms rather than generic ones that may lack the domain expertise required to deliver real value.

IDC’s The Rise of Agentic AI report found that nearly all (90.9%) of organizations expect vertical, domain-specific agents, and 62% buy pre-built agents, then customize them.

The trend is very clear in the marketing industry, where a trove of specialized AI tools has risen to prominence to fill gaps left by generic, all-purpose solutions. Many of them are built to help marketers implement AI agents into their workflows. Supermetrics, for instance, supports end-to-end marketing intelligence workflows with AI agents augmenting each step of the process, from data connection to activation.

5. Most organizations expect agentic AI to open up more time for strategic work across functions

Leaders across functions and industries are betting that agents will let them shift human roles from tactical execution to strategic judgment. Adobe’s 2026 AI and Digital Trends report found that nearly two-thirds (63%) of organizations expect agentic AI adoption to free up more time for employees to focus on strategic and creative work.

There’s plenty of evidence that the expected shift is coming to fruition. For instance, Microsoft’s 2026 Work Trend Index Annual Report found that two-thirds of AI users say it has allowed them to spend more time on high-value work.

In some industries, compressing execution timelines while freeing up time for strategic work can deliver outsized value. Take marketing, for example. Supermetrics’ 2026 Marketing Data Report identified that 45% of marketers don’t have the time to analyze data, while 17% barely have any time at all.

Agents like Supermetrics’ Insights Agent are solving that problem, letting marketers conduct deep analysis across complete sets of marketing data in minutes, saving hours for actual decision-making and strategic work.

6. Multi-agent systems are becoming more popular than single-agent workflows

As organizations advance in their AI adoption, they’re shifting from single-agent deployments to interconnected systems of multiple, individually specialized agents.

IDC’s The Rise of Agentic AI study found that in 2025, over one-third of organizations (37%) were already implementing multi-agent solutions, while 44% set up systems in which their own custom-built agents coordinate with purchased pre-built ones.

Anthropic’s 2026 Agentic Coding Trends Report predicts that multi-agent systems will gradually become more common, eventually replacing single-agent deployments as organizations seek to maximize performance gains from AI.

However, multi-agent systems are complex, and businesses are encountering significant challenges during and after implementation. For example, IDC’s report shows that 3 in 10 businesses cite difficulties getting their agents to operate together effectively.

One potential source of these difficulties is inconsistent data. Agents working from different versions of the same metric will give different answers to the same question. That’s exactly the problem that Supermetrics solves for marketers.

Each of your agents in Supermetrics (custom or pre-built) works from the same numbers, enabling consistently trustworthy agentic analysis and campaign management.

7. Enterprises are outpacing smaller companies on agentic AI adoption and scaling

Large-scale organizations have significantly outpaced small businesses in agentic AI adoption over the past year.

McKinsey’s The State of AI in 2026 report found that the percentage of organizations with over $1 billion in revenue that are scaling AI agents rose from 27% to 40% year over year. Meanwhile, only 22% of small organizations reported scaling AI agents, the same rate reported last year.

Notably, the same report shows that over 4 in 10 (41%) of businesses below the $1 billion revenue threshold aren’t using agents in any form, not even experimenting, compared with (27%) of those over $1 billion.

Budget and sheer resource capacity are the obvious contenders for the primary factors preventing widespread adoption among SMBs. However, agentic AI solutions designed with affordability in mind are emerging across different verticals. Supermetrics, for example, starts at $44/month billed annually, and gives marketers full access to Supermetrics Studio and external AI connections for agentic automated workflows.

8. AI agents are being used to execute long-running tasks

As agentic AI capabilities develop, organizations are finding that they can deploy agents to execute tasks that may span hours or days. Anthropic’s 2026 Agentic Coding Report points out Rakuten as an example, which employed Claude Code to work autonomously for seven hours in a single run to implement a feature inside vLLM, an open-source library with 12.5 million lines of code, and matched the reference method with 99.9% numerical accuracy.

In the same report, Anthropic predicts that scenarios like these will only get more common. While they’re speaking specifically in the context of software development, the general concept can also apply to other verticals as AI providers continue to develop agents to handle more complex work.

It’s also worth noting that just because agents can handle more intensive tasks doesn’t mean that organizations are trying to automate that work in every case. IDC’s The Rise of Agentic AI report reveals that 61% of organizations fully trust agents with analysis and reporting work, and an even slimmer 42% trust them to execute customer service autonomously.

IDC’s trust figures show that agents have some headway to make in terms of trust on fundamental, lower-intensity tasks before we’re likely to hear of widespread automation on the most complex workflows and decisions.

9. MCP is becoming the standard way to connect AI agents to business data

The Model Context Protocol (MCP), introduced by Anthropic in November 2024, has become the most widely adopted open standard for giving agents access to live, accurate enterprise data. MCP is an open-source standard connection that lets large language models (LLMs) access data from external platforms. This overcomes one of the largest limitations of standalone LLMs: They can only reference the data they were trained on.

Demand has driven competitive behavior across verticals, with several platforms now offering native MCP solutions as a differentiator. For example, Supermetrics is one of a few platforms in the marketing data space that offers MCP as a core capability, allowing marketers to connect their cross-platform data with popular LLMs like Claude and ChatGPT.

Supermetrics’ Claude integration, specifically, is the fastest-growing aspect of the platform. This shows there’s real appetite among marketing teams for using an MCP to query their data conversationally and update it automatically, rather than relying on time-consuming traditional methods.

10. Non-technical teams are building their own AI automations

Agentic tools are letting people who’ve never written a single line of code build useful technical automations. Naturally, non-technical teams are the primary beneficaries here.

In its 2026 Agentic Coding Report, Anthropic predicts that teams across areas such as marketing, sales, and operations will be able to automate workflows and build custom tools without little or no engineering help. They point to an example from their own legal team, in which a lawyer with no technical expertise built a self-service triage tool in Claude Code, reducing turnaround time from two to three days to 24 hours.

As the possibilities for non-technical users grow, so will organizations' ability to get value from their agentic deployments across functions. Instead of sending requests to a potentially backlogged development and engineering team, teams and departments can build valuable automations on their own without needing to know how to code.

11. Most organizations expect AI agents to handle half of customer support within 18 months

One of the clearest agentic AI trends is that organizations are forecasting a fairly aggressive transition to AI agents for customer service functions. Adobe’s 2026 AI and Digital Trends study found that nearly 8 in 10 (78%) organizations expect AI agents to handle about half of their customer support interactions within the next 18 months. Sixty-nine percent and 62% predicted the same for customer sales and conversational customer engagement, respectively.

With that said, actual adoption lags behind: the same study shows that only 16% of respondents have embedded agentic AI for customer support across their entire organization. This is a space worth watching in the next couple of years to see whether the pace of adoption matches the aggressive forecasts.

12. An agentic AI trust gap is emerging between companies and customers

There’s an elephant in the room that needs to be addressed with agentic AI: organizations are significantly overestimating customers' willingness to trust them.

Adobe’s 2026 AI and Digital Trends report reveals the following gaps in trust, among others:

• 21% of consumers would feel comfortable trusting AI agents to make final and unchecked decisions about a product or service, 14 percentage points below organizations at 35%
• 21% of consumers would feel comfortable with an AI agent providing deeper personal information (such as preferences and hobbies) for curated experiences, seven percentage points below organizations at 28%

Adobe found that when customers expect to interact with a person but instead encounter an AI agent, 37% disengage. Thirty-one percent will do the same if they’re able to identify that a piece of content is AI-generated.

Disclosure is always the safest option, so customers know what to expect. In the marketing industry, disclosure or permission is often legally required, especially regarding the use of customer data. As AI laws like the EU AI Act come into effect, disclosure requirements are likely to expand. Taking an “honesty is the best policy” approach from the start will likely position you better to adapt to regulations and retain customer trust.

13. Most organizations lack a clear leadership vision for agentic AI

Agentic AI adoption, as with any major change to a business, starts with a clear vision and path forward from leadership. Alarmingly, data shows that most organizations don’t have this, not just for agentic AI, but across AI implementation as a whole.

Microsoft’s 2026 Work Trend Index Annual Report revealed that just over one-quarter (26%) of AI users say that their leadership is aligned on the topic of AI. Adobe’s 2026 AI and Digital Trends Report corroborates this, with respondents citing executive misunderstanding of AI as the top cause for leadership misalignment.

So, in many cases, organizations face barriers to AI adoption at the very start, before any technical problems or resource limitations have a chance to surface.

14. Technical and resource limitations are a top barrier to agentic AI adoption

Leadership alignment doesn’t clear the path to adoption on its own. Even in organizations where executives are bought in and understand how agentic AI can resolve cross-functional inefficiencies, technical gaps and resource constraints still stall implementations.

Leadership alignment doesn’t clear the path to adoption on its own. Even in organizations where executives are bought in and understand how agentic AI can resolve cross-functional inefficiencies, technical gaps and resource constraints still stall implementations.

IDC’s The Rise of Agentic AI study found that a lack of skilled personnel is the top implementation challenge (55%), and 67% say users need more skills and training to drive adoption.

At an even more fundamental level, implementation and usage are a costly endeavor, and it’s one that organizations can’t ignore. Forty-one percent of respondents in IDC’s study expressed concerns about a lack of predictability in costs and ROI. McKinsey’s The State of AI in 2026 report corroborates this, showing that one in five respondents has had to constrain their usage of AI due to operating costs.

15. Agentic AI is redefining roles, responsibilities, and expectations across functions

AI agents are already changing human roles and responsibilities at a fundamental level. Google Cloud’s 2026 AI Agent Trends study describes a model in which employees at all ranks serve as supervisors of AI agent systems.

Rather than executing every mundane or repetitive task themselves, employees instead set desired outcomes and delegate that work to systems of agents. The employee then serves in a strategic oversight role, using their judgment for complex decision-making and quality control of outputs.

Google Cloud clarifies that specific new roles around agentic orchestration or AI management aren’t yet present in the market. It’s the existing roles themselves that are changing.

For a marketing analyst, the shift is concrete. Half of marketers in Supermetrics' 2026 report wait one to three days for data answers; an agent working on unified data answers in minutes, leaving the analyst to decide what it means for spend.

16. Agent governance and permissions gap emerging

As agents move from reading data to taking actions, organizations need rules for what an agent can do without human approval. In marketing, that line usually sits at spend: an agent can analyze performance and draft a budget change, but a person approves it.

Adobe’s 2026 AI and Digital Trends report found that while 65% of organizations have developed guidelines for the responsible use of generative AI, only 37% have done so for agentic AI. So, the category that needs the most rules has the fewest.

No organization wants AI agents to make a negative impact with unauthorized actions. While agents are a fairly new development in the AI space, it stands to reason that we can expect more organizations to build out formal governance frameworks as adoption increases.

Level up marketing intelligence workflows with Supermetrics Agents

Of the 16 trends, three matter most for marketing teams in 2027: vertical agents built for marketing data, MCP connecting that data to tools like Claude and ChatGPT, and the data-quality gap that decides whether either works.

If you’re a team or company in the marketing space, Supermetrics offers a launch point to get your people started with agentic AI. Supermetrics connects data from marketing and sales sources into a unified, governed layer that both pre-built agents and custom proprietary agents can query.

Start a free trial with Supermetrics AI today, and join the platform trusted by over 200,000 companies in more than 120 countries.

FAQ

What are the best AI agents for marketing?

The best AI agents for marketing analysis and reporting include Supermetrics Agents, which analyze cross-channel marketing data from 150+ sources; HubSpot's Breeze agents for CRM and content tasks; and Salesforce Agentforce for sales and service workflows.

What is the most popular agentic AI?

While there’s no consensus data on the single most popular agentic AI, common examples of agentic AI solutions developed that are widely used across industries include Claude, ChatGPT, and Gemini.

Is agentic AI overhyped?

Whether agentic AI is “overhyped” depends on your perspective and expectations. The capabilities are real, and you can get real value out of implementing it, provided that it’s built on solid infrastructure.

However, there’s some truth to the claim that expectations are outpacing measurable results. McKinsey’s The State of AI in 2026 report showed that just 37% of respondents attributed a positive EBIT impact to AI, essentially unchanged from 2025.

What metrics are organizations using to measure agentic ROI?

Measurement remains an underdeveloped aspect of agentic AI adoption, and while there are common indicators to track (such as time and money saved, output quality, or revenue impact), your definition of success will vary significantly by the function or vertical in which you operate.

How do multi-agent systems handle errors and exceptions?

Multi-agent systems handle errors and exceptions by equipping agents to detect, report, and recover from failures. Agents may check other agents’ outputs while retrying or redirecting failed tasks.

Multi-agent systems also use limits (like memory limits or timeouts) to avoid error spirals. Higher-risk or unresolvable errors typically get escalated to a human for manual review.

Which industries are adopting agentic AI the fastest?

Some of the industries where agentic AI adoption is outpacing others include finance, technology, healthcare, retail, and manufacturing.