I see Profound’s MCP evolution as a meaningful shift for Marketing Engineers. It now connects agents to a knowledge graph and adds 15 new capabilities built around how marketing teams actually work.
For retailers, I believe this demands a serious reframe. Answer engines are already shortlisting products and shaping purchase decisions long before shoppers ever land on retail or ecommerce websites. That compresses the shopping funnel and makes traditional search less reliable as the primary channel for customer acquisition.
Instead of waiting for shoppers to arrive through search, I need to think about how retailers can be recommended throughout the entire shopping journey. That means understanding how people use answer engines for Christmas gifting, how brands earn mentions and citations in relevant AI responses, and how visibility can be maximized across AI search experiences.
I see this report as a practical edge for retailers preparing for the next holiday cycle. It uses real shopper behavior from Christmas 2025, analyzed through Profound’s AI visibility lens, to show how people are using AI to shop for the holidays.
Most importantly, it turns those insights into actionable takeaways. By understanding where answer engines influence discovery, comparison, and purchase decisions, I can see how ecommerce teams should optimize product visibility before the 2026 season ramps up and compete more effectively for the AI shelf this Christmas.
I see Christmas shopping moving beyond the search bar. More shoppers are now turning to AI answer engines to research products, compare gift options, and decide what to buy long before they land on a retailer’s website.
For retailers, I believe this shift requires a serious reframe. Answer engines can shortlist products, shape preferences, and guide purchase decisions earlier in the journey than traditional search ever did. That compresses the shopping funnel and makes search alone too limited as a customer acquisition strategy.
Instead, I need to think about how retailers can earn recommendations across the entire AI-assisted shopping journey. That means understanding how people use answer engines for Christmas gifting, how brands earn mentions and citations in relevant AI responses, and how ecommerce teams can improve visibility across AI search.
In this report, I give retailers a clearer path to that advantage. I draw on real shopper behavior from Christmas 2025, analyzed through Profound’s AI visibility lens, to show how people are using AI to shop for the holidays.
I also focus on practical takeaways retailers can use now, before the 2026 season ramps up. The goal is simple: optimize ecommerce products early, show up in the AI answers that matter, and win the AI shelf this Christmas.
I believe your brand may already be getting misrepresented in AI search, and the hard part is that you might not even know it is happening.
When I looked at how AI search responses behave, one pattern stood out immediately: nearly half of AI responses include unsolicited comparisons, opinions, and recommendations that the user never directly asked for.
That creates a second dimension marketers cannot afford to ignore. It is not just whether AI systems mention your brand. It is how they frame your brand, what they compare it against, and which assumptions they repeat back to users.
To understand the scale of the problem, I analyzed 50,000 prompts across seven industries. I wanted to see when AI search stays factual, when it adds its own judgment, and how often brands are pulled into recommendations or comparisons without the user asking for them.
What I found shows why AI visibility is no longer only about being included in the answer. It is also about making sure the answer represents your brand accurately, fairly, and in the right context.
In this article, I break down what I found, why this “parrot problem” matters for marketers, and what you can do to protect your brand as AI search becomes a bigger part of the customer journey.
I am paying close attention to how Reddit conversations are shaping AI search, especially after Profound collaborated with Reddit to analyze how conversational data informs AI-generated answers.
What stands out to me is how much value AI systems can draw from real discussions, lived experiences, and community-driven context. Reddit’s conversational data helps reveal the kinds of answers people are looking for, the language they use, and the perspectives that can influence how AI-generated responses are formed.
As I dive into the data we’ve amassed from over 80 SaaS clients between 2022 and 2026, this report paints a vivid picture of freemium model effectiveness. Together, we’ll explore industry averages, see how visitors transition to becoming free users, and how these free users convert to paid customers. I’ll also guide you through the nuances of various freemium offerings compared to free trial success rates.
From February to May 2026, I dove deep into the fascinating world of agentic AI adoption. I explored how it’s being embraced by enterprises, mid-market players, and SMBs across the U.S. and worldwide. By gathering insights from top consulting firms like McKinsey, Gartner, and IDC, as well as academic institutions and AI leaders, I pieced together a comprehensive overview of agentic AI’s current landscape.
This report fuses insights from over 30 research efforts and industry surveys, covering 15,000+ businesses. It provides a granular look into how businesses are integrating autonomous AI agents this year, breaking it down by company size, industry, deployment stage, primary use cases, and adoption and abandonment patterns.
*Statistics are based on data up to May 14, 2026, unless indicated otherwise.
While generative AI generates immediate outputs, agentic AI shifts the way systems function entirely. This piece zeroes in on agentic AI’s adoption, defined as follows:
Agentic AI revolves around AI systems autonomously planning, deciding, and executing complex tasks from beginning to end.
The term adoption signifies any case where an organization uses at least one agentic AI system at any stage, from initial trials to full-scale implementation.
Meanwhile, abandonment involves halting an agentic AI program or specific projects. This doesn’t always mean closing an organization’s entire AI operations, as they might continue other initiatives.
Agentic AI adoption significantly varies by organization size. A breakdown of recent adoption rates across different segments unveils fascinating trends.
As I dug into the data, I discovered enterprises are leading the way with 25% adoption, thanks to their resources and AI budgets. However, smaller sectors, like mid-market firms and SMBs, are catching up fast. Their year-on-year growth rates are even outpacing those of enterprises!
I predict that SMBs and mid-markets will continue adopting agentic AI faster than their larger counterparts. This trend is partly driven by accessible solutions such as Salesforce Agentforce and Microsoft Copilot Studio, which empower companies with tighter budgets. In contrast, enterprises face challenges due to their intricate systems and diverse data environments.
Agentic AI deployment spans various maturity stages, presenting unique challenges depending on available resources. For SMBs, scaling can be costly, making it particularly challenging.
The table showcases deployment stages among adopters, revealing that 62% of enterprises, despite higher resources, linger in the experimentation phase. Notably, only 13% achieve full deployment.
A few patterns stand out from the data:
Firstly, experimentation predominates across sizes, with a 56% average gap to partial deployment. This highlights caution across sectors in deploying agentic AI.
Despite enterprises’ resources, mid-market companies are seeing greater partial deployment rates, likely due to fewer approval bottlenecks and more budgetary leeway compared to SMBs.
Also, scaling correlates with resources. Enterprises, despite early-stage phases, manage full-scale deployment at rates double those of mid-markets.
These patterns reveal that most organizations are still exploring, with few transitioning to production deployment.
It’s not all smooth sailing. According to Gartner, around 40% of agentic AI projects might be canceled by 2027, due to challenges encountered during deployment.
Although abandonment rates generally decline over time, mid-markets still see higher rates due to their broader range of obstacles and fewer resources compared to large enterprises.
Summarizing the common reasons for project failures:
Data quality matters. Without quality data, agents struggle, highlighting a universal need for centralized and uniform data pre-deployment.
Clear expectations are vital. Projects without well-defined success criteria often fail to demonstrate value, risking cuts in resources when results are inconspicuous.
Costs weigh heavily on SMBs. For SMBs, financial constraints dominate abandonment reasons, overshadowing other factors. Mid-market firms display more varied primary drivers.
Such insights explain why full implementation is elusive for many, despite significant investments. Companies need to address multiple challenges concurrently to progress beyond experimentation.
On an industry level, exploring adoption across sectors shows where agentic AI thrives and lags. Regulatory factors, data readiness, and competitive dynamics result in differing adoption levels.
Industries like education, construction, and real estate lag, owing to budget constraints, less advanced data infrastructures, and fewer automation opportunities. Nonetheless, even these sectors demonstrate notable enterprise adoption, signaling a broader reach beyond tech and financial services.
Finally, examining use cases underscores where agentic AI is making headway. Customer service and supply chain coordination rank high due to their structured processes. On the other hand, finance sees lower adoption due to stringent regulatory scrutiny.
If you fancy obtaining a PDF copy of this insightful report or learning more about our work, feel free to reach out here.
For further exploration into agentic AI and its surrounding trends, consider delving into the following reads:
