Inside APAC’s Shift to Agentic Marketing

That spectrum is a useful way to think about how AI and agentic AI are showing up inside enterprise technology B2B marketing teams right now.

Take a typical Google search campaign.

In the first scenario, AI assists. Your team does a lot of the hard thinking: Reading product docs, understanding the ICP, and extracting keywords. Then an AI helps draft ad copy and variations. The machine makes pedaling easier.

In the second scenario, AI shares the load. One agent pulls keywords. Another researches the product and the ICP. A third agent combines those inputs and generates ad copy using guardrails. You’re still steering, but machines are doing real chunks of work.

Then there’s the third mode. AI doesn’t just help build the campaign; it runs it. The agentic system does all of the above, and also sets up the campaign, allocates spend within defined limits, and launches. (Okay, maybe not launches, but you get the idea.)  You input the destination, and machines take you there. That’s autonomy.

If you’re like most enterprise technology marketers in APAC, you’re probably somewhere in between.

We Ran the Numbers

That’s what one of the first, if not the first, large-scale surveys exploring AI and agentic AI inside B2B organizations shows.

The findings come from the Agentic Organizations Report, one that we at ROI·DNA produced in partnership with Hotwire. It draws on a survey of 900+ AI-using B2B professionals across the US, Europe, and Singapore, combined with expert interviews and practitioner conversations.

The report captures a pattern we kept seeing in real teams. We frame it as Assist → Share → Autonomy: The shift from AI as a tool, to a teammate, to an autonomous actor inside real workflows.

Here’s a quick tour of the numbers:

• The biggest gains people report are: Speed (78%), quality of output (60%), and creativity (58%).
• AI is already showing up as more than a tool: 21% say it feels like a colleague, and 14% say it feels like a decision-maker.
• The ‘shared agency’ shift is real: 43% say they’d be comfortable being managed by an AI.

How Agentic AI Shows Up in Practice

Enough theory. If you’re wondering how all this ‘Assist → Share → Autonomy’ plays out inside real organizations in APAC, here’s an example.

Who: A global project and workflow software company serving product and engineering teams.

The Challenge: The company’s ABM team in APAC was trying to solve a classic scale problem that plagues one-to-one ABM initiatives: How do you create deep, credible, multi-faceted account intelligence quickly, consistently, and cost-effectively?

Each report needed to cover a lot of ground. To be useful, it had to give sales and marketing teams a clear, multi-dimensional view of each target account, including:

• The account’s strategic and IT priorities
• Its financial performance, including core business segments and growth trends
• The market landscape and macroeconomic environment it operated in, including key competitors, in-country trends, and regulations
• Its challenges and opportunities
• A list of key stakeholders to target along with their backgrounds and priorities
• And competitive positioning, which articulated how the company could win the account based on the account’s challenges and goals

Agentic AI Solution: The company turned to ROI·DNA, and we deployed our proprietary ROI·DNA Ignite platform combined with consultancy services.

ROI·DNA Ignite is an agentic AI research platform that uses multiple AI agents to conduct account and market research, competitive analysis, SWOT assessments, and persona profiling. For account research, it synthesizes deep web analysis with internal data sources including CRM systems, intent signals, technographics, and competitive intelligence to produce tailored, accurate, multi-dimensional, and comprehensive reports.

Benefits: The most immediate impact for the company’s ABM team was speed. Account research reports that could previously take weeks to assemble (and risked parts of the report becoming stale) could now be produced in a fraction of the time. That speed had a knock-on effect: empowerment. The marketing team felt far less constrained about how many accounts they could realistically pursue.

Just as importantly, the reports were accurate. We used third-party data including  intent, wallet share, and technographics to not only enrich insights, but to cross-validate information surfaced in web research.

By tailoring the structure of the account dossiers with ROI·DNA’s ABM/ABX strategists, the company not only improved consistency, it also ensured that each dossier clearly mapped an account’s specific business and technology challenges to how the company could help, and position itself.

The dossiers also included personalized outreach emails aligned to specific personas within each account, allowing sales and marketing teams to move quickly from insight to action.

Overall, the agentic AI solution enabled the company to lower costs, improve collaboration between sales and marketing, and significantly accelerate time-to-value for its ABM programs.

Where Do We Go From Here?

First, let’s situate ourselves. At this point, pretty much everyone’s using AI to assist. And we’re seeing more B2B marketing teams in APAC move into the share stage.

It’s unlikely we are going to stop here. Not after we’ve had a taste of all the goodness agentic AI brings to the table. More likely, marketing leaders like you are going to keep experimenting intentionally. You’re going to keep pushing at the idea of shared agency. And you’re going to keep compounding gains.

Because compounding is the point. Keep pedaling.