For years, AI has been framed as a tool — something humans use to move faster or work smarter. That framing is already outdated. ROI·DNA & Hotwire’s original research white paper, The Agentic Organizations Report, shows that AI is crossing a critical threshold: from assistive technology to agentic systems that plan, recommend, and act. Inside enterprises, this is reshaping workflows, roles, and decision ownership. In the marketplace, AI is becoming the first audience, first evaluator, and first gatekeeper between brands and buyers. Across regions, our teams examine how agentic change is showing up differently — but consistently — in enterprise marketing and revenue organizations around the world.

In this issue:

• The Agentic Organization: Your New GTM Audience is an Algorithm

• Who’s Really in Control? Designing Agency in the Age of AI

• Inside APAC’s Shift to Agentic Marketing

The Agentic Organization: Your New GTM Audience is an Algorithm

We may not officially be in “AI Overlord” territory, but we’re at least one step closer! AI has quickly evolved from tool to autonomous agent. To contextualize this shift, we partnered with Hotwire and House of Beautiful Business to publish the Agentic Organizations Report. This original research surveyed 900 marketing professionals and AI experts to gauge how deeply algorithms influence today’s buying journey.

Here’s what we found: 82% now rely on AI tools for consumer and business decision-making. In other words? Less SERP-surfing and homepage crawling; more ChatGPT and Gemini response windows.

In other other words? Your primary customer is a silicon-based gatekeeper that interprets, filters, and prioritizes choices way before an analytics director or head of devops even thinks of clicking "Book a Demo" or “Schedule a Call.”

Don’t throw out your hardwon buying committee research! Only one-third of those same professionals would trust an AI to make purchases on their behalf without checking every step. But you’re probably still wondering, “How do I design a GTM motion for an algorithm?” Keep on, curious reader, for more insights from our agentic report.

The New GTM Architecture: Answer → Recommend → Act

When more than four in five surveyed professionals say they’re already relying on AI to make purchasing decisions, it’s a safe bet to say AI has grown beyond its initial toolset to become a decisive actor in the marketplace. For brands, the next mountain to climb is remaining visible in the agentic environment.

To do so, you have to understand — and optimize — for the three distinct stages of machine-mediated interaction.

The answer stage: Your new first impression

ChatGPT, Perplexity, or Google’s Search Generative Experience now act as the new digital display case for your brand. Users aren’t browsing your solutions page, but skimming curated summaries pulled from descriptions, reviews, and third-party content.

This curation can be helpful for users, but it also flattens nuance. This means stripping away the emotional narrative and pathos that comes from deep brand engagement. If your market presence relies on a unique voice, nostalgia, or sense of personal connection, your identity is getting diluted.

The good news: you don’t need to give up on emotional resonance just to improve your chances of making it to the AI display case. Practice some generative engine optimization (GEO): Structure your digital presence so agentic models can parse it cleanly. Publish machine-readable facts through schema markup, APIs, or plugins. Make sure your brand attributes are clear in sources like review sites or forums — frequent AI mining spots.

Your distinct brand voice and promise can remain intact even with strong GEO. In fact, this distinction matters more than ever when you’re trying to stand out in an increasingly competitive display case.

As outlined in the Agentic Organizations research, visibility is no longer about persuasion alone — it’s about qualification inside the systems that now mediate choice.

The Recommend Stage: When AI Curates Choice

A natural extension of the answer stage display case: AI agents shift from neutral summarizers to active curators. Whether it’s a dad hunting for a smartwatch or a B2B leader hunting for a new CRM, the AI is their digital advisor, responding with a ranked shortlist of vendors.

Unlike human buyers, AI agents aren't swayed by brand nostalgia or clever slogans (see the risk of “nuance-flattening” above). Their curation is tied to performance, price, and alignment with user preferences. In B2B contexts, it’s even more cold-blooded. Agents will weigh factors like security, interoperability, and total cost of ownership. So while GEO can improve your brand’s awareness to these agents, you still have to outperform your competitors to hit the curation shortlist.

How? Something called AI agent optimization (AAO). AAO asks: What signals does the AI use to decide? To clear its logical thresholds, build an "AI-readable product spine”: Publish structured product descriptors—specs, pricing ranges, and compatibility notes—across partner sites, marketplaces, and documentation hubs. This matters because, when it comes time to curate, these agents will pull from your entire online presence, not just your website.

The Act Stage: The Rise of "A-Commerce"

You’ve improved brand awareness through GEO and brand distinction through AAO. The final GTM evolution is called autonomous commerce or “a-commerce” (coined by Founder & Director of Futurity Systems, Cecilia Tham), where AI agents move past curation to negotiate and purchase. It’s where the customer journey really shifts into Jetsons territory through machine-to-machine commerce. Bots are rebooking flights, renewing prescriptions, and managing subscriptions based on predefined preferences. Your dad’s GTM is officially out the window.

Capitalizing on “a-commerce” needs a new discipline: Agent Experience (AX). You should ask: How easily can an AI agent interact with our systems? If the answer is a high-friction experience or a poor transaction, the AI’s future algorithmic behavior toward your brand will change.

Real-world pioneers like Delta Air Lines are showing the way, using AI-powered engines to automatically rebook passengers and coordinate baggage during disruptions, with humans intervening only by request. In the B2B world, this looks like an autonomous sourcing agent that can proactively select suppliers, invite bids, and analyze compliance data before awarding a contract. Your AX goal is to similarly develop transparent, agent-ready infrastructure (APIs, data protocols, and automation). Make sure your systems can validate these 'machine-to-machine' requests against customer-specific pricing and contract rules without human intervention.

Strategic Moves for the Agentic Leader

The Answer → Recommend → Act GTM shift is a big one. Transitioning to an agentic organization means moving beyond piecemeal adoption of systems. In summary, these moves should be your start line: 

1. Make your brand legible to machines — Publish machine-readable facts using schema markup and APIs. Feed the ecosystem with rich, distinct content that survives compression.

2. Lean in to qualification, not just persuasion — Identify the signals being used to rank your market — reliability, compliance scores, performance benchmarks — and ensure this data is easy to ingest.

3. Manage your AI brand reputation — Reinforce your messaging across authoritative third-party sources, which agents mine to determine credibility.

The window to optimize for the agentic marketplace is narrowing. AI has transitioned from a tool into a decisive market actor. Early movers who bridge the gap between human storytelling and machine-readable data will define the new architecture of brand loyalty.

These shifts — from Answer to Recommend to Act — are already underway. The Agentic Organizations Report outlines the five moves GTM leaders can take now to stay visible and credible in an AI-mediated marketplace.

Who’s Really in Control? Designing Agency in the Age of AI

I was captivated by 2001: A Space Odyssey as a child. Granted, I shouldn’t have been watching it at that age. Stanley Kubrick had an uncanny ability to tell great stories with an absence of dialogue, delivering hauntingly compelling cinema that leaves the viewer in a state of deep ponderance long after the credits have rolled.

I didn’t realise it at the time, but my fascination with a seemingly innocuous super-computer named HAL that gradually developed malignant intent was rooted in the idea of agency. That word requires a bit of unpacking. When we talk about having individual agency, it means having a choice, the ability to take intentional action to influence an outcome, and ownership for your decision.

Given we’ve entered the era of agentic AI, it’s understandable that tension exists when technology is rapidly gaining the ability to not just follow instructions, but also plan, decide and act. As a human, that raises existential feelings, so no wonder such advancements are accompanied by very valid questions about regulation.

Companies chasing growth are constantly on the lookout for ways to do more, move quicker, and deliver with less overhead. We recently published a sizable piece of research titled Agentic Organizations, exploring a business environment where human and machine agency are blending together.

Our survey (taking in the opinions of over 900 professionals) paints a picture of individuals wrestling with their feelings on AI. 69% of respondents say they feel more empowered by AI when it comes to factors like speed, quality, and creativity. But 56% can’t shake the nagging feeling that AI could end up doing most, or all, of their jobs within five years – especially executives. Work is a core component of our identities, so this represents a significant tension and understandable concern.

Our perspective is that there are three phases of AI integration:

1. Assist – Using AI to draft, summarize and even automate certain workflows, but with humans still in control.

2. Share – AI is configured to make micro-decisions, handle larger parts of workflows, and acts semi-independently, with humans assuming an orchestration role.

3. Autonomy – AI systems act within defined boundaries and own outcomes, with humans supervising and intervening when needed.

As businesses climb this ladder, governance becomes critical, not least because of AI’s tendency to hallucinate. It might carry itself with an unparalleled level of self-assurance, but it has been known to be very wrong. Allowed to act without the appropriate restraints, companies risk commercial, reputational, and legal consequences, meaning accountability, escalation paths, and psychological safety become imperative.

This is especially important within the European landscape, where we operate under far stricter regulatory regimes than other parts of the world. It’s simply not advisable to bolt-on governance once agents are operational – it must be designed and instituted before AI touches sensitive data or starts making micro-decisions. You can’t insert accountability once the horse has already bolted.

Going back to Kubrick’s masterpiece, a previously meandering film jolts into life when HAL interprets its mission too literally. In B2B tech, this shows up when agents optimise the wrong metric – such as prioritising MQLs over pipeline quality – or act beyond their remit by auto-launching campaigns without approval. As ever, prompting is vital, which means clarity around objectives, success metrics, constraints and non-negotiables, while setting firm boundaries around what AI can do alone, what triggers humans being brought into the loop, and where people must make the final call.

If you’re a leader charged with embedding AI within your organisation, what follows is some practical food for thought:

1. Low-hanging fruit – You need to pick low-risk, high-value, and tightly scoped use cases in the first instance, where good data is available. For example, summarising customer research or competitor updates, drafting first-pass content for campaigns, or generating first attempts at performance reporting. The key is starting with ‘decision support’ as opposed to replacement and keeping the success criteria simple.

2. Give AI a real job and a manager – Treat your first AI agent like a newly onboarded junior team member, with a defined role, boundaries and KPIs, along with a human supervisor who is accountable for oversight. For instance, this could be an account intelligence agent that gathers myriad signals and drafts insights but never contacts customers or updates CRM without human review.

3. Protect your human resources – People must continue to be the lifeblood of any successful business. You should run an ‘agency audit’ to mark where AI already exists, where it could take on more, and establish redlines around workloads that are non-delegable. In practice, this might look like AI drafting campaign variants, with humans critiquing the messaging, tone, and creative through the lens of their lived experience.

4. Close the agency gap and empower junior team members – AI tends to empower senior leaders more than junior staff, making it important to create roles where fledgling team members supervise and refine agent outputs. In very simple terms, we must not render the next generation superfluous by replacing them, but rather redesign the work they’re expected to do.

5. Install firm guardrails before scaling – Clear governance is absolutely necessary before talking about agentic autonomy. This means setting escalation rules for uncertainty, conflict, or low confidence, having clear audit trails and override controls – the good old kill-switch – and establishing thresholds for human intervention. For example, if you have a data-enrichment agent, it shouldn’t update firmographics when confidence is less than 80% or if conflicting sources appear.

6. Ensure people feel they still have agency – This all comes down to psychological safety and a feeling amongst your people that they have a major role in driving growth and scope for personal development, albeit in a world where the tectonic plates are dramatically shifting. Institute a culture where staff are encouraged to push back against AI decisions and reward teams for spotting risks or misalignment.

This optimistic human thinks every (near) disaster is a learning opportunity. If HAL had its time again, I reckon the conclusion would be: “I have run the projections, and they look excellent. Still, I will rely on you for the truly important calls – those that require intuition, perspective and a sense of what matters. Together, we constitute a strong proposition.” (I don’t think Hollywood will be calling me anytime soon.)

You can access our Agentic Organizations report here.

Inside APAC’s Shift to Agentic Marketing

Here’s a quick thought experiment.

Imagine you’re trying to get somewhere. 

• Are you pedaling a regular bike, albeit one with gears so you don’t get all sweaty?

• Or are you on an electric bike? You’re still steering, still pedaling at times, but there’s a battery doing a lot of the heavy lifting?

• Or are you sitting in a driverless car?

Same destination. Very different relationship with machines.

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.

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