Toshiba's Marketing SVP Sees The Marketing Org Chart Holding People And AI Agents Together

Marketing leaders used to have somewhere to hide. The "let me come back with next month's numbers" stall, the "we need another quarter to read the trend" delay, and the comfortable lag between a campaign launch and a real read on what was working all bought political cover when things missed. AI agents have closed that window. Friday nights now come with live notifications. The leaders adapting fastest are restructuring their departments from the org chart down.

Trish Nettleship is Senior Vice President of Marketing at Toshiba Global Commerce Solutions, the retail technology provider behind point-of-sale and self-checkout systems for retailers in more than 120 countries. With 25 years of marketing leadership across global technology and SaaS organizations, she previously held CMO roles at NCR Voyix and ResMed's SaaS business, and earlier in her career, she helped launch some of AT&T's first enterprise social media efforts.

Nettleship is putting that thinking into practice at Toshiba now, and her central argument is that the shift is structural rather than tactical. "Stop talking about AI as a tool. It's the operating system that underlines what we do," Nettleship says.

That reframe is what separates leaders who can run inside the always-on cadence from leaders who buckle under it. Adding AI tools to an unchanged department only changes how quickly the team learns things are off. Treating AI as the operating layer changes what happens next, starting with the data the team is actually working from.

The clock never stops

Reporting cadence is the first thing to change. Monthly cycles, quarterly check-ins, and the multi-week gap between launching a campaign and getting a real read on it have collapsed into something closer to live feedback. AI agents now flag underperformance as it happens, eliminating the buffer that marketing leaders once relied on to course-correct quietly between board updates.

"You can no longer hide behind the excuse of lacking data and needing to wait a month. We were always looking backwards at dashboards to see what didn't work before making tweaks. You can't hide behind that anymore," Nettleship says.

The shift exposes capability gaps in leaders who have not adapted to working in real time. Notifications surface behavioral patterns at the precise moment a campaign needs adjustment, and they do not respect business hours. A quiet weekend that used to mean a quiet inbox now comes with active recommendations attached.

"I've used AI agents where you're looking at data in real time and getting notifications on a Friday night that your results aren't where they need to be, and here are the recommendations. You can't beat that kind of real-time data," she adds.

Marketing's seat at the agentic table

The new pace of feedback rewires what is expected of marketing leaders. The role is shifting toward growth orchestration, with CMOs accountable for enterprise-level decisions on where to automate, where to invest, and where human judgment still matters. Leading that conversation now requires technical fluency that used to live two layers down on the org chart. Marketing chiefs are expected to understand the data architecture, the AI agents running on top of it, and what each one means for the work. Delegating that to a data team or an agency cedes the seat that the role itself now depends on.

"I've got to be a data scientist. I've got to understand the data, analyze it, say what it means, and pivot in real time what we're doing from a marketing perspective," Nettleship says.

That hands-on fluency is what gives a CMO credibility to claim a different kind of seat in the broader enterprise AI conversation. Agentic investment has historically flowed to engineering and product, where coding agents and developer tools have been the obvious early wins. The function with the most direct line to the customer has sat outside that conversation, and Nettleship sees the gap as marketing's opening.

"Marketing is uniquely positioned to be at the center of AI when you think about agentic AI. Initially, it was all focused on coding and product. But when it comes to really getting closer to the customer, marketing is uniquely positioned to be at the forefront of leveraging AI to bring in that voice of the customer," she says. 

AI on the org chart

For organizations facing shrinking marketing budgets, the redesign Nettleship describes is the practical response. Marketing teams are consistently asked to do more with less, and treating AI as a productivity layer that sits above the existing org structure misses the point. The bigger move is to bring AI agents into the team itself, creating hybrid human-agent workforces where one marketer can oversee multiple agents handling the bulk of execution.

"Think about AI as part of your team, an operating layer your team rides on. That's what's going to make different marketing leaders stand out," Nettleship says.

The implication is that the organizational chart itself starts to look different. Headcount lines now sit alongside agent roles, with both contributing to the same campaigns and outputs. For Nettleship, that shift is what allows resource-constrained departments to keep pace with growing expectations and to give her existing team room to develop new skills without burning out on capacity work.

"I look at my org chart not just as people, but as people and AI agents working in collaboration. You have to figure out how to do more with what you already have. That is the key to unlock, and it allows my team to upskill," she says. 

None of this holds together without a leader willing to keep learning at the same pace. The conversation around AI in marketing has compressed faster than any prior technology shift, with the gap between "we have time to prepare" and "we are visibly behind" shrinking from years to months. Reading widely and trading notes with peers facing the same pressures has kept Nettleship current in a way that formal training cannot, and the same posture of listening and learning before acting shapes how she steps into a new team.

The 30-day listening tour

The restructuring Nettleship is laying out only works if the team buys into it, which is where most enterprise AI rollouts quietly fall apart. Mandates from the top stall in the middle, education-first programs without a real problem to solve fizzle, and tool pilots get rationalized away the moment they require behavior change. Nettleship's counter is patience. She treats early ambiguity as deliberate restraint, since the structure has to be built collaboratively to hold.

Workplace culture has shifted in her favor on the timing. The first wave of job-loss anxiety has given way to a more pragmatic posture, with employees focused on staying relevant as more work moves to AI. That shift makes the conversation about AI adoption easier to start. Succeeding at adoption requires its own methodology. Foundational education comes first. Collaborative identification of the team's biggest problem areas comes next. Only after that does the discussion turn to which agents and tools actually fit. The first thirty days of any new role get dedicated entirely to listening before she proposes anything at all.

"For my first 30 days, I make no decisions, and I just listen. When I hear someone say they want to bring in a new AI tool, I listen with no judgment. Then we start having conversations about what problem we are actually trying to solve. I lead with questions, not yes or no judgments," she says.

Strategy beats stack

All of these moves, from collapsing the reporting buffer to claiming marketing's seat at the agentic table to redrawing the org chart to leading the team through it, assume the strategy underneath is worth scaling in the first place. That is the assumption Nettleship is most insistent on testing before anything else. She helped launch some of AT&T's first enterprise social media efforts early in her career without a defined problem behind the work. The lesson she draws from that experience is that a new channel without a clear problem to solve is a coin flip dressed up as a strategy, and a new channel that scales faster than you can correct it is a coin flip with longer odds.

"AI in general can make mediocre marketing just explode. If you're doing poor marketing, AI is not going to improve it. It's just going to make it more prevalent," she says. 

The check on that risk, in her playbook, is keeping humans inside the loop the agents are running. The strategic judgment, the customer reads, and the editorial sense for what should and should not get shipped are the inputs the system has to learn from, and they only stay sharp when the people closest to the customer stay involved. An operating layer is only as good as what gets fed into it, and a marketing department that lets AI run without a human anchor will discover, at speed, that the system has scaled the wrong thing.

"Marketing isn't about leads. It's not about revenue. It is about the people that do the marketing and the people that you're marketing to. AI is an enabler to help us understand the people better. But AI has to learn off of what we tell it. If we continue to stay out of it, it's not going to know our customers or the market as well as we do," Nettleship says.