How to get your organization aligned for the AI age

At the September 2025 MarTech Conference, Jessica Kao, director of B2B GTM transformation advisory at Adobe, moderated a powerhouse panel on one of the hardest questions facing enterprises today: How do you get your organization aligned for the AI age?

Joining her on the panel were:

• Verl Allen, CEO of Clarine.
• Julz James, director of GTM systems, Fleetio.
• Ali Schwanke, founder of Simple Strat.
• AJ Sedlak, marketing technology & governance lead, Ford Motor Company.
  

Over 40 minutes, the panel returned again and again to three themes: data quality, organizational alignment and cultural readiness. The consensus was clear: AI doesn’t create order from chaos. If organizations don’t evolve their culture and their standards, AI will accelerate dysfunction, not fix it.

Clean data isn’t optional anymore

Allen set the tone from the executive perspective. He argued that enterprises must build alignment on high-quality, structured and standardized data within teams and across workflows, applications and departments.

“Clean data in, better results out,” Allen warned. “If not, we’re just accelerating chaos with AI.”

His emphasis:

• Commitment to metadata standards so context moves with the data.
• Cross-functional culture shifts — quality and consistency can’t be the responsibility of only one team.
• The realization that multiple systems and applications now touch the same workflows, demanding shared accountability.

James agreed from the operator’s side. Fragmented systems, she said, create not just inefficiency but biased outputs. “Bad data in, bad customer experience out,” she stressed. Accuracy isn’t just a technical issue — it directly affects ROI measurement.

Kao reinforced the point with a marketer’s perspective: “For years, we got away with garbage in, garbage out. But with AI, you can’t. It forces us to adopt best practices we’ve been talking about for a decade. There’s no choice anymore.”

When is data ‘good enough’?

Marketers can’t wait for perfect data before using AI. Schwanke compared it to starting a diet: “There’s always a tomorrow. You’ll never have perfect data. The question is: when is it good enough to start?”

Her framework: the Three Cs:

1. Context: What will the data be used for?
2. Consequence: What happens if we’re wrong?
3. Confidence: How will we test and validate assumptions?

She argued that AI projects are iterative. Perfection is impossible, but confidence comes from guardrails and testing. Kao added that organizations should compare the “old way vs. new way,” not to prove perfection but to build trust in forward momentum.

What blocks marketing-IT alignment?

When the audience for the session answered a poll about the top obstacles to marketing-IT alignment, the top answers were:

1. Siloed teams.
2. Data quality.
3. Resistance to change.

Sedlak pointed out that alignment isn’t about swapping updates in meetings. It’s about shared goals, shared priorities and joint roadmaps. Otherwise, IT builds for long-term scalability while marketing scrambles for quick wins — leading to mistrust.

Allen added that misaligned incentives deepen the divide. “IT may push a multi-year build,” he said. “Marketing may just want a lightweight solution they can use tomorrow. Without trust, both sides lose.”

James emphasized that today, everyone — from sales to finance — is more tech-savvy than five years ago. Marketing can draft requirements and sometimes even bypass IT entirely. That shift demands more coordination, not less.

Schwanke closed the loop: the secret weapon is a business requirements document. Without it, misalignment is inevitable.

Breaking down silos: AI as a forcing function

Paradoxically, AI may be the best antidote to silos. Kao observed: “AI doesn’t recognize departments. It forces us to work cross-functionally.”

The panelists outlined practical steps:

• Shift from tools to systems thinking. James described moving from “just MOps” to “GTM systems,” aligning sales, IT, finance and CX around shared platforms.
• Evolve skillsets. Sedlak argued MOps leaders must go beyond button-pushing and understand how systems work and integrate. “That’s where you unlock power — solving problems creatively.”
• Introduce new roles. Allen highlighted the rise of the Chief Data Officer as a bridge between IT and marketing. This role “doesn’t referee, but ensures decisions carry data implications and context across the org.”

Pre-purchase vs. post-purchase journeys

Audience Q&A turned practical: are pre-purchase and post-purchase journeys aligned in one tool?

Schwanke’s take: Don’t believe every vendor promising end-to-end visibility. “When a platform promises everything to everyone, it’s not true. Focus on the minimum viable data you need across the journey, then reverse-engineer systems to deliver it.”

That systems-thinking approach — start with outcomes, not tool features — earned broad agreement.

Garbage in: Did humans hide it before AI?

Another question asked whether humans covered for bad data before AI. James answered bluntly: “Yes.” Humans manually checked spreadsheets, scrubbed lists and validated data before campaigns. With AI, those guardrails vanish unless the system is trained and monitored.

Her advice is to require human-in-the-loop validation early, then release AI when confidence is proven. “At first, check every email. Once you know it’s not hallucinating, let it rip.”

Future-proofing AI choices

Kao raised a critical forward-looking issue: how do we avoid creating new silos by buying too many AI tools?

Her prediction: AI agents will soon need interoperability. Marketers must choose platforms with openness and standards, or risk recreating today’s fragmentation tomorrow.

Schwanke reframed the challenge as leadership: “We’ve managed people for decades. AI isn’t ‘set and forget.’ It’s like managing robotic employees. You need parameters, guardrails, and accountability.”

Sedlak extended the metaphor: organizations should run “AI employee reviews.” Leaders must demand explainability, evaluate decision quality, and continuously monitor results — just as they would with human staff.

Key session takeaways for leaders

1. Data standards aren’t optional. Without metadata, context and hygiene, AI accelerates chaos.
2. Perfection is impossible — confidence is enough. Use frameworks like the Three Cs to know when to move forward.
3. Alignment requires shared goals. Stop building in silos; roadmaps must cross IT, marketing, sales and finance.
4. Business requirements documents are a secret weapon. Clarity avoids wasted builds and mistrust.
5. AI breaks silos — but only if leaders embrace systems thinking. Marketing ops must evolve into GTM systems leadership.
6. New roles matter. The Chief Data Officer can bridge IT and marketing priorities.
7. Future-proof with interoperability. Choose tools that can connect with others in an agentic AI ecosystem.
8. Treat AI like employees. Set expectations, monitor performance, and review outputs regularly.

The bottom line

AI adoption is no longer a marketing project; it’s an enterprise transformation. Data quality, alignment and cultural readiness—not just tools—will decide who wins.

As Allen warned, “If we don’t set standards and context, we’re just accelerating chaos.” Schwanke reframed this, arguing that AI is not a black box but a new kind of workforce — one that needs leadership, accountability, and trust.

Organizations that recognize this shift, align around business outcomes and future-proof their systems will not just survive the AI age — they’ll lead it.

Listen to an audio overview of the September MarTech Conference

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