From data overload to decisive action: How Fujitsu proved the power of Agentic AI in ABM

Every ABM leader today wrestles with the same fundamental problem: making sense of sprawling account data. Sales systems, marketing platforms, third-party intent tools, CRM notes, spreadsheets, campaign results – the 'tapestry of data' is rich, but also fragmented.

The challenge is not collecting the data. It’s stitching it together into a coherent, rationalized view that enables confident decision-making at speed.

And when that fails, programs suffer:

• Scaling with confidence becomes near impossible
• Resource and budget allocations drift away from true opportunity
• Sales and marketing teams operate on different versions of the truth
• Engagement strategies lack the precision needed to resonate with decision makers

This is the backdrop against which Fujitsu’s EMEA Marketing Transformation team, led by Andrea Clatworthy, challenged MomentumABM to prove what Agentic AI could do.

The proof of concept

Working with Fujitsu, we designed a secure Agentic AI solution and underpinned by our ABM best practice frameworks. The AI agent interrogated over 200 files of structured and unstructured account intelligence across four strategic accounts (Mazda, Daikin, NatWest, Mölnlycke).

It delivered:

• Dynamic scoring of account attractiveness and business strength across 30+ weighted criteria
• Scenario analysis and flexible weighting to adapt as priorities shift
• Account-specific summaries linking surfaced business challenges directly to Fujitsu’s solutions
• Tailored engagement plays and value propositions ready to feed into ABM execution

The impact

For Fujitsu, the proof of concept delivered clarity, speed, and confidence in decisions that would normally take weeks of manual synthesis.

Andrea Clatworthy put it plainly:

“MomentumABM’s expertise alongside their cutting-edge Agentic AI, combined with our rich, structured and unstructured data, created a powerful synergy for us. This great PoC didn't just prioritize accounts; it yielded actionable insights, specific plays, and engagement strategies. It's a splendid combination that clearly demonstrates the game-changing power of gen AI in modern ABM.”

Why it matters for ABM leaders

This proof point is about more than one client. It demonstrates what’s possible when ABM programs confront the systemic issues caused by fragmented data head-on:

• Program scalability: AI rationalizes inputs so ABM teams can confidently expand to more accounts.
• Resource alignment: Marketing budget and effort are directed where the true potential lies.
• Sales/marketing alignment: Shared, evidence-backed scoring models replace competing interpretations of opportunity.
• ROI transparency: Clearer account prioritization and challenge mapping make it easier to prove the business value of ABM.

The way forward

Every ABM program is facing its own unique version of this challenge. The good news? As Fujitsu’s case shows, Agentic AI – applied within the guardrails of proven ABM frameworks – can transform it from a barrier into a competitive advantage.

If you lead an ABM program and are wrestling with the complexity of fragmented account data, now is the time to explore what Agentic AI can do. Read the full case study and connect with our team to discuss how AI can help solve systemic ABM performance challenges.

I'll be diving deeper into Agentic AI and how it can reshape ABM program orchestration and personalization during my session at Rethink New York on October 30.