How Americana Foods deployed an AI agent, announced company-wide as a team member, to automate flash reporting, variance analysis, and management-ready financial summaries across their Foods business.

The Problem Nobody Talks About

Finance teams at large enterprises face a quiet tax on every reporting cycle: the cost of assembly.

At Americana Foods, a leading regional food company operating across multiple markets and categories since the 1960s, the numbers told a familiar story. Monthly flash reports consolidated manually across multiple business units and geographies. Variance explanations assembled in Excel, taking days per cycle with inconsistent quality across regions. A 20-30 year legacy on-prem Oracle ERP with no APIs, all reporting done through screen navigation.

The FP&A team was spending the majority of its time on data assembly and formatting, not strategic analysis.

"Previously, we were sending reminder emails manually, consolidating multiple inputs, drafting CFO-ready summaries," explains the team. Analysts were stuck in "assembler" mode: chasing inputs, formatting reports, drafting commentary manually each cycle.

The industry's default answer: accept that finance teams are data assembly lines. Accept the trade-off between speed and insight.

Americana didn't accept that.

"We Wanted to Be More Proactive in Our Decision-Making"

Americana's Shared Services and Business Excellence function, the company's centre of excellence for operational efficiency, standardisation, and transformation, had scaled from shared services to business excellence. The next step was clear: move from reactive reporting to proactive decision-making.

"That's when we felt we need to introduce the AI agents," says Praveen Jagadeesan, who leads Business Excellence. "AI agents accelerated our transformation agenda, I must say."

But this wasn't going to be a typical vendor engagement. Americana needed a partner who understood how to build AI into their existing processes, not someone selling a product and disappearing.

Start Small. Prove Value. Then Expand.

Instead of pitching a complete finance transformation, Beam suggested starting with one concrete use case: month-end flash reporting.

The approach was pragmatic. Build an AI agent that could handle the repetitive cycle: input collection, follow-ups, escalations, consolidation, and generating CFO report commentary. Prove it works. Then expand.

The agent went live in production in less than 30 days.

"Beam's approach was highly collaborative and innovative," says Sangeeta, Senior Director of Business Excellence & Shared Services. "They invested time understanding our processes, worked closely with end users and adapted solutions quickly based on feedback — which is often missing in traditional vendor engagements."

Meet Anova: The AI Agent With a Role Title, a Manager, and a Start Date

Here's where Americana did something no other company we've worked with has done.

They announced the AI agent, named Anova, to the entire company via an official circular. Anova was introduced as a team member. With a role title. A reporting manager. A start date.

"Giving Anova a role, manager, and a start date helped," says Praveen. "It created transparency, it built trust, encouraged adoption, reinforced accountability, humanised the technology."

This wasn't a gimmick. It was a deliberate change management strategy.

"When we talk about humanising the technology, that's where the emotional intelligence plays a role," Praveen explains. "It also signalled our leadership's commitment to embedding AI into daily operations."

Amina Hakkim from FP&A became Anova's assigned manager, working directly with the agent since day one.

The Numbers

18 weeks of analyst time reclaimed every year. Not by cutting corners, but by removing the repetitive assembly work so analysts could focus on what actually matters: strategic analysis and advising leadership.

From Assembler to Advisor

The biggest shift wasn't in the numbers. It was in the role.

"The biggest shift was in month-end flash reporting," says Amina. "Now, Anova manages input, follow-ups, and escalations, consolidation, generating CFO report commentary. Instead of spending hours assembling the report, I can now focus on validating insights."

The team went from chasing data and formatting spreadsheets to reviewing AI-generated summaries with embedded explanations, corrective actions, and severity ranking. CFO-ready outputs, with a human-in-the-loop consent step where analysts review and validate before reports reach leadership.

"That shift from assembler to advisor made the biggest difference," Amina says.

The Maturity Journey: Shadow Mode to AI Department

What makes Americana's story particularly compelling is how trust was built systematically:

Month 1-2 (Shadow Mode): Anova ran alongside the team. Outputs were compared side-by-side with manual work. No one was asked to trust anything they hadn't verified.

Month 3 (Co-Pilot): The team started relying on Anova's output as the starting point for all reports. The quality had earned it.

Month 4+ (Autonomous): Anova began surfacing cross-regional insights that manual processes had missed entirely.

Expansion (AI Department): Americana is now building a parallel AI department alongside the human org. Multiple agents across FP&A, Market Insights, and HR, all under one platform and one license.

"The vision is to expand AI from isolated use cases into a scaled digital workforce model across all functions," says Praveen. "Ultimately, AI will become an integral part of how work gets done."

"It Didn't Feel Like a Vendor Engagement. It Felt Like a Partnership."

Technology didn't make this work. Partnership did.

"Working with the Beam team has been extremely collaborative from day one," says Amina. "It didn't feel like a vendor engagement, it felt more like a partnership."

Beam embedded itself in Americana's processes — understanding the workflows, the pain points, the reporting cycles — before building anything.

"They didn't just build an AI agent — they built it around our processes," Amina explains. "They are structured, responsive, and genuinely focused on long-term capability building. What I appreciate most is that they are also guiding us on how to build our own agents for additional use cases. So it's not just implementation, it's enablement."

This is what separates a vendor from a partner. A vendor delivers a tool. A partner builds a capability.

What This Means for Enterprise Finance Teams

If you're running finance operations at scale, the challenge isn't finding AI tools. There are plenty of those.

The challenge is finding a partner who understands how to build AI into your existing processes, especially when those processes run on legacy systems with no APIs, span multiple geographies, and need to produce outputs that a CFO will trust for board reporting.

Americana found that partner. The results: a team transformed from assemblers to advisors, CFO-ready reports in minutes not days, 18 weeks of analyst time reclaimed every year, and a parallel AI department alongside the human org, expanding across FP&A, Market Insights, and HR.

"Adopting AI at scale represents a mindset shift towards innovation and agility," says Praveen. "Transformation is not limited to digital natives. Traditional enterprises can also leverage AI to modernise operations, and they also stay competitive."