October 30, 2025

This Month in AI - From Readiness to Real ROI

Payal

AI’s influence across the enterprise has entered a new phase, it’s now less about experimentation and more about execution. This month’s edition spotlights five leading perspectives from global analysts and technology leaders. Each unpacks what it really takes to move from AI potential to AI performance.

From BCG’s deep dive into the widening AI value gap to McKinsey’s blueprint for AI-driven procurement, these insights explore how organizations are scaling responsibly, governing confidently, and realizing measurable business outcomes. Whether your focus is enterprise readiness, automation in ERP, or embedding AI into financial and operational workflows, the message across every report is consistent: AI value isn’t created by adoption alone, but by disciplined integration, governance, and scale.

The AI Winners are Pulling Away - The Rest may Get Left Behind

Despite record AI investments, most organizations aren’t seeing the promised returns. According to BCG’s latest report The Widening AI Value Gap, only 5% of companies are realizing significant, scalable value from AI, while 60% struggle to achieve material gains. The gap between these “future-built” leaders and the rest is widening fast, creating what BCG calls the AI Value Gap.

The rise of the “future-built” company

Only 5% of companies are “future-built.” These leaders report up to 3.6x higher shareholder returns and 1.7x faster revenue growth than peers. Meanwhile, the majority remain stuck in early experimentation. These top performers, what BCG calls future-built organizations, have figured out how to translate AI ambition into measurable business outcomes. They share five key traits:

A multiyear AI ambition, led from the top — CEOs and boards drive a unified vision with explicit financial goals.
End-to-end workflow reinvention — AI is used not just to automate, but to reshape how work gets done.
An AI-first operating model — human–AI collaboration replaces traditional silos, supported by strong governance.
Skilled, AI-fluent teams — more than 50% of employees are actively upskilled to work alongside digital agents.
Fit-for-purpose tech and data foundations — interoperable, secure architectures enable scale and reuse.

Agentic AI: The new force multiplier

One of the biggest accelerators of the value gap is Agentic AI - intelligent systems that can reason, learn, and act autonomously. These agents already account for 17% of total AI value in 2025, and are projected to reach 29% by 2028.

Unlike traditional automation, Agentic AI combines predictive and generative intelligence to execute entire workflows - from dynamic pricing to supply chain orchestration - under human oversight. The most successful implementations embed these “digital workers” across workflows rather than running isolated pilots.

How Agentic AI is changing the game

BCG’s findings highlight several use cases where Agentic AI is already reshaping value creation:

Predictive maintenance in energy, reducing downtime by up to 30%.
AI-powered underwriting in insurance, cutting approval times and fraud risk.
Virtual sales assistants in consumer industries, delivering double ROI on e-commerce conversions.
Agentic orchestration in manufacturing, driving up to $300M in EBIT impact through automation and governance at scale.

Why the gap keeps widening

For AI transformation laggards, the challenge isn’t technology, it’s execution discipline. Many companies scatter resources across pilots, lack data integrity, or fail to embed AI in governance. The result is fragmented progress that doesn’t scale.

Future-built companies, by contrast, are turning AI into a strategic engine - one that powers reinvention, not just automation. Their advantage grows every quarter as Agentic AI becomes a force multiplier for speed, accuracy, and adaptability.

The takeaway

The message from BCG’s research is clear: AI value doesn’t come from tools, it comes from transformation. Companies that act now by building trusted data foundations, scalable governance, and true human–AI collaboration will define the next era of enterprise performance. Those that wait risk being left behind for good.

Agentic AI: The Natural Evolution of ERP

ERP has always adapted from mainframes to the cloud. Now, Agentic AI marks the next leap, moving systems from recording and reporting to thinking and acting.

According to Computer Weekly’s Is Agentic AI the beginning of the end for ERP? 79% of executives say AI agents are already in use, with two-thirds reporting productivity gains. Yet adoption remains early — only 12% have scaled enterprise-wide. The top goals: efficiency (51%), new AI-driven services (42%), and greater adaptability (42%).

But as Agentic AI rises, so does the debate. Rimini Street’s CEO Seth Ravin predicts that ERP, as we know it, will fade away “transaction by transaction,” replaced by agile AI agents that automate above existing systems without the cost of traditional upgrades. It’s a bold, disruptive vision of leapfrogging the old ERP model entirely.

Not everyone agrees. Conor Riordan of the UK & Ireland SAP User Group argues that ERP isn’t dying, it’s transforming with AI augmenting rather than replacing it. The goal isn’t to abandon ERP foundations but to build smarter, more adaptive layers on top. The real challenge lies in balancing innovation with governance, transparency, and cost predictability.

The shift is already visible: ERP is evolving from copilots which suggest to agents that act. As BlackLine CTO Jeremy Ung says, “Agentic AI represents the next step beyond copilots, moving from suggestion to action.” But that leap only works when systems are transparent, auditable, and grounded in trust.

This is not the end of ERP, but its natural evolution toward platforms that can manage complexity, learn from data, and make proactive decisions with transparency and control. Agentic AI is moving enterprise tech from systems of record to systems of intelligence — and rewriting how companies run.

AI is Rewriting the Procurement Playbook

Procurement is stepping into the spotlight. McKinsey’s latest research Transforming procurement functions for an AI-driven world shows that functions once focused on cost control are now becoming strategic value creators - powered by AI, automation, and smarter operating models.

AI turns insight into impact
Nearly 40% of procurement teams have already implemented or piloted generative AI and advanced analytics. The results are tangible: one organization uncovered over $10 million in value leakage by analyzing supplier data through AI models.

From transactional to strategic
Leading companies are redesigning procurement to separate strategic sourcing from transactional work. The shift allows teams to spend less time processing orders and more time shaping growth, supplier resilience, and sustainability goals.

Efficiency with intelligence
McKinsey estimates that AI-driven procurement can boost efficiency by 25–40 %, as digital tools, agile structures, and hybrid human-AI workflows become the norm.

Forty percent of organizations report using gen AI in procurement. — [[Image]]

The takeaway:
Procurement’s transformation is well underway. For organizations willing to reimagine the function, AI is turning procurement from a cost center into a competitive advantage.

Finance is Quietly Winning at Gen AI

Gen AI might still be finding its footing in most business functions, but finance is already cashing in. According to Bain’s article Gen AI in Finance Isn’t Failing—It’s Working Where It’s Built In, finance teams are proving that AI delivers results when it’s baked into core workflows, not tacked on as an experiment.

Where AI’s actually working
Forget flashy chatbots, the real action is in automation. About 77% of AI use cases in finance run through APIs, quietly speeding up things like reconciliations, reporting, and forecasting. When AI lives inside everyday systems, the efficiency gains show up fast.

Repeatable beats experimental
The biggest wins come from structured, rule-based work - think payables, receivables, or management reports. These aren’t glamorous, but they’re predictable and decision-rich, making them perfect for AI to streamline.

The setup matters more than the software
Finance leaders say the hurdle isn’t the tech, it’s the setup. Without clean data, clear processes, and a bit of change management, even the smartest AI will stumble. The takeaway? Less hype, more housekeeping.

Closing thought:
Finance is showing what “working AI” really looks like - embedded, invisible, and quietly compounding value every day. Everyone else might want to take notes.

AI Readiness is Now a Competitive Advantage

In global business today, ambition alone no longer guarantees AI value. Cisco’s Realizing the Value of AI reveals a striking insight: readiness is the margin that separates those who succeed from those who sit in an endless pilot loop. The survey of more than 8,000 senior IT and business-leaders across 30 markets shows that roughly 13% of organizations the so-called “Pacesetters” consistently outperform their peers across deployment speed, value capture and infrastructure maturity.

What the Pacesetters Do Differently

From pilot to production: These leaders are 4× more likely to move AI use-cases into production and ~50 % more likely to report measurable value than typical organizations.
Infrastructure built for scale: Nearly all Pacesetters are designing networks that anticipate next-gen computing demands, compared with fewer than half of other firms.
Governance meets ambition: They combine bold AI goals with clarity in data, security and change-management frameworks. In short: ambition without readiness is a recipe for churn.

Why It Matters for Executive Teams

Readiness is not an IT checklist. Rather, it’s a strategy. Organizations that treat AI as a side project risk sinking into a cycle of costly pilots and muted returns. Conversely, companies that govern their data, connect infrastructure to business objectives, move from pilot to scale, and track AI outcomes are capturing value now and setting up resilience for what comes next.

“When AI readiness is done right, it becomes a competitive advantage, not just a technology project.”

In short: As AI continues to mature and agentic systems become mainstream, the winners will not be the fastest adopters, but those who are ready to scale, govern and monetize.