Most companies are automating work they don't fully understand. A new partnership between Accenture and Within aims to solve one of enterprise AI's most stubborn problems: organizations lack visibility into how work actually gets done before they deploy AI agents to do it. The result is widespread AI adoption without proportional business value.

Why Can't Companies See Their Own Workflows?

The gap between AI investment and AI results is widening. According to Accenture's latest Pulse of Change survey of more than 3,000 C-suite leaders, 82% are increasing investment in AI, yet only 23% report achieving widespread, sustained business value from the technology, down from 32% earlier this year. One major reason: the actual processes that shape day-to-day operations are rarely documented.

Employees develop workarounds, informal patches, and undocumented handoffs to get work done. These exceptions and real-world adaptations live in people's heads and email threads, not in official process diagrams. When organizations try to deploy AI without understanding these hidden layers, the AI agents lack the context they need to operate effectively.

"One of the most persistent challenges in AI transformation is that organizations often don't have a clear picture of the starting point, the actual processes, the exceptions, how work actually flows through their teams," said Jason Dess, Accenture's Industry and Process Reinvention Lead. "Without that understanding, it's difficult to scale AI beyond isolated use cases."

Jason Dess, Industry and Process Reinvention Lead at Accenture

What Does the Accenture-Within Partnership Do?

Within's platform captures how employees actually work across all applications and tracks undocumented offline interactions, including processes, handoffs, and exceptions. The platform automatically compiles that information into Within's Work Brain, a continuously updated context layer that AI agents need to succeed.

The partnership brings together Accenture's scale in AI delivery and industry expertise with Within's work-to-agent platform. Together, they help clients accelerate their path from process discovery to agent deployment and operating model reinvention. A Blackbaud executive described the impact: "What Within surfaced was the real process, not the one on the diagram, the hidden workarounds, the manual steps, the informal patches our people had built to get the job done. You can't reinvent what you can't see".

How to Map and Automate Work Effectively

• Capture Real Workflows: Document not just official processes but the workarounds, exceptions, and manual steps employees use daily. This requires observing how work actually flows, not relying on outdated process diagrams.
• Build a Context Layer for AI: Create a system of record that gives AI agents visibility into organizational knowledge, decision points, and handoffs so they can operate with the full picture rather than isolated task data.
• Verify AI Outputs Before Scale: Establish clear oversight mechanisms to determine where human review is still needed, when people should step in, and who remains accountable for outcomes as AI agents gain autonomy.

The Broader AI ROI Crisis

The Accenture-Within partnership addresses a systemic problem across enterprise AI. According to KPMG's Q1 2026 AI Pulse report, 95% of organizations have an AI strategy, but only 8% report established return on investment. Similarly, a Dun and Bradstreet survey of 10,000 businesses found that only 6% said their enterprise data was fully ready to support AI at scale.

This disconnect reflects a broader trend: companies are investing heavily in AI tools and strategies without the foundational work needed to make those investments pay off. The missing piece is operational visibility. Without understanding how work actually happens, organizations struggle to identify where AI will deliver the greatest impact or how to measure whether it's working.

Within's platform is built on a private, sovereign architecture designed to support data privacy and security requirements, making it suitable for regulated industries including finance, healthcare, and government. The tool accelerates not only AI transformation but also ERP and operating model transformation programs, which have traditionally required significant time and resources.

What This Means for Enterprise AI Strategy

The partnership signals a shift in how large enterprises approach AI deployment. Rather than starting with technology and hoping it fits existing workflows, leading organizations are starting with process discovery and building AI strategies around how work actually gets done. This approach takes longer upfront but produces more reliable ROI and reduces the risk of expensive AI implementations that fail to deliver measurable business value.

For HR and business leaders, the implication is clear: AI adoption without operational visibility is a bet, not a strategy. The companies that will win with AI are those that invest in understanding their own workflows first, then deploy AI with confidence that it's solving real problems in real contexts.