GrayCyan unveils AI readiness scorecard for industry

GrayCyan has launched an AI Readiness & ROI Assessment Scorecard for operational leaders in manufacturing and industrial organisations. It is positioned as a governance-first benchmark for whether ERP, MES, and cross-functional workflows can support responsible automation.

The scorecard focuses on operational structure rather than attitudes to AI. It examines whether workflows are enforced, systems integrate cleanly, and data and controls are consistent enough for automation projects to move beyond pilots.

Manufacturers have increased spending on AI-related initiatives over the past two years, but many programmes run into problems when teams try to integrate models into day-to-day operations. Fragmented processes, inconsistent master data, and manual handoffs across departments can block automation even when a model performs well in testing.

The assessment is designed to address that gap between AI ambition and operational execution. GrayCyan develops human-in-the-loop systems integrated into ERP, MES, and other operational platforms.

"This is not a survey about AI enthusiasm," said Nishkam Batta, founder and CEO of GrayCyan. "It is a structural evaluation of how work actually flows through an organisation. AI only delivers results when workflows are enforced, systems are integrated, and governance is disciplined."

Operational indicators

The assessment uses 50 structured indicators across six areas: administrative coordination; finance and supply chain workflows; sales and service operations; warehouse and production synchronisation; engineering and change management; and ERP and system integration.

Each question measures workflow enforcement, system depth, data consistency, and governance maturity. The structure reflects the operational reality of many industrial firms, where process exceptions and local workarounds sit alongside standardised systems.

The framework separates questions of readiness from questions of return. That distinction has become more prominent as manufacturers try to quantify benefits from automation while also addressing governance and risk concerns.

Three modelling layers

The framework also adds three indices intended to distinguish the scorecard from conventional assessments.

The Manual Effort Index quantifies coordination friction embedded in everyday workflows. It is intended to capture manual effort that often sits outside formal process maps. Many industrial organisations rely on email, spreadsheets, and informal approvals even when core transactions sit inside ERP and MES platforms.

The Volume Exposure Index measures transaction frequency and operational repetition, focusing on areas where small inefficiencies compound at scale. In manufacturing, that pattern can appear in purchase order changes, production schedule updates, quality holds, shipping documentation, and inventory adjustments.

The Automation Feasibility Level evaluates whether system integration, data governance, and controls are structurally capable of supporting automation responsibly. It centres on whether a business can run an automated workflow with appropriate controls, rather than whether the workflow is attractive in principle.

Together, the three layers link ROI projections to operational conditions inside existing ERP and production systems, rather than basing estimates on strategic intent alone.

Two outputs

Participants receive two outputs. The first is an AI Readiness Score, a maturity-based rating that reflects workflow automation discipline, operational system integration, data quality, governance controls, and leadership alignment.

The second is a directional ROI Opportunity Estimate focused on administrative and coordination effort. This is framed as work associated with the "hidden factory", a term used in manufacturing operations to describe effort that sits outside formal value streams.

The framework is designed to avoid inflating scores based on company size or stated AI ambition. By separating readiness from ROI, it aims to help organisations identify near-term efficiency opportunities without assuming they are ready for broader automation programmes.

Intended users include chief operating officers, IT leaders, finance executives, engineering teams, and digital transformation leaders. The cross-functional focus reflects how automation projects in manufacturing often span procurement, planning, production, warehousing, and customer service, with shared dependencies on master data, approvals, and exception management.

GrayCyan builds human-in-the-loop AI systems for mid-sized organisations and develops AI middleware that integrates with legacy ERPs, CRMs, and operational platforms. Batta said, "AI only delivers results when workflows are enforced, systems are integrated, and governance is disciplined."