Transforming order-to-delivery operations with agentic AI
C · Weak signal A 12-page Capgemini point-of-view that sells its own O2D services; every figure is borrowed from third parties and no client results are measured.
Summary based on the full text·Original: about 12 minutes to read
In short
A March 2026 point-of-view paper from Capgemini's Intelligent Supply Chain Operations practice on applying AI agents to order-to-delivery (O2D): order capture, allocation, claims, invoicing, freight audit and collections. Its argument is that the best companies already automate most orders, and that agents are useful for the remaining exceptions that need judgement. It sets out eight principles and six use cases, and closes with a pitch for Capgemini's own frameworks.
Key numbers
- over 90 percentshare of orders top-performing enterprises already process without human intervention (per Capgemini)
- 27 percentorganizations that already have dedicated agentic AI teams
- more than 40 percentagentic AI initiatives Gartner expects to be discontinued by end of 2027
- 65 percentexecutives anticipating rising supply chain costs over the next two years
As reported by the source.
English·PDF·opens capgemini.com
What it reports
- Top-performing enterprises process over 90 percent of orders without human intervention; the rest need manual handling for pricing discrepancies, customer-specific terms, incomplete orders, stock allocation, credit risk and compliance.
- 27 percent of organizations have dedicated agentic AI teams; nearly one in ten deploy multi-agent systems.
- 65 percent of executives expect supply chain costs to rise over the next two years.
- Cites Gartner: more than 40 percent of agentic AI initiatives will be discontinued by end of 2027, due to cost, unclear outcomes and weak risk management.
- Cites an MIT survey of 153 business leaders and over 300 AI initiatives: 95 percent of companies saw zero return so far.
- States some AI models show an error rate of 60 percent, without naming which.
- Eight principles: business impact, process re-architecture, data quality, integration with ERP/TMS/WMS, customer-centricity, human-AI collaboration, governance with traceable decisions, and talent evolution.
- Six use cases: order capture and correction by email, stock and quota allocation by margin and SLA, claims handled by a supervisory "chief" agent coordinating specialist agents, invoice price checks, freight cost auditing, and dunning.
How they got it
Consultant synthesis. Market statistics are taken from Gartner, MIT and unnamed surveys; use cases are described as possibilities ("could", "can") rather than as deployments with results.
Limits
- No client case, baseline, cost or measured outcome for any of the six use cases.
- The 60 percent error rate and the 90 percent touchless figure have no cited source in the text.
- Written to promote Capgemini's D-GEM and ESOAR frameworks.
Why it matters for companies
The useful idea is scoping: agents belong in the residual exceptions of an already automated order process, not as a replacement for basic process harmonisation. The paper's own principle that a messy process must be fixed before agents are layered on top is the main precondition to check. The six use cases are a reasonable list for an O2D team to cost out, but each needs its own baseline and business case, which this paper does not supply.
Summary based on the full text. Read the original for the full argument and data.Open PDF on capgemini.com
- Tags
- AI agents, Operations