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Capgemini’s diagnosis, summarized by executive Steve Jones in a July 2024 VentureBeat presentation, is that the main barriers are poor operational data, undefined digital boundaries and an organization that has not changed its processes, controls or responsibilities. A better model alone rarely fixes those gaps.
Proof of concept, pilot and production are different achievements
A proof of concept (PoC) demonstrates technical feasibility in a controlled setting. It may use a hand-curated data set, a small number of users and a workflow that is not connected to operational systems.
A pilot tests a more realistic version with a limited business unit, process or customer group. Production means the system is part of normal operations and must meet requirements for security, privacy, availability, support, monitoring, cost and accountability. Scaled production adds multiple teams, regions or processes without multiplying risk and expense at the same rate.
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The common mistake is treating a successful demonstration as evidence that the organization is ready for the last two stages.
Capgemini’s three-part diagnosis
1. The data is available, but not usable at the point of work
Language models can sound authoritative while drawing on incomplete records, obsolete policies, conflicting document versions or information that lacks business context. A database may technically contain an answer without making the current, authorized answer available when an employee or automated process needs it.
Capgemini’s 2024 material reports that only 42% of surveyed organizations had the data foundations required to use generative-AI models effectively, and only 46% considered themselves well prepared for data accuracy and reliability. These are survey responses, not independent technical audits. See the Capgemini 2024 Integrated Annual Report and its research infographic.
The issue applies beyond model training. Retrieval systems, enterprise search and AI agents all depend on freshness, provenance, permissions and clear definitions. Historical human workarounds often hide bad data: an experienced employee knows which spreadsheet is current or asks a colleague for an exception. An automated system cannot safely assume that invisible correction will happen every time.
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Capgemini uses “digital boundaries” to describe explicit limits on what an AI system may read, infer, recommend and change. A useful boundary specifies:
- the business problem and permitted inputs;
- systems, users and data the system may access;
- actions it may take without approval;
- decisions it may influence;
- conditions that require a stop or escalation;
- outcomes it must never pursue; and
- the logs and owner required for audit.
For example, a collections assistant could rank accounts and draft messages, but it should not waive a debt, alter a legal status, contact a protected customer segment without review or modify a ledger without authorization. Positive permissions and negative constraints are both necessary.
This is why a single “AI brain” controlling an enterprise is a poor design target. Separate, bounded systems—a finance assistant, customer-service agent, supply-chain planner or compliance reviewer—can each have their own data, rules, escalation path, owner, metrics and audit requirements. “Digital employee” is a useful concept, not a guarantee of safe autonomy; an assistant that drafts text has a very different risk profile from an agent that changes records.
3. The operating model remains unchanged
Putting an assistant beside an old process does not create a new capability. Production requires redesigned workflows, named accountability, employee training, revised job responsibilities, review procedures, incident response and continuing funding for maintenance.
Capgemini’s research found that only 40% of data executives regarded their organizations as mature on nontechnical foundations such as culture, ethical guardrails, governance and legal or regulatory frameworks, compared with 56% on technical foundations. The gap is described on the Capgemini Data-powered enterprises 2024 page. The survey does not prove that every stalled project has the same cause, but it illustrates why model selection is only one part of readiness.
Why a successful demo can lose its business case
A controlled demonstration often excludes the costs that determine whether a deployment pays for itself:
- data preparation, quality controls and source-system changes;
- identity, access, privacy and security testing;
- retrieval, integration and legacy-system work;
- model and inference charges at realistic volume;
- evaluation sets, monitoring and drift detection;
- human review, support and incident response;
- training, adoption and process redesign; and
- vendor lock-in, downtime and performance requirements.
Only 18% of respondents in Capgemini’s infographic said they were aware of how to productionize and monitor large-language-model applications, while 51% said they had defined a scaling roadmap. A roadmap is not proof that execution is funded or effective. Calculate cost per completed task using fully loaded operating costs, not just an API invoice.
The five-gate scale test
Use these gates before authorizing a larger rollout. A “stop” result is a useful decision, not a failure.
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Gate 1: Business value
- What measurable outcome changes—revenue, margin, loss, service time, quality or risk?
- What is the baseline and who owns it?
- Is the expected benefit large enough to pay for integration and governance?
Stop condition: the team can describe the technology but not the metric.
Gate 2: Data readiness
- Are all required sources identified, current, accurate and permissioned?
- Can the system obtain the information when the decision is made?
- Who corrects stale or conflicting data, and how is the correction recorded?
Stop condition: the demonstration depends on manual curation that will not exist in production.
Gate 3: Boundary and risk design
- What may the system read, recommend or change?
- Which actions always need human approval?
- What happens when confidence is low or sources disagree?
- Can every consequential action be audited?
Stop condition: nobody can state what the AI must not do.
Gate 4: Workflow and operating model
- Where exactly does the system enter the process?
- Which roles change, and what training is required?
- Who monitors quality, updates policies and handles incidents after the pilot team leaves?
Stop condition: there is no named owner for the live system.
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- What is the cost per completed task at realistic volume?
- Do latency, availability and accuracy remain acceptable across regions, products and languages?
- Are support, security and maintenance included?
Stop condition: the economics work only at pilot volume or with unpaid internal labor.
Metrics that matter after the demo
Track measures tied to the workflow rather than enthusiasm or model benchmarks:
- accuracy on a defined evaluation set and unsupported-claim rate;
- task completion, human override and escalation rates;
- handling time and cost per completed task;
- error severity and customer or employee satisfaction;
- revenue, margin, loss reduction or other agreed business result;
- security incidents and data-access violations; and
- time required to update the system after a policy or source-data change.
Prompt counts, numbers of demos, employee access totals and raw benchmark scores are vanity metrics unless they connect to these outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design choices that require judgment
Fine-tuning or retrieval
Retrieval-augmented generation is generally suited to changing enterprise knowledge; fine-tuning can help with consistent classification, format or style. Neither fixes stale sources, incorrect permissions or ambiguous rules.
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Human review or autonomous execution
Human-in-the-loop controls add safety but also review cost, bottlenecks and automation bias. Reviewers need sufficient time, context, authority and expertise; a nominal approval click is not meaningful control.
Centralized or federated governance
Central standards improve consistency and security. Function-level owners preserve local rules and accountability. A practical model is central requirements with business owners for each bounded system.
Build, buy or combine
| Approach | Best fit | Limitation |
|---|---|---|
| Managed platform | Speed, security controls and support are priorities. | Less control and possible ecosystem dependence. |
| Custom layer | Differentiated data, unusual workflows or strict regulatory needs. | Higher engineering and maintenance burden. |
| Hybrid | Managed foundation models with proprietary retrieval, policy and evaluation. | More components to integrate and govern. |
Capgemini and AWS announced a commercial collaboration aimed at moving enterprise generative-AI investments from pilots to production, citing services including Amazon Bedrock. That announcement is a vendor offering, not independent evidence that deployments succeed. Platform selection should follow the use case, data, boundaries, ownership and economics.
What to do next
- Select one narrow, repeated workflow with a clear owner and baseline.
- Map every data dependency, permission and correction process.
- Write allowed actions, prohibited actions, escalation rules and audit requirements.
- Design evaluation data and production metrics before expanding access.
- Estimate full operating cost, including people, integration and maintenance.
- Set an explicit scale-or-stop decision date and record what the experiment learned.
A canceled PoC can be a successful investment if it demonstrates that the data, economics, risk or adoption case is not ready. The costly failure is allowing an experiment to drift without a decision gate.
How enterprise platforms fit
Organizations may evaluate implementation partners and platforms such as Capgemini’s data and AI services, Amazon Bedrock, Microsoft Azure AI Foundry, Google Vertex AI, Databricks Data Intelligence Platform or Snowflake Cortex. Each can supply infrastructure or services; none automatically supplies clean data, process ownership, employee adoption or an acceptable risk boundary. Check current regional pricing and availability directly with each provider.
The Bottom Line
Capgemini’s central lesson is straightforward: scaling generative AI is primarily an enterprise operating-model and data problem, with model capability as only one necessary component. Make the workflow measurable, the data usable, the authority bounded and the ownership permanent before committing to production.
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