AI is everywhere… except in production
Over the past two years, artificial intelligence – and especially generative AI – has become a strategic priority for most organizations. Executive teams, business units, and IT departments are multiplying experiments: copilots, chatbots, automated workflows, and intelligent agents.
Yet one reality stands out: industrialization remains the main bottleneck.
- 72% of organizations already use AI in at least one business function.
- But only 31% have successfully scaled it across the enterprise.
- And fewer than 20% of companies are currently achieving a significant competitive advantage from their AI initiatives. (BCG, 2024)
In other words, AI has entered the enterprise – but it has not yet transformed operating models at scale.
The “production gap”: the real challenge for organizations
Most companies do not lack ideas or use cases.
They lack:
- reliable, usable data,
- scalable architectures,
- appropriate governance,
- operating models compatible with AI,
- integration and deployment capabilities.
This gap between AI’s promise and its real-world deployment is often referred to as the “AI production gap.”
In practice, many organizations:
- accumulate proof-of-concepts without scaling them,
- build isolated solutions disconnected from the core IT landscape,
- underestimate data, security, and change management challenges.
Why AI industrialization is so complex
AI is not just another technology.
It requires transformation across several layers simultaneously.
1. Data foundations
- quality,
- governance,
- integration,
- real-time accessibility.
Without solid data foundations, AI cannot deliver reliable outcomes.
2. Technology architectures
- cloud and scalable infrastructures,
- integration with legacy systems,
- model and data security.
3. Processes and operating models
- redesign of workflows,
- new roles and responsibilities,
- automation of low-value tasks.
4. Workforce adoption
- AI literacy and training,
- change management,
- evolution of roles and skills.
This is why AI industrialization is not a standalone technology project.
It is a full-scale operating model transformation.
What market leaders are doing differently
Organizations that successfully scale AI share several characteristics.
1. A strategy aligned with business priorities
AI is not treated as an innovation experiment.
It is positioned as a performance lever.
2. A data-centric approach
Leading organizations invest first in:
- data integration,
- governance,
- data platforms.
3. Scalable, high-ROI use cases
Instead of multiplying pilots, they focus on:
- a limited number of high-impact use cases,
- quickly scalable initiatives.
4. An end-to-end transformation approach
They work with partners capable of covering:
- strategy,
- data,
- technology,
- cybersecurity,
- adoption and change.
The Alan Allman Associates approach: from experimental AI to operational AI
Across the Alan Allman Associates ecosystem, we see a growing demand for:
- operational AI solutions,
- intelligent agents,
- data-driven platforms,
- hyperautomation initiatives.
Our approach is based on a simple principle:
AI only creates value when it is embedded in business processes and deployed at scale.
We support clients across the entire value chain:
- Identifying high-impact use cases
- Structuring AI-ready data foundations
- Designing scalable AI architectures
- Deploying and integrating solutions
- Driving change and upskilling teams
Examples of interventions across the ecosystem
H4H – Intelligent automation of business processes
Specialized in smart automation and low-code platforms, H4H helps organizations optimize and automate processes to improve operational performance.
Typical engagements:
- automation of IT and business service processes,
- large-scale automation deployments,
- integration of AI capabilities into operational workflows.
Argain Consulting Innovation – Data governance and value creation
Argain focuses on:
- organizational performance,
- project governance,
- data-driven transformation.
Typical engagements:
- data governance frameworks,
- AI-powered contract management solutions,
- transformation programs based on data insights.
we+ France & Benelux – Scalable AI and digital solutions
we+ supports organizations from:
- needs assessment,
- to development,
- through full-scale deployment of digital and AI solutions.
Typical engagements:
- modernization of financial systems,
- development of AI-enabled applications,
- creation of delivery and service centers for industrialization.
Phoenix DX – AI agents and scalable digital architectures
Phoenix DX helps organizations:
- design modern application architectures,
- deploy AI agents,
- transform digital platforms.
Typical engagements:
- production-ready AI agent architectures,
- platform modernization to support AI workloads,
- integration of AI solutions into core business systems.
From pilot to impact: the four key steps
To successfully industrialize AI, organizations should structure their transformation around four key stages.
1. Prioritize high-ROI use cases
Focus on:
- productivity gains,
- quality improvements,
- measurable financial impact.
2. Build strong data foundations
- data integration,
- governance,
- quality management.
3. Design scalable and secure AI architectures
- cloud-based platforms,
- modular architectures,
- cybersecurity by design.
4. Transform processes and skills
- workflow automation,
- role redefinition,
- team training and adoption.
Toward a new generation of transformation programs
AI marks a turning point in how transformation programs are designed.
Organizations can no longer:
- stack isolated digital projects,
- multiply pilots,
- operate in silos.
They must move toward:
- end-to-end approaches,
- data-driven decision-making,
- performance-oriented transformations.
This is precisely where consulting is evolving:
less analysis in isolation, more operational and measurable execution.
Conclusion: AI is no longer a project, it is an operating model
The challenge for organizations is no longer to experiment with AI.
It is to embed it sustainably into their operations.
The companies that succeed will:
- align AI with business priorities,
- invest in strong data foundations,
- work with partners capable of delivering end-to-end solutions.
At Alan Allman Associates, we are building an ecosystem designed to support this transformation: from strategy to industrialization.