scaling AI: rewiring the operating model for sustained success

In our first article, we outlined a blueprint for scalable AI transformation - highlighting that success requires a holistic approach, not just endless technology POCs. Organisations must align AI initiatives with strategy, robust tech and data foundations, an evolved operating model, and a future proofed AI augmented workforce.

We then explored the critical role of technical readiness, highlighting the need for strong data, automation, and infrastructure to enable AI’s full potential in driving business effectiveness.

While these foundations provide the bedrock for success, AI’s real impact is scaled & unlocked when businesses evolve their operating model - their structures, workflows, and culture - to fully capture AI’s potential across the enterprise.

Why operating models must evolve for AI

Traditional operating models were built for efficiency in a pre-AI world - characterised by hierarchical decision-making, siloed teams, and rigid workflows based on departmental structures. The emergence & democratisation of AI, however, introduces new capabilities that demand more agility, continuous learning, and human-machine collaboration. While some businesses can evolve existing structures & workflows, many find transformative capabilities are being shoehorned in to legacy models.  This limits their effectiveness, creates inefficiencies & ultimately becomes the biggest blocker to adoption & scalability – getting stuck in the POC phase.  By sequencing this change strategically, businesses can ensure AI is deeply integrated & embedded into the organisation effectively - not just layered on top.

Lumo blueprint: a structured approach

At Lumo, our AI readiness assessment is the first step in operating model transformation, ensuring organisations have a clear baseline for AI maturity, key business priorities, and operational changes required for successful execution, adoption & scaling.  We outline some of our key insights & pragmatic tips below:

Build AI-optimised structures

To fully capture AI’s potential, businesses need to evolve & flex traditional hierarchies and rigid operating models. Organisations should look to:

• Adopt a federated AI model - enabling business units to retain autonomy while benefiting from centralised AI governance, compliance, and best practices.

• Establish AI-powered CoE’s - providing tooling, enablement, and AI expertise while supporting domain specific teams.

• Embed AI into existing business functions - integrating AI and ML specialists into cross-functional teams rather than operating AI as a separate initiative.

Redesign workflows for AI-driven effectiveness

Traditional workflows were designed for human-led, linear processes which often won’t maximise AI's capabilities.  Existing processes & workflows will need to be redesigned from the ground up, to fully integrate & scale AI capabilities:

Start by mapping workflows to capabilities

• Identify which tasks or processes should be automated, augmented, or remain human led.

• Rethink handoffs between machines & humans - ensuring AI-driven insights seamlessly inform decision making.

Automate where it adds real value

• AI isn’t just about replacing repetitive tasks - it should also enhance workflows by reducing bottlenecks and inefficiencies.

• Organisations must track how automation improves resource utilisation - ensuring efficiency gains lead to measurable business impact.

Integrate human-AI collaboration into workflows

• AI should be a co-pilot, not a replacement - organisations must define where human oversight, validation, and intervention are critical.

• ‘Human in the loop’ models should be built into governance frameworks to ensure AI reliability and trust.

Continuously monitor & optimise AI-enabled workflows

• AI focussed workflows should evolve over time - organisations must set up continuous monitoring to track efficiency, accuracy, and business outcomes.

• Regular feedback loops should be in place to refine AI models and improve workflow integration over time.

Embed a culture of change beyond technology

Technology alone is not enough - mindset shifts and cultural adaptation are critical at all levels, from leadership to front-line employees.

• Upskilling and reskilling: AI training must be tailored to business context - ranging from basic AI literacy (e.g., using GPT models for productivity) to advanced AI enablement (e.g., integrating AI powered decision making into workflows). One size fits all training won’t drive scaled enablement & adoption.

• Adaptive ways of working: AI transformation is not a one-off project - it requires a shift to more agile, experimentation-led approaches where teams are enabled to continuously test, learn, and refine how AI integrates into workflows. Organisations must foster cross-functional collaboration, AI champions, and a culture where iteration is encouraged rather than resisted.

• Trust and transparency: Trust in emerging capabilities is built incrementally - by proving value through small, controlled use cases, ensuring transparency in AI decision-making, and gradually scaling its role across the business.  Communicating across the organisation what has worked, & more importantly what hasn’t, will help build credibility in your change programme.

Practical steps for evolving and measuring the operating model

• Assess current AI maturity: Conduct a comprehensive evaluation of the current operating model, identifying key pain points, inefficiencies & gaps. Use maturity assessments & benchmarking frameworks to quantify readiness.

• Redesign key workflows: Prioritise workflows that deliver measurable outcomes when enhanced with new capabilities. Select a workflow, redesign it, and adjust team structures to integrate AI capabilities where relevant.

• Pilot AI-embedded teams: Launch pilots that embed AI experts into teams rather than running AI initiatives in isolation. These pilots should focus on practical application, gathering insights, and iterating based on real world feedback.

• Create a governance framework: Establish AI governance policies, including human oversight, compliance mechanisms, and stage gates to ensure responsible AI use, building trust & regulation through the process.

• Develop robust KPIs: Implement key metrics to measure AI adoption, efficiency gains, and business outcomes, ensuring impact is continuously tracked end to end.

• Invest in change management: AI adoption requires strong leadership, structured communication plans, and continuous training to ensure sustainable success at scale.

First step: assess your AI readiness

Reinventing the operating model is a critical step in achieving, scaling & sustaining successful outcomes from AI & emerging capabilities.  At Lumo, we guide organisations through this transformation with a pragmatic, business first approach - helping leaders design AI-ready structures, workflows, and cultures that drive sustainable success.   

Our structured approach to evaluating AI maturity, identifies key transformation priorities, and ensures the right foundations for scalable success.  If you’d like to explore how we can help shape your AI transformation strategy for real & scalable impact, get in touch.