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Process optimisation: why AI is now a strategic imperative

By leveraging AI in your process optimisation, you can optimise workflows and transform inefficiencies into strategic advantages. Here, we outline a three-step roadmap for success: from discovery to redesign to implementation.

7 minutes to read
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Sebastian Schweitzer

Principal Data Consultant

Processes: the true levers of competitive differentiation 

The way a company runs its processes can make or break its competitive edge. Streamlined, efficient processes have long been a way of differentiating yourself from your competitors, ensuring faster service, higher quality, and a better customer experience. 

In the past, efficiency gains have been delivered by traditional process improvement methods (think digitalisation and rule-based automation). However, these approaches tend to suffer from diminishing returns and can struggle with the growing complexity and speed of business today. 

This is where AI-driven process optimisation comes into play. By leveraging advanced artificial intelligence (from machine learning to generative AI), organisations can analyse vast amounts of data, uncover hidden inefficiencies, and continuously improve processes in ways that were previously impossible. 

The result is not just doing things right, but learning to do them better over time, giving early adopters a significant head start. 

A new strategy: from human-crafted to AI-driven processes 

AI is driving tangible business impact and momentum is building fast. Gartner predicts that by 2028, AI agents will support at least one-third of business decisions, up from less than 1% today. That’s not incremental change: it’s a complete rethinking of how businesses operate. 

But, instead of viewing AI as a lever for automating specific niche areas, shouldn’t companies be looking deeper to unlock new opportunities across their entire business?  

As Harvard Business Review puts it, ‘Smart companies are viewing the introduction of AI as the rationale for a new look at end-to-end processes’. 

With the right strategy, companies can leverage AI not just to fix broken workflows or cut costs, but to fundamentally reshape how work gets done. This is the essence of truly AI-driven processes: systems that don’t just run faster but that get smarter over time. They don’t just support business decisions, they elevate them. 

So what sets AI-driven processes apart from traditional digitalisation or rule-based automation (RPA)? 

AI-driven process optimisation can tap into any of the following features: 

  • AI-driven enhancements: Enhancing existing processes with adaptive, predictive, or context-aware capabilities that go beyond static rules.
  • Human-AI synergy: Redesigning workflows to optimise collaboration between humans and AI, leveraging the strengths of both.
  • Autonomous AI ecosystems: Building towards agents that operate with minimal oversight, and take self-directed decisions and actions. 

In essence, AI-driven process optimisation isn't merely about adding artificial intelligence to existing workflows; it's about reimagining those workflows from the ground up to place genuine human machine intelligence at their core. 

This means designing processes where human and artificial intelligence are not just integrated, but co-dependent and mutually reinforcing, enabling entirely new levels of adaptability, insight, and impact. 

Man operating machine is checking the metal parts before start the yoke machine with digital data display. factory and manufacturing concepts

The three phases of AI-driven process optimisation 

Phase 1: Process discovery 

The first step in your process optimisation efforts should always be analysing processes in depth in order to understand which processes offer the greatest potential for AI-driven gains. This can be accomplished by screening processes and evaluating them in terms of AI-fficiency and achievability.  

You should start by looking for processes with: 

  • Employee frustration 
  • High manual effort 
  • Delays 
  • Bottlenecks 
  • Errors or gaps 
  • Repetition 
  • Too many people involved 
  • Budget inefficiencies 

Then assess them for: 

  • AI-fficiency gain, prioritising process optimisation that is able to deliver genuine financial impact, is a strategic fit, and can improve the workflow experience. 
  • Achievability, taking into consideration the cost and resources needed for deployment, the level of risk to the business, and technical feasibility.  

After completing the screening and evaluation phases, you can map your processes to the AI-fficiency process matrix. This enables you to identify which processes should be prioritised and, most importantly, to identify your prime movers, offering both high achievability and AI-fficiency gains. 

Phase 2: Process redesign 

Identifying the right opportunities is just the beginning. True transformation takes place when you uncover the structural root causes behind inefficiencies and start enhancing or entirely redesigning processes for AI-driven operations.  

The process redesign phase has four main steps. Following these steps should help you to understand the problem space and the possible solutions space.

  • Step 1 – Capture 'as-is' process flow & pain points

    The first step is to develop a clear, shared understanding of the current state. In this step, your priorities should be to: 

    • Define process goals, success metrics, and ownership
    • Capture existing workflows, pain points, needs, and constraints
    • Map the 'as-is' journey 

  • Step 2 – Determine perspectives and  root causes 

    It’s not enough to understand what’s broken, you need to understand why. To do so, you need to: 

    • Develop root cause hypotheses for pain points
    • Validate root cause hypotheses with key stakeholders
    • Confirm root cause frequency and severity 

    Root causes can typically be located across four structural dimensions: 

    1. Structure & workflow
    2. Systems & tools
    3. Skills & expertise
    4. Culture & organisation 

  • Step 3 – Explore possible solution ideas and pathways 

    With the problem space clearly defined, the focus shifts to identifying and shaping potential solutions. In this phase, you should: 

    • Organise the root causes into clusters and prioritise them
    • Confirm which of the four structural dimensions (see above) can be changed
    • Conduct ideation sessions with stakeholders and experts to identify possible candidate solutions 

  • Step 4 – Assess solution elements and prioritise 

    With ideas for solutions at hand, your next move is to evaluate and prioritise the ideas that will drive the most value. To do this, you should: 

    • Cluster solution candidates across pain points and prioritise
    • Assess solution candidates using the DFV framework (Desirability, Feasibility, Viability)
    • Design the new 'to-be' journey based on candidate solutions  

Phase 3: Process implementation and continuous optimisation 

Discovering inefficiencies and redesigning processes for AI-driven operations are critical steps, but without strong implementation even the best designs remain theoretical. 

At Zühlke, we know from experience that execution is where most initiatives stall, and where leadership commitment makes the difference between pockets of success and enterprise transformation. That’s why the final phase of AI-driven process optimisation focuses on a clear roll-out plan, supported by a detailed blueprint, agile delivery, and structured change initiatives, to turn ideas into real, measurable outcomes.

  • Step 5 – Detail blueprint and roll-out plan 

    The first step in implementation is turning your vision into a clear and actionable roadmap. To do this: 

    • Clearly document your functional and non-functional requirements from both user and technical perspectives and turn them into a feature backlog
    • Develop an architecture blueprint that empowers your new business process and that covers the full spectrum of data, application, and technology layers
    • Create a release roadmap that includes key milestone deliverables across both technical and supportive workstreams, such as change management, upskilling, and governance. 

  • Step 6 – Realize solution elements and change initiatives according to roll-out plan 

    With the backlog, blueprint, and roadmap in place, it’s time to move from planning to execution. Here’s how you drive change effectively: 

    • Deliver value fast by incrementally & continuously shipping features and improvements in an agile manner
    • Execute a structured change management plan with clear communications, targeted training, and employee engagement
    • Provide strong post-launch support to drive adoption, address issues early, and reinforce new behaviours 

What makes AI-driven process optimisation so successful? 

AI-driven process optimisation offers a practical and scalable way to embed artificial intelligence at the heart of your operations - not just as a tool, but as a catalyst for business transformation. By pursuing an end-to-end focus on real processes, rather than isolated use cases, it delivers tangible impact fast. 

AI-driven process optimisation is successful because it: 

  • Meets you where you are: It works across all levels of process maturity, creating value from day one while enabling future scalability. 
  • Takes an end-to-end approach to optimisation: It takes an end-to-end look at your processes - not at isolated tasks - driving systemic improvements across your core business operations. 
  • Sets your AI flywheel in motion: It answers the question of how to adopt AI quickly and meaningfully, acting as a lever for overcoming stagnating growth and mounting cost pressures in the new AI-driven economy. 

Three potential next steps for C-level leaders

  • Vision & scope workshop

    Unite your executive team around a shared AI transformation vision. In this facilitated session, you'll: 

    • Define clear, actionable strategic objectives for AI
    • Prioritize high-impact business domains
    • Scope your transformation so that it aligns with your long-term goals 

    Outcome: A unified leadership vision and a focused, AI-ready initiative scope. 

  • Process discovery

    Map your current operations to uncover high-value opportunities. Through structured analysis, you’ll: 

    • Identify inefficient, manual, and resource intensive processes 
    • Understand the impact of AI-driven process optimisation on your processes 
    • Develop a business case detailing the economic potential of making the selected processes AI-fficient 

    Outcome: A clear transformation roadmap grounded in your business reality. 

  • Process redesign

    Choose one high-value process and reimagine it with AI at the centre. This deep dive empowers you to: 

    • Enhance and redesign your process to make it truly intelligent and adaptive 
    • Identify existing process inefficiencies and root causes 
    • Define a target picture that creates confidence and generates internal momentum for enterprise-wide AI-ficiency adoption 

    Outcome: A ready-to-implement blueprint for AI-driven process optimisation. 

By taking these steps, leaders can unlock significant cost efficiencies, drive innovation, and secure an enduring competitive advantage. The time to act is now - transform your operational processes to thrive in the AI era. 

How Zühlke can help you leverage AI for process optimisation 

At Zühlke, we partner with organisations across a range of industries to deeply analyse, redesign, and implement business-critical processes tailored to their unique operational and strategic needs. 

Whether you are looking to enhance efficiency, drive agility, or scale intelligently, we can help you unlock the full potential of AI. Talk to us today to explore how we can transform your processes into a true competitive advantage.

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