Augmented intelligence, not AI: An ACHIEVE framework for process excellence leaders

Across boardrooms and business transformation offices, generative artificial intelligence (AI) is often framed in extremes: utopia or disruption, productivity miracle or job killer. However, there is a more pragmatic and powerful framing for process excellence professionals – augmented intelligence.

Augmented intelligence is not about replacing human judgment, creativity, or leadership. It is about amplifying them. It is the ‘exoskeleton for the mind’ that helps people think better, coordinate better, and scale better. For a discipline like process excellence – rooted in human-centered improvement, structured problem solving, and continuous learning – this framing is not only reassuring; it is strategically sound.

The question, then, is not whether to use generative AI, but how to use it in a way that strengthens human capability and organizational performance. To that end, consider the ACHIEVE framework: a practical lens for deploying generative AI as augmented intelligence.

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From automation to augmentation

Process excellence has always balanced efficiency with effectiveness. Lean removed waste but respected people. Six Sigma reduced variation but relied on human insight. Agile increased speed but emphasized collaboration and learning.

Augmented intelligence sits naturally in this lineage. It helps:

• Reduce cognitive and administrative waste.
• Surface insights humans may overlook.
• Enable faster experimentation and learning.
• Scale good ideas across the enterprise.

It does not remove the need for human thinking – it raises the bar for it.

The ACHIEVE framework

A: Aid human coordination

Many organizational failures are not technical; they are coordination failures. Misaligned assumptions, unclear ownership, and ambiguous decisions derail even the best-designed processes.

Generative AI can act as a neutral ‘second set of eyes’ to:

• Summarize meeting notes and decisions.
• Identify ambiguities in plans or requirements.
• Highlight missing data or unclear ownership.
• Suggest follow-up actions and accountabilities.

For example, after a cross-functional planning meeting, a leader can ask AI to flag ambiguities or potential gaps. Even if the tool is imperfect, it stimulates the right conversations. The value lies in prompting clarity and alignment.

In process excellence terms, this is waste reduction in communication and decision flow.

C: Cut out tedious tasks

Process professionals spend significant time on low-value cognitive work:

• Cleaning and grouping qualitative data.
• Drafting first versions of reports.
• Reformatting insights for different audiences.
• Summarizing survey responses.

Generative AI excels at this ‘first-pass’ work. It can:

• Cluster free-text responses into themes.
• Generate draft summaries.
• Reorganize information in multiple ways.
• Produce initial report structures.

This does not eliminate the analyst; it frees them to do what matters – interpretation, stakeholder engagement, and strategic thinking.

In Lean language, this is removing non–value-added cognitive effort.

H: Help provide a safety net

Even experienced leaders make mistakes. Slides use undefined jargon. Plans contain hidden conflicts. Assumptions go unchallenged. AI can function as a safety net by:

• Flagging undefined technical terms for a given audience.
• Identifying conflicting decisions across teams.
• Stress-testing plans for logical gaps.
• Reviewing communications for clarity and tone.

Imagine asking AI to review a transformation roadmap and highlight unclear assumptions. The goal is not blind trust, it is structured skepticism. This aligns well with Six Sigma’s emphasis on risk reduction and error-proofing (poka-yoke for knowledge work).

I: Inspire better problem solving

Humans are creative but also biased. We anchor on familiar solutions. We confirm what we already believe. We default to past playbooks. Used well, generative AI can challenge these patterns by:

• Offering alternative solution paths.
• Generating multiple approaches to a problem.
• Playing ‘devil’s advocate’ to proposals.
• Producing hard questions leaders should answer.

A powerful prompt for any transformation leader: “Act as a skeptic. What assumptions am I making? What could go wrong?”

This does not replace expertise; it stretches it. It pushes teams beyond habitual thinking into more innovative territory.

E: Engage the human in the loop

The biggest risk is passive use. Copy-paste thinking leads to shallow outcomes and skill erosion. Augmented intelligence requires active human engagement:

• Treat outputs as drafts, not final answers.
• Edit, refine, and contextualize results.
• Apply domain judgment and ethics.
• Iterate through dialogue, not one-shot prompts.

The competitive advantage will not go to those who use AI most, but to those who think best with it.

V: Value-driven scaling

One of AI’s superpowers is scaling good ideas quickly. A leader can:

• Personalize communications at scale.
• Generate role-specific examples for training.
• Tailor improvement ideas by function or region.
• Create customized learning scenarios.

For instance, post-training follow-ups can include department-specific prompts that help participants apply concepts immediately. What was once too time-intensive becomes feasible. This supports what process excellence strives for: scaling best practices across the enterprise.

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A practical starting point for leaders

For leaders wondering where to begin:

1. Create a compelling vision: Position AI as augmentation, not replacement. Link it to productivity, quality, and employee empowerment.
2. Set guardrails: Define data privacy rules, acceptable use, and review mechanisms. Governance builds trust.
3. Start internally: Pilot in HR, finance, PMO, and transformation teams, areas rich in knowledge work.
4. Focus on use cases, not hype: Target real pain points: reporting burden, analysis backlog, communication overload.
5. Build capability: Teach teams how to prompt, critique, and iterate. AI literacy will become a core professional skill.

The future of process excellence

The future of process excellence is not just faster processes – it is smarter people supported by smarter tools. Augmented intelligence allows professionals to spend less time formatting and more time thinking, less time chasing data and more time interpreting it, less time on rework and more time on innovation. In that sense, generative AI is not a departure from process excellence philosophy, it is its next evolution.

The organizations that win will be those that use AI not to minimize humans, but to maximize human potential. In the end, business transformation is still a human journey. Augmented intelligence simply gives us a stronger engine for it.

All Access: Future of BPM 2026

You asked, and we listened. Business process management (BPM) remains the cornerstone technology for driving organizational transformation, according to the survey results featured in the latest PEX Report. As we look toward 2026 and beyond, generative AI, agentic AI, and intelligent process orchestration are redefining how processes are designed, executed, and optimized. BPM is your key to adapting swiftly and effectively in this new era.

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