By Building on Momentum and Strength to Achieve Success

Let’s Work Smarter, Not Harder

At Illuminate, we believe organizations can approach AI in a smarter way by building on what already works. That’s the heart of AI²: Appreciative Inquiry × Artificial Intelligence. This piece introduces the AI² framework and explores how it helps organizations adopt AI with clarity, integrity, and impact.

For more than two decades, our founder, Beeta Tahmassebi, has helped organizations tackle complex challenges not by fixing what’s broken, but by scaling what already works. This approach, rooted in Appreciative Inquiry, is a proven method for driving faster and more sustainable results.

Artificial intelligence is often described as a force multiplier. Appreciative Inquiry is one too. It focuses on building from strengths rather than ignoring problems. It encourages leaders to look at what’s going well, what’s possible, and how to build momentum. That mindset is especially useful when navigating something as complex and fast-moving as artificial intelligence.

As leaders in the development and nonprofit sectors, we have seen that meaningful change starts with curiosity, connection, and the courage to build on what’s working. Fear and deficit thinking rarely move organizations forward.

So, when artificial intelligence began dominating boardroom agendas, we asked ourselves: What would it look like to apply Appreciative Inquiry to AI adoption? Not as a metaphor, but as a practical framework that helps teams think clearly, act responsibly, and build confidently.

That idea became AI²: Appreciative Inquiry × Artificial Intelligence. AI² is a values-driven, strengths-based approach to digital transformation. It invites curiosity, centers people, and helps organizations move forward with imagination and integrity.

From Hesitation to Possibility

Today’s leaders are under immense pressure to “figure out AI.”
Some feel excited. Most feel overwhelmed.

In nearly every client conversation, we hear some version of the same tension:

“We know we need to wrap our heads around this, but where do we start?”

The dominant narratives around AI are binary: it’s either the end of the world or the key to unlimited productivity. But these extremes don’t help leaders make grounded decisions.

Instead of asking whether AI is good or bad, AI² begins with a different question: “What are we already doing well, and how could AI help us do it better, faster, or with less strain?”

What Is AI²?

AI² is an approach to artificial intelligence grounded in the core practices of Appreciative Inquiry – a change methodology that seeks to amplify strengths, surface positive deviance, and co-create futures built on what gives life to an organization.

As David Cooperrider and Diana Whitney write in Appreciative Inquiry: A Positive Revolution in Change, real transformation happens when we focus on what gives life to people, teams, and systems, not just on what’s broken.

Instead of focusing only on risk management or compliance (important, but not sufficient), AI² invites organizations to explore:

• Where AI is already adding value in small, organic ways
• What values should guide future adoption
• How to scale and steward use cases that reflect the organization’s mission

This isn’t about blind optimism. It’s about grounded, ethical experimentation, with a bias toward learning and alignment.

While much of today’s focus is on generative AI (tools that create text, images, or code), the AI² approach applies just as well to analytical and assistive AI systems, including those used for prediction, search, and classification.

The Problem with “Risk-First” Thinking

Most AI governance frameworks focus on risk related issues like bias, transparency, privacy, and misuse. That is vital. But when organizations begin with only “what could go wrong,” they often get stuck.

Teams hesitate to try anything. Innovation gets siloed. Momentum dies in committee.

AI² doesn’t ignore risks, it reframes them. Rather than treating AI as inherently dangerous, it asks:

• What are the decisions that should never be outsourced to machines?
• How do we safeguard values while enabling progress?
• Where can AI support human judgment, not replace it?

For example:

• Don’t expect AI to replace deep listening or real dialogue. But do use it to transcribe interview notes so you can spend more time on interviews and bringing in new voices.
• Don’t let AI make assumptions about your stakeholders. But do use it to help synthesize themes from your documents so you analyze sources/background reports in a fraction of the time it used to take you to do the same work.

That’s what responsible possibility looks like.

AI² in Action: Practical Use Cases

Imagine a team faced with hundreds of documents from prior research, evaluations, and stakeholder interviews, needing to inform an upcoming strategy refresh.

Instead of starting from scratch or reading everything line by line, they could use AI tools to rapidly scan and summarize key insights by theme.

Then, they gather staff to do quality control on the AI analysis, reflect on what the emerging insights mean, layer in lived experience and organizational values, and update their plans based on what matters, not just what surfaced.

AI supports the work. It doesn’t replace it. That’s AI² in action.

Other practical applications include:

• Survey Analysis & Thematic Clustering: AI can process open-ended feedback at scale, surfacing key themes and sentiments, allowing human analysts to focus on interpretation and insight.
• Proposal Drafting or Past Performance Tailoring: AI can assist in customizing capability statements and RFP responses, saving time while ensuring alignment with client needs.
• Meeting Note Capture & Action Summaries: AI-powered transcription tools generate clear records of decisions and next steps, improving internal alignment for small teams.

These are not future use cases. Companies are already doing this, and adoption rates are growing fast. According to McKinsey’s 2024 State of AI report, 78% of organizations now use AI in at least one business function, and 71% report regular use of generative AI. While much of the attention is on marketing and IT, adoption is growing rapidly in knowledge-intensive domains like strategy, compliance, and knowledge management, where tools like text summarization and pattern detection can support deeper learning and better decisions.

What Leaders Can Do Now

If you’re a leader trying to navigate AI with integrity, here are a few starting points:

• Start with strengths. Look for places where your teams are already using AI productively, even informally. These are bright spots worth nurturing.
• Name what AI is for. Instead of chasing novelty, define the outcomes you care about. Ask: What problems are we trying to solve, and how can AI help?
• Set guardrails with purpose. Be explicit about what AI shouldn’t do in your organization. That clarity builds trust and lowers resistance.
• Invite people in. AI² is participatory. Engage staff, partners, and communities in co-creating how AI tools are used and governed.

Leading with Courage and Clarity

The promise of AI isn’t that it will do our jobs for us. It’s that it might help us do more of what matters—with focus, creativity, and alignment.

We don’t need to outsource our judgment.
We need to strengthen it.
We don’t need to fear every new tool.
We need to shape how they’re used, together.

That’s the heart of AI²: Appreciative, intelligent, and human.
Let’s build from what’s working.

What’s Next

This is the foundation of AI²: a practical, people-centered approach to artificial intelligence that starts from strengths and scales what works. Curious about how AI² can support your organization? Explore our services at illcglobal.com or reach out to btahmassebi@illcglobal.com.