When discussing Generative Artificial Intelligence (GenAI), our attention often gravitates toward the word “Intelligence.” However, the most crucial word in GenAI is “Generative.”

The core purpose of GenAI is to generate.

• “Generative” is the ‘why’ behind GenAI.

• “Artificial” represents the ‘what’ – a technological rather than biological process.

• “Intelligence” is merely the ‘how,’ describing the computational methods that enable generation.

Yet, we routinely invert this hierarchy, becoming distracted by the word ‘intelligence’. This leads to our struggles in finding scalable applications of GenAI, despite a desire for significant investment in AI.

Teams often ask, “Let’s find use cases to use AI?” as if the solution is seeking a problem, leading to forced use cases or siloed scenarios that are disconnected from practical business activities.

Instead, what if there’s a pattern that reveals a treasure trove of use cases for GenAI? A systemic way to look at the work your organization does every day?

There is… we just need to tilt the angle of our curiosity and question.

Instead of asking where we can apply AI, ask:

What is being ‘generated’? And should we co-generate it using GenAI?

The Word ‘Generative’ Is the Key to Unlocking GenAI Use-Cases

The focus on the word ‘generative’ unlocks the difficulty in discovering use cases. Instead of starting with AI capabilities and hunting for applications of AI, organizations should inventory what they’re already generating:

• Reports

• Presentations

• Code

• Designs

• Analyses

• Communications

• Proposals

• Training materials

Anything being generated in the form of text, voice, images, or data by people is a candidate use case for GenAI.

The question becomes: “What generative work is currently consuming our time and talent, and how can AI augment this generation?”

The fundamental principle is simple: if it is being generated by humans, it can be and should be generated by machines in collaboration with humans.

Consider how this applies across every form of output in your organization.

• Your finance team generates text through risk reports and regulatory documents.

• Your marketing department generates images for social media and promotional materials.

• Your training teams generate videos for onboarding and safety demonstrations.

• Your developers generate code, documentation, and testing scripts.

• Your call center representatives generate spoken responses to customer inquiries, following similar patterns hundreds of times daily.

The pattern is universal: if humans are generating it, machines can collaborate in that generation process.

Just as autonomous cars shift the role of humans from driving to directing, deciding, and supervising the driving, GenAI shifts the role of humans from generating content to directing, deciding, supervising, handling exceptions, and assuring the quality of the content being generated.

A Systematic Approach: Conducting Your Generative Inventory

The most effective way to systematically uncover AI’s potential is to catalog the ‘generative’ activities already happening across your organization.

Begin by examining every output your teams produce across all functions, including intermediate work products and communications, not just final deliverables.

This comprehensive scope reveals that every department is already a ‘generative’ powerhouse.

Whether it’s risk analysts writing credit assessments, marketing teams designing graphics, training departments recording videos, developers documenting code, or support agents explaining solutions, the pattern is consistent: humans are continuously generating content that follows recognizable structures and workflows.

• The finance team generates text through risk reports, regulatory documents, and financial analyses.

• Marketing departments create images for social media, promotional brochures, and multimedia content alongside written proposals and presentations.

• Training teams produce instructional videos and documentation while developers generate code, API guides, testing scripts, and technical specifications.

• Call centers create spoken responses and chat explanations following similar patterns daily.

• Even R&D teams generate synthetic data for testing, research summaries, and analytical reports.

Once this generative landscape is clear, then for every output, ask:

• If humans are writing it, why can’t it be drafted by GenAI?

• If humans are speaking it, why can’t GenAI speak instead or enhance that dialogue?

• If humans are coding it, why not leverage GenAI to accelerate it?

• If humans are designing it, what role could GenAI play in ideation or iteration?

• If it is generated in one format (e.g., text), can GenAI regenerate it in another format (e.g., audio, video)?

These questions force us to flip the discovery process.

Instead of searching for AI applications, you’re questioning why human-generated work couldn’t be enhanced with GenAI.

Suddenly, AI opportunities become glaringly obvious, no longer hidden in plain sight.

This isn’t about automation or replacing humans with machines. It’s about “autnomation”: augmenting human generative capacity to shift roles from execution to supervision, from creation to curation, from doing to directing.

When Toyota revolutionized manufacturing, they introduced the concept called autonomation – automation with a human touch. Workers moved from repetitive tasks to roles directing, validating, and managing automated processes.

GenAI amplifies human judgment, enabling teams to scale output exponentially while freeing their time and talent to generate more.

Scaling to Systematic Industrialization

Most AI initiatives are unable to scale because they focus on finding use cases rather than fundamentally reimagining and reengineering the way work is getting done.

The purpose of GenAI is to ‘generate.’

The best way to align with this purpose is to activate everyone in the organization to look for what they generate and then ask: “Can this be generated with GenAI assistance?”

This approach is more straightforward for everyone to understand and far less threatening.

When the focus is on the word ‘generating’, people can recognize a pattern in their work that can relate to the purpose of GenAI tools.

• The finance analyst sees their weekly reports as ‘generative’.

• The marketing coordinator sees their social media posts as ‘generative’.

• The customer service representative sees their response patterns as ‘generative’.

• The developer sees their documentation as ‘generative’.

It’s intuitive, immediate, and personal. It reveals the potential and enormous scope of GenAI.

This focus on generating is key to grassroots-driven adoption and transformation. Instead of top-down AI mandates that create resistance, you’re empowering every employee to identify opportunities in their own work. They’re not being asked to learn AI, they’re being asked to recognize what they already generate.

This shifts the conversation from “How do we implement AI?” to “How can we enhance and amplify what we’re already generating?”

This approach transforms AI from merely a technology into a significant competitive advantage.

By systematically focusing on generative tasks, companies can quickly integrate GenAI into every aspect of their operations. Rather than searching for new applications, organizations are refining existing workflows that already involve substantial human effort. Businesses that adopt this strategy can achieve productivity gains of 10 to 20 times in generative work, not because the technology itself is revolutionary, but because they are enhancing processes that are already in place.

By enhancing their generative capacity in this manner, organizations can achieve compounded benefits across all business functions, effectively reshaping their operational foundation through grassroots recognition and the systematic enhancement of generative work.  

Activating a GenAI Transformation

CIOs can activate this transformation right in their department first and be the role model for the larger organization.

IT function is a “generative powerhouse,” as most IT tasks involve generation. Now IT professionals can shift their focus from generating outputs to analytical reasoning, which includes analyzing, designing, directing, ensuring quality accountability, and managing exceptional situations.

Everything can be co-generated.

CIOs can envision and aspire to autonomous IT (if not all, then most sub-functions), where the role of team members evolves from doing tasks to directing outcomes, much like how an autonomous car allows humans to transition from actively driving to directing the driving outcomes.

Here is the call to action for CIOs:

• Reframe IT’s strategy around generative transformation.

• Focus on outputs like code, documentation, tickets, and incident reports… almost everything in IT is generative.

• Conduct a Generative Inventory of all IT activities that produce content.

• Empower and enable teams to embed GenAI tools in every workflow of IT.

• Enforce governance controls and adjust as the maturity and dependability of GenAI grow.

• Track impact on productivity, quality, and agility.

• Upskill teams and position GenAI as an assistant, not a replacement.

• Make the GenAI transformation visible to other departments.

The Wrap

The breakthrough you’ve been seeking is in recognizing that your organization is a generative powerhouse.

Every report, presentation, analysis, and communication represents an opportunity waiting to be amplified.

The transformation you’ve been looking for lies in transforming your generative capacity and agility. A GenAI transformation or Generative Agility can be measured as the percentage of organization’s generative tasks co-generated with GenAI.

Set an aspirational goal of 100% to reshape every act of generation as co-generation with Artificial Intelligence, rather than being solely a manual effort.

This metric changes everything, from counting AI pilots or measuring technology adoption, to tracking the behavioral shift toward AI-augmented ‘Generation’ of outputs across every role, department, and workflow.

• When your finance analyst drafts reports with AI assistance…

• When your marketing team co-creates content with GenAI…

• When your developers code with AI support…

• When your customer service reps respond with AI-enhanced dialogue…

you’re approaching true ‘Generative’ transformation.

Imagine if every team member in your company is empowered to use GenAI to generate their outputs and keeps score of what percentage of their work is generated with AI assistance.

What if everyone starts to see GenAI as their Personal Generator Assistant instead of a threat to their role, and an amplifier of their creative and analytical capabilities? Every worker deserves to have a Personal Generator Assistant just like they have a computer or mobile device to amplify their work and its impact.

Shift everyone’s focus to the word ‘Generative’ and ask:

“What are we generating, and what percentage of it is currently co-generated with your Personal Generator Assistant?”

The gap between your current percentage and 100% reveals your GenAI transformation roadmap and the inventory of use cases.

The opportunity is right in front of us.