For a technology that’s designed to assist, advise, and augment human capabilities, it’s astounding how often AI gets treated like an enigma that requires endless analysis and deliberation before it’s put to work. The most recent iteration of this paradox can be seen in the growing calls for assessments, ROI filters, and feasibility studies before businesses commit to using Generative AI (GenAI).
Consultants are being hired to develop frameworks, strategists are crafting PowerPoint decks, and entire meetings are being dedicated to the question: How can we make AI work for us?
Here’s the irony: While everyone is busy over-analyzing how to integrate AI into their operations, they’re ignoring the most powerful resource at their disposal—the AI itself. Generative AI models aren’t just tools you deploy after you’ve figured out a strategy. They can help you create the strategy. If you’re wondering where AI can add value, why not simply ask it?
It’s time for companies to recognize that one of the best ways to discover how to use AI is to collaborate with it directly. Instead of commissioning yet another ROI study or engaging in more bureaucratic filtering, why not let AI be part of the discovery process? Let’s break down why it’s time to stop overthinking and start leveraging GenAI’s capabilities more intuitively.
The traditional approach to implementing new technology in business has always been methodical and measured. You identify potential use cases, run feasibility studies, measure expected ROI, and then decide whether to proceed. This approach makes sense when dealing with static tools or technologies with narrowly defined functions. But Generative AI doesn’t fit neatly into that category.
Generative AI models like GPT-4 or DALL-E aren’t rigid solutions that only work in predefined scenarios. They’re flexible, adaptable, and capable of performing a wide range of tasks—from drafting reports and generating marketing ideas to brainstorming product features and suggesting process optimizations. Treating them as if they’re static technologies, requiring weeks of deliberation before use, is like bringing a Swiss Army knife to a survival scenario and spending all your time deciding which blade to use while missing the fact that the knife itself can show you what it’s best suited for.
So, why not use AI as a collaborative partner in figuring out how it can add value? Let the AI itself help you uncover its own best use cases. Ask it to analyze your existing processes, identify bottlenecks, suggest content strategies, or even create a preliminary draft of your very own AI adoption roadmap. The responses might surprise you, and at the very least, they’ll serve as a starting point for further refinement and discussion.
Another stumbling block in AI adoption is the insistence on extensive ROI calculations before the technology is even tested in a real-world context. Managers want to know exactly how much time or money will be saved, which KPIs will be impacted, and how soon they’ll see returns. While these questions are important, they’re not easy to answer when dealing with a technology as versatile and dynamic as GenAI.
The reason is simple: GenAI’s value often extends beyond traditional ROI metrics. It can improve the quality of customer interactions, speed up ideation processes, and enable more personalized experiences—all of which contribute to long-term brand equity and competitive advantage but might not show up immediately on a balance sheet.
And guess what? If you’re struggling to calculate ROI, ask the AI for help. GenAI models can analyze cost-benefit scenarios, simulate outcomes, and even create preliminary ROI frameworks tailored to your specific context. If you’re unsure about where to start, you can ask the AI questions like:
You’ll likely receive a starting point that’s far more specific and actionable than anything a whiteboard brainstorming session could yield.
One of the most powerful, yet underutilized, aspects of GenAI is its ability to identify opportunities for its own application. It’s a self-recommending technology. You can pose open-ended questions to an AI model to help pinpoint areas where it can add value. Here are some examples of how you can “ask” AI about its own use:
Instead of spending weeks creating internal reports that may or may not capture AI’s potential, these questions can give you concrete starting points in a matter of minutes. The AI might suggest things you hadn’t considered—like automating customer follow-ups, generating SEO-optimized blog posts, or even training the sales team on product knowledge using AI simulations.
Imagine how much time companies could save if, instead of waiting for the perfect ROI calculation or a polished proposal from a consultancy firm, they just started experimenting with AI in small, manageable ways. Want to see if AI can help improve your social media strategy? Ask it to draft a month’s worth of posts, and see what you get. Curious about using AI to assist in onboarding new employees? Have it draft some training materials, and test them out with a few new hires.
These small-scale experiments offer immediate feedback and can highlight real value much faster than formal assessments ever could. They also help companies build internal expertise, create a culture of innovation, and—crucially—understand AI’s capabilities firsthand. This hands-on approach is where the true ROI of AI begins to reveal itself, often in unexpected ways.
Another reason companies hesitate to “just ask AI” is fear. Fear of AI giving the “wrong” answers, fear of AI being misused, or fear of AI making existing roles redundant. But these fears are often exaggerated, based on a misunderstanding of AI’s role as a collaborator rather than a replacement for human ingenuity.
If you’re worried about AI misuse, then the solution isn’t to avoid it altogether—it’s to create guardrails and define roles. AI can provide suggestions, but humans must be in the loop to validate, refine, and implement these suggestions. Fear shouldn’t paralyze companies into inaction. If anything, the best way to understand the risks is through controlled, small-scale experimentation where failures are learning experiences rather than catastrophes.
It’s time for companies to stop seeing GenAI as an intimidating, “all-or-nothing” investment that needs to be fully justified before being used. AI is a dynamic, evolving tool that thrives on interaction and iteration. By asking AI to help shape its own use case, companies can demystify the technology and discover new ways to leverage it that traditional ROI filters would never reveal.
Instead of debating how to use AI behind closed doors or waiting for the perfect business case to emerge, why not simply engage with the technology and see where it takes you? The best way to understand AI’s potential isn’t through endless deliberation and analysis—it’s by using it, experimenting with it, and letting it guide you toward new opportunities.
The next time someone at your company proposes another feasibility study or ROI assessment before greenlighting a GenAI project, ask yourself: Why not just ask AI where it can add value? Why not let it participate in its own discovery process?
After all, the AI isn’t just sitting there waiting to be used—it’s ready to engage, advise, and adapt. All you have to do is start the conversation. So, let’s stop the overthinking, stop the hesitations, and start collaborating with AI in real, impactful ways. Because if we keep getting caught up in analysis paralysis, we’ll miss out on the true value that AI brings: a partner that’s willing and ready to help, if only we’d ask it to.
About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com