Most organizations dive into AI with enthusiasm but stumble right out of the gate. They ask the wrong question from day one: "Can AI do this?" It seems logical enough, but this approach puts AI in the driver's seat when humans should be steering.
The better question transforms everything: "What can I do with AI?" This subtle shift changes AI from the protagonist of your story to a powerful tool in your hands. Instead of wondering if AI can write content, generate reports, or analyze data, start asking how AI can help you create better content, deliver more insightful reports, or make smarter decisions.
This mindset difference isn't just semantic—it fundamentally changes how organizations approach AI adoption. When you make AI the hero, you end up chasing shiny tools and impressive demos. When you make your team the hero, you focus on solving real problems and achieving business outcomes.
The path from AI potential to performance requires more than just technology. It demands strategic thinking, cultural change, and a framework that keeps humans at the center. Organizations that get this right don't just implement AI—they transform how work gets done.
The most common conversation AI consultants have with marketing leaders starts with "Can you help me with AI adoption?" When asked what they want to accomplish, there's often an awkward pause. The CEO asked about AI strategy, so they're exploring AI—without knowing what problem they're trying to solve.
This backwards approach leads to scattered efforts and disappointing results. Successful AI adoption starts with establishing a strategic north star built around clear business objectives. These objectives typically fall into two categories: pain points you want to mitigate and opportunities you want to leverage.
Consider a large enterprise marketing organization with 200 global marketers facing two potential AI projects. The first involved using AI to recategorize assets in their decade-old digital asset management system that nobody used effectively. The second focused on AI-powered cold prospecting for a new vertical they were entering.
Both were technically valid AI use cases. But when viewed through the lens of business value, the choice became clear. Developing pipeline for a new vertical directly tied to revenue growth and market expansion. The digital asset management project, while potentially useful, ranked lower on the value chain.
This doesn't mean the asset management project was wrong—just that timing and relative importance matter. Organizations should evaluate AI initiatives against their strategic priorities and plan them in cycles. What bubbles up as critical today might shift in six months based on changing business needs.
The key insight: start with strategy and business outcomes, then find where AI fits. Don't start with AI capabilities and try to force them into your business.
While organizations obsess over AI technology, they consistently overlook the most critical factor: people. Change management represents the single most neglected aspect of AI adoption journeys. Companies focus intensely on what AI can do while ignoring what their people think and feel about it.
The vulnerability manifests differently across organizational levels. Middle managers often feel most threatened, worried they'll be replaced by younger, AI-savvy employees. Senior executives feel lost without a clear vision of what their AI-infused strategy should look like. They're concerned about governance, brand risks, and the fear of AI initiatives backfiring publicly.
Meanwhile, younger employees frequently feel frustrated that their companies aren't investing enough in proper AI training and development. They see the potential but lack the organizational support to pursue it effectively.
A common thread runs through all these groups: confusion about guidelines and governance. Employees want to do the right thing, but they don't know what's okay to use AI for and what isn't. This uncertainty creates paralysis that stifles adoption even when leadership supports AI initiatives.
The training investment gap makes things worse. Survey after survey—from Salesforce, Adobe, and the Marketing Institute—identifies lack of training and knowledge as the primary barrier to AI adoption among marketers. Yet organizations remain surprisingly reluctant to invest in AI education for their teams.
As one executive worried: "If I train my team, now they're AI-savvy marketers and they might leave." The response captures the paradox perfectly: "What if you don't train them and they stay? Now you have a bunch of dinosaurs who don't know AI and struggle to stay relevant."
Creating successful AI adoption requires cultural transformation. Organizations need to build experimentation cultures with risk tolerance for new technologies. They need open dialogue about AI fears and concerns. Most importantly, they need leadership that sets clear vision and expectations while providing time and resources for teams to learn and experiment.
Transforming from AI potential to performance requires fundamental mindset changes. Four key pillars create the foundation for this transformation.
Pillar 1: From "Can AI do this?" to "What can I do with AI?"
This shift makes humans the protagonists of their AI story. Think of it as the human-AI sandwich: humans provide creativity, intuition, and strategic vision at the beginning, AI processes and analyzes in the middle, and humans verify and refine the output at the end.
This approach acknowledges that while AI continues improving rapidly, humans remain essential for directing AI effectively and ensuring quality outcomes. The goal isn't to replace human judgment but to amplify human capabilities.
Pillar 2: From Outputs to Outcomes
Many organizations get excited about AI's ability to increase output volume. They celebrate going from 50 blogs per year to 500 blogs per year without asking whether 500 blogs actually serve their business better than 50 high-quality pieces.
This output mentality misses the point. The real question isn't whether you can produce more content, reports, or analyses—it's whether increased production drives better business outcomes. Focus on the results you want to achieve, then determine if AI helps reach those goals more effectively.
Pillar 3: Reclaiming Time for Human-Centric Work
Nobody works 40-hour weeks anymore. Most professionals put in 60-plus hours, leaving little time for strategic thinking, relationship building, or creative problem-solving. AI offers the opportunity to reclaim time from routine tasks and redirect it toward uniquely human activities.
This means more time to think, imagine, lead, and inspire. It also means better work-life balance and stronger personal relationships. When people understand that AI can free them to do more human things rather than replace human value, they become much more enthusiastic about adoption.
Pillar 4: Embracing the Previously Unimaginable
Consider how Uber combined four existing elements—maps, internet, phones, and apps—to completely revolutionize transportation. The individual pieces existed, but their combination created something nobody had imagined before.
AI presents similar possibilities. The challenge is remaining open to ideas and applications that seem impossible today. This requires willingness to experiment with approaches that don't fit current workflow patterns and comfort with uncertainty about where AI might lead.
Understanding AI's potential requires distinguishing between doing things differently and doing different things altogether. Most organizations start with the first approach—using AI to improve existing processes. But the real transformation comes from reimagining what's possible.
A marketing analytics team at a large B2C organization initially wanted to use AI to accelerate their data pipeline processes. They envisioned taking data through existing systems faster and creating visualizations more efficiently. This represented doing things differently—same process, better execution.
The breakthrough came from challenging that assumption. Instead of faster visualizations, what if there were no visualizations at all? The team developed a conversational interface where marketers could simply "talk to their data." Instead of navigating complex dashboards, they could ask natural language questions like "What was our most successful campaign last quarter?" and receive immediate, contextual insights.
This approach transformed the entire concept of data analysis. The underlying data sources and pipelines remained, but the human interaction became completely different. Marketers no longer needed to interpret charts and graphs—they could have conversations with their data and receive insights served up directly.
This evolution from different to transformational requires patience. Like any major technology adoption, AI follows the familiar bell curve. Early enthusiasts lead the charge while skeptics resist change, and the majority gradually moves from healthy skepticism to cautious optimism. Organizations need patience and consistent leadership commitment to guide this transition successfully.
For individuals feeling motivated but unsure where to start, the key is avoiding overwhelm. The constant AI buzz on LinkedIn and in business publications can create FOMO that leads to paralysis. Instead, take a measured approach.
Start by dipping your toes in rather than diving deep immediately. Plenty of free resources exist, from Coursera's AI courses to industry-specific learning opportunities. If formal courses feel too intensive, follow genuine business leaders in your field who curate and share relevant AI developments. Focus on people who discuss practical applications rather than hype-driven content.
For organizations, the investment strategy requires formalization. Set aside specific budget for AI training and development. Create communities where team members can share experiences and learn from each other. Take a long-term perspective on AI skill development rather than expecting immediate returns.
The goal should be building toward AI self-reliance—the ability to identify opportunities, implement solutions, and adapt to new developments without constant external guidance. This requires both individual skill development and organizational culture change.
Turning AI potential into performance isn't about finding the perfect tool or implementing the most advanced technology. It's about maintaining a human-first, strategy-driven approach that treats AI as an enabler of human potential rather than a replacement for human value.
The organizations that succeed will be those that invest in their people, establish clear strategic priorities, and remain open to transformation beyond their current imagination. They'll ask better questions, focus on meaningful outcomes, and create cultures where AI amplifies human capabilities rather than threatening human relevance.
The future belongs to those who can effectively combine human creativity, judgment, and leadership with AI's processing power and analytical capabilities. That future requires strategic thinking, cultural transformation, and the courage to embrace possibilities that don't exist yet.
The question isn't whether AI will change how work gets done—it's whether your organization will lead that change or get left behind by it.
"The first is to shift from 'can AI do this?' to 'what can I do with AI?' The second is to shift from outputs to outcomes. The third is to shift towards reclaiming time to think, imagine, lead, and inspire. And the fourth is to shift towards embracing ideas that we would have never considered otherwise." - Aby Varma
02:51 - Why "Can AI do this?" is the wrong question
06:22 - Enterprise case study: asset management vs. cold prospecting AI
10:19 - The overlooked change management problem in AI adoption
28:18 - The "human AI sandwich" concept introduced
33:22 - Complete four-pillar mindset framework
38:18 - Practical next steps for getting started
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Aby is the founder and principal at Spark Novus, transforming marketing through AI, digital, content, and brand strategies. He aligns marketing, sales, and product teams to drive brand positioning and demand activation. Known for his strategic vision and energy, Aby builds inclusive, high-performing teams.
He advances AI in marketing through the 'Marketing AI SparkCast' podcast and as the founder of the Marketing AI Pulse and Future Crafters communities. Aby is a member of the Forbes Communication Council.
