DOSSIER

TECH

Rethinking How We Sell AI: From Transactions To Transformation

For decades, the growth playbook for tech giants was simple: sell consumption. Microsoft, Amazon, and Google built empires by charging for computing, storage, and licenses. Spin up more, use more, pay more. The model was frictionless, scalable, and ruthlessly efficient. But here’s the truth no one wants to say: AI strains that model.

AI isn’t a utility. It’s a transformation. And transformation doesn’t come packaged in an SKU.

The miracles people expect from AI—automating entire workflows, revolutionizing supply chains, predicting disease, ending hunger—don’t live in some pre-trained model you can swipe a credit card to access. These outcomes require time, trust, and tailored guidance. They need someone to go beyond selling and start story-building.

That’s the new game: storytelling over specs, guidance over gigabytes.

The original cloud sales model was about spinning the meter. Sell services, then sit back and watch as usage ballooned. But AI isn’t about usage. It’s about insight. It’s about outcomes. And outcomes don’t scale just because you allocated more GPUs.

The dirty secret? You can’t sell a miracle like it’s a microwave. AI demands context. Without it, all that “compute” generates noise.

A colleague recently reminded me of a key tension: many LLM providers today are doubling down on a usage-based model—metering tokens, charging per API call, and selling access to compute-intensive endpoints. They’re treating intelligence like storage or bandwidth. But here’s the problem: building tomorrow’s business on the billing logic of yesterday is a recipe for missed potential.

The future of AI isn’t just pay-per-token—it’s pay-per-trust, pay-per-outcome, and pay-per-transformation. What we’re seeing now is less a long-term model and more a legacy reflex: an effort to monetize something revolutionary using familiar levers.

The real constraint? Imagination.

Many AI business models today are shaped by what’s historically worked, not what’s now possible. We’re still looking backward—optimizing revenue per inference—when the value lies ahead: in orchestration, alignment, and intelligence that reshapes the enterprise.

Selling AI: The Narrative Era Has Arrived...We’ve entered the narrative era of tech. The companies doing this well are the ones helping clients imagine what’s possible before ever touching a line of code.

AI transformation is a hero’s journey. The enterprise is the protagonist. The villain is inefficiency, fragmentation, or the legacy process. And the AI solution? That’s the sword, not the story.

You can’t shortcut this with an upsell.

Take NVIDIA’s recent GTC announcements—yes, they talked about chips. Still, they spent most of their time painting a future of AI-powered everything: digital twins of factories, AI assistants for every profession, and even AI used in drug discovery. That’s not a product pitch. That’s narrative dominance. They’re selling a vision of the world, not a widget.

Or look at OpenAI and the custom GPTs. The entire play is: What story does your business want to tell, and how can a GPT be shaped to deliver it? That’s narrative-first, and it’s changing how organizations think about go-to-market.

Selling AI: Consultative by Design...Every company has mountains of data, but most don’t know what it’s worth or how to use it. The real opportunity lies not in reselling models but in orchestrating a roadmap.

The winners will be the ones who help their clients understand what should be optimized, automated, or reimagined. AI isn’t a strategy; it’s a toolset. And tools without a blueprint collect dust.

Recent headlines prove this. Last month, IBM signed a significant deal with NASA to build AI models trained on earth science data—but it wasn’t a purely transactional sale. The partnership was built on co-development, shared data, and clear strategic milestones. That’s consultative AI in action.

Meanwhile, companies like Accenture and Deloitte are growing AI advisory services faster than deployment arms because clients aren’t looking for tools but clarity. They’re asking: What do I do with all this? How do I trust it? How do I govern it?

Selling AI: The Org Chart Wasn’t Built for This...Here’s the uncomfortable truth: Most companies aren’t structured to succeed with AI. Silos are a top barrier to AI adoption, along with data fragmentation and unclear accountability.

Budgets are set quarterly. Product roadmaps are set annually. Org charts are fixed in silos. Meanwhile, AI models evolve weekly, and breakthroughs don’t wait for fiscal alignment.

“Our executive leadership team asked ourselves, if we were starting this 70-year-old agency from scratch today, would it look the same? The answer was probably not,” said Jeff Beringer, Chief AI Officer at Golin. “That thinking is essential when trying to build AI into the DNA of how a business works.”

The CMO has some data, the CIO has another part, and the CTO is experimenting with its sandbox. None of it is connected and does not map cleanly to the kind of collaborative infrastructure AI requires.

So what happens? You try to innovate, and the system resists. You introduce a new model, and suddenly, your legacy platforms can’t talk to it. You find the insight but have no mechanism to act on it. AI isn’t just hard to implement. It’s hard to organize around.

And that’s the real unlock: AI isn’t just a technology shift. It’s an organizational redesign challenge disguised as a tech opportunity.

Selling AI: The Complexity Curve: Adoption Takes Time—and Alignment

Adopting AI isn’t fast. It’s not linear. And it’s not just technical—it’s organizational.

Most enterprises are functionally fragmented, with the CMO, CIO, and CTO holding pieces of the puzzle—data, platforms, and priorities that rarely align. Their systems don’t talk, their roadmaps don’t sync, and their incentives often compete.

But AI doesn’t thrive in silos. It doesn’t just need data—it needs connected, contextualized, and collaborative data. It requires alignment across functions, not just access across systems.

Now, layer in the rate of change. New models drop weekly, foundation models shift, and entire frameworks deprecate overnight. Enterprises are being asked to bet big while the ground is still shaking.

None of that fits neatly into Big Tech’s existing sales motions. It doesn’t align with quarterly budget cycles or conforms to short-term metrics.

That’s why consultative selling and strategic guidance aren’t nice-to-haves—they’re mandatory. AI transformation needs patience, precision, and partnership.

But here’s the kicker: once you set it up, it can eliminate massive friction. It can flatten silos. It can automate the tedious and unlock the profound. However, it has to be implemented with full awareness of this complexity. Anything less is theater.

Selling AI: The Role of the Employee...AI transformation doesn’t stop at leadership alignment—it lives or dies on the front lines.

As Doug Llewellyn, CEO of Data Society Group, put it: “Employee data and AI literacy is the often-forgotten key to successful AI transformation. Front-line employees can either supercharge their efforts through AI or hold back the value of transformation through misuse or misunderstanding. And companies that are successfully enabling AI in the employee ranks are doing a lot more than granting access to new tools. They are ingraining it in their culture, investing in extensive training, and retooling their processes from the ground up with empowered teams.”

That’s the real unlock: AI isn’t just a tech adoption play. It’s an organizational readiness exercise—from boardroom to breakroom. Companies that treat it like a tool rollout will stall. The ones that treat it like a capability build will thrive.

Selling AI: Sales Org Reset: From Pitch to Partnership...Sales teams need a complete reboot. The skill set is shifting from closing deals to co-creating outcomes. That means:

-Incentivizing long-term change over short-term usage

-Hiring for empathy, domain fluency, and vision

-Rewarding collaboration between product, sales, and client success

And at the center of this reset? One of the most critical new roles in the modern enterprise.

Selling AI: Enter the Chief AI Officer (CAIO): The New Architect of Trust

If the old model treated tech adoption like a vending machine, AI is more like building a cathedral. It needs vision, patience, and someone who can read both the blueprints and the soul of the business. That’s the Chief AI Officer.

The CAIO is not just a technologist. They are the translator between ambition and execution. They ensure the organization doesn’t just buy AI—it becomes AI-capable.

It’s not yet standard, but the role is growing rapidly and becoming essential as organizations mature their AI posture. Without a trusted guide, AI efforts stall, hallucinate, or backfire. Plug-and-play quickly becomes “plug-and-pray.”

Selling AI: Counsel for the New Sales Era...If you’re leading sales, strategy, or product at a company trying to sell AI solutions, here’s the new playbook:

-Shift from selling to shaping. Don’t sell features—frame futures.

-Value time over transaction. Trust must be built before architecture can.

-Elevate narrative over numbers. Possibility precedes pricing.

-Co-design; don’t just demo. The client is the hero.

Selling AI: Sell less. Shape more.

AI isn’t just a new chapter in the enterprise playbook—it’s a different language altogether. And most companies? They’re still flipping through the index, hoping the answers are in the appendix.

It doesn’t care about your fiscal year. It doesn’t pause for your procurement cycle. It doesn’t wait for alignment across your fragmented org chart.

This isn’t a sales evolution—it’s a structural upheaval. The companies that thrive won’t just adopt AI. They’ll dismantle the scaffolding of how they plan, decide, and grow—and rebuild it around intelligence.

Because AI doesn’t break one department, it breaks the tempo of the entire enterprise. And when the beat changes, you either learn to move differently or fade out entirely. The ones who get it won’t be the loudest. They’ll be the ones with the quiet confidence of builders—guiding, shaping, trusting, aligning, not pitching.

Because AI doesn’t need a sales team—it needs architects.

And the future? It belongs to those with AI blueprints in one hand and a compass in the other.

Reporter: Jason Snyder