This year’s Adobe Summit was more than a showcase of innovation. It marked a shift in the enterprise AI conversation. From what we saw in Vegas, AI has now very much moved from pilots to practice. Leaders are feeling the pressure to deliver concrete outcomes, not just savvy experiments.

Yet, amid the rush to implement AI, many organizations face an uncomfortable question: are they winning the race to deploy AI while falling behind on AI readiness? Without the right foundations in place, speed becomes a liability instead of a competitive advantage.

What Adobe unveiled and why readiness matters

Summit showcased an impressive vision for the agentic enterprise, with Adobe unveiling CX Enterprise, AI-powered orchestration, Brand Intelligence, and new tools designed to connect content, data, decisioning and customer journeys at scale. While the stack promises highly personalized experiences and autonomous workflows, many CMOs are still grappling with fragmented data, disconnected teams, unclear governance and a lack of operational maturity. 
 
The gap between what's possible and what's practical remains one of the biggest obstacles to realizing AI's full value. This was echoed in the recent Gartner 2026 CMO Spend Survey, where 70% of CMOs set their sights on becoming AI leaders in 2026, yet only 30% reported mature or fully developed AI readiness capabilities. The ambition is there, and so is the investment, with CMOs allocating an average of 15.3% of marketing budgets to AI initiatives. But spending money on AI is not the same as being ready for it.  

“What's standing in the way is rarely a lack of vision. It's the infrastructure underneath: trusted data, clear governance, the right skills and the operating models needed to turn experimentation into impact.”

Ben Hall, VP, Go-To-Market, North America

Over-automation erodes customer experience

The race is on to deliver measurable impact from AI investments. That brings the temptation to automate every touchpoint, often at the expense of thoughtful design and human connection. Yet the pressure is real. Real-time personalization has become a baseline expectation, with customers demanding experiences tailored to their needs, preferences and context.  

At the same time, AI agents are increasingly shaping how products are discovered, evaluated and purchased. The rise of agent-driven commerce is already visible in Adobe data: traffic from AI assistants such as ChatGPT and Google Gemini to retailer websites has grown by 269% in the past year. These visitors convert 31% better than traditional search users and generate 254% more revenue per visit.

This means that your brand strategy becomes more critical and more complex. It now has to cater to two customers: the actual person who feels your brand and the agent who calls the shots. Automation has a place here, sure. But without empathy or orchestration, you risk reducing customer experiences to a series of transactional, impersonal exchanges.

“At its core, orchestration is about connecting the dots across brand, media, pricing, loyalty and commerce. Without that coordination, automation simply helps you make disconnected decisions faster.”

Ben Hall, VP, Go-To-Market, North America

Data: the foundation AI depends on

As AI agents become the new gatekeepers of discovery, visibility depends on more than a well-designed website. These systems rely on data to understand products, evaluate options and make recommendations. If your content, product data and brand signals aren't connected, you're less likely to show up when it matters. Which brings us to one of the biggest barriers to AI readiness: fragmented data.

In many organizations, information is scattered across CRMs, ad platforms, commerce systems and analytics tools that don't integrate seamlessly. The result is an incomplete picture of your customer and a weak foundation for AI. When the underlying data is inconsistent, AI outputs become unreliable, making it difficult for teams to trust the recommendations, insights and decisions being generated. And once that trust is lost, adoption quickly stalls.

“Rebuilding confidence in AI is far harder than investing in the right data foundations from the start.”

Ben Hall, VP, Go-To-Market, North America

So, what does AI readiness look like?

Adobe Summit 2026 made one thing clear: the technology is ready. The question is whether organizations are ready to make the most of it. Closing the gap between AI ambition and AI readiness requires more than new tools. It demands the right operating model. At Empathy Lab, we like to call this a Growth Operating System: an orchestrated way of connecting data, intelligence, teams and execution. It focuses less on automating individual tasks and more on helping teams align decisions across complex environments.

As an Adobe Platinum Partner for more than a decade, we’ve helped organizations navigate every major shift in customer experience, commerce and digital transformation. We understand not only the technologies unveiled at Summit, but also the organizational change required to make them work in practice. This could be: