1. 2026 is the year that systems with AI-enhanced workflow capabilities become more important to business decision makers than the debates concerning the AI models themselves. The potential of orchestrating multiple AI models using a composable framework of functions, workflows and data integration capabilities will start to open up possibilities where business architects can position a more ‘agentic’ approach to resolving longstanding business process challenges.
2. AI agents working in combination with composable application platforms will provide more dynamic routing of tasks towards easily re-composable workflows rather then being reliant on the sometimes brittle, hard to maintain and numerous pipelines coded into existing applications. This means that in 2026, selecting the right composable application platform to work in hybrid fashion with existing traditional applications becomes a key consideration for IT leaders, whereas selecting composable platforms has recently been driven more by the Business directly.
3. 2026 is the year when we crossover from pilot projects to real production value. However safety, security and oversight remain as the major obstacles to adoption. We can expect a big focus on manpower-intensive, industry/company-specific low-to-medium skilled tasks (ones that initially benefit from data orchestration and process automation) to be tackled as a first step to enable subsequent enhancement with AI capabilities as handling these concerns becomes more mainstream.

As we navigate the breakneck speed of AI evolution, it’s easy to get caught up in the “magic.” But at Cyferd, we spend our time thinking about what happens when the magic trick ends and the business reality begins.

To help steer the conversation, I’ve put together three predictions—and reality checks—for the coming months.

1. The LLM Plateau and the Long Game for World Models

We’ve all been captivated by Large Language Models. They have proven more successful than even their creators at Google anticipated; as Demis Hassabis recently noted in his discussion with Hannah Fry, the way language can decode our complex landscape is nothing short of miraculous.

However, expect the “buzz” to cool. We are reaching the limits of what pure language prediction can do for high-stakes decision-making.

• The Reality: LLMs are linguistic architects, not physical ones.
• The Future: The next frontier is World Models—AI that understands cause, effect, and the physical/logical constraints of reality. While these are coming, they are still a way off from being “reliable” for the enterprise. Until then, LLMs remain a powerful tool in our arsenal, but they are not the whole armory.

2. “Vibe Coding” vs. Enterprise Integrity

There is a growing sentiment that “anyone can build an app now” just by chatting with an LLM. This “vibe coding” is fun for a weekend project or a flashy demo, but it hits a brick wall the moment it meets a governed environment.

• The Gap: A simple app with no security layer is easy. A complex, organization-ready solution—complete with holistic data modeling, intricate workflows, and zero-trust security—is another story entirely.
• The Challenge: Turning “vibes” into managed, trusted code is the industry’s biggest hurdle.
• The Cyferd View: The winners in this space won’t just be the ones with the best prompts. It will be the platforms that possess a holistic data approach. If a platform knows your data across all your apps, it has the context required to resolve complexity that a standalone LLM simply can’t see.

3. The Great Governance Pushback

We’ve seen a rush to give users LLM tools, but the honeymoon period is ending. High-profile failures—like the recent controversies surrounding unmoderated image generation in tools like Grok—are a wake-up call.

There is no excuse for tools that bypass basic human decency or corporate safety.

• Predictable Friction: As errors, hallucinations, and ethical breaches kick in, we will see a massive organizational pushback.
• The Demand: Organizations will (and should) start demanding “Enterprise Grade” governance from AI providers. We are moving away from the “move fast and break things” era of AI and into an era of accountable intelligence.

Final Thoughts

The “cool” factor of AI is maturing into a “consequence” factor. At Cyferd, we believe the future belongs to those who can marry the agility of AI with the rigors of enterprise governance.

Podcast reference: https://podcasts.apple.com/ae/podcast/google-deepmind-the-podcast/id1476316441?i=1000741556387

As we move into 2026, it is clear to me that the conversation around artificial intelligence in the enterprise has fundamentally shifted. These views are my own, shaped by the work I do every day with large, complex organisations as Chief Customer Officer at Cyferd. They are grounded less in hype, and more in what I am seeing customers actually build, deploy, struggle with, and ultimately succeed with.

The most important change I see is not whether enterprises are adopting AI, but how they are doing it.

From AI as an Add-On to AI as a Foundation

Over the last few years, many organisations experimented with AI by bolting it onto existing applications. A chatbot here. A prediction model there. Often impressive in isolation, but rarely transformative at scale. In 2026, that approach is reaching its natural limit.

Enterprises are now maturing to a point where they are designing and deploying end-to-end business process flows with AI built in from the outset, not added as an afterthought. AI is no longer a feature. It is becoming a core design principle.

This shift matters because AI delivers its real value when it is embedded directly into the operational fabric of the business. When decision-making, orchestration, exception handling, and continuous improvement are all AI-assisted by design, organisations move beyond incremental gains and start to unlock structural efficiency.

Redesigning Processes from the Ground Up

One of the strongest patterns I see across enterprise customers is a willingness to go back to first principles. Instead of asking, “Where can we add AI to this application?”, they are asking, “If we were designing this process today, knowing what AI can do, what would it look like?”

That question leads to uncomfortable but necessary conclusions. Many existing processes were never designed for automation, let alone AI. They were designed around human handoffs, static systems, and fragmented data.

In 2026, organisations are increasingly accepting that true AI enablement requires process redesign, not superficial enhancement. This is hard work, but it is also where the real competitive advantage lies.

Data Capture Is the Weakest Link

Despite major advances in AI, data capture remains the Achilles’ heel of most automation strategies. If a critical business process still starts with a PDF, an email attachment, or an image, the organisation is immediately on the back foot.

Even the best AI models struggle when they are fed late, low-fidelity, or poorly structured data. Enterprises are recognising this and are going back to the very beginning of their processes to rethink how work is initiated.

The organisations making the most progress are obsessing over data quality, structure, and timeliness from the first touchpoint. They understand that capturing high-quality data early unlocks AI value immediately and compounds that value downstream as the process progresses.

Why Traditional SaaS Is Starting to Show Its Limits

Another clear trend I see is growing frustration with traditional SaaS architectures. Point solutions, each optimised for a narrow use case, have served enterprises well for many years. But they struggle when organisations try to orchestrate complex, end-to-end processes infused with AI.

Handing one stage of a process to one vendor, then another stage to a different vendor, inevitably introduces gaps, friction, silos, and rigidity. Innovation slows. Data fragments. AI potential is diluted.

In 2026, more enterprises are concluding that maximising AI impact requires holistic control over data, process design, orchestration, and intelligence. This is increasingly difficult to achieve by stitching together off-the-shelf tools that were never designed to work as a coherent whole.

Platforms as the Next Value Unlock

This is why I believe we are entering a phase where modern enterprise platforms become the primary vehicle for AI-driven transformation. Platforms that allow organisations to design bespoke processes, manage data end-to-end, embed AI natively, and evolve continuously are becoming essential.

Rather than optimising isolated steps, enterprises want to optimise outcomes across the entire value chain. The ability to look at the full end-to-end need and solve it coherently is emerging as one of the biggest value unlockers of the next few years.

Industry analysts are increasingly describing this shift through the lens of business orchestration and automation technologies. From my perspective, this framing aligns closely with what customers are already doing in practice.

Why This Matters Now

The organisations that succeed in 2026 will not be the ones that adopt the most AI tools. They will be the ones that rethink how work gets done, starting with data capture, redesigning processes around intelligence, and choosing architectures that allow them to adapt and innovate continuously.

From where I sit, working hands-on with enterprise customers, this is no longer theoretical. It is happening now. And it is fundamentally reshaping how operational systems are built, how value is created, and how AI delivers lasting impact at scale.