Twisted Loop’s Predictions for 2026
Every year, there’s no shortage of noise about what’s coming next in technology. But as leaders, what we really need to know is which shifts will shape growth, where to lean in, and where to tread carefully.
To get into the heart of this challenge, we asked the Twisted Loop team to answer four questions:
What technological advancements are going to impact scaling businesses in 2026?
What should business leaders be wary of?
Where should they be bold?
Where should they invest their budget outside of AI?
Here’s what they said.
From experimentation to acceleration
For us, 2025 has felt like the year where many organisations discovered what AI could do. In 2026, business leaders will have to confront what this really means for them.
The experimental phase is ending, and the acceleration curve is beginning.
We’re seeing the tools that once sat only with technical specialists move across the business, shifting who gets to drive innovation and where growth will come from.
In practice, that will mean that over the next 12 months, time to value will shrink from quarters to weeks. We expect the leaders who pull ahead won’t be those who chase every new platform, but those who focus on redesigning one to three core workflows where cycle time, cost, or risk, hit the P&L.
Our advice is to avoid spreading bets wide and start building deep capabilities where it matters most, your high-impact use cases.
The rise of the agent economy
In 2026, we believe AI agents will finally move from proof-of-concept to everyday productivity. By agents, we don’t mean static chatbots or dashboards. We mean autonomous digital teammates, AI systems that can take goals, navigate systems, and execute multi-step tasks on your behalf, often coordinating with other agents or humans.
What started as a handful of developers wiring up workflows is about to spill into the mainstream, because approaches like Model Context Protocol (MCP) and tools such as Microsoft Copilot Studio are tearing down the barriers.
It feels a lot like the early days of spreadsheets or email - suddenly everyone, not just the specialists, can create and use them as part of daily work.
The bigger shift, though, is cultural. When we talked this through, we kept coming back to the same point: once anyone can spin up an agent, proactive employees won’t wait in line for IT or developers to solve their problems, they’ll start building proof-of-concept solutions themselves.
That could be an exciting development, because the people closest to the work are usually the ones with the sharpest ideas about fixing it. But it could also become messy. Without structure, you get fragmentation – lots of proactive colleagues building mini-tools without governance or guardrails set by the board. Leaders will need to set the right standards so bottom-up innovation services the company’s core goals.
Avoiding the familiar traps
This brings us on to the “pilot trap” - a problem we believe will intensify in 2026.
The pilot trap is where organisations launch lots of proofs of concept that never scale, either because they’re built in isolation, often within innovation teams, or because there isn’t a path to production built in. Working within echo chambers can also mean that success is wrongly measured solely on technical performance rather than business outcomes.
The result is a shelf full of demos that look impressive but never deliver real enterprise value.
With AI tools becoming easier for anyone to make, businesses could quickly find themselves with dozens of disconnected agents, each useful in a pocket but not landing promising value.
To tackle this, think about scalability, governance, and integration from day one. Assume that any pilots you build will be used by your entire organisation, creating repeatable value - and plan accordingly.
Agents creating agents
Perhaps the most provocative theme we explored was recursion - the idea that agents will increasingly be able to generate other agents to solve subtasks.
Think of this like a senior consultant breaking down a project and delegating pieces to junior colleagues. Each junior then brings in a specialist to help. The core AI agent delegates, supervises, and integrates, allowing complex work to be broken down and accelerated.
It sounds like science fiction, but the technical pieces are already falling into place, and 2026 may be the year this starts to enter everyday workflows.
The opportunity here is speed and sophistication: complex problems can be broken down and solved faster than a single human or agent could manage alone. But the risk is just as real. Without the right design, these multi-agent systems can loop endlessly, contradict one another, or even chase down irrelevant paths - all while burning time, compute, and trust.
When this happens, it becomes very hard to see where an error originated. Without robust agentic evaluation, leaders won’t know which part of the chain failed which translates to wasted costs, unreliable outputs and frustrated teams.
That’s why we believe resilience will become as important as capability. Arbitration agents to resolve disagreements, loop limits to keep systems efficient, and clear human-in-the-loop checkpoints will all be critical. In other words: it’s not enough to make these systems powerful; they have to be predictable and safe enough to rely on at scale.
Beyond automation: the strategy vacuum
One of the points we kept circling back to was what actually happens after automation does its job?
Stripping out repetitive work sounds like progress, but unless leaders have already decided where the freed capacity should go, it quickly disappears into the ether.
Automation on its own isn’t growth. You'll need to outline a pipeline of priorities waiting to soak up the extra capacity - whether that’s entering new markets, developing products, or doubling down on customer segments.
Where to invest now
We’ve learned the smartest investment isn’t another platform, it’s process literacy. Process literacy means being able to clearly describe what happens in your workflow, who does what, in what order, with what inputs and decisions. It’s how we move from assumptions to actionable design. Rather than asking“what can this tool do?" it starts with "what is the actual shape of our work and how could it be improved?”
Most people know their jobs inside out, but very few have ever mapped the actual decisions, inputs, and exceptions that shape the work.
When we train teams to articulate that, it’s like unlocking a door - suddenly automation makes sense, because it’s grounded in how the work really happens. Once those processes are visible, it opens up the bigger opportunity: not just automating what exists today, but redesigning workflows entirely around agent capability, stripping out unnecessary steps and reshaping roles so the work fits the future, not the past.
We pair that with stronger data foundations, retiring legacy systems, and cleaning and connecting data. It’s not glamorous, but it’s what makes agents capable of taking the right actions, not just generating the right answers.
Perfect data doesn’t exist, and it doesn’t need to. What matters is improving integrity step by step, so the value of your AI tools compounds over time rather than collapsing under bad inputs.
In a nutshell: our 2026 predictions
What technological advancements are going to impact scaling businesses in 2026?
AI agents will move from proofs of concept to mainstream productivity, embedded in the everyday platforms we already use.
Recursion will arrive: agents creating other agents to break down complex tasks. Done right, this unlocks speed and sophistication; done wrong, it creates loops, contradictions, and wasted cost.
Internal agent marketplaces will emerge, where employees can share, refine, and even be rewarded for useful agents they create. Think of this like an app store within your business. These marketplaces let employees find, reuse, and improve agent tools created by peers, making innovation viral.
What should business leaders be wary of?
The “pilot trap”: clever proofs of concept that never scale, leaving businesses with impressive demos but no enterprise value.
Fragile ecosystems: multi-agent systems without guardrails that collapse under their own complexity.
The trust gap: over-reliance on AI-generated communication that erodes authenticity just as customers, employees, and investors demand it most. From auto-generated emails to investor updates, teams risk sounding robotic, right when human clarity and trust are most needed.
Where should leaders be bold?
Empowering employees to become creators, not just users - giving them the tools, frameworks, and incentives to build agents safely and productively.
Channelling freed capacity into new markets, products, and customer segments rather than letting it drift into side projects.
Treating resilience as a competitive edge - building arbitration agents, loop limits, and checkpoints so multi-agent systems are reliable enough to trust.
Where should leaders invest budget outside of AI?
Process literacy: training teams to map how their work really happens, so automation is grounded in reality, not assumptions.
Data foundations: retiring legacy systems and cleaning and connecting data. Perfect data isn’t the goal - improving integrity step by step is what compounds value over time.
How twisted loop can help your business
Our team has been delivering data and AI solutions for over 30 years.
We know how to start fast, align strategy to business goals, and build momentum with confidence.
We offer a range of AI Strategy and Rapid Prototyping services, with delivery timescales as short as two weeks - so you can choose the balance of speed, rigour, and scope that best fits your business priorities.
Want to rapidly accelerate your AI strategy or test a high-impact use case?
