A Partridge in an AI Tree: Agentic AI Becomes a Standard Part of Knowledge Work
- William Beresford
- 1 day ago
- 3 min read
Prediction: Over the next twelve days, we’re publishing twelve predictions for 2026, each rooted in what we see across clients, data science teams, and global research. No hype. No futurist theatrics. Just what we believe will genuinely shape leaders, customers and organisations over the coming months.
We begin with a shift already underway: one that is more transformative than the last decade of digital change combined.
Agentic AI Becomes a Standard Part of Knowledge Work
By the end of 2026, agentic AI will not be autonomous. It won’t be deciding strategy. It won’t be running organisations. But it will be running work. Invisible work. Essential work. Work that humans never had the capacity, or patience, to complete.
From “using AI” to “AI completing tasks”
The breakthrough will be in the agency layer, so that AI:
updates reports
drafts code
triages inboxes
enters data into systems
prepares first-pass analysis
runs routine tasks across software
takes small operational actions
closes loops humans forget to close
This is the “in-between work” that consumes a disproportionate amount of organisational time. As Gartner notes, by 2026, 25% of enterprise workflows will include autonomous agents performing operational tasks. And McKinsey estimates that up to 60–70% of current employee hours involve activities AI can partially automate, especially in knowledge-heavy roles.
Why 2026 is the tipping point
1. Tooling becomes embedded, not experimental
Agentic capabilities are being built into platforms workers already use, including: Microsoft 365, Google Workspace and Salesforce amongst others. Harvard Business Review highlights that productivity gains happen only when AI is integrated directly into daily workflows rather than bolted on. Consequently, by 2026, many employees won’t even know that they are “adopting AI”; their everyday tools will just start doing more and more.
2. Enterprise APIs finally unlock automation at scale
Enterprise systems are becoming accessible for AI-driven actions through standardised APIs and governance layers. OpenAI’s, Anthropic’s and Cohere’s “agent frameworks” are enabling multi-step tasks with constraints, oversight, and audit trails, which is crucial for risk-sensitive industries.
3. Agentic AI is cheaper and more energy-efficient
The 2024–2025 period is defined by optimisation rather than scale. Sparse models, retrieval-based systems, and small domain-specific LLMs reduce cost enough for organisations to deploy AI agents inside real workflows without exploding the P&L.
4. Organisations finally have the data foundations
The same aforementioned McKinsey research finds that organisations with strong data governance are 3× more likely to achieve measurable AI impact. You cannot automate tasks if your underlying data is messy, unstructured or inconsistent. This is a major theme across our entire twelve-day series.
What this means for leaders
AI becomes “expected” rather than “innovative”
Just as cloud stopped being a strategy and became an assumption, agentic AI will become the default expectation in most functions — finance, HR, operations, marketing, customer service.
Employees shift from doing to directing
Work will become more supervisory, more judgement-driven, less administrative. The question will no longer be: “Can AI help me?” But rather: “What should I still be doing manually?”
Organisations must redesign workflows, not just deploy tools
Leaders who treat AI as a plug-in will see incremental gains. Leaders who redesign operating models around hybrid human–AI teams will see exponential ones.
This is the cultural shift of the decade.
Beyond: Putting Data to Work
Agentic AI only succeeds when the foundations are right: clean data, well-defined processes, clear governance, and a view of where automation genuinely drives value.
At Beyond, we help organisations:
map where AI agents can take over real tasks
automate workflows using your existing tech stack
build trustworthy data pipelines that enable reliable AI
use experimentation to prove ROI before scaling
design hybrid human–AI operating models that actually work
You can find out more about how we are working with AI on our blog here: https://www.puttingdatatowork.com/post/building-an-ai-first-beyond-organisation-and-what-we-are-doing-behind-the-scene
And for a first-hand experience we also offer an AI pathway workshop which is designed as a structured first step — aligning ambition, surfacing high-impact opportunities, and setting out clear next actions.
If you want 2026 to be the year AI does the work, rather than generate more of it, get in touch.
Let’s put your data to work: intelligently, safely, and commercially.
