AI: happening with us, not to us

In my last article for Business Reporter, I explored why process is king in the age of AI. Today, I explore how employees are taking a more active role in shaping processes.

 

For years, specialists managed process improvement. Employees were the subjects of change, not the drivers of it.

 

AI is beginning to change that dynamic. We’re now seeing a redistribution of process intelligence, and with it a democratisation of decision-making power. As Google DeepMind COO Lila Ibrahim told LinkedIn Big Ideas 2026, we’ve moved from “AI happening to us” to “AI happening with us”.

 

AI is a revolution in the hands of its users. So, what does that mean in practice?

 

 

Optimisation, redistributed

This shift differs from many earlier technological revolutions. During the railway revolution of the nineteenth century, industrialists concentrated power and control. By contrast, AI tools increasingly reach individuals and teams across organisations.

 

This shift is most visible in how process improvement is changing hands. Historically, employees worked around inefficiencies or escalated them up the chain of authority – rather than fixing them directly because they lacked the tools or authority.

 

That’s all changing, and fast. Employees are adopting generative AI tools rapidly and often faster than leadership expects, according to McKinsey’s Superagency in the Workplace report.

 

AI and automation used to be distant, specialist capabilities. Today, user-centric tools empower employees to describe outcomes in plain language and let AI agents orchestrate the steps or customise their own applications. Non-technical users now design workflows, automate tasks, and address bottlenecks without having to wait in IT queues. 

 

When employees take a larger role in process improvement, best practice spreads more quickly and outcomes improve across the organisation.

 

 

The risk of overreliance

The user-centric convenience of the AI revolution does raise an important question. If AI-powered tools are making process intelligence, decision support, and data so widely and readily accessible, what happens to human expertise? How do you build deep institutional knowledge if AI recommendations are available on demand?

 

This concern is legitimate. Many people rely on navigation apps and feel they have lost their natural sense of direction. Similarly, some worry that uncritical use of AI could erode professional intuition. If people do not understand the processes that underpin their business, meaningful human oversight becomes difficult.

 

However, when teams use AI well, it doesn’t replace understanding; it creates more space for it. When people are no longer buried in manual work, they can step back and develop as true knowledge holders. AI removes the noise of low-value, repetitive tasks, so that teams can spend more time spotting problematic processes, refining decision logic, and exercising judgment where it matters most.

 

Organisations should not outsource thinking to AI, but they can use it to strengthen human judgement.

 

 

Expertise, amplified

When more team members develop a deeper strategic understanding of their work, organisations unlock a powerful force for improvement. Rather than relying on isolated experts or tribal knowledge, employees begin to recognise when processes are problematic, and are empowered to fix them not just once, but at scale.

 

Organisations can embed domain knowledge into AI assistants and digital coworkers to share expertise across roles. A standard loan originator, for example, can draw on the insights and best practices of the most experienced underwriters. A claims processor can execute with the accuracy and efficiency of a senior adjudicator.

 

These AI-enabled helpers don’t replace professional judgment. They extend expertise and ensure it is continually strengthened, rather than outsourced.

 

 

Governing process improvement at scale  

AI democratisation fuels innovation, but it also introduces invisible risk. Without proper governance, the automations and agents that teams build can become dependencies without ownership or oversight. For example, a departing employee may leave behind a proliferation of workflows nobody else understands.

 

This “creation sprawl” carries new risk. As Gartner notes in its recent report, Securing No-Code Application Development Requires New Approaches, newfound employee developer capabilities could “introduce a new class of exposure that bypasses conventional security controls and governance.”

 

Containing this risk is a challenge. They must balance governance at scale with the creativity AI enables. The solution is guided autonomy: environments where employees can innovate within guardrails that ensure security and accountability. Organisations must create environments where employees are free to innovate, but within guardrails that ensure security and accountability.

 

In other words, democratisation works best when it is structured. AI may empower individuals, but the systems they build must still operate as part of a coherent whole.

 

 

Democratised, but disciplined

The future of AI-enabled organisations will depend on balance. Empowering employees to optimise their own processes unlocks enormous value, but only when that empowerment is paired with strong governance and a clear understanding of where human judgement must remain central.

 

AI should not become a substitute for expertise, nor a black box that people rely on without question. Instead, it should act as a force multiplier: surfacing knowledge, strengthening expertise, and enabling employees to innovate more effectively.

 

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Mark Talbot is Director of Architecture and AI at Appian

 

Main image courtesy of iStockPhoto.com and tadamichi