How AI Can Turn Every Employee into a Top Performer

Most businesses are trying to solve the wrong problems

The conversation around AI has moved so quickly it’s hard for business owners to keep up. Early discussions focused on what the technology could do, where it might apply, and how quickly it would change things. 



Most organisations have now moved past that stage. The question is no longer whether AI has a role, but why it is not yet delivering consistent impact across the business.

The constraint sits elsewhere,

In most organisations, AI is being introduced into operating environments that were never designed for it. Here’s why:


• Workflows have been layered over time


• Data sits across disconnected systems, often without clear ownership or structure


• Employees rely on outdated information and personal workarounds to compensate for gaps in the system


The Microsoft Work Trend Index 2025 found that 80% of the global workforce lacks the time or energy to do their job. This is a reflection of how much of the working week is spent navigating these system constraints.


AI, when implemented properly, changes that dynamic. It allows your team to operate with the same level of context, clarity and cuts through delays and inconsistencies.

Freeing up time by removing low-value work

The starting point is usually straightforward. With our clients, the first step is to identify where significant amount of time is being absorbed across the business.

In most organisations, there are persistent pockets of work created fby accumulated processes - re-entering data across platforms, manual routine queries. None of it is complex, but it is tedious and time consuming.

In a typical professional services firm, senior consultants can spend hours each week compiling client updates by pulling information from CRM, finance and other internal tracking tools - all essential information, but it is not the best use of senior time. Once that process is automated end-to-end, those same consultants shift their focus to preparing for client conversations and identifying risks earlier in the engagement cycle. The output improves, not because the team changes, but because their time is being spent differently.

This is where AI tends to deliver its first, and often most immediate, return. Removing repeatable, low-value work creates capacity across the team and allows more of that time to be redirected towards work that drives outcomes.

Improving decisions by fixing access to information

Time alone does not resolve the issue if decisions are still being made with incomplete context.

Most organisations have the data they need. It is rarely in one place, and it is not always accessible in a way that supports consistent decision-making. From a leadership perspective, this shows up as variation. Similar decisions are made differently across teams because individuals are working with different inputs.

A common example is commercial decision-making. A recent client of ours was managing pricing across multiple segments with data spread across several systems.
Account managers made decisions based on what was available to them at the time, which led to inconsistent pricing outcomes and margin leakage. Once those data sources were connected and surfaced in a single interface, decisions became more consistent across the team. The underlying capability did not change. The quality of the inputs did.

There is also a growing gap between leadership perception and employee behaviour. McKinsey’s research found that executives estimate around 4% of employees use generative AI for at least 30% of their daily work, when the actual figure is closer to 13%. Employees are already using AI to fill information gaps, often without visibility or structure.

When AI is structured properly with governance, it becomes a consistent layer between data and decision-making. It reduces reliance on individual knowledge and makes context more broadly accessible.

Connecting teams by breaking down silos

Even with time and information addressed within teams, performance is still shaped by how information moves across the organisation.

Functional department systems are necessary, but they often come with fragmented views of the business. Sales, operations, and finance each operate with valid but incomplete perspectives. Bringing everything together requires manual coordination, which introduces delay and inconsistency.

This is most visible where workflows cut across teams. Customer information, for example, often sits across CRM, support, and billing systems. Each function operates effectively within its own environment, but resolving issues that span these systems requires multiple handovers and manual reconciliation. The result is slower resolution, inconsistent handling, and unnecessary escalation.

When those systems are connected and surfaced through a unified layer, those handovers reduce materially. Resolution becomes faster and more predictable, and escalation drops as teams are able to operate with the same context.

The objective here is not more collaboration in itself, but less friction in how teams arrive at a shared view. When information moves cleanly across systems, alignment becomes a byproduct rather than an effort. AI, built on a clear information architecture, enables this by allowing context to move with the work, rather than needing to be rebuilt each time.

Accelerating how quickly people become effective

The final constraint is how quickly capability develops within the organisation.

Most businesses carry a lag between hiring and full productivity. New and junior staff rely heavily on experienced team members, and knowledge is transferred through a mix of documentation and informal guidance. It works, but it does not scale well. The same questions are answered repeatedly, and senior staff become a bottleneck.

In a growing team onboarding multiple hires, we found senior operators spending a significant portion of their time responding to repeat operational queries. By structuring that knowledge and making it accessible through AI within the workflow, new hires were able to resolve most queries independently. Within weeks, the reliance on senior staff reduced, and those individuals were able to redirect their time to higher-value work.

This is where AI has a compounding effect. It reduces the time it takes for new hires to become effective and frees up experienced staff to focus on work that actually requires their input.

What the top-performing companies are already doing

The organisations seeing results are not treating AI as an overlay.

They are using it to reshape how work is done.

They mandate experimentation, but within defined governance. Teams are encouraged to test and learn, but within clear boundaries around data access and acceptable use. This avoids the risk of uncontrolled adoption while still allowing progress.

They start with operational friction rather than visibility. The focus is on the processes that consume the most time, where removing inefficiency has a direct impact on performance.

They also resist scaling too early. Value is proven in specific areas before being expanded.

Deloitte’s State of AI in the Enterprise 2026 highlights that only 34% of organisations are truly reimagining how work gets done, despite rising investment. The remainder are introducing tools without addressing the underlying structure, which is why many initiatives fail to deliver meaningful results.

Where organisations do take a more deliberate approach, the impact is reflected in outcomes. The Microsoft Work Trend Index 2025 reports that 71% of employees at organisations with mature AI deployment say their company is thriving, compared to 37% globally. That difference reflects a change in how work operates, not just the introduction of new technology.

Where to start?

The starting point is not a platform. It is a clear view of where time is being lost.

Focus on the tasks that consume the most time for the most people. These are often not the most visible processes, but they are where inefficiency compounds.

Prove value at a small scale before expanding. This creates internal alignment and provides a reference point for what works.

From there, build the infrastructure that allows those improvements to be repeated. Without that, progress tends to remain isolated.

Where TwinTech fits

We work with Australian businesses on the elements that determine whether AI delivers value in practice.

That includes architecture, data readiness, and process design. These define how information moves through the organisation, where automation sits, and how decisions are made.

Without that foundation, most deployments underperform. With it, the impact is sustained and measurable.

FAQs

How does AI improve employee performance?
By removing low-value work and improving access to information. The result is more consistent output across the team, not just stronger performance from a few individuals.


What should we automate first?
Start with repetitive, time-intensive tasks that are widely distributed across the business. Reporting, admin workflows, and routine queries are usually the right entry point.


Can AI actually improve decision-making?
Yes. Most decisions suffer from incomplete context. AI connects the data behind those decisions and makes it accessible when it is needed.


How does AI reduce silos between teams?
By connecting systems and standardising information. Teams operate from the same context, which reduces friction and improves alignment.


How quickly can we expect results?
In well-defined use cases, within weeks. The key is to start small, prove value, and then expand.