AI co-workers aren’t a futuristic possibility – they’re an imminent one. According to Microsoft’s 2025 Work Trend Index, 82% of leaders are confident they’ll use digital labour to expand workforce capacity within the next 12-18 months. This represents a fundamental shift in how organisations view AI: from a collection of tools for improving productivity to a ‘digital workforce’ that can augment human talent at scale.
But although companies are now moving beyond generative AI pilots, only around 24% have deployed it at the organisation-wide level. This suggests that while confidence in the transformative potential of AI is high, there’s still a gap between ambition and reality.
To date, AI tools have mainly been deployed to support individuals or teams in siloed areas of the business. This approach has benefited individual and organisational productivity by enabling people to code faster, summarise documents or quickly create a presentation. But an ‘app for that’ mentality focused on tools rather than transformation misses the full potential of the technology and limits ROI. Instead, leaders need to view AI as an operating system for the organisation.
“Rather than looking at very specific areas of the business and building targeted solutions that incorporate elements of AI, it’s about looking more horizontally across the business, at end-to-end workflows and cross-department ones, to see how AI could actually benefit the end-to-end workload,” says John Clarke, head of cloud and AI advisory at Telana, which provides AI, data, software development and cloud engineering services.
Although we’re still in the early days of this shift, “There’s now a general realisation that this is what’s required – and that it’s also an opportunity,” he adds. Autonomous agents capable of planning and executing complex, multi-step tasks across applications and departments have huge potential to improve organisational efficiency and competitiveness, for instance. The goal is not to replace human workers, per se. It’s more that these agents can help humans to focus more on strategic work, innovation and client relationships.
Navigating cultural change
All of these activities require human capabilities – judgement, style, taste, empathy, gut instinct and creativity – that will remain integral to a well-functioning society and workforce. Nevertheless, properly embedding AI across the organisation inevitably creates a degree of disruption. Processes need to shift, roles need to evolve, and leaders will need to guide people through this period of significant cultural change.
One of the most effective strategies for reassuring employees about the value of AI is to start with ‘lighthouse projects’ – focused initiatives that demonstrate how AI can benefit both employees and the wider organisation. “People are already experimenting with AI on a daily basis,” says Clarke. “They’re producing content and getting insights from various data sources quicker than they ever could before. Lighthouse projects can help to reinforce that narrative and expand it across the organisation.”
He points to a recent app Telana worked on for a large multinational as an example. It provides field technicians with a GenAI tool that summarises all job data into a single overview, eliminating time-consuming preparation before a site visit. Since its introduction, each technician has been able to complete one additional site visit per day, thanks to a dramatic reduction in the amount of time they have to spend on information gathering. As adoption of the tool expands, the efficiency gains will likely continue to increase.
Champion networks are equally important for creating the right messaging around AI and embedding it within workflows. These initial end-users can tell others: “‘We’ve been given this tool, it’s made me more efficient, I can spend more time thinking about the next job or planning ahead and strategising,’” says Clarke. Ideally, this should reflect top-down messaging from leadership that says: “‘We’re trying to make your work more efficient and free up time for you to pursue different things, not replace you.’”
Scaling adoption
Of course, it’s also important to have the right metrics in place to measure success. Returning to the field technician case study, Clarke says that analysing data from various channels allowed Telana to assess whether technicians were completing more site visits, as well as providing more recommendations to customers based on the insights provided by the app. “Within the solution, we also included a feedback form to capture the response of users – things like ‘Is this enabling your work?’ and ‘How are you finding the UX?’ We get that information directly from them so that they feel part of the adoption, development and usage of the app.”
Successful horizontal AI adoption also tends to follow an iterative “crawl, walk, run” model. This approach begins with ideation workshops and hackathons to identify the right problems, then moves into prototypes and minimum viable products before shifting into production. “We think it’s the right approach because of the collective learning and understanding of what’s possible,” says Clarke. “It’s about making incremental investment, measuring the impact it has and moving through each stage into a scaling solution.”
There will be fully automated agentic workflows and organisations in the future
As more of these projects are embedded across organisations and agentic AI becomes mainstream, many human workers will shift from ‘doers’ to orchestrators of AI teams. Instead of promoting AI agents to carry out single tasks, they’ll need to prompt them to carry out business outcomes. It’s therefore not surprising that critical thinking, strategic oversight and AI orchestration skills are seen as increasingly essential, with 47% of leaders listing upskilling existing employees as a top workforce strategy for the next 12–18 months.
“There will be fully automated agentic workflows and organisations in the future, where we trust what they do, we’ve tested them well enough and they automate all sorts of processes and interactions that don’t require any human interaction,” Clarke explains. “But the capability being there and the speed that organisations adopt it are two different things.”
Organisations could be restrained by a variety of factors, including investment capacity, legacy debt, data silos and skill sets. Some may be forced to move quickly due to competitive threats, while others will lag behind. But regardless of the speed of change, AI’s greatest promise lies not in replacing human work, but in elevating it. Leaders who recognise this and successfully embed AI across end-to-end workflows will therefore find their organisations become not just more efficient, but fundamentally more human too.
For more information, visit telana.com
AI co-workers aren’t a futuristic possibility – they’re an imminent one. According to Microsoft’s 2025 Work Trend Index, 82% of leaders are confident they’ll use digital labour to expand workforce capacity within the next 12-18 months. This represents a fundamental shift in how organisations view AI: from a collection of tools for improving productivity to a ‘digital workforce’ that can augment human talent at scale.
But although companies are now moving beyond generative AI pilots, only around 24% have deployed it at the organisation-wide level. This suggests that while confidence in the transformative potential of AI is high, there’s still a gap between ambition and reality.
To date, AI tools have mainly been deployed to support individuals or teams in siloed areas of the business. This approach has benefited individual and organisational productivity by enabling people to code faster, summarise documents or quickly create a presentation. But an ‘app for that’ mentality focused on tools rather than transformation misses the full potential of the technology and limits ROI. Instead, leaders need to view AI as an operating system for the organisation.
