AI and the human factor: balancing new priorities in TMT

Navigating disruption at the leading edge

If one thing unites the TMT sector, it is a heightened awareness of the need for digital transformation. At the same time, TMT brings together a wide range of businesses, with technology, media and telecommunications companies experiencing different pressures, opportunities and adoption paths when it comes to AI. Working in close proximity with the sources of innovation, companies in the sector tend to adopt innovations rapidly. The lengthy list of new technologies adopted by the sector’s companies during the past decade includes cloud computing, 5G, software-as-a-service, adtech, cloud-based gaming and more.

This is a sector prepared for change, four out of 10 business leaders across all sectors define digital transformation as a leading strategic priority, in TMT 58% of respondents take this view. In addition, TMT companies are clearly prioritising their response to AI. Seventy-one percent of sector executives say that AI is already having a major impact on their organisation, compared to 58% across all sectors. In TMT, two-thirds (63%) of senior leaders see AI as a leading success factor in digital transformation, compared to half (50%) of respondents across all sectors.

This is just as well, as mounting challenges confront many companies in the sector. Some of these challenges are structural. Telecommunication companies, for example, are struggling to deliver adequate returns as they emerge from a period of sustained investment in 5G and fibre networks. The advertising industry continues to grapple with deep-seated disruption triggered by new technologies and changing client preferences. In both sub-sectors, substantial digital transformation programmes are common.

On the other hand, the impact of AI is being felt in unpredictable ways. AI search has dramatically reduced website traffic and advertising revenues among online publishers. Enterprise software companies and professional information vendors, previously thought to possess reliable defences against disruption, have come under pressure as the potential of agentic AI becomes clear. Over all of this looms anxiety about the durability of the business models that underpin AI innovation itself. As has often been the case in the digital era, TMT remains at the epicentre of change. The inevitable response is a continuing focus on the potential of digital transformation.

Technology: new tools, new workforce

The technology sector includes a range of specialist and diverse sub-sectors, including social media platforms, e-commerce retailers, the gaming industry and semiconductor manufacturers. As AI becomes the dominant preoccupation for technology companies, the pace of change is accelerating rapidly.

In software businesses, AI is transforming the entire product lifecycle. In product development, assumptions about what users want are being replaced by large-scale automated analysis of user behaviour, customer feedback and analysis of competitor roadmaps. Generative AI is rapidly automating software development. Increasingly, developers using platforms like Cursor delegate the writing of code to AI agents, which build, test and demo new features. AI coding assistants make context-aware suggestions, manage projects, identify security threats and automate software testing. The results include reduced costs, improved quality and accelerated time to market.

Computer and electronics manufacturing accounts for more of the world’s most advanced manufacturing facilities than any other industrial sector, according to the World Economic Forum’s Global Lighthouse Project. In semiconductor design, for example, machine learning helps engineers squeeze more functionality on to silicon wafers, increase energy efficiency and improve scalability. Within the sector’s high-velocity supply chains, the Internet of Things and AI-driven scenario modelling increasingly helps participants navigate disruption in real time.

Many companies in technology sub-sectors are already running AI-driven products and services at scale. This creates a need to optimise costly AI chips, ensure high availability, orchestrate workloads, monitor performance and accelerate networks and databases. Collectively, these approaches, often described as AI-Native Computing, have become a primary focus of digital transformation in the technology industry. The same approaches to building AI-ready computing infrastructure are rapidly becoming available to other enterprises as well via major cloud platforms.

Technology companies understand the requirements of AI-driven transformation. Most are aggressively focused on asking the right questions about data quality, cybersecurity and infrastructure as AI buildouts continue. By contrast, the sector’s unifying challenge remains human talent. Finding it, training it and retaining it has always been a challenge, but AI is intensifying demand in highly specific niches. For example, among developers able to make the transition to working as orchestrators of agentic AI systems that increasingly write code. For most technology companies, the answer will involve reskilling and upskilling existing employees.

Media and entertainment: using AI to extend human creativity

Surrounded by the noise of the attention economy, media owners struggle to maintain engagement, especially among young consumers who primarily use social media for news consumption. Those that cultivate first-party data and generate revenues from subscriptions, e-commerce and events enjoy some degree of insulation from the volatility of digital advertising markets.

AI’s potential to redefine content production remains substantial. But the media industry’s approach has become increasingly nuanced. AI has largely emerged to augment, rather than replace, human skills in newsrooms. Consumer attitudes suggest caution is warranted. Only a small minority of readers and viewers approve of news being produced entirely or mostly by AI. In film and TV, by contrast, AI is rapidly reducing the cost of pre-production (eg 3D set modelling) and post-production (eg dialogue replacement). Digital transformation is common among traditional broadcasters. In the U.S. and Europe, video streaming platforms (e.g. Netflix) and pure-play digital/social video (e.g. YouTube, TikTok) now account for the majority of TV viewing time, at the expense of traditional, linear broadcasting. In response, broadcasters are adopting IP-based distribution and modernised content management systems to streamline operations, monetise programming and extend distribution on to social platforms.

Disruption continues to scale new heights of intensity within the advertising industry itself. Machine learning is helping media owners improve advertisement targeting. Increasingly, ad buying involves dynamic pricing, adjusting the cost of exposure depending on demand, competition and engagement. Less positively, AI is encouraging an ad fraud arms race in which bots get better at imitating human behaviour, while fraud detection tools improve too. Meanwhile, agencies are struggling to integrate new forms of advertising, including retail media and commerce media. As many big brands shift digital ad buying in-house, traditional agencies have responded by investing heavily in digital transformation. Automation, improved transparency for clients and value-based pricing are all on the agenda.

Manufacturers dream of fully automated ‘lights-out’ factories, but a hit-driven sector like media and entertainment must strike a delicate balance between exploiting the process efficiencies of AI and nurturing human creativity. Resources need to be ring-fenced for bottom-up transformation initiatives alongside top-down directives. Paradoxically, this logic applies with greatest force in the parts of the sector, like the world of advertising agencies, where commercial pressures are at their most intense.

Telecommunications: automating the core, identifying new revenue streams

Telecommunications operators, or telcos, have proved largely unable to translate insatiable demand for bandwidth into significant revenue and profit growth. Recently deployed 5G and fibre broadband networks have encouraged rises in data consumption, yet average revenue per user is broadly stagnant or declining in most territories. Nearly all telcos operating in markets with stable long-term levels of inflation are expected to grow annual revenues by 2% or less between now and the early 2030s.

In the near future, however, opportunities exist for telcos to exploit AI-driven digital transformation to refine and extend their business models. Major network operators are using advanced analytics and AI to restructure wholesale deals that have fuelled low-cost competition in the past. They are also using AI to segment the market more effectively, personalising and contextualising consumer offers. In general, the telecommunications industry’s ability to do this has lagged behind comparable sectors, including online travel, media streaming and e-commerce.

For many years, telcos have focused on becoming lean utilities thanks to digitalisation, operational simplification and headcount reduction. Further gains are now possible by exploiting the full potential of AI in areas like network engineering, operation and maintenance. In the long run, pressing forward with the digital transformation of networks themselves is likely to become the single most valuable route to improving the industry’s financial performance.

Telcos also stand to benefit from selling services to enterprises and SMEs adopting AI as the potential extends beyond familiar offerings such as cybersecurity and sovereign cloud. Telcos that have invested in the most advanced form of 5G network also stand to benefit by building out networks of small, advanced data centres in locations where logistics, transportation and manufacturing companies require accelerated network capacity to support advanced AI use cases.

In telecommunications, process discipline continues to surpass everything. Despite nearly a decade’s worth of efficiencies and rationalisation, operating expenditure across much of the industry remains stubbornly high. The next chapter involves AI’s potential to vastly improve both customer-facing operations and network management. Under these circumstances, the key challenge involves finding ways to mitigate the cultural and knowledge-based deficits that open up as workforces continue to shrink. Attracting talent is always a requirement. Arguably, in telecommunications, the more immediate challenge involves retaining the talent that matters the most.

From digital capability to organisational confidence

TMT companies have more experience than most with AI-driven digital transformation. Senior leaders in TMT place greater emphasis than those in other industries on data governance, cybersecurity and technology infrastructure as critical enablers of successful digital transformation and are more likely to prioritise investment in these areas. As a result, the sector is more confident in the outcomes of transformation initiatives, with 50% of TMT executives reporting that cybersecurity is the digital transformation investment most likely to deliver the strongest returns, compared with 39% across all sectors.

The second lesson involves the human dimension. In TMT, as in most other vertical sectors, restructuring and change management typically is not prioritised as part of digital transformation strategy. For seven out of 10 firms in the sector, change management is simply ‘included’ in transformation strategy, rather than prioritised. As a result, resourcing frequently comes under pressure at the point of deployment. The picture is similar across other sectors with just 19% of executives describing change management as exerting a major impact on the success of digital transformation projects.

 As AI becomes the dominant component of digital transformation, it is time to reassess the low level of urgency surrounding change management initiatives. Research has repeatedly shown relatively high levels of employee optimism about AI in the workplace. However, areas of anxiety persist, most notably among those employees who feel overwhelmed by the potential of AI in the absence of industry-specific training. As AI increases pressure on employees to adapt, paying more attention to the humans in the loop will become increasingly important. In this respect, the TMT sector may once again be called upon to deal with the impact of change in advance of other sectors.

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