{"id":99,"date":"2026-09-21T14:30:32","date_gmt":"2026-09-21T14:30:32","guid":{"rendered":"https:\/\/jbmipublisher.org\/blog\/?p=99"},"modified":"2026-09-21T14:33:05","modified_gmt":"2026-09-21T14:33:05","slug":"ai-redefining-management-2026","status":"publish","type":"post","link":"https:\/\/jbmipublisher.org\/blog\/2026\/09\/21\/ai-redefining-management-2026\/","title":{"rendered":"AI Is Not Replacing Managers: How Artificial Intelligence Is Redefining Management in 2026"},"content":{"rendered":"\t\t
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For years, one of the most persistent predictions about artificial intelligence has been that organisations would become flatter, middle managers would disappear and algorithms would increasingly make decisions once handled by people.<\/p>\n\n

The evidence emerging in 2026 tells a more complicated story.<\/p>\n\n

Artificial intelligence is certainly automating parts of managerial work. Scheduling, reporting, routine analysis, monitoring and information processing can increasingly be supported by software. Yet new research suggests that companies adopting AI may not simply need fewer managers. They may need\u00a0different managers<\/strong>.<\/p>\n\n

A 2026 study published in\u00a0Strategic Management Journal<\/em>\u00a0found that US firms with higher levels of AI adoption posted both more managerial vacancies and a larger share of managerial vacancies than firms adopting AI less intensively. At the same time, the skills requested from those managers were changing. Routine administrative capabilities became relatively less important, while coordination, communication, creativity, sales and other growth oriented capabilities gained importance.<\/p>\n\n

This matters well beyond the United States. AI adoption among UK businesses has risen sharply, while organisations across Europe are trying to determine whether investments in artificial intelligence will translate into productivity, innovation and competitive advantage.<\/p>\n\n

The emerging lesson is that the future of management may not be a choice between\u00a0human managers and artificial intelligence<\/strong>. It may be about how effectively managers learn to organise work around artificial intelligence.<\/p>\n\n

AI Adoption Is Accelerating, but Deep Integration Is Still Rare<\/h2>\n\n

The United Kingdom provides a useful picture of both the speed and limitations of current AI adoption.<\/p>\n\n

According to the Office for National Statistics, the proportion of UK businesses with at least 10 employees reporting the use of at least one AI technology increased from around 12% in late 2023 to around\u00a035% by June 2026<\/strong>. Adoption was considerably higher in digitally intensive industries. Some 58% of businesses in information and communication reported using AI, compared with only 13% in construction.<\/p>\n\n

The headline adoption rate, however, hides an important detail.<\/p>\n\n

Among UK businesses already using AI, the average number of AI technologies used increased only modestly, from approximately 1.4 in 2023 to 1.6 in 2026. Just 10% of businesses using AI reported using it extensively, while only 15% said that more than half of their employees used AI in their daily work.<\/p>\n\n

In other words, AI is spreading faster than it is becoming deeply embedded in organisations.<\/p>\n\n

The United States displays a similar pattern. US Census Bureau research based on nationally representative business data found that approximately 18% of firms used AI in a business function during the November 2025 to January 2026 reference period. When weighted by employment, the proportion rose to 32%, reflecting much greater use among larger organisations.<\/p>\n\n

Yet 57% of US businesses using AI deployed it across no more than three business functions. Sales and marketing, strategy and business development, and information technology were among the most common areas of deployment.<\/p>\n\n

This distinction between\u00a0adoption and integration<\/strong>\u00a0may become one of the most important management questions of the AI era.<\/p>\n\n

Buying an AI system is comparatively easy. Redesigning an organisation so that the technology generates sustained value is much harder.<\/p>\n\n

A Surprising Finding: More AI Can Mean More Demand for Managers<\/h2>\n\n

This is where new management research becomes particularly interesting.<\/p>\n\n

Alekseeva, Azar, Gin\u00e9, Samila and Taska examined US job posting data to investigate how firms changed their demand for managers as they adopted artificial intelligence.<\/p>\n\n

Their dataset covered 823 firms and examined AI related hiring and managerial vacancies over the period from 2010 to 2022. Importantly, this period largely reflects\u00a0predictive AI<\/strong>, including technologies used for classification, forecasting and optimisation. It predates the widespread adoption of ChatGPT style generative AI and today’s emerging agentic systems. The findings therefore should not automatically be treated as predictions of what generative AI will do to management.<\/p>\n\n

Even with that qualification, the results challenge a simple automation narrative.<\/p>\n\n

Firms with greater AI adoption showed greater demand for managerial positions. In the researchers’ instrumental variable estimates, a one percentage point increase in their measure of AI adoption was associated with a 1.2 percentage point increase in the managerial share of vacancies and approximately 9% more managerial vacancies. The relationship was particularly pronounced in manufacturing and in research intensive firms.<\/p>\n\n

Why might a technology capable of automating tasks increase demand for managers?<\/p>\n\n

Because automation is only one part of technological transformation.<\/p>\n\n

When organisations adopt new technologies, somebody still has to determine how they fit into existing processes, coordinate teams, redesign responsibilities, resolve conflicts between technological and organisational requirements, govern risks and turn technical capability into commercial results.<\/p>\n\n

AI may reduce some forms of management while simultaneously creating more demand for others.<\/p>\n\n

The Managerial Job Description Is Changing<\/h2>\n\n

The more revealing finding may therefore concern\u00a0what managers are expected to do<\/strong>.<\/p>\n\n

The same study found that stronger AI adoption was associated with changing demand for specific managerial skills.<\/p>\n\n

Requirements related to activities such as budgeting, routine planning, scheduling, staff administration and conventional office software became relatively less prominent. In contrast, skills related to sales, coordination, written communication, listening and other growth oriented activities gained importance. In manufacturing firms, creativity, collaboration, stakeholder management and training became particularly relevant.<\/p>\n\n

The direction of change can be summarised as follows:<\/p>\n\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Traditional emphasis<\/th>\nEmerging AI era emphasis<\/th>\n<\/tr>\n
Collecting information<\/td>\nInterpreting information<\/td>\n<\/tr>\n
Routine reporting<\/td>\nJudgement and decision framing<\/td>\n<\/tr>\n
Scheduling activities<\/td>\nCoordinating human and AI workflows<\/td>\n<\/tr>\n
Monitoring standard processes<\/td>\nManaging exceptions and uncertainty<\/td>\n<\/tr>\n
Administrative supervision<\/td>\nCross functional coordination<\/td>\n<\/tr>\n
Producing routine analysis<\/td>\nChallenging and validating AI output<\/td>\n<\/tr>\n
Managing people alone<\/td>\nManaging people, data and intelligent systems<\/td>\n<\/tr>\n
Following established processes<\/td>\nRedesigning processes<\/td>\n<\/tr>\n
Technical control<\/td>\nGovernance and accountability<\/td>\n<\/tr>\n
Information ownership<\/td>\nStrategic interpretation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n\n

This does not mean budgeting, planning or people management suddenly become unnecessary. Rather, it suggests that technologies can absorb portions of routine managerial work, allowing the relative value of other capabilities to rise.<\/p>\n\n

That represents a fundamental change in what organisations may mean when they describe someone as an effective manager.<\/p>\n\n

Why Better AI Does Not Automatically Produce Better Businesses<\/h2>\n\n

The technology industry understandably focuses on model capability. Managers face a different problem.<\/p>\n\n

An AI model may perform exceptionally well technically and still create little organisational value.<\/p>\n\n

Consider a company that introduces generative AI for customer support. The model may successfully generate responses in seconds. But management still has to decide when customers should interact with AI, when cases should be escalated to employees, who is accountable for incorrect advice, how performance should be measured and whether productivity gains should translate into lower costs, better service or expanded capacity.<\/p>\n\n

The technology does not make those strategic choices.<\/p>\n\n

This helps explain why economists continue to distinguish rapid AI adoption from economy wide productivity transformation.<\/p>\n\n

A July 2026 US Bureau of Economic Analysis study examining business expectations and AI outcomes found that firms’ motivations for adopting AI did not yet translate neatly into measurable outcomes across the economy. The researchers did find links between AI use cases and increased research and development intensity, suggesting that organisational restructuring and complementary investment may still be developing before their full effects appear in productivity statistics.<\/p>\n\n

The implication for managers is important: implementation is not the same as transformation.<\/p>\n\n

European Evidence Points to the Importance of Complementary Investment<\/h2>\n\n

Research from Europe provides further evidence.<\/p>\n\n

A 2026 European Investment Bank working paper examined more than 12,000 non financial firms and found that AI adoption increased labour productivity by approximately 4% among European firms in its analysis. The productivity gains were driven largely by capital deepening rather than immediate employment reductions and were concentrated particularly among medium sized and large companies.<\/p>\n\n

Crucially, the study emphasised the role of complementary investments in areas such as\u00a0software, data capabilities and workforce training<\/strong>.<\/p>\n\n

That finding fits a much older lesson from management research: general purpose technologies rarely create their full economic value simply by being installed. Organisations usually have to change structures, processes, skills and sometimes business models around them.<\/p>\n\n

The same is likely to be true of AI.<\/p>\n\n

A business that purchases advanced AI but retains fragmented data, slow approval processes, unclear accountability and employees who do not understand how to use the technology may gain very little.<\/p>\n\n

A competitor using a less advanced model but combining it with cleaner data, better processes and stronger managerial capabilities may produce substantially greater value.<\/p>\n\n

The UK Faces an Innovation Management Challenge<\/h2>\n\n

The UK’s broader innovation data makes this question especially relevant.<\/p>\n\n

The 2025 UK Innovation Survey, published in June 2026, found that\u00a034% of UK businesses were innovation active during 2022 to 2024<\/strong>, down from 36% during 2020 to 2022. Large businesses were substantially more likely to be innovation active, at 47%, than small and medium sized businesses, at 34%.<\/p>\n\n

These figures measure a different period and concept from the ONS AI adoption statistics, so they should not be directly compared as if they describe the same phenomenon.<\/p>\n\n

They do, however, highlight an important management issue.<\/p>\n\n

Rapid access to a new technology does not guarantee that firms will become more innovative.<\/p>\n\n

Innovation requires organisations to convert technology into new or improved products, services, processes and business practices. That conversion depends partly on leadership, investment, workforce capability, organisational learning and willingness to redesign established ways of working.<\/p>\n\n

Indeed, the UK Innovation Survey identifies skills as an important component of firms’ capacity to introduce innovations and reports that businesses classified as broader innovators have a larger proportion of highly qualified employees than non innovators.<\/p>\n\n

For UK businesses, therefore, the central competitive question may increasingly shift from:<\/p>\n\n

\u201cDo we use AI?\u201d<\/strong><\/p>\n\n

to:<\/p>\n\n

\u201cHave we developed the managerial and organisational capabilities required to obtain value from AI?\u201d<\/strong><\/p>\n\n

What the AI Era May Require From Managers<\/h2>\n\n

The manager of the next decade may need a broader set of capabilities than either the traditional administrator or the technology specialist.<\/p>\n\n

Managers will increasingly need enough AI literacy to understand what models can and cannot reliably do. They will need to distinguish confident sounding outputs from trustworthy evidence and understand when human review is necessary.<\/p>\n\n

They will also need process design capabilities. Simply inserting AI into a poorly designed workflow can make a bad process faster without making it better.<\/p>\n\n

Governance will become another core management responsibility. Decisions will have to be made about appropriate AI use, access to organisational data, accountability for automated recommendations, privacy, security, intellectual property and escalation when systems fail.<\/p>\n\n

Perhaps most importantly, managers will need to manage organisational change.<\/p>\n\n

AI implementation alters roles, information flows, expertise and sometimes the distribution of authority. Employees may need to abandon familiar routines while learning new ones. Teams may have to determine which decisions remain human, which can be automated and which should involve collaboration between people and machines.<\/p>\n\n

These are management problems as much as technological ones.<\/p>\n\n

SMEs May Face the Hardest Transition<\/h2>\n\n

The challenge may be particularly significant for small and medium sized enterprises.<\/p>\n\n

Large organisations often have dedicated technology teams, data specialists, compliance functions and training budgets. Smaller businesses frequently have to make the same technological decisions with far fewer resources.<\/p>\n\n

JBMIJ’s own recently published research on the\u00a0AI adoption friction gap in SMEs<\/strong>\u00a0reflects growing scholarly interest in precisely this problem: the distance between recognising the potential value of AI and possessing the organisational capabilities required to implement it effectively.<\/p>\n\n

The UK government has similarly identified digital adoption among SMEs as a strategic priority, with the SME Digital Adoption Taskforce setting an ambition for UK SMEs to become among the most digitally capable and AI confident businesses in the G7 by 2035.<\/p>\n\n

For smaller firms, successful AI adoption may therefore depend less on purchasing the most sophisticated system and more on selecting a narrow business problem, establishing reliable data, training employees and assigning clear managerial ownership.<\/p>\n\n

What Researchers Still Need to Find Out<\/h2>\n\n

The evidence is moving quickly, but major questions remain unanswered.<\/p>\n\n

Most rigorous firm level research available today measures earlier generations of AI. Generative AI has spread at an extraordinary pace only since late 2022, while genuinely autonomous agentic systems are newer still. The organisational consequences of these technologies may differ significantly from those of predictive machine learning.<\/p>\n\n

Researchers therefore have an opportunity to investigate questions such as how generative AI changes managerial decision quality, whether AI reduces or increases management layers, how managers evaluate uncertain machine recommendations, whether AI adoption changes organisational centralisation, how human and AI teams divide decision authority and which management practices enable SMEs to realise measurable productivity gains.<\/p>\n\n

Cross country comparisons will also become increasingly valuable.<\/p>\n\n

The United Kingdom, United States, European Union economies and emerging markets operate under different labour markets, regulatory institutions, skills systems and business structures. The same technology may therefore produce very different organisational outcomes across countries.<\/p>\n\n

Conclusion: AI May Make Management More Important, Not Less<\/h2>\n\n

Artificial intelligence is undoubtedly capable of performing tasks that were previously carried out by managers.<\/p>\n\n

But automating a managerial task is not the same thing as eliminating management.<\/p>\n\n

Current evidence suggests something more interesting may be happening. AI is changing the composition of managerial work. Some routine administrative activities can increasingly be automated, while coordination, judgement, communication, innovation, governance and organisational redesign become more important.<\/p>\n\n

US firm level research has already found greater managerial demand among more intensive AI adopters. UK statistics show rapid adoption but relatively shallow integration. European evidence suggests that productivity gains depend partly on complementary investments in technology, data and people.<\/p>\n\n

Taken together, these findings point toward a different interpretation of the AI revolution.<\/p>\n\n

The organisations that gain the most from artificial intelligence may not be those that replace the greatest number of people.<\/p>\n\n

They may be those that\u00a0redesign management most effectively around what humans and intelligent systems each do best<\/strong>.<\/p>\n\n

Sources and Further Reading<\/h3>\n\n

Alekseeva, L., Azar, J., Gin\u00e9, M., Samila, S., & Taska, B. (2026).\u00a0Artificial intelligence adoption and the demand for managerial expertise<\/em>. Strategic Management Journal.<\/p>\n\n

Office for National Statistics. (2026).\u00a0Artificial intelligence in UK businesses: 2023 to 2026<\/em>.<\/p>\n\n

US Census Bureau. (2026).\u00a0The Microstructure of AI Diffusion: Evidence from Firms, Business Functions, and Worker Tasks<\/em>.<\/p>\n\n

European Investment Bank. (2026).\u00a0AI adoption, productivity and employment: Evidence from European firms<\/em>.<\/p>\n\n

Highfill, T., & Samuels, J. D. (2026).\u00a0AI Expectations and Outcomes<\/em>. US Bureau of Economic Analysis.<\/p>\n\n

Department for Business and Trade. (2026).\u00a0UK Innovation Survey 2025<\/em>.<\/p>\n\n

Call for Research<\/h3>\n\n

How is artificial intelligence changing management, innovation and organisational performance in your country or industry?<\/strong><\/p>\n\n

The\u00a0Journal of Business Management & Innovation<\/em>\u00a0welcomes original research, case studies, reviews and comparative studies examining artificial intelligence, strategic management, entrepreneurship, innovation, organisational change and emerging business models.<\/p>\n\n

Research comparing the United Kingdom with the United States, Europe, Africa, Asia and other regions is particularly valuable as businesses navigate different technological, regulatory and organisational environments.<\/p>\n\n

Submit your manuscript to JBMIJ<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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