Year: 2026
Type: editorial
Abstract: The influence of AI in businessArtificial intelligence (AI) has moved from a specialised technical pursuit to a core strategic lever across industries.Its influence in business is multifaceted: it shapes what firms produce, how they compete and how work is organised.This editorial synthesises current scholarly thinking on three interrelated domains where AI exerts its strongest influenceproductivity and value creation, the organisation of work and skills and strategic/ organisational transformation.Productivity and the economics of prediction: One of AI's most immediate economic contributions is its enhancement of prediction.Machine learning methods lower the cost of forecasting, enabling more informed decisions across marketing, operations, finance and supply chains (Agrawal, Gans, & Goldfarb, 2019).The framing that "AI is a prediction technology" helps explain how firms extract value: by improving decision quality where uncertainty previously constrained action (Agrawal et al., 2019).Yet the macroeconomic impact of AI on measured productivity has been uneven.Despite spectacular advances in capabilities, empirical work documents a "productivity paradox"rapid technical progress not yet fully reflected in aggregate productivity statistics -which likely reflects diffusion lags, measurement problems and organisational frictions in adopting AI at scale (Brynjolfsson, Rock, & Syverson, 2017).Firms that successfully integrate AI tend to pair it with process redesign, managerial changes and complementary investments in data infrastructure; absent these, AI projects can deliver narrow gains without broad productivity uplift (Davenport & Ronanki, 2018).Thus, AI's productivity potential is real but conditional: prediction must be embedded in decision processes and supported with organisational change.Work, tasks and labour dynamics: A central debate concerns AI's effects on employment.Early influential work estimated that many occupations contain tasks susceptible to computerisation, raising concerns about large-scale job displacement (Frey & Osborne, 2017).Subsequent scholarship has complicated this picture.Economists emphasise that technology substitutes for tasks, not whole occupations and that complementarities between AI and human labour can increase demand for different skills (Autor, 2015).Moreover, industry-and firm-level outcomes depend heavily on demand-side dynamics: automation can spur new demand (creating jobs) or saturate markets and depress employment, depending on the elasticity of demand and market responses (Bessen, 2018).Practically speaking, firms are more likely to redeploy human labour than to eliminate it wholesale.AI tends to automate routine, codifiable tasks while increasing the value of social, creative and managerial skills (Autor, 2015).Organisations face pressing choices about reskilling, job redesign and workforce transition.The managerial imperative is to anticipate task shifts and invest in complementary human capital and organisational processes that allow human-AI teams to deliver higher-value outcomes.Strategic and organisational transformation: Beyond productivity and labour, AI reshapes strategy.Firms that view AI as a modular technology (a feature to bolt onto existing products) miss larger strategic opportunities.AI can create new business models by enabling personalisation at scale, dynamic pricing, autonomous operations and data-driven orchestration of ecosystems (Agrawal et al., 2019).Successful adopters develop capabilities in data governance, model development and product integration -and crucially, align AI initiatives with clear metrics of customer value.The organisational challenge is coordination.AI projects often require cross-functional collaboration among data scientists, domain experts, engineers and managers.Davenport and Ronanki (2018) observe that the most successful cognitive projects are business-driven, start small with clear metrics and scale iteratively; moonshots without aligned processes commonly IIM Ranchi journal of management studies 1
Author: Aditya Shankar Mishra
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7128437735