AI-Driven Business Decision-Making Research in 2026

AI-Driven Business Decision-Making in 2026: Emerging Research Opportunities for Management Scholars

Artificial intelligence is rapidly becoming more than a technology implementation issue for organisations. It is increasingly becoming a management, strategy, governance and organisational design question. In 2026, businesses are moving beyond basic experimentation with artificial intelligence and exploring how AI can support forecasting, strategic planning, customer analysis, financial decisions, workforce management, supply chain operations, risk assessment and many other areas traditionally dependent on human managerial judgement. This shift creates an important research opportunity for management scholars. The central question is no longer simply whether organisations will adopt artificial intelligence. Researchers now have an opportunity to examine how AI changes business decision-making, when it improves managerial outcomes, where human judgement remains essential, and what organisational capabilities are required to use AI responsibly and effectively. The ISB&M Journal of Business Issues & Research (ISBMJBIR) encourages scholarly investigation into these emerging questions across business, management and related interdisciplinary disciplines. Researchers working in strategic management, marketing, finance, human resource management, organisational behaviour, operations, entrepreneurship, information systems, business analytics and corporate governance can contribute substantially to understanding the developing relationship between artificial intelligence and managerial decision-making.

From Business Automation to AI-Assisted Decision-Making

For many years, business technology was primarily used to automate repetitive activities, store information and improve operational efficiency. Artificial intelligence represents a broader shift. Modern AI systems can analyse large amounts of structured and unstructured information, identify patterns, generate scenarios, summarise complex information, support forecasting and provide recommendations. This means AI can increasingly influence not only how work is performed but also how organisational decisions are developed. Consider a marketing manager deciding which customer segment should receive a new campaign. AI may analyse historical purchase behaviour, engagement patterns and customer characteristics. A finance team evaluating future cash requirements may use predictive models to analyse multiple financial scenarios. A human resource manager may use analytics to identify patterns related to employee retention. A supply chain manager may use AI-supported forecasting to anticipate demand fluctuations. A senior management team may use generative AI tools to synthesise competitor information before discussing a strategic decision. Each example creates research questions. Does the use of AI actually improve decision quality? Does it accelerate decision-making? How does it affect managerial confidence? When do managers accept or reject algorithmic recommendations? What happens when AI-generated recommendations conflict with managerial experience? Who is accountable when an AI-supported decision produces an undesirable result? These questions demonstrate why AI-driven decision-making should increasingly be studied as a management phenomenon rather than exclusively as a technical one.

Why 2026 Is an Important Period for AI and Management Research

The early stages of generative AI adoption were dominated by experimentation. Employees used AI for drafting, summarisation, coding, brainstorming and information retrieval. Organisations explored pilots to determine how the technology might improve productivity. The next stage is more complex. Businesses increasingly need to decide how AI should be integrated into organisational processes, which decisions may be delegated or partially automated, which decisions require human approval, and how AI-related risks should be governed. This creates a significant transition from AI as a productivity tool toward AI as part of organisational decision systems. For management scholars, this transition provides opportunities to investigate AI using established theories from strategic management, organisational behaviour, information systems, decision science, human resource management, marketing and corporate governance. It also creates opportunities to develop new theoretical frameworks that explain forms of collaboration between human managers and increasingly capable AI systems.

Research Opportunity 1: Human-AI Collaboration in Managerial Decisions

One of the most important research areas concerns the division of decision-making responsibility between humans and artificial intelligence. It is unlikely that every organisational decision should be fully automated. Routine, data-rich and low-risk decisions may be suitable for substantial AI involvement. However, complex strategic decisions involving ethical considerations, uncertain environments or significant stakeholder consequences may require greater human judgement. Researchers can explore questions such as:

When should managers rely on AI recommendations?
When should human judgement override algorithmic recommendations?
Does AI improve or weaken managerial decision quality?
How does AI influence managerial confidence?
Can excessive dependence on AI reduce critical thinking?
How should organisations divide decision authority between humans and AI?
What types of decisions are suitable for automation?
How do managers respond when AI challenges their existing beliefs?

An important emerging concept is not simply automation but augmentation. Under an augmentation model, artificial intelligence strengthens human capabilities while managers retain appropriate oversight and accountability. Research comparing automation and augmentation approaches could make valuable contributions to management literature.

Research Opportunity 2: Generative AI and Strategic Management

Strategic decisions often involve uncertain information, competitor behaviour, market developments and long-term organisational consequences. Generative AI can potentially help managers process information more rapidly, compare alternatives, identify market patterns and generate potential strategic scenarios. However, strategic management cannot simply be reduced to algorithmic optimisation. Competitive strategy also requires interpretation, organisational knowledge, understanding of stakeholder behaviour and judgement regarding uncertain future conditions. Potential research themes include:

Generative AI in strategic planning
AI-supported competitive analysis
AI and strategic agility
Artificial intelligence in scenario planning
AI and business model innovation
Algorithmic support for strategic decisions
AI adoption and competitive advantage
AI capabilities and firm performance
Strategic risks of AI dependence
AI-driven organisational transformation

Researchers may also investigate whether access to similar AI technologies reduces competitive differentiation or whether organisations with better data, management capabilities and implementation processes are able to create greater value from the same technologies.

Research Opportunity 3: AI in Marketing and Consumer Decision-Making

Marketing represents one of the richest environments for AI-related research because organisations increasingly use data and automated systems to understand and influence consumer behaviour. AI can support customer segmentation, recommendation systems, personalised communication, content generation, pricing analysis, customer service and advertising optimisation. This creates significant research opportunities involving both organisational performance and consumer response. Potential themes include:

AI-powered personalisation
Generative AI in digital marketing
AI-generated advertising content
Chatbots and customer experience
Algorithmic recommendations
Predictive customer analytics
AI and brand trust
Consumer acceptance of AI
AI disclosure and consumer behaviour
Artificial intelligence and customer loyalty
AI-powered pricing strategies
Neuromarketing combined with AI analytics

An important research question is whether increased personalisation always improves the customer experience. Consumers may appreciate relevant recommendations while simultaneously becoming uncomfortable if personalisation appears intrusive. Researchers can therefore explore the relationship between personalisation, privacy, transparency and consumer trust.

Research Opportunity 4: AI-Driven Financial Decision-Making

Finance is another major area in which artificial intelligence can influence organisational decisions. AI and machine learning can support forecasting, fraud detection, credit analysis, investment research, risk assessment and financial planning. Generative AI may also change how financial professionals analyse reports, interpret information and prepare decision materials. Potential research topics include:

AI-assisted corporate financial planning
Machine learning in credit assessment
AI and investment decision-making
Artificial intelligence in financial risk management
AI-based fraud detection
Predictive financial analytics
FinTech and artificial intelligence
Algorithmic investment behaviour
Generative AI in financial analysis
AI adoption in banking and financial services

Researchers should also consider potential biases in financial AI systems. An algorithm may appear objective while reproducing patterns contained within historical data. Research examining fairness, explainability and accountability in AI-supported financial decisions can therefore be particularly valuable.

Research Opportunity 5: Artificial Intelligence in Human Resource Management

Human resource management is being transformed by data analytics and increasingly sophisticated AI tools. Organisations may use AI to support recruitment, candidate screening, workforce planning, skills analysis, employee engagement and performance management. However, HR decisions directly affect individuals, making fairness and transparency especially important. Research opportunities include:

AI-based recruitment
Algorithmic candidate screening
Employee attitudes toward workplace AI
AI and performance evaluation
People analytics
AI-assisted workforce planning
Artificial intelligence and employee productivity
AI-related job redesign
Employee reskilling for AI-enabled workplaces
Algorithmic fairness in HR decisions
AI and organisational culture
Human-AI collaboration at work

Researchers can investigate whether AI-supported HR processes improve consistency or instead introduce new forms of bias and employee distrust.

Research Opportunity 6: AI Agents and the Changing Organisation

One of the most significant developments for management research is the emergence of increasingly autonomous AI systems capable of performing multiple connected tasks. These systems are often described as AI agents or digital colleagues. Traditional workplace technologies generally required humans to initiate individual tasks. More autonomous AI systems may be able to identify information, perform analysis, complete workflows and interact with other systems with considerably less direct human intervention. This development raises fundamental organisational questions. Researchers could investigate:

How AI agents affect organisational structures
Changes in managerial spans of control
AI agents as members of organisational teams
Coordination between employees and autonomous AI
AI agents and organisational productivity
Delegation of decision authority to AI systems
Trust in autonomous AI
Effects on middle management roles
AI agents and knowledge work
Organisational accountability for autonomous actions

Management research can play an important role in determining how organisations should redesign roles and workflows when AI becomes not merely a tool but an active participant in business processes.

Research Opportunity 7: AI Adoption Among SMEs

Small and medium enterprises provide a particularly important context for artificial intelligence research. Large corporations may have significant technology budgets, specialised data teams and dedicated AI functions. SMEs often operate under different constraints. They may have limited budgets, smaller datasets, fewer specialised employees and less formal technology governance. At the same time, accessible generative AI tools may allow smaller businesses to use advanced capabilities that previously required substantial technology investments. Research questions include:

What determines AI readiness among SMEs?
What prevents smaller organisations from adopting AI?
Does generative AI reduce digital capability gaps?
How does management awareness influence AI adoption?
Which AI applications generate the greatest SME value?
How do SMEs evaluate AI investments?
What role does employee training play in adoption?
How do resource constraints affect AI implementation?

This area is particularly relevant for Indian management research because MSMEs represent an important component of economic activity and provide diverse environments in which to investigate technology adoption.

Research Opportunity 8: AI, Entrepreneurship and New Business Models

Artificial intelligence is not simply changing existing organisations. It is also enabling new entrepreneurial opportunities. Entrepreneurs can potentially use AI for market research, software development, customer support, marketing, design, financial modelling and business operations. This may reduce some traditional barriers to starting and scaling businesses. Researchers can investigate:

AI-enabled entrepreneurship
Generative AI and start-up formation
AI-driven business models
Entrepreneurial productivity
AI adoption in start-ups
Technology entrepreneurship ecosystems
AI and entrepreneurial decision-making
AI-enabled innovation
Entrepreneurial skills in an AI economy
Artificial intelligence and venture creation

An especially interesting question is whether AI democratises entrepreneurship or primarily benefits founders who already possess advanced technological, managerial and financial capabilities.

Research Opportunity 9: AI Governance, Ethics and Accountability

As artificial intelligence becomes involved in more consequential organisational decisions, governance becomes increasingly important. Organisations need to determine who can use AI, what data can be provided to AI systems, which outputs require verification, which decisions require human approval and how AI-related incidents should be addressed. Research opportunities include:

Corporate AI governance
Responsible AI frameworks
AI accountability
Algorithmic transparency
Explainable AI in management
Data governance
Privacy and artificial intelligence
AI ethics
Bias in algorithmic decisions
Human oversight of AI
AI risk management
Board oversight of artificial intelligence

AI governance should not necessarily be studied only as a compliance issue. Governance can also influence innovation. Excessively restrictive controls may prevent organisations from experimenting effectively, while insufficient oversight may create unacceptable operational, legal, ethical or reputational risk. Research examining how organisations balance these competing objectives can make an important contribution.

Research Opportunity 10: Trust, Explainability and Acceptance of AI Decisions

A technically accurate AI recommendation does not automatically become an organisationally accepted decision. Managers and employees need to trust the system sufficiently to use its recommendations. This introduces behavioural and organisational questions around explainability, transparency and confidence. Researchers may examine:

Managerial trust in AI systems
Algorithm aversion
Algorithm appreciation
Explainability and decision acceptance
Employee perceptions of AI
Trust in generative AI
Managerial experience and AI reliance
Effects of AI errors on future trust
Human reactions to automated recommendations

Researchers can also study whether managers place too much trust in AI. An organisation faces risks both when employees ignore useful recommendations and when they accept AI outputs without sufficient verification.

Research Opportunity 11: AI, Leadership and Organisational Change

AI implementation is frequently framed as a technological project, but its success may depend substantially on leadership. Senior managers need to determine where AI should be deployed, how investment priorities should be established, what skills employees require and how organisational resistance should be managed. Relevant research questions include:

Leadership capabilities for AI transformation
Digital leadership
Management support and AI adoption
Organisational resistance to AI
Change management for AI implementation
AI literacy among senior executives
Leadership communication during automation
Employee participation in AI transformation

Studies connecting leadership behaviour with measurable AI implementation outcomes could offer valuable insights for both scholarship and practice.

Research Opportunity 12: Measuring Business Value from AI

Perhaps one of the most important questions for organisations is also one of the simplest: Does the AI investment create measurable value? Businesses may experiment with AI because competitors are doing so or because management believes adoption is strategically necessary. Research is needed to determine what actually produces results. Potential research themes include:

AI and organisational productivity
Return on AI investment
AI and firm performance
Cost reduction through AI
Revenue enhancement from AI adoption
AI and innovation performance
AI-enabled process improvement
Measuring generative AI productivity
AI maturity and organisational performance

Researchers should distinguish carefully between adoption, usage and value creation. An organisation that has purchased AI software has adopted technology. An organisation whose employees actively use AI has achieved usage. An organisation that demonstrates improved productivity, decision quality, customer outcomes or financial performance has achieved measurable business value. These are different research constructs and should not automatically be treated as equivalent.

Beyond Adoption Studies: Where Management Research Needs to Go Next

Early technology research often asks whether organisations intend to adopt a particular innovation. Such research remains useful, but the rapid diffusion of accessible AI tools means management scholars can increasingly investigate deeper questions. Instead of asking only: “Will managers adopt generative AI?” Researchers might ask: “How does generative AI change managerial decision quality?” Instead of: “Do employees intend to use AI?” Researchers could examine: “Under what circumstances do employees accept or override AI recommendations?” Instead of: “Does the company use AI?” Research could explore: “Which organisational capabilities convert AI adoption into measurable competitive advantage?” This movement from adoption-focused research toward outcome-focused research may produce more significant managerial and theoretical contributions.

Methodological Opportunities for AI and Management Researchers

AI-related management research can benefit from methodological diversity. Researchers may use:

Large-scale surveys
Experiments
Field experiments
Longitudinal studies
Organisational case studies
Interviews with managers and employees
Mixed-method research
Panel data analysis
Secondary business datasets
Digital trace data
Systematic literature reviews
Bibliometric analysis
Comparative industry studies

Longitudinal research may become particularly important. AI technologies evolve rapidly, and the organisational effects visible immediately after implementation may differ substantially from the effects observed after employees have developed experience with the technology.

The Importance of Indian and Emerging-Market Research

Much of the global discussion surrounding artificial intelligence focuses on large technology firms and multinational enterprises. There remains substantial scope for research in emerging-market contexts. India offers opportunities to examine AI across:

MSMEs
Start-ups
Banking and financial services
Retail
Manufacturing
Healthcare management
Education businesses
E-commerce
Logistics
Professional services
Family-owned businesses
Rural enterprises

Researchers should, however, move beyond treating India merely as another geographical dataset. Studies become more theoretically valuable when they explain how institutional structures, resource constraints, workforce characteristics, cultural factors or market conditions affect AI adoption and outcomes.

What Makes a Strong AI and Management Research Paper?

Artificial intelligence is a popular subject, but popularity alone does not create a strong scholarly contribution. Researchers should avoid simply adding “AI” or “generative AI” to an established management topic without demonstrating why the technology meaningfully changes the underlying research problem. A strong AI-management manuscript should ideally contain:

A clearly defined managerial problem
A meaningful research gap
A relevant theoretical foundation
Clearly defined AI-related constructs
An appropriate methodology
Transparent data collection and analysis
Careful interpretation of findings
Managerial implications
Limitations
Future research directions

Researchers should also clearly identify which form of artificial intelligence they are studying. Machine learning, predictive analytics, generative AI, large language models and autonomous agents are related technologies but should not automatically be treated as interchangeable concepts.

Opportunities for Interdisciplinary Research

AI-driven business decision-making sits naturally at the intersection of several disciplines. Management researchers may collaborate with scholars working in:

Information systems
Computer science
Psychology
Economics
Statistics
Finance
Marketing
Operations research
Business ethics
Public policy

For example, a study of AI-based recruitment may combine HRM, information systems and organisational psychology. Research on algorithmic pricing may connect marketing, economics and data science. AI governance studies may combine strategic management, information systems, ethics and corporate governance. Such interdisciplinary approaches can help scholars address the complexity of AI-enabled organisations more effectively.

ISBMJBIR as a Platform for Emerging Business and Management Research

The ISB&M Journal of Business Issues & Research provides a scholarly platform for research addressing emerging challenges affecting organisations, managers, entrepreneurs and markets. AI-driven decision-making is particularly relevant to this mission because artificial intelligence is influencing numerous business disciplines simultaneously. Researchers examining AI in strategy, finance, marketing, entrepreneurship, HRM, operations, governance, business analytics or organisational behaviour can contribute to a growing conversation about how modern organisations should make decisions in technology-intensive environments. Authors considering submission should review the journal's current Aims & Scope, Author Guidelines, Call for Papers and manuscript submission requirements before preparing their work.

Digital Research Publishing and the Role of Modern Journal Infrastructure

The transformation occurring within business organisations also has parallels in scholarly publishing. Researchers increasingly expect digital submission systems, organised peer-review workflows, clear manuscript tracking and structured online access to published research. ISB&M Journal of Business Issues & Research is powered by ScholarJMS, providing digital infrastructure for the journal website, manuscript submission and editorial workflow. Universities, management institutes, scholarly societies and publishers planning to establish their own academic journals can similarly benefit from building structured digital workflows from the beginning. ScholarJMS: https://www.scholarjms.com Institutions operating OJS journals may require a different type of support. OJSCloud provides OJS hosting, technical support, migration assistance, journal launch consulting and ISSN consulting. OJSCloud: https://www.ojscloud.com For journals preparing to register persistent identifiers for published articles, GetDOI provides Crossref DOI support, sponsorship guidance and DOI workflow assistance. GetDOI: https://www.getdoi.com For publishers exploring greater transparency and research trust, Scholar9 supports transparent peer-review and research credibility workflows. Scholar9: https://www.scholar9.com

Why Research Journals Need Structured AI Publishing Workflows Too

As AI becomes increasingly relevant to academic research, journals will also need clear policies surrounding responsible use. Editors may encounter questions involving AI-assisted writing, disclosure of AI use, research integrity, generated content and reviewer responsibilities. This adds another dimension to modern journal management. A journal launching today should therefore consider not only its website and manuscript submission system, but also:

Publication ethics policies
AI use and disclosure policies
Peer-review procedures
Editorial decision workflows
Research integrity standards
ISSN readiness
Article metadata
Crossref DOI workflows
Long-term indexing readiness

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Frequently Asked Questions

What is AI-driven business decision-making? AI-driven business decision-making refers to the use of artificial intelligence, machine learning, predictive analytics, generative AI or related technologies to analyse information and support or automate organisational decisions. Applications may involve strategy, marketing, finance, human resources, operations and other business functions. What are the major AI management research opportunities in 2026? Important areas include human-AI collaboration, generative AI in strategic management, AI agents, AI governance, AI adoption among SMEs, AI-supported marketing, financial analytics, HR decision-making, organisational transformation and measurement of business value from AI investments. Can management scholars conduct research on generative AI? Yes. Generative AI raises important management questions involving productivity, organisational decision-making, employee behaviour, leadership, strategy, customer engagement, governance and innovation. Is AI research suitable for a business and management journal? AI research is highly relevant when the research question has a clear business, organisational, managerial or economic dimension. A technical AI model without a meaningful management connection may be better suited to a technology journal. What is human-AI collaboration? Human-AI collaboration describes situations in which human employees or managers and artificial intelligence systems work together. AI may provide analysis or recommendations while humans contribute contextual judgement, creativity, ethical evaluation and accountability. What is agentic AI in business? Agentic AI generally refers to AI systems capable of carrying out connected tasks with greater autonomy than traditional AI assistants. From a management perspective, this creates research questions concerning delegation, organisational design, governance, accountability and human oversight. Why is AI governance becoming important? As AI influences increasingly consequential decisions, organisations need policies defining how systems may be used, what data can be processed, when human approval is required, who is accountable and how risks such as bias, privacy breaches or inaccurate outputs should be managed. What AI research opportunities exist for Indian scholars? Indian researchers can investigate AI adoption among MSMEs, digital entrepreneurship, banking and FinTech, retail, manufacturing, workforce transformation, consumer behaviour, family businesses and other sectors. Context-sensitive research can also contribute to global management theory. How can researchers measure the business value of AI? Researchers may examine outcomes such as productivity, decision accuracy, cost reduction, customer experience, revenue growth, innovation, employee performance or organisational agility. The appropriate measure depends on the AI application and research question. Can doctoral scholars submit AI-management research to ISBMJBIR? Doctoral and early-career researchers can consider submitting work that aligns with the journal's scope and meets its scholarly and manuscript preparation requirements. Authors should consult the latest Author Guidelines before submission. Does ISBMJBIR welcome interdisciplinary AI research? Research connecting management with information systems, psychology, economics, data analytics, finance, marketing, operations or other relevant disciplines can be appropriate where the central contribution relates clearly to business and management issues. How can an institution start an AI, business or management research journal? Institutions should establish the journal's aims and scope, editorial board, publication policies, peer-review process, website, submission workflow, publishing schedule and research integrity standards. ISSN readiness, DOI workflows, metadata and future indexing requirements should also be considered early in the launch process.

Conclusion: From Artificial Intelligence Adoption to Intelligent Management

Artificial intelligence is changing the way organisations analyse information, design processes and make decisions. For management scholars, however, the most important research opportunity is not simply documenting that businesses are using AI. The deeper challenge is understanding how artificial intelligence changes organisations. Researchers need to examine when AI strengthens managerial judgement, when it creates new risks, how it changes organisational roles, how employees respond to algorithmic recommendations and how firms translate rapidly advancing technological capabilities into sustainable business value. The strongest research in this field will move beyond technology enthusiasm. It will investigate measurable outcomes. It will examine failures alongside successes. It will distinguish between AI adoption and AI value creation. It will critically investigate questions of accountability, governance, ethics and human judgement. Most importantly, it will help explain how managers and organisations can make better decisions in environments where intelligent systems increasingly participate in business processes. The ISB&M Journal of Business Issues & Research encourages researchers, academicians, doctoral scholars and practitioners to explore these emerging issues and contribute rigorous research that strengthens both management theory and business practice. Authors working on artificial intelligence, business analytics, digital transformation, strategy, marketing, finance, entrepreneurship, HRM, organisational behaviour and related interdisciplinary areas are encouraged to review the journal's current Call for Papers and Author Guidelines. For journal owners, universities and institutions If your institution is planning to launch, migrate or modernise a scholarly journal, support is available for: