Applied AI for Business Management, Strategic Planning and Decision Support

About This Course

This course prepares managers and planners to use artificial intelligence as a practical aid to business decisions. It covers where AI can support analysis, forecasting, and option-testing in strategy and operations, and where human judgement, data quality, and accountability must remain with management.

Participants learn to frame a management question for an AI-supported analysis, assess the data and the limits of a model, and use outputs in planning without treating them as instructions. The emphasis is on applied use in the organisation, not on building models from first principles

Course Objectives

Explain, at a management level, what current AI tools can and cannot do for planning and decisions.
Identify business questions where AI support is useful, and those that should remain fully human.
Specify the data a decision-support use needs, and the risks of poor or biased data.
Interpret AI-assisted analysis for strategy, performance, and operational choices.
Set rules for human review, accountability, and documentation of AI-supported decisions.
Assess cost, capability, and vendor claims before adopting a tool.
Design a small pilot for one management use case.
Govern the use so that confidentiality, fairness, and auditability are protected.

What You Will Learn

What AI can usefully do for managers, and what it should not be asked to decide
How to frame a planning or operational question for AI support
How data quality and bias affect a management conclusion
How to use AI-assisted analysis in strategy without surrendering ownership
How to read an AI-supported recommendation critically
How to set human review and accountability rules
How to judge a tool or vendor claim at a management level
How to design a small, measurable pilot
How to protect confidentiality and keep an audit trail
How to leave with a proposal for one real management use case

Skills You Will Gain

Management use-case selection for AI
Question framing for decision support
Data-suitability assessment
Critical review of AI-assisted analysis
Strategic-assumption testing with AI support
Human-in-the-loop decision rules
Vendor and tool evaluation at management level
Pilot design for an AI management use
Documentation of AI-supported decisions
Governance of confidentiality and accountability

Why Choose Komatek Academy

Built for managers and planners, not as a technical introduction to algorithms
Use cases taken from strategy, planning, and operational decisions
Limits and review rules practised, so adoption does not become uncritical reliance
Clear boundary: the course supports lawful, accountable decision support and does not assist deception or the evasion of oversight
A pilot participants can take back, scoped to one question the organisation actually faces

Who Should Attend

Strategy, planning, and performance managers

Operations and business-unit managers who use analysis in decisions

Management accountants and business analysts supporting leadership

Digital, innovation, and transformation leads

Risk and compliance officers concerned with AI-supported decisions

Advisers helping an organisation adopt AI in management practice

Course Content

MODULE 01

AI in the Management Context

1. What managers need to know: prediction, classification, generation, and search over organisational data

2. Difference between a decision-support tool and an autonomous decision

3. Where AI fits in the planning cycle: sensing, analysis, options, and monitoring

4. Limits: incomplete data, outdated patterns, and confident error

5. Accountability: the manager remains responsible for the decision

6. Workshop: Sorting a set of management tasks into suitable, unsuitable, and conditional for AI support

MODULE 02

Data, Questions and Use Cases

1. Turning a strategic or operational question into a form a tool can assist

2. Data the organisation already holds, and data it would need

3. Quality, bias, and representativeness at a management level

4. Confidential and personal data: what may be used, and under what control

5. Prioritising use cases by value, feasibility, and risk

6. Workshop: Defining one use case with the question, the data, and the decision it would inform

MODULE 03

Using AI in Strategic Planning

1. Environmental scanning and synthesis of external information

2. Scenario inputs and stress-testing of assumptions

3. Comparing options against stated criteria

4. Detecting inconsistency in a plan or a business case

5. Keeping strategy ownership with the leadership team

6. Workshop: Using a structured AI-assisted brief to test one planning assumption

MODULE 04

Decision Support in Operations and Performance

1. Forecasting demand, workload, or risk indicators

2. Exception reporting and management dashboards

3. Drafting options and briefing notes for a decision meeting

4. Checking an analysis for missing factors and over-confidence

5. When to stop and seek specialist or human review

6. Workshop: Reviewing a sample AI-assisted recommendation and deciding what management should accept, amend, or reject

MODULE 05

Adoption, Governance and the Pilot

1. Evaluating tools and vendors: capability, data handling, and cost

2. A pilot: scope, success measures, and stop rules

3. Roles: business owner, data steward, and users

4. Documentation of AI-supported decisions for later review

5. Staff capability and the risk of uncritical reliance

6. Capstone: Present an applied-AI proposal for one management use, covering the question, data, human review rules, pilot design, and governance