SMART DECISION-MAKING: AI TOOLS FOR LEADERS & TEAMS

Live-Online Training: N210,000.00
Classroom Training: N 280,000.00
3 - 4 participants: 5% discount
5 or more participants: 10% discount

(Available also for Customised Training by Duration, Venue & Fee)

Course Description

The business ecosystem is volatile, uncertain, complex, and ambiguous (VUCA), yet leaders are required to make faster, higher-quality decisions: often with incomplete information. Artificial Intelligence (AI) has emerged as a powerful decision-support capability, enabling leaders and teams to analyse data at scale, identify patterns, simulate scenarios, and reduce bias in judgment.

This three-day programme equips leaders and teams with practical AI tools, frameworks, and governance principles to enhance strategic, operational, and people-related decisions. The training focuses on AI as an augmentation tool—not a replacement for leadership judgment—helping participants integrate AI into everyday decision-making while maintaining accountability, ethics, and organisational values.

Training Objectives / Learning Outcomes

Participants will be able to:

• Distinguish between human judgment, data analytics, and AI-augmented decision-making.
• Apply AI tools to scenario planning, forecasting, and prioritisation.
• Use AI to support budgeting, performance tracking, and risk management.
• Improve team decision quality through collaborative AI use.
• Identify and mitigate bias in data and algorithms.
• Ask better questions and prompts to extract actionable insights from AI tools.
• Develop an AI-Enabled Decision Framework for their teams or units.

Course Content

DAY ONE: Foundations of AI-Enabled Decision-Making

The New Decision Environment for Leaders

• Complexity, speed, and uncertainty in modern organisations
• Limitations of intuition-only decision-making
• Why traditional decision models struggle today
• Role of AI as a decision support system

Data Insight:
Leaders using analytics and AI are 2.9x more likely to outperform peers in decision accuracy (McKinsey).

Understanding AI in Simple Business Terms

• What AI is—and what it is not
• Machine learning, predictive analytics, and generative AI explained simply
• AI vs automation vs traditional analytics
• Where AI adds value in leadership decisions

Interactive Session:

• Mapping current decisions that are slow, subjective, or error-prone.

Types of Decisions AI Can Support

• Strategic decisions (growth, investment, policy)
• Operational decisions (resource allocation, scheduling, optimisation)
• People decisions (talent, performance, engagement)
• Risk and compliance decisions

Case Snapshot:

• How AI improves forecasting accuracy by 20–30% in planning processes (Gartner).

Cognitive Bias and Decision Errors

• Common leadership biases (confirmation, anchoring, overconfidence)
• How AI helps reduce bias—and where it can introduce new ones
• Combining human judgment with machine insight

Exercise:

• Bias identification in recent leadership decisions.

DAY TWO: Practical AI Tools for Leaders & Teams

AI Tools for Strategic and Business Decisions

• Scenario modelling and “what-if” analysis
• Demand and revenue forecasting
• Market and competitor intelligence
• Strategy prioritisation using AI insights

Data Insight:
Companies using AI-based forecasting reduce planning errors by up to 50% (BCG).

AI for Operational and Performance Decisions

• AI-supported budgeting and cost optimisation
• Performance dashboards and real-time insights
• Resource allocation and productivity optimisation
• AI-enabled risk identification

Group Activity:

• Redesigning a traditional decision workflow using AI tools.

AI for Team and People Decisions

• AI in performance management and feedback
• Workforce planning and skills analysis
• Decision support for promotions and succession
• Managing fairness and transparency

Data Insight:
AI-supported people analytics improves talent decision accuracy by 25% (Deloitte).

Asking the Right Questions: Prompts & Insight Framing

• Why AI quality depends on leadership questions
• Structuring prompts for clarity and insight
• Translating AI output into executive decisions
• Avoiding blind reliance on AI recommendations

Practical Session:

• Crafting leadership-level AI prompts for real business challenges.

DAY THREE: Governance, Ethics & Sustainable Adoption

Ethical and Responsible AI Decision-Making

• Risks of algorithmic bias and data misuse
• Accountability in AI-supported decisions
• Regulatory and compliance considerations
• Building trust with stakeholders

Data Insight:
85% of executives cite ethics and trust as the most significant barriers to AI adoption (Accenture).

Integrating AI into Leadership and Team Culture

• AI as a collaborative team member
• Overcoming fear and resistance to AI
• Building AI confidence among non-technical staff
• Leadership behaviours that support adoption

Discussion:

• “What decisions should never be fully automated?”

Measuring the Impact of AI-Supported Decisions

• Decision speed, quality, and consistency metrics
• Linking AI insights to KPIs and outcomes
• Learning loops and continuous improvement
• Avoiding decision paralysis through over-analysis

Developing an AI-Enabled Decision Roadmap

• Identifying high-impact decision use cases
• Tool selection and capability requirements
• Governance, training, and change management
• 30-60-90-day implementation roadmap

Capstone Exercise:

• Developing an AI-Supported Decision Framework for participants’ teams or organisations.