Pideya Learning Academy

AI in Business Strategy and Applications

Upcoming Schedules

  • Live Online Training
  • Classroom Training

Date Venue Duration Fee (USD)
13 Jan - 17 Jan 2025 Live Online 5 Day 3250
31 Mar - 04 Apr 2025 Live Online 5 Day 3250
28 Apr - 02 May 2025 Live Online 5 Day 3250
23 Jun - 27 Jun 2025 Live Online 5 Day 3250
18 Aug - 22 Aug 2025 Live Online 5 Day 3250
08 Sep - 12 Sep 2025 Live Online 5 Day 3250
27 Oct - 31 Oct 2025 Live Online 5 Day 3250
08 Dec - 12 Dec 2025 Live Online 5 Day 3250

Course Overview

Artificial Intelligence (AI) is no longer a futuristic concept—it is a present-day catalyst driving business reinvention, shaping organizational strategy, and transforming competitive landscapes across all industries. As companies navigate increasing complexity, rising customer expectations, and digital disruption, AI offers the tools to reimagine value chains, streamline operations, and create intelligent business models. At Pideya Learning Academy, the AI in Business Strategy and Applications course is expertly curated to help professionals decode AI’s real potential, evaluate its strategic relevance, and confidently lead AI-driven innovation within their organizations.
Global trends underscore the importance of understanding and integrating AI into business strategy. According to the 2024 McKinsey Global Survey on AI, 55% of surveyed organizations have adopted AI in at least one function, a sharp rise from just 20% in 2017. Companies that have implemented AI at scale are now seeing measurable returns—1.5 times more likely to report increased revenues and 2.3 times more likely to reduce operational costs. Additionally, PwC projects that AI will contribute up to $15.7 trillion to the global economy by 2030, with the largest gains driven by productivity improvements and product personalization. As AI continues to accelerate, the ability to strategically deploy it is becoming a core leadership competency.
This forward-thinking program by Pideya Learning Academy empowers participants with an executive-level understanding of AI’s strategic role, its key technological components, and its enterprise-wide implications. You will explore the full lifecycle of AI adoption—from identifying suitable business problems and designing value propositions to overseeing AI project outcomes and measuring return on investment. More than just technical exposure, the training enables you to engage in the strategic, ethical, and governance dimensions of AI, ensuring your initiatives align with regulatory standards and societal expectations.
Integrated into this learning journey are key features that make the training deeply valuable:
Strategic insights into aligning AI capabilities with core business objectives
A comprehensive understanding of AI tools such as machine learning, neural networks, and algorithmic models
Guided frameworks for assessing AI readiness and building credible AI business cases
Effective techniques for collaborating with technical teams, data scientists, and stakeholders
Insightful exploration of AI ethics, bias mitigation, and governance structures
Templates and strategic planning tools for AI adoption roadmaps and performance metrics
Enhanced decision-making confidence in advocating and executing AI transformation initiatives
Throughout this course, learners are equipped to make informed, high-impact decisions that integrate AI into strategic business functions such as operations, finance, customer service, product development, and human resources. Real-world examples and scenario-based discussions allow participants to grasp how AI is being used to generate value across industries including healthcare, retail, banking, logistics, and manufacturing.
By the end of the AI in Business Strategy and Applications training, participants will be well-positioned to transition from passive observers of AI disruption to active change leaders driving sustainable growth. They will gain the fluency to navigate the technology’s potential and challenges, enabling their organizations to remain agile, relevant, and future-ready.
With a commitment to excellence and relevance, Pideya Learning Academy ensures that every participant walks away not only with foundational knowledge of AI but also with the strategic foresight to shape the future of their business through responsible and innovative AI adoption.

Course Objectives

After completing this Pideya Learning Academy training, the participants will learn to:
Assess and identify opportunities for AI integration across business processes
Understand the foundational technologies driving AI, including machine learning and deep learning
Build persuasive business cases for AI investment and deployment
Analyze the implications of AI in business strategy, ethics, and operations
Communicate technical concepts of AI to cross-functional and non-technical teams
Navigate the challenges and risks of AI implementation in various organizational contexts
Lead AI-focused change initiatives aligned with corporate goals and stakeholder expectations

Personal Benefits

Enhanced leadership skills in AI-driven business environments
Stronger understanding of current and emerging AI trends
Broadened strategic perspective on technological transformation
Increased ability to collaborate with data and IT professionals
Better preparedness for future roles involving AI decision-making

Organisational Benefits

Improved AI strategy alignment with business goals
Enhanced capability to lead digital transformation initiatives
Strengthened cross-departmental communication on AI projects
Reduction in operational inefficiencies through AI optimization
Increased innovation capacity and competitive market positioning

Who Should Attend

This Pideya Learning Academy course is ideal for:
C-suite executives and senior leaders spearheading digital transformation initiatives
Department heads and strategic planners evaluating AI use cases
Mid-career professionals aiming to gain expertise in AI applications
Business development professionals and consultants exploring tech-driven innovation
Data analysts and project managers involved in AI implementation
Any professional seeking to strengthen their understanding of AI’s role in modern business strategy

Course Outline

Module 1: Foundations of the AI Landscape
Historical evolution of Artificial Intelligence The role of AI in digital transformation AI trends shaping modern industries Overview of the AI ecosystem: stakeholders and drivers Key AI terminologies and definitions Intersections between AI, data science, and automation
Module 2: Core Machine Learning Paradigms
Introduction to machine learning and algorithmic logic Supervised learning: classification and regression Unsupervised learning: clustering and dimensionality reduction Reinforcement learning: agents, rewards, and environments Bias-variance trade-off in machine learning Black-box models vs. interpretable models
Module 3: Neural Networks and Deep Intelligence
Architecture of neural networks Activation functions and layers Convolutional Neural Networks (CNNs) for image processing Recurrent Neural Networks (RNNs) for sequence modeling Transfer learning and pre-trained models Deep learning frameworks and tools
Module 4: Intelligent Systems and Automation
Defining machine intelligence and autonomy Human-machine collaboration in workspaces Robotic Process Automation (RPA) and AI integration Predictive maintenance with AI-enabled sensors AI's influence on workforce transformation Industrial and service-sector AI applications
Module 5: AI Ethics, Governance, and Compliance
Principles of ethical AI design Fairness, accountability, and transparency in AI Data privacy, consent, and anonymization Regulatory frameworks for AI implementation Risk mitigation in algorithmic decision-making Responsible AI adoption strategies
Module 6: Strategic AI Implementation in Business
Identifying AI value drivers in business models Strategic alignment of AI with business objectives Organizational readiness and change management Use case development and cost-benefit analysis AI project roadmaps and lifecycle planning KPI frameworks for AI success measurement
Module 7: Data Infrastructure for AI Initiatives
Data collection and preprocessing strategies Big data architecture and storage systems Data pipelines and workflow automation Structured vs. unstructured data for AI models Metadata management and cataloging Data quality metrics and validation
Module 8: AI Tools, Platforms, and Technologies
Overview of popular AI development platforms Open-source libraries for AI and ML (e.g., TensorFlow, PyTorch) Cloud-based AI solutions (e.g., AWS SageMaker, Azure AI) AutoML and low-code/no-code AI tools Deployment pipelines: model training to production Monitoring AI performance in real time
Module 9: Emerging Trends and Future of AI
Explainable AI (XAI) and model interpretability Generative AI and large language models (LLMs) Edge AI and embedded systems AI in cybersecurity and fraud prevention Quantum computing and AI intersections Sustainable AI and green computing trends
Module 10: Capstone: Designing AI-Driven Innovation
Mapping organizational problems to AI solutions Building AI implementation plans Stakeholder alignment and presentation skills Pitfalls and lessons from global AI failures Preparing for future AI disruptions Drafting AI policy and internal governance

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