Master the art of AI-driven human-machine collaboration
Duration: 14 hours.
This program equips you with the strategic framework and tools to navigate the AI landscape, unlock business value, and achieve sustainable competitive advantage. You will learn how to augment your job performance, collaborate with other stakeholders, and create better business outcomes by leveraging the different levels of capabilities offered by AI solutions, such as Analytical, Collaborative, and Autonomous AI. The program emphasizes a human-centric approach to AI adoption, ensuring that your expertise is leveraged alongside AI capabilities to drive innovation and achieve organizational success.
Program Objective
To empower business professionals with the foundational knowledge, analytical tools, metrics, and strategic framework needed to effectively harness the potential of AI in Human-Machine collaboration, drive operational efficiency, and achieve enterprise-wide strategic agility and transformation. This program provides a step-by-step learning journey, equipping enterprises to integrate AI into their operations and decision-making for sustained competitive advantage.
The StrategyOps 2.0 Four-Course Program offers enterprises a comprehensive, structured, and actionable pathway to success with AI. Progressing from foundational concepts to advanced strategic agility, this learning journey equips business professionals to lead their organizations into an AI-powered future with confidence, clarity, and measurable impact. Enterprises that invest in these courses position themselves to unlock AI's full potential, enabling sustained growth, resilience, and innovation in an increasingly competitive landscape.
Why Enterprises Must Implement This Program
1. AI Competence Progression: Enterprises must navigate the stages of AI adoption with clarity and strategy. This program ensures business professionals understand the evolving roles of AI and how it impacts the Human-Machine workforce.2. Causation Analysis for Better Outcomes: With AI influencing every aspect of business operations, enterprises need a robust framework to identify, analyze, and optimize the factors driving AI outcomes.3. Measuring What Matters: Implementing AI solutions without measuring their impact is a missed opportunity. This program teaches enterprises how to evaluate their AI initiatives against operational and strategic metrics, ensuring a focus on ROI and measurable success.4. Strategic Agility for Transformation: The future belongs to enterprises that can adapt quickly and innovate continuously. By mastering hyperadaptive AI capabilities, businesses can achieve unparalleled agility and long-term success.5. Holistic and Practical Approach: This program seamlessly blends theoretical insights, practical tools, and real-world applications, ensuring participants are equipped to drive meaningful AI transformations in their organizations.
Course Description
Session 1: Business AI Foundation: Development Stages of Human-Machine Work.
- Objective: To introduce business professionals to the stages and goals of AI in Human-Machine collaboration and competence progression in enterprises.
- Key Topics:
- • Types of Business AI and Human-Machine Competence Progression Stages.
- • Overview of AI capability levels (from Observability to Hyperadaptive Strategic Agility).
- • Human-machine work ratios and their implications for business operations.
- • Characteristics of Analytical AI, Collaborative AI, and Autonomous AI.
- • Case studies showcasing the evolution of AI competence in AI solutions to enable business needs, including the roles of AI Agents and AI Orchestrators.
- • Examples of how AI categories address specific business challenges (e.g., improving productivity, enhancing decision-making).
- Outcome: Learners will understand AI's roles in business, including its current and progressive stages of AI competence that impact the human-machine workforce, with specific insights into how AI addresses real-world challenges.
Session 2: Business AI Causation: StrategyOps Causation Analysis
- Objective: Explore how AI-driven analytics impact enterprise value creation and outcomes realization.
- Key Topics:
- • StrategyOps Causation Analysis Elements.
- • Core impacts of AI on assets, workflows, and output generation.
- • AI Goals of Increasing, Sustaining, and Restoring Business Outcomes.
- • How AI enables enterprise journeys, customer journeys, shared services, and infrastructure.
- • Layered outcomes strategy for using AI across business and technology functions (e.g., supply chain, IT).
- • StrategyOps Methods for identifying and addressing causality in AI-supported outcomes.
- • Examples of practical scenarios (e.g., using AI to mitigate supply chain disruptions, enhance customer satisfaction, or improve IT efficiency).
- Outcome: Learners will gain insights into how AI enhances business outcomes and operational resilience through structured causation analysis, with a clear understanding of real-world applications in diverse business scenarios.
Session 3: Measuring AI Value: Metrics for AI Value Realization
- Objective: Teach participants how to measure the operational and business value of AI solutions and the competence and performance of Human-Machine teams enabled by AI.
- Key Topics:
- • Metrics and StrategyOps methods assessing and realizing AI Business Value.
- • Operational and Business Outcomes KPIs (from operational outputs to stakeholder experience and strategic outcomes).
- • Technology Causality KPIs for AI solution characteristics.
- • Top performance metrics (e.g., error reduction, time efficiency, and optimization).
- • Real-world application of causality KPIs in enterprise AI deployments.
- • Dependency mapping and constraints assessment in AI outcomes realization.
- • Workshop activity: Learners will build a KPI model for a specific business scenario using AI solutions, such as reducing customer service delays or improving supply chain efficiency.
- Outcome: Learners will be equipped to measure AI solutions' tangible and intangible value, integrate these metrics into strategic decision-making, and apply them through hands-on activities.
Session 4: Strategic AI: Reaching Hyperadaptive Agility with AI
- Objective: Examine advanced AI capabilities that drive hyperadaptive strategic agility and business transformation.
- Key Topics:
- • Characteristics of Adaptive, Generative, Agentic, and Strategic AI.
- • Hyperadaptive strategic agility for enterprise-wide AI.
- • AI-driven co-creation, collaboration, and self-orchestration.
- • Business benefits of advanced AI: innovation, resilience, and competitiveness.
- • StrategyOps methods for strategic AI goal alignment and risk mitigation.
- • Case studies of AI-led strategic decision-making in enterprises.
- • Introduction to specific StrategyOps methods and tools for strategic AI alignment (e.g., scenario planning tools, risk assessment frameworks).
- Outcome: Learners will understand how to leverage advanced AI capabilities to transform business strategies, achieve sustained competitive advantage, and align AI initiatives with long-term organizational goals using actionable tools.
What are the exam characteristics?
- Time allocated: 120 minutes (onsite or virtual)
- Number of questions: Approximately 50 multiple-choice.
- Passing score: 65% correct answers.
- Format: electronic; open book.
Contact kate.anderson@valtics.com to receive additional information or schedule a call with a VALTICS representative.
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