OGSM Framework for AI Strategy Development

AI Strategy components using OGSM

In a world where artificial intelligence (AI) is rapidly transforming industries, developing a cohesive and effective AI strategy is crucial for enterprises looking to stay competitive. But a challenging question still for most is how to get started with developing an effective AI strategy for their organization. One approach to come up with an effective strategy would be based on a popular framework called the OGSM framework — an acronym for Objectives, Goals, Strategies, and Measures. This structured approach could provide clarity, focus, and alignment in driving organizational priorities, especially in a rapidly evolving domain like AI.

In this blog, we will delve into how OGSM works, why it’s a powerful tool for crafting enterprise AI strategies, and how it can be applied to support three key AI objectives: building trust, enabling automation, and making a meaningful impact on business outcomes.

What is OGSM and Why is it effective?

The OGSM framework originated in the corporate strategy world and has been widely adopted by organizations globally, including Fortune 500 companies. The simplicity and adaptability of OGSM make it a preferred choice for aligning teams, especially around shared priorities.

At its core, OGSM links high-level aspirations (Objectives) to actionable, measurable plans (Measures) through clear intermediate steps (Goals and Strategies). This logical flow ensures that long-term visions remain actionable while keeping every stakeholder on the same page.

AI strategies require balancing long-term innovation with short-term operational efficiency. OGSM’s structured approach ensures that no element — be it trust, automation, or business impact — is overlooked. Furthermore, it helps enterprises categorize investments, align diverse stakeholders, and track success through measurable outcomes.

Let’s look at Objectives, Goals, Strategies, and Missions individually and how the framework can be used to build an effective AI adoption strategy.

O: Objectives

When developing an AI strategy for an enterprise, it’s essential to ground the approach in objectives that reflect both the technical and business imperatives of AI adoption. The three core objectives —

1. building trust,
2. enabling automation, and
3. making a meaningful business impact,

collectively form the backbone of a successful enterprise AI strategy.

Objectives expected from an AI Strategy

Each of these objectives shapes how an enterprise prioritizes investments, deploys resources, and manages risks in its AI initiatives. Here’s how these objectives connect directly to the development of a comprehensive enterprise AI strategy.

Trust: The Foundation of Responsible and Scalable AI

Trust is not just an ethical consideration — it is a strategic necessity. The “trust” here, is not just between humans and machines, but also within the brand and customers. Without trust, AI initiatives will face resistance from employees, customers, and regulatory bodies, which can derail even the most promising technologies. For enterprises, trust must underpin every element of their AI strategy, ensuring that AI adoption is responsible, compliant, and aligned with the organization’s values.

How This Shapes Enterprise AI Strategy

• Governance and Policies: Developing an AI strategy requires clear governance policies that ensure the ethical and transparent use of AI. This includes establishing frameworks for data governance, bias mitigation, and explainability. For instance, an enterprise might adopt guidelines such as NIST’s AI Risk Management Framework or OWASP Security Guidance, or create its own AI ethics board to oversee major projects.
• Customer-Centric Design: Enterprises must design AI systems that build customer confidence. For example, a bank introducing an AI-based loan approval system should focus on transparency and provide clear explanations for approval or rejection.

Development Considerations

• Include explainability tools and frameworks in model development pipelines to address transparency requirements.
• Make data audits and security assessments routine checkpoints in the AI development lifecycle.
• Implement training programs for employees to foster trust and understanding of AI tools.

Automation: The Key to Scaling AI for Operational Excellence

Automation is at the heart of enterprise AI strategies. AI’s ability to reduce manual workloads, streamline decision-making, and operate at scale makes it a vital tool for modern enterprises looking to stay competitive. However, incorporating automation into an AI strategy is more than implementing technology — it requires rethinking workflows, processes, and organizational structures.

How This Shapes Enterprise AI Strategy

• Operational Alignment: Enterprises must evaluate which areas of their operations are ripe for automation and prioritize accordingly. For instance, back-office processes, supply chain operations, and customer support are common candidates for returns on automation investments.
• Technology Investment: Automation requires investment in AI tools such as robotic process automation (RPA), natural language processing (NLP), and predictive analytics. The AI strategy must allocate resources to the right technologies and employee training based on the organization’s goals.

Development Considerations

• Identify processes that create bottlenecks or require high manual intervention, such as invoice processing, customer query resolution, or compliance reporting.
• Measure potential automation success with pilots, testing how much time or cost is saved.
• Build feedback loops to continuously improve automated systems through retraining and refinement.

Impact: The Ultimate Goal of Enterprise AI

Every AI strategy must ultimately deliver measurable and meaningful impact. For enterprises, this means focusing on initiatives that create tangible business value — whether through cost savings, revenue growth, or innovation. The impact is what differentiates experimental AI efforts from strategic, enterprise-wide transformations.

How This Shapes Enterprise AI Strategy

• Business Alignment: A meaningful AI strategy starts with a clear understanding of business goals. For example, an enterprise seeking to increase profitability might focus on cost-saving AI initiatives like predictive maintenance or operational optimization.
• Prioritization of Use Cases: Not all AI initiatives will deliver equal value. Enterprises must prioritize high-impact use cases that align with their strategic objectives. Tools like ROI analysis and feasibility studies can guide these decisions.

Development Considerations

• Establish baseline metrics (e.g., revenue growth, cost reduction, or time savings) for every AI initiative.
• Use Agile or iterative development approaches to test and refine high-impact use cases quickly.
• Create cross-functional teams to ensure AI initiatives address both technical and business needs.

G: Goals

In the OGSM framework, while the objectives provide an overarching vision and direction, the goals translate those objectives into specific, measurable outcomes. Objectives are broad, qualitative aspirations that define what an organization seeks to achieve, whereas goals are more precise and focus on how success will be realized in alignment with the objectives. This structured relationship ensures that high-level strategic aims are connected to actionable outcomes, enabling organizations to track progress, prioritize efforts, and achieve clarity in execution.

In the context of enterprise AI strategy, each objective — building trust, enabling automation, and making a meaningful impact — guides the focus of the strategy, while the corresponding goals define the tangible results needed to fulfill those aspirations.

Goals expected from an AI Strategy

The above figure shows the relationship between objectives and goals expected from an AI strategy.

Trust → TRiSM

The goal that aligns with the objective of building trust is TRiSM (Trust, Risk, and Security Management). Trust in AI systems hinges on their ability to operate transparently, ethically, and securely. TRiSM focuses on critical aspects such as data quality, model accuracy, explainability, security, and risk management. These attributes are essential to building confidence among stakeholders, ensuring compliance with regulations, and mitigating risks. Without a structured approach like TRiSM, the broader objective of trust remains unattainable.

Automation → Autonomy

The goal that aligns with automation is Autonomy. Automation in enterprises is about more than just automating repetitive tasks — it’s about creating AI systems that can operate independently, make intelligent decisions, and adapt dynamically. Autonomy emphasizes developing AI systems that deliver faster decision-making, reduce human intervention, and improve operational efficiency. By focusing on autonomy, enterprises unlock the potential of AI to scale processes and achieve true automation at a strategic level.

Impact → Value

The goal that aligns to make a meaningful business impact is Value. The ultimate aim of any AI initiative is to generate measurable business impact, whether through cost savings, revenue growth, or innovation. The goal of value ensures that AI projects deliver tangible returns on investment, align with strategic business priorities, and maximize resources. By focusing on outcomes such as time saved, costs reduced, and new opportunities created, this goal ensures the meaningful impact promised by the objective is achieved.

M: Measure

Measurement is the cornerstone of the OGSM framework, providing the accountability and clarity necessary to turn strategic aspirations into tangible results. While objectives define the “what” and goals specify the “how,” measures “quantify progress” and ensure alignment with organizational priorities.

In the OGSM framework, measuring is the glue that connects aspirations to reality. It provides clarity, structure, and evidence, ensuring that the strategies and actions undertaken are impactful, efficient, and aligned with the organization’s goals. Without robust measures, even the most well-thought-out objectives and goals risk falling short of their potential.

Measures to track progress of the AI strategy

Key Measures of TRiSM

1. Data Quality: High-quality, unbiased, and well-labeled data is crucial for trustworthy AI systems. Poor data quality undermines model accuracy, fairness, and reliability.
2. Security and Audit: Ensuring robust security measures protect sensitive data, while regular audits maintain compliance with regulations like GDPR or HIPAA.
3. Accuracy and Explainability: Stakeholders must understand how AI systems arrive at decisions. Transparency builds confidence, while accuracy ensures the system delivers reliable outcomes.

Key Measures of Autonomy

1. Intelligence: AI systems must continuously improve through learning. Intelligence is demonstrated by a system’s ability to adapt to new data and contexts.
2. Swift Decision-Making: Speed is a key metric for autonomy. Systems that make faster decisions reduce time-to-market and enhance operational efficiency.
3. Independence: The accuracy and success rate of AI-driven decisions determine how effectively autonomous systems can achieve desired outcomes with or without human oversight, depending on the use case.

Key Measures of Value

1. Time: Measuring how AI reduces operational timeframes, accelerates processes, or increases speed to market.
2. Money: Tracking cost savings or revenue growth attributable to AI initiatives.
3. Resources: Evaluating how AI optimizes resource allocation, such as workforce efficiency or supply chain improvements.

S: Strategy

The 3-pillar strategy — comprising Movers, Shakers, and Disruptors — is a guiding approach for categorizing and prioritizing enterprise AI use cases based on their business impact. Each pillar defines distinct areas of impact, helping organizations strategically allocate resources, align efforts with objectives, and ensure measurable results.

The 3-pillar strategy to guide AI adoption

When integrated into the OGSM framework, this 3-pillar strategy serves as a decision-making funnel that aligns AI initiatives with the organization’s broader aspirations, enabling enterprises to channel investments toward high-value opportunities.

The 3-pillar strategy plays a critical role in ensuring that enterprise AI use cases are evaluated, categorized, and executed in alignment with the objectives, goals, strategies, and measures of the organization. In addition, one of the biggest challenges in enterprise AI is identifying and prioritizing the right use cases from a sea of possibilities. The 3-pillar strategy addresses this by creating a structured funnel for evaluation:

1. Establishing a Clear Decision Framework

• By categorizing use cases into Movers, Shakers, and Disruptors, organizations can evaluate their potential business impact, feasibility, and strategic alignment.
• This framework allows enterprises to balance short-term operational improvements with long-term innovation and growth.

2. Guiding Resource Allocation and Investment

• Movers require moderate investment but deliver immediate, tangible benefits, making them ideal for early-stage AI adoption or quick wins.
• Shakers may require more sophisticated systems and data but provide measurable financial returns, justifying mid-level investments.
• Disruptors demand the highest investment and risk tolerance but have the potential to transform the business, making them priority areas for innovation-focused enterprises.
• Example: An enterprise might prioritize a Mover project, like automating HR workflows, for immediate productivity gains while simultaneously investing in a Disruptor project, like launching an AI-powered subscription service, for future growth.

3. Balancing Strategic Focus and Flexibility

• The 3-pillar approach ensures that no area is neglected. Enterprises can simultaneously pursue operational excellence (Movers), financial efficiency (Shakers), and innovation (Disruptors), maintaining a balanced AI portfolio.
• This balance prevents over-commitment to high-risk projects while ensuring long-term competitive advantage.

4. Evaluating and Reprioritizing Continuously

• As each use case progresses, its impact can be measured using OGSM’s Measures. This data helps organizations continuously evaluate and reprioritize their portfolio.
• Example: If a Shaker project initially expected to deliver significant cost savings starts underperforming, resources can be redirected to a Disruptor initiative with higher potential returns.

Conclusion

The OGSM framework is a powerful tool for structuring and implementing an enterprise AI strategy. By focusing on the three core objectives — building trust, enabling automation, and creating meaningful impact — organizations can align their AI initiatives with long-term business goals. Additionally, categorizing investments into Movers, Shakers, and Disruptors provides clarity on where to allocate resources for maximum strategic advantage.

As enterprises embrace AI, a thoughtful strategy built on the OGSM framework ensures that their investments not only deliver value but also position them for sustained growth in the AI-driven future.