AI Strategy

An AI strategy is a comprehensive plan that outlines how an organization will leverage artificial intelligence to create business value, enhance competitive positioning, and achieve strategic objectives. It consists of three key layers:

• AI strategic competency,
• AI use cases,
• AI enabling factors.

Without a structured approach, approximately 34% of AI projects fail or are abandoned, according to research by Rackspace Technology. A well-defined AI strategy helps businesses avoid costly mistakes, align AI initiatives with business goals, and ensure systematic integration of AI technologies across the organization.​

While digital transformation broadly encompasses the adoption of digital technologies across all business areas, an AI strategy specifically focuses on integrating self-learning technologies that can analyze data, make predictions, and automate decision-making processes.

Digital transformation may include cloud adoption, process digitization, and automation, whereas AI strategy zeroes in on machine learning, predictive analytics, natural language processing, and intelligent automation capabilities. AI strategy is increasingly becoming a critical component within broader digital transformation initiatives, as AI technologies enable more sophisticated data-driven decision-making.​

A comprehensive AI strategy typically includes several essential components:

The timeline for developing an AI strategy varies based on organizational complexity, existing data maturity, and scope of AI ambitions. Typically, an initial AI strategy assessment and AI roadmap can be completed within 4-12 weeks. However, the full implementation of AI initiatives spans longer periods, often 1 – 2 months for AI proof of concepts and another 2 – 3 months for Full AI Custom Development. Organizations should view AI strategy development as an iterative process, continuously refining approaches based on learnings and evolving business needs.​

AI implementations can deliver substantial returns, though outcomes vary significantly by use case and industry. Research indicates that AI-driven personalization can improve conversion rates by 15-30%, while intelligent automation can reduce operational costs by 20-40%.

The return on investment depends on factors such as the quality of data available, organizational readiness, and the strategic alignment of AI initiatives with business goals. A well-structured AI strategy helps organizations prioritize high-value use cases and measure outcomes systematically to maximize returns.

The EU AI Act introduces stringent requirements for AI systems, particularly those classified as high-risk. An AI strategy helps organizations navigate compliance by establishing risk-based regulatory frameworks, implementing ethical principles in technology development, and creating policies for regulatory risk management.

Key compliance areas include

• transparency obligations,
• algorithmic accountability,
• data governance,
• continuous monitoring. 

Working with AI consultants experienced in European regulations can help businesses align their AI initiatives with these requirements while maintaining innovation capacity.

Identifying high-value AI use cases requires a systematic assessment of business processes, data availability, and potential impact. Effective approaches include:​

• Conducting AI workshops to identify pain points and opportunities across departments​

• Analyzing processes with high data volumes and repetitive decision-making​

• Evaluating use cases based on feasibility, business value, and strategic alignment​

• Starting with AI Proof of Concepts to validate concepts before scaling​

Industries commonly see strong AI applications in customer service automation, predictive maintenance, demand forecasting and personalized marketing.​

The recommended first step is conducting an AI readiness assessment to evaluate your organization’s current capabilities, data maturity, and strategic priorities. This assessment typically examines:​

• Current technology infrastructure and data landscape​

• Organizational culture and change readiness​

• Existing skills and talent gaps​

• Business processes suitable for AI enhancement​

• Competitive landscape and industry AI adoption trends​

Following the assessment, organizations can develop a prioritized AI roadmap that identifies quick wins alongside longer-term strategic initiatives, ensuring both immediate value creation and sustainable AI capability building.​