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Maximizing AI Investments: Strategies for Delivering Business Value
To successfully implement AI and maximize its business value, companies must focus on four key areas—comprehensive data strategy, acquiring the right technology and skills, aligning AI initiatives with business priorities, and measuring AI's impact.
Date of writing
February 9, 2025
Time of reading
2 minutes
Artificial Intelligence (AI) has rapidly become a cornerstone of modern business strategies, with a McKinsey report indicating that 72% of companies worldwide are either utilizing AI technology or actively exploring its implementation, according to Forbes. Executives are optimistic about AI's potential to address business challenges and drive growth. However, the substantial investment of $1 trillion in AI represents one of the most significant gambles in business history, especially considering that over 80% of AI projects fail to deliver the anticipated outcomes.
To harness AI effectively and maximize its business value, established companies should focus on four critical components:
1. Comprehensive Data Assessment and Strategy
Begin with a thorough evaluation of your organization's data assets. This involves identifying data sources, ensuring data quality, and establishing a clear strategy aligned with your operating model. For instance, customer information often exists in various formats across different departments—emails, databases, spreadsheets—making it essential to determine which data is most reliable.
Legacy systems may pose challenges in data extraction or real-time access, underscoring the importance of data quality, especially when grounding AI-generated content. Developing a robust data strategy will help set priorities and outline the necessary investments for your AI initiatives.
2. Acquisition of Appropriate Technology and Skills
With a defined data strategy, the next step is to build a data platform supported by the right technology and expertise. This includes setting up infrastructure as a service (such as a cloud environment) and utilizing tools required to build, train, and deploy AI models. Understanding machine learning datasets and selecting suitable AI development platforms are crucial.
Collaborating with strategic partners can be beneficial, provided they work alongside your team and contribute to skill development. Building the platform, hiring new talent, and training existing team members will take time, but it's a vital investment for long-term success.
3. Alignment of Data Maturity, IT Spending, and Business Priorities
Analyze your current environment to inform which outcomes can be achieved and establish a realistic timeline for realizing business benefits. It's essential to identify and prioritize business cases across all lines of business, acknowledging that resources will be simultaneously building and learning.
Clear definition of business priorities and alignment among the executive team, technology team, and line managers are crucial. Starting with use cases involving structured data, such as a knowledge base for support, might be more manageable before tackling more complex data sources.
4. Measurement of AI Initiative Value
The executive team and the board will want to understand the value derived from AI investments. The finance team should lead efforts to define how value is measured and identify key performance indicators to track. Costs may include professional services and technology, while benefits might encompass resource reductions, such as decreased call times or volumes.
Decisions on cost allocation, whether spread across AI projects or considered foundational investments, should be made collaboratively between business owners and finance. It's important to recognize that initial measurements may require adjustments as you learn more about the model and its impact.
Investing time and resources in these areas can make the difference between successful business outcomes and a failed AI program. Creating a comprehensive plan that drives investment in AI will have a tangible impact on the bottom line.
To harness AI effectively and maximize its business value, established companies should focus on four critical components:
1. Comprehensive Data Assessment and Strategy
Begin with a thorough evaluation of your organization's data assets. This involves identifying data sources, ensuring data quality, and establishing a clear strategy aligned with your operating model. For instance, customer information often exists in various formats across different departments—emails, databases, spreadsheets—making it essential to determine which data is most reliable.
Legacy systems may pose challenges in data extraction or real-time access, underscoring the importance of data quality, especially when grounding AI-generated content. Developing a robust data strategy will help set priorities and outline the necessary investments for your AI initiatives.
2. Acquisition of Appropriate Technology and Skills
With a defined data strategy, the next step is to build a data platform supported by the right technology and expertise. This includes setting up infrastructure as a service (such as a cloud environment) and utilizing tools required to build, train, and deploy AI models. Understanding machine learning datasets and selecting suitable AI development platforms are crucial.
Collaborating with strategic partners can be beneficial, provided they work alongside your team and contribute to skill development. Building the platform, hiring new talent, and training existing team members will take time, but it's a vital investment for long-term success.
3. Alignment of Data Maturity, IT Spending, and Business Priorities
Analyze your current environment to inform which outcomes can be achieved and establish a realistic timeline for realizing business benefits. It's essential to identify and prioritize business cases across all lines of business, acknowledging that resources will be simultaneously building and learning.
Clear definition of business priorities and alignment among the executive team, technology team, and line managers are crucial. Starting with use cases involving structured data, such as a knowledge base for support, might be more manageable before tackling more complex data sources.
4. Measurement of AI Initiative Value
The executive team and the board will want to understand the value derived from AI investments. The finance team should lead efforts to define how value is measured and identify key performance indicators to track. Costs may include professional services and technology, while benefits might encompass resource reductions, such as decreased call times or volumes.
Decisions on cost allocation, whether spread across AI projects or considered foundational investments, should be made collaboratively between business owners and finance. It's important to recognize that initial measurements may require adjustments as you learn more about the model and its impact.
Investing time and resources in these areas can make the difference between successful business outcomes and a failed AI program. Creating a comprehensive plan that drives investment in AI will have a tangible impact on the bottom line.