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2017 Artificial Intelligence Global Executive Study and Research Project Summary

Core Content

This report presents findings from the 2017 Artificial Intelligence Global Executive Study and Research Project, conducted by MIT Sloan Management Review and The Boston Consulting Group. It explores the current state of AI adoption and understanding across industries and organizations, highlighting the gap between ambition and action. The study surveyed over 3,000 executives, managers, and analysts from 112 countries and 21 industries, and included in-depth interviews with more than 30 technology experts and executives.

Main Findings

• High Expectations for AI: Across all industries, company sizes, and geographies, executives have high expectations for AI's impact on their organizations. While only 14% believe AI is currently having a large effect, 63% expect significant effects within five years.
• Limited Adoption: Despite high expectations, actual AI adoption is still in its early stages. Only about 25% of organizations have adopted AI, with only 5% extensively incorporating it. Less than 40% of companies have an AI strategy in place.
• Diverse Applications: AI is being applied to various areas such as information technology, operations, and customer-facing activities. For example, Ping An Insurance uses AI for customer service, and BP employs AI to improve drilling operations.
• Maturity Clusters: Four distinct organizational maturity clusters emerged based on AI understanding and adoption:
  • Pioneers (19%): Understand and have adopted AI, with strong leadership and business cases.
  • Investigators (32%): Understand AI but are not deploying it beyond the pilot stage.
  • Experimenters (13%): Are piloting or adopting AI without deep understanding.
  • Passives (36%): Have no adoption or limited understanding of AI.
• Barriers to Adoption: The main barriers to AI adoption include unclear business cases, competing investment priorities, and lack of data and analytics expertise. Pioneers are more likely to overcome these challenges due to better data infrastructure and leadership support.
• Data and Training Needs: Training AI algorithms requires high-quality data and understanding of the process. Pioneers are 12 times more likely to understand the process for training algorithms, 10 times more likely to understand development costs, and 8 times more likely to understand data needs. Data collection and preparation are often the most time-consuming aspects of AI development.
• Misconceptions About Data: There are several misconceptions about the role of data in AI. Some organizations believe that sophisticated algorithms alone can solve problems without sufficient data. However, data is critical, and its quality and availability significantly affect AI outcomes.
• Make Versus Buy: The traditional make-versus-buy decision is being re-evaluated in the context of AI. While Pioneers focus on internal skill development, Passives rely more on external resources. However, even those relying on external support need internal expertise to structure problems and manage data effectively.

Key Insights

• Strategic Opportunity and Risk: Over 80% of organizations see AI as a strategic opportunity, while almost 40% also perceive it as a risk. This reflects a growing awareness of AI's potential to both enhance and disrupt business operations.
• Organizational Readiness: The readiness of organizations to adopt AI varies significantly. Pioneers are more likely to have a strategic view and the necessary infrastructure, while Passives are still in the early stages of understanding AI's value.
• Cultural and Leadership Challenges: Implementing AI requires a cultural shift and strong leadership commitment. Large companies, in particular, face significant challenges in adapting to AI-driven processes.
• Future Implications: The report suggests that AI will continue to reshape business practices, creating new opportunities and risks. Organizations that fail to adopt AI may fall behind in the competitive landscape.

Conclusion

The study underscores the importance of aligning AI ambitions with realistic implementation strategies. It highlights the need for robust data infrastructure, leadership support, and a clear understanding of AI's business value. As AI continues to evolve, organizations must navigate both the opportunities and risks it presents to remain competitive and innovative.