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Artificial Intelligence

Building an AI Strategy: Key Steps for Aligning AI with Business Goals

July 2024
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Your Presenter(s)

Cindi Howson Tembakan kepala

Cindi Howson

Chief Data & AI Strategy Officer at ThoughtSpot and host of The Data Chief podcast.

Cindi Howson is the Chief Data & AI Strategy Officer at ThoughtSpot and host of The Data Chief podcast.

Cindi is an analytics and BI thought leader and expert with a flair for bridging business needs with technology. As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy.

Cindi was previously a Gartner research Vice President, as the lead author for the data and analytics maturity model and analytics and BI Magic Quadrant, and a popular keynote speaker. She introduced new research in data and AI for good, NLP/BI Search, and augmented analytics and brought both the BI bake offs and innovation panels to Gartner globally. She’s frequently quoted in MIT,Harvard Business Review, Information Week and is rated a top 12 influencer in big data and analytics by Analytics Insight, Onalytca, Solutions Review, and Humans of Data.

In 2022, CDO Magazine named her a Leading Data Consultant, and Global Data Power Woman. In 2021 she was named data leader of the year by Women in Data and as a finalist for motivator of the year by Women Leaders in Data and AI.

Prior to joining Gartner, she was founder of BI Scorecard, a resource for in-depth product reviews based on exclusive hands-on testing, contributor to Information Week, and the author of several books including: Successful Business Intelligence: Unlock the Value of BI & Big Data, Analytics Interpreted, and SAP BusinessObjects BI 4.0: The Complete Reference.

She served as The Data Warehousing Institute (TDWI) faculty member for more than a decade. She serves on the board for Drexel University’s LeBow Business Analytics program and is a volunteer for Girls Plus Data, Women in Data, and the Mark Cuban Foundation, AI bootcamps.

Prior to founding BI Scorecard, Howson was a manager at Deloitte & Touche and a global BI standards leader for Dow Chemical. She has an MBA from Rice University.

Vin Vashishta Tembakan kepala

Vin Vashishta

Founder & AI Advisor at V Squared

Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning.

Sonali Bhavsar Tembakan kepala

Sonali Bhavsar

Managing Director at Accenture

Sonali leads Accenture's North America Data Management and Governance practice for Data and AI, helping enterprises make data-informed decisions and realize the full potential of their data using data products in GenAI, data products in data mesh, operational innovation, and strong data foundations in cloud. With more than 22 years of experience in data, IT, and business strategy, she has a proven track record of delivering robust and innovative data foundation capabilities with machine learning and AI for Fortune 100 firms across industries. Sonali was previously a Managing Director at KMPG, a Senior solutions Executive for Watson at IBM, and a director at HP Autonomy.

Summary

Artificial Intelligence (AI) strategies are becoming a necessary part for contemporary businesses aiming to use technology effectively. Specialists underline the significance of aligning AI strategy with business objectives to prevent the common mistake of pursuing AI for its novelty rather than its practicality. In particular, AI should improve existing business practices and customer experiences, leading to revenue growth or cost reduction. The integration of AI into business operations is intricate, requiring a clear understanding of data strategy as a foundation. Companies must prioritize data quality and governance, ensuring that AI tools are used responsibly and ethically. The role of leadership in AI strategy cannot be overstated, with an emphasis on having a clear ownership of the strategy to ensure alignment with the company's goals. In addition, the cultural readiness of a team to embrace AI is essential, along with a focus on upskilling employees to close the gap between technical capacities and business needs. The webinar also highlighted the need for a balanced approach to AI, where immediate wins are pursued alongside long-term strategic goals, ensuring that the AI initiatives are sustainable and beneficial in the long run.

Key Takeaways:

  • AI strategy must align with business objectives to be effective and avoid pursuing trends.
  • Data quality and governance are important components of a successful AI strategy.
  • Leadership and clear ownership are necessary for driving AI strategy within a company.
  • Upskilling and cultural readiness are essential for successful AI adoption and implementation.
  • A balanced approach in AI initiatives can provide both immediate gains and long-term strategic benefits.

Deep Dives

Aligning AI Strategy with Business Goals

Aligning AI strategy with business goals is essential to avoid the trap of using AI simply because it is a trend. As Cindy Howson pointed out, AI should be seen as a tool to improve business processes, enhance customer service, and ultimately boost revenues. Without this alignment, businesses risk investing in AI technologies that do not add value. Jamie Dimon, CEO of JPMorgan Chase, has highlighted AI's transformative potential, comparing its impact to that of the printing press and the internet. This underlines the importance of integrating AI into business strategies thoughtfully, ensuring it serves as a complement to existing objectives rather than a distraction.

Data Strategy as a Foundation for AI

A solid data strategy is the backbone of any successful AI implementation. AI initiatives rely heavily on the availability and quality of data. Vin Vashishta emphasized the need to treat data strategy as synonymous with AI strategy, as the data collected dictates the opportunities available for analytics and AI applications. This involves creating data-generating processes that offer competitive advantages and ensure the reliability of the AI models used. The integration of business context and customer insights into data collection can make AI processes more efficient and cost-effective.

The Role of Leadership in AI Strategy

Leadership plays a significant role in the success of AI strategy within an organization. It is important to have a clear ownership of the AI strategy that aligns with the organization's overall direction. Cindy Howson noted the trend of combining roles, such as chief data and AI officers, to ensure cohesive leadership. This approach allows for a unified strategy that covers data governance, quality, and the selection of technological tools. Effective leadership ensures that AI initiatives are not conducted in silos but are integrated into the broader organizational strategy.

Cultural Readiness and Skill Development

The cultural readiness of a company to adopt AI is as important as the technological tools themselves. Sonali Bhavsa highlighted the need for organizations to assess their cultural and team readiness when considering AI implementations. This involves upskilling existing staff to ensure they can work effectively with AI technologies. The focus should be on encouraging critical thinking and creativity, skills that are less likely to be automated by AI. Organizations must manage change effectively, ensuring that employees understand and are comfortable with the new technologies and processes being introduced.


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