8 steps to build a successful AI strategy for your business
Customer AI strategist Bill Schmarzo explains how organizations can successfully implement AI by focusing on people, process and purpose over technology.
As AI tools become more sophisticated, organizations are increasingly struggling to derive real value from them. Many have rushed to adopt solutions without a clear strategy in place, leading to missed opportunities and disappointing results from the tools in question.
How AI goes beyond traditional analytics
Unlike traditional analytics tools that optimize based on past data, AI can optimize around future goals. This allows organizations to create AI systems that reflect their aspirations, rather than just improving their current operations.
However, the full potential of AI can only be realized through proper implementation. Organizations need to make sure everyone is involved in the process -- from frontline workers to senior management -- and that they understand their role in defining, developing and deploying AI models that deliver meaningful outcomes.
A human-centric approach to AI strategy
In the podcast, Bill Schmarzo emphasized that successful implementation requires AI strategy to move beyond the "high priesthood of data scientists" and involve frontline workers -- the doctors, teachers, technicians and operators who possess valuable tribal knowledge.
"The people who have the best insights, who have the tribal knowledge … which we can use to drive where we want to take the organization -- it's the people at the front lines of customer engagement," Schmarzo explained in the interview.
According to Schmarzo, the key to successful AI implementation lies in understanding that "AI and data is not an end point. They're a means to an end point." He outlined eight critical steps for successful AI implementation:
- Start with business value, not technology. Before diving into technical solutions, figure out how your organization delivers stakeholder value and align your AI initiatives with concrete business objectives.
- Build awareness and education. Ensure all employees understand both the professional and personal implications of AI technology by implementing comprehensive training programs that develop data literacy across the organization.
- Engage stakeholders early. Involve internal and external stakeholders in the planning process, gathering their input on desired outcomes and essential decision-making requirements while managing any potentially competing interests.
- Develop use cases strategically. Design a methodical roadmap of use cases that build on accumulated knowledge, prioritizing initiatives that are based on potential value and practical feasibility.
- Build organizational structure for success. Encourage collaboration and empower frontline workers to make decisions by creating flexible innovation frameworks and fostering a culture that views failure as an essential component of growth.
- Take advantage of the economies of learning. Establish systems to capture and apply insights from each implementation, recognizing that organizational learning capability often supersedes scale in knowledge-intensive industries.
- Ensure inclusive development. Create a democratized approach to data science that values both technical expertise and practical experience by integrating perspectives from across the organization -- particularly those of workers with frontline access to customers and operations.
- Focus on balanced outcomes. Design AI implementations that effectively balance multiple objectives, considering not only financial metrics but also community impact, environmental sustainability
,and ethical implications.
Listen to the full interview now. Click here to subscribe to Tech Beyond the Hype on Apple Podcasts.
Ana Salom-Boira is an editorial manager within TechTarget's Editorial Summits team. She also produces and hosts the podcast series Tech Beyond the Hype, which explores how emerging technologies and the latest business trends are shaping the future of work.
