AI Success Boils Down to Two Issues

In the 1980s companies were awoken to the need for improving quality to increase customer satisfaction in order to keep and increase market share and profitability. Everything about the customer was analyzed, dissected and correlated back to products and services in order to move the needle of profitability and market share forward.

In the 1990s companies focused on business process re-engineering as a management strategy, focusing on the analysis and design of workflows and processes within an organization. BPR aimed to help organizations fundamentally rethink how they do their work to dramatically improve customer service, cut operational costs, and become world-class competitors. As many as 60% of the Fortune 500 companies claimed to have initiated re-engineering efforts.

For the first nineteen years of the 21st-century companies awoke to an era of disruption. Adoption of the internet and related technologies grew exponentially, and the voice of the marketplace shifted from the suppliers to the customers and employees. Disruption of markets has been common, and the world became one big marketplace of connections, conversations, and transparent intents. These new dynamics have created new languages that transformed the shape of the marketplace and business philosophies on a global scale. Everything has changed and will undoubtedly change again, yet the barriers to change remain the same.

Back to the Future

As we move further into the 21st Century technology will continue to transform the business landscape yet the barriers to change will remain the same. These barriers are the crucibles of progress for organizations, industries, and societies at large.

A recent survey of the participants of the Artificial Intelligence World Conference listed several changes needed for AI success within the government and the private sector. The critical changes needed are in the areas of workforce training, data and culture.

Improved data governance, data-centric architecture, and increased consistency of data formats and tagging were the top three needs in terms of data. This is also the area where survey respondents expect the quickest returns:77% expect better data analytics to be the top AI mission outcome.

Workforce needs are also significant, mainly centered on staff. We need increased training for our current workforces in data science and AI, the survey found, as well as increased hiring of AI-specific subject matter experts, the respondents said. And on the process side, survey respondents requested formal processes and methods to guide AI implementation.

Finally, the survey respondents identified needs in company culture. Senior management needs a more data-driven strategic vision around AI, they said, and culture must shift to value data across all functions. They’d like to see a commitment to data-driven decision making across government and enterprise.

It appears the faster things change the more things remain the same. Whether the target is to improve quality or adopt the latest technology the consistent restraint to progress is understanding and using data as well as cultural transformation. To truly overcome these restraints leaders must slow down and learn in order to overcome the constraints and accelerate the transformation. The alternative is to simply go back to the future.

As the great historian George Santayana said: “Those who cannot remember the past are condemned to repeat it." I think there is an AI algorithm that can help leaders remember the past. What say you?

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This article is written by Jay Deragon, Managing Partner of Accelerate InSite.

Mr. Deragon has over 30 years of experience in numerous business ventures involving digital technologies. He has built and sold several digital ventures, led a global management consulting practice and served organizations large and small. He is currently a Managing Partner of Accelerate InSite with a focus on AI Strategies.