The CEO's Role in Scaling AI as a Technology Native

What if a firm created each component of its product with every purchase, without any standard or uniform parts, procedures, or quality-assurance standards? It's doubtful that any CEO would look favorably on such an approach.

This is how many businesses engage with artificial intelligence (AI) and analytics in general: by putting themselves at a significant competitive disadvantage. As teams are dispersed throughout the company and begin new initiatives from the ground up, working manually rather than using enterprise processes for deploying and monitoring real AI models.

To make a significant impact on a company's bottom line, organizations must scale the technology throughout their organization, integrating it into key business processes, workflows, and customer journeys in order to improve decision-making and operations every day.

Experiments are mostly done

Gone are the days when businesses could only afford an experimental AI approach and a general understanding of analysis. It's now time to put AI to work.

Managers can no longer afford to test things out. The era of experimenting with AI is over, as artificial intelligence is already a key competitive advantage for organizations that are still developing—those who fail to do so will be left behind rapidly.

In this post, I'll assist CEOs in recognizing the proper levers they may use to help and support their AI leaders' efforts to implement these procedures and technologies on a permanent basis.

What is worth doing to move from experiment to market implementation and accelerate the application of AI?

a) Use pre-designed blocks to create company processes that allow for more consistency and efficiency.

Organizations should invest in a wide range of reusable assets and components. One example is developing "ready-to-use" "products" that standardize a certain set of data (for example, combining all customer information to create a 360-degree customer picture), using common standards, built-in security and surveillance, and self-service capabilities.

Weight and Biases is an example of a firm that can significantly speed up the process from research to market introduction. The firm's solution, which includes artificial intelligence model testing, versioning, and tracking, allows for quick evaluations of AI models.

If your company has consumer data, Weights & Biases can help you create AI models quickly and improve the speed of AI applications. These will also let you forecast customer behavior and evaluate client attrition at what price point would customers consider switching to a different provider.

Organizations invest a lot of time and money in developing AI solutions only to discover that a company stops utilizing nearly 80% of them because they no longer provide value. Using ready-to-use solutions with scalable tools in the service model reduces waste by just 30%. How is this possible? specialized platforms enable faster and more effective conclusions from processed data.

What should be monitored in an organization interested in accelerating artificial intelligence adoption?

• % of built artificial intelligence models that have been implemented. It is worth talking about how many models have been trained but have not been approved for implementation in the production environment.?
• Total Automation Impact and AI Investment ROI.
• The number of departments involved in joint implementations.

b) Building smooth cooperation between business and IT to accelerate the application of AI

The aligning of business executives' objectives with those of AI teams and IT departments is one of the key factors behind the acceleration of AI projects. The majority of the goals of AI and data teams should, ideally, be in line with those of corporate executives. Business leaders should also determine how much value they anticipate from AI and how it will be monetized. Artificial intelligence is most useful when it works in harmony with specific processes. It would be wrong to think that AI itself will define which areas are more suitable, given that there are many decisions to be made about where to use it and how it can deliver additional value.

At one of my clients, I discovered a disconnect between the IT department's goals and those of the business units. The IT department concentrated on its operation strategy, while the business divisions focused on their northern lights, which pushed them toward different operational objectives.

As a result, over 80% of the 50 business units did not link their expectations with the IT department's corporate and technological road map. As a consequence, this resulted in the development of more than 200 projects. 55% of them were isolated islands, without any chance to leverage machine learning or deep learning.

The BITA model may help organizations achieve AI strategy alignment by bringing IT and business divisions together.

Figure 1: Business and IT alignment model, Henderson, and Venkatraman (ResearchGate).

c) Investments in talent

The job of data analysts and technical engineers is transforming. Their work used to be focused primarily on low-level coding before. They must now build models out of modular components and develop an outcome that is ready for production and scaling. The use of artificial intelligence necessitates a different strategy.

In addition, there are newer job responsibilities in AI teams. One of them is a machine learning engineer who can convert AI models into enterprise-class production systems that function at scale. Business executives should communicate the shift throughout the company and collaborate on the talent development map with human resources managers.

Conclusion

The three ways to move from experiment to market implementation and accelerate the application of AI are using pre-designed blocks, building smooth cooperation between business and IT, and investing in talent.

1. As an executive, what do you think of the CEO's role in scaling AI as a business?

2. What are your thoughts on developing strategies for the adoption of AI technology?

3. How has the CEO's leadership style changed with the introduction of new technology?

4. What is your experience with implementing or scaling AI at your company?

5. Have you had any difficulties scaling up an artificial intelligence strategy across various departments?

Share your thoughts!