04.03.2023 Executive Data Bytes – Driving AI Success Through Strategic Investment

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, let's dive into AI Driven Success, and how strategic investments propels its growth.

Focus piece: “AI-Driven Leadership ”

Executive Summary

Driving AI success necessitates the involvement of various components such as strategy, operations, culture, and a diverse ecosystem. Leaders are at the forefront of this mission to make AI successful, and their actions and communications set the tone and standard for what is to come. In this MIT Sloan Management Review article, we see the seven characteristics of AI-Driven leaders, which we will discuss below.

Key Takeaways

• The ability to have clear business objectives for the implementation of AI systems in their organizations is the first and most important attribute of AI-Driven leaders. They don't just decide to invest in and use AI systems because it's nice to have; they can clearly see how it can improve business operations and reduce costs while increasing the bottom line.
• Leaders who are AI-driven also make an effort to learn the technologies. Leaders will need to understand how AI systems work in order to make sound decisions about them. They are not expected to be technical experts who understand how these systems work intricately, but a high-level understanding of how these systems make the decisions they do will allow them to determine which AI projects or systems will be most beneficial to the organization's success.
• AI-driven leaders have a healthy dose of ambition. They are aware of the resources at their disposal as well as the possibilities that these resources provide. They do not make unrealistic assumptions about what is possible because it is "AI," but instead tailor their expectations and ambitions to what is achievable at the time. In summary, they don't mix up the time to pursue moonshot projects with the time to harvest low-hanging fruit when necessary.
• Finally, AI-driven leaders prepare their followers for the journey. While some people believe that AI systems will eventually replace humans, AI-driven leaders understand that greater productivity will be achieved when smart people collaborate with smart machines; thus, AI leaders will work to dispel any fear of total AI dominance in order to achieve this level of collaboration.

Focus piece: “How Investing in AI is About Investing in People, Not Just Technology”

Executive Summary

While leaders lead the way, it is critical to bring along the employees who will implement the visions and strategies. Without them, these ideas will remain just that: ideas. People are in charge of creating these systems and ensuring that they are used by the intended audience. As a result, investing in people is critical to driving AI success in any organization. This Entrepreneur article explains how investing in AI is about investing in both people and technology.

Key Takeaways

• One of the first things to consider when investing in people is to invest in factors other than technical abilities. AI systems cannot be built solely by data scientists and machine learning engineers. An AI team composed of product leaders, subject matter experts, designers, end users, front-line associates, data scientists, and technical builders should create AI systems. Having a team that understands the entire scope of what it takes to build a system will help propel the system's success.
• Leaders, and by extension organizations, should consider investing in addressing cultural stigma. New technologies that promise disruption tend to frighten those who are unaware of their capabilities, and AI in particular challenges people's sense of importance and relevance, especially given the current threat of job losses. Addressing these fears and insecurities will allow employees across the organization to feel at ease with these systems and work in accordance with the leadership's strategies and plans.
• In addition to addressing cultural stigma, organizations should work to integrate an AI mindset within the organization. This mindset entails taking a data-driven, experimental, and innovative approach to problem solving and decision making, with AI viewed as a tool to augment human intelligence and enable new possibilities. This includes adopting a culture of learning and experimentation in which failure is viewed as an opportunity to learn and improve.

Focus piece: “Designing and building artificial intelligence infrastructure” 

Executive Summary

Technology is the platform and tool that businesses use to get their visions, plans, and strategies in gear. This puts AI success into action. Choosing the right tools and establishing the right infrastructures can mean the difference between beating your competitors and being overtaken by them. While having the right tools is important, knowing how to use them together is preferable to simply having them. This TechTarget article series explains how to design and build artificial intelligence infrastructure to propel AI success.

Key Takeaways

• Investing in infrastructure necessitates first investing in data. There can be no adequate working AI system without data. It's common knowledge that AI systems rely on massive amounts of data to learn and improve over time. As a result, organizations must invest in data collection, storage management, and processing to ensure that their AI systems have access to high-quality data relevant to their use cases.
• With the necessary data in place, organizations can shift their focus to establishing the technological resources to support the development of these systems. AI systems are generally computationally intensive to build, and large amounts of data storage space are required to store both data and models used in these systems. As a result, putting these resources in place based on the project's requirements will aid in achieving stability during the system-building process.
• With the available data and technological resources, businesses will need to focus on putting in place the right machine learning operations (MLops) to ensure that these AI systems, once built, can be easily deployed and consistently kept online in production environments without breaking. A solid MLops strategy will also assist businesses in detecting and correcting data drifts, model drifts, and an overall decrease in model accuracy.

Let's start strategizing!

Who We Are

Data Products partners with organizations to deliver deep expertise in data science, data strategy, data literacy, machine learning, artificial intelligence, and analytics. Our focus is on educating clients on varying aspects of data and modern technology, building up analytics skills, data competencies, and optimization of their business operations.