When you hear the term “artificial intelligence,” what comes to mind?
The idea that AI is upending the modern workforce is a big obstacle companies have to overcome when thinking about how to best integrate and adopt artificial intelligence into their business. While it’s true that automation may displace 75 million jobs, statistics also indicate that it will generate 133 million new ones worldwide by 2022. COVID-19 is only accelerating this change, transforming business models in every industry and challenging the skill sets of their teams.
While leaders know that artificial intelligence can unlock incredible insight, many companies — especially traditional ones — are struggling to adopt it. The ability to identify and implement AI technology can be overwhelming and complicated in equal measure. This stems from four critical challenges with emerging technology:
- Complex change management: There is often a disconnect between how companies anticipate using AI solutions and the ability of leaders and managers to handle the degree of adaptation that’s required. In general, the lack of understanding about what artificial intelligence actually is and what it does gets in the way of fully planning for this integration in order to really improve the business.
- Lack of examples: There aren’t enough publicly available common use cases from which leaders can learn.
- Need for ethics guidelines: There isn’t enough awareness of how to make good decisions around the ethical and responsible use of artificial intelligence.
- Skills gaps for practitioners: There aren’t enough people with hard skills in AI, DevOps, cloud, machine learning, and data engineering to really enable this change, even as leaders begin identifying the right opportunities for it. About 95% of the AI-impacted workforce population (as identified by the adoption map below)* does not have skills in these sectors.
To address these challenges, we partnered with Microsoft to form the AI & Data Science Standards Board, representing a broad set of AI and data executives across the tech, auto, media, healthcare, professional services, and hospitality sectors. The board aims to increase the clarity around and access to AI skills and careers so that organizations and leaders can responsibly realize the opportunity of artificial intelligence.
Board members agree that, while machine learning and automated tools have the potential to drive better business decision-making, the No. 1 barrier to adoption is an organization’s inability to identify and implement artificial intelligence into its business model. This begs the question: How do you transform a business model?
It starts by taking a look at your workforce strategy.
Defining Roles in the AI Workforce
A true picture of AI encompasses how a workforce collaborates across teams, how workers do their work, and how they communicate with leadership. With this in mind, we created an AI & Data Science Adoption Map to help organizations evaluate what’s required for their artificial intelligence and data science adoption journey. The map captures the board’s perspective on which groups and functions should be responsible for the different elements of enabling a change to create a successful AI organization.
Click to download for more detail.
Let’s break down the map’s key features. At a high level, we have three groups: Leaders, Creators, and Users. These were chosen specifically to display their interconnectivity and showcase each group’s responsibilities. Leaders set the vision and are accountable for the responsible adoption of AI, which influences the Creators’ work. Creators implement the vision (set by the Leaders) while keeping in mind the needs and processes of the Users. Users then leverage the outputs of this AI and data science adoption to improve speed, efficiency, and quality of work. This map is not a reflection of an org chart, rather, it provides a bird’s-eye view of who should be involved in AI adoption and how it impacts their function. Download the map for more detail about the key functions and titles that fall within each group.
Many of our board members and enterprise partners acknowledge that few leaders truly know where to start when it comes to implementing and effectively leveraging artificial intelligence. But Gretchen O’Hara, VP of U.S. AI & Sustainability Strategy & Partnerships at Microsoft, stated that the “map will help companies reskill faster and at scale.”
If you could benefit from using the AI & Data Science Adoption Map, we encourage you to join the board in reviewing this framework to evaluate your organization and the strengths and gaps in your AI system.
Implementing AI and data science strategy within an organization is complex, and the AI & Data Science Adoption Map is a first step toward achieving improved clarity and definition for the field. The board’s next focus will be to more clearly articulate the skills required across different functions. Of course, we will also be evolving our training and certifications for relevant roles to ensure that team members at every level can better execute their goals.
This is only the beginning, and we can’t wait to hear from you about how you plan to use the map. We look forward to continuing the conversation in the months to come.
Meet the AI & Data Science Standards Board
Since 2011, General Assembly has trained individuals and teams online and on campus through experiential education in technology, data, marketing, design, and product. Learn more about how we can transform your talent and our solutions to upskill and reskill teams around the globe.