AI for the Entire Organization: Specialists, Generalists, and Executives

As artificial intelligence (AI) continues to redefine the boundaries of technology and business, it becomes ever more essential for organizations to cultivate AI readiness. But what does this readiness entail, and who carries the responsibility of driving it? 

The answer lies in understanding that readiness for AI is not a solo act, but a collective effort, best achieved through a balanced integration of three crucial types of personnel: specialists, generalists, and executives. Each plays a crucial role in driving AI adoption and implementation. Specialists, with their deep technical expertise, act as the essential gear wheels in the AI machinery, handling tasks ranging from advanced programming to data analysis. Meanwhile, generalists serve as the indispensable bridges, connecting the specialist's technical world with the strategic realm of executives. Lastly, executives, with their strategic vision and leadership, provide the necessary direction and create an environment conducive to innovation.

The collaboration between these three roles forms the backbone of AI readiness, fostering a culture of innovation and ensuring the effective leverage of AI technologies. In this article, we will delve deeper into the roles, responsibilities, and skills each of these personas bring to the table, along with the necessity of a harmonious collaboration between them, and the ways to foster this talent at all levels. 

AI Specialists Lay the Foundation

An AI specialist is someone with in-depth technical expertise who knows how to develop, use, and implement AI technologies and platforms. They may have designed AI algorithms and models, or implemented specific AI systems and tools for businesses in the past. AI specialists can include data scientists, machine learning engineers, software engineers, and AI researchers. 

Specialists typically have a background in mathematics, computer science, advanced programming, statistics, statistical modeling, or domain knowledge. The day-to-day tasks of AI specialists may include developing and testing AI algorithms and models, collaborating with an organization's cross-functional teams when implementing AI, and researching new AI, implementing it, and monitoring it over time. Specialists also program AI systems, which means they create specific prompts or tasks for their organization that the AI will perform.

AI specialists play a crucial role in the evolution of AI in the enterprise, but their role is not without its challenges. Steeped in technical details, specialists might find it challenging to explain complex AI concepts, algorithms, or models to non-technical team members or executives. AI specialists may also sometimes find it difficult to align their technical work with the broader business strategies defined by the executives.

To overcome these challenges, organizations should: 

  • Emphasize Data Storytelling. Data storytelling is a powerful communication tool that blends data, visuals, and narrative to explain complex AI concepts or insights in a more accessible way. Encouraging AI specialists to use this approach when presenting their work to non-technical stakeholders can help bridge the comprehension gap and make AI projects more relatable to the broader business objectives. Consider conducting workshops or bringing in external experts to train your teams in effective data storytelling techniques. 
  • Promote Cross-Functional Collaboration. Create opportunities for regular interactions and meetings among specialists, generalists, and executives. This could include project teams, brainstorming sessions, and strategy discussions. Such interactions can foster mutual understanding, encourage knowledge sharing, and lead to more synergistic decision-making.
  • Establish Clear Communication Channels and Processes. Develop a robust communication framework that ensures seamless flow of information across all levels. This could include regular status updates, project dashboards, and feedback mechanisms. Make sure that technical teams are articulating their ideas and findings in a way that non-technical stakeholders can comprehend, and vice versa.

AI Generalists Bridge the Gap

AI generalists are team members who bridge the divide between technical AI roles and business-centric functions within the company. They may not have the specialized skills to develop or program AI, but they need to understand how to interact with and leverage AI-generated insights in the course of their role. 

Generalists’ backgrounds are typically in a non-technical field, such as marketing or accounting, but they use AI to help them complete tasks more efficiently. For example, a generalist in the finance function might use AI-based predictive modeling tools to analyze large volumes of transactional data for unusual patterns or anomalies. In this role, the AI generalist would not necessarily be developing these machine learning models but rather applying these AI tools, interpreting the results, and translating these insights into actionable risk mitigation strategies. 

AI generalists often find themselves in a unique position within the enterprise, operating at the intersection of technology and business. While this offers them a holistic view of the organization, it also presents several challenges. AI generalists might find it difficult to fully grasp the technical details of AI solutions, given their breadth of focus as opposed to the depth of AI specialists. Similarly, generalists might struggle to translate intricate AI concepts or results into easily understandable insights for executives or other non-technical team members. 

There are several steps organizations can take to overcome these challenges and tap the full potential of AI generalists within the organization: 

  • Prioritize Data Literacy. Given that AI and data are intrinsically linked, a strong understanding of data is vital for anyone involved in AI projects. Therefore, employers should prioritize data literacy among their generalists—including understanding how to read, work with, analyze, and interpret data. Employers can offer training sessions, workshops, or online courses to build these skills. By promoting data literacy, organizations empower their generalists to understand the nuances of AI-generated insights, contribute effectively to data-driven decisions, and collaborate more efficiently with AI specialists. 
  • Establish Clear Communication Channels. Ensure there are clear channels for communication between AI specialists and generalists. This could involve regular updates, feedback sessions, and an open-door policy for queries and clarifications. Good communication can prevent misunderstandings and ensure all team members are on the same page.
  • Support Continuous Learning. Foster a culture of continuous learning within the organization. Encourage generalists to stay updated with the latest AI trends and technologies. This could be supported by offering access to learning resources, sponsoring attendance at relevant conferences, or providing time off for self-study. Continuous learning can help generalists stay relevant in the rapidly evolving AI landscape and enhance their ability to work with AI specialists.

Executives Set the Direction

Executives play a critical role in driving an AI readiness strategy and enablement within their organizations. Executives define the organization's overall AI vision and strategy. They identify where and how AI can be deployed to drive business value, aligning it with the broader strategic objectives of the organization. They are responsible for the allocation of resources—both human and financial—towards AI initiatives, and they lead the charge in fostering a culture of innovation and learning, creating an environment where experimentation with AI is encouraged and failure is seen as an opportunity to learn.

Although executives are not expected to be deeply technical, a basic understanding of AI, its potential, and its limitations is essential for informed decision-making. AI can transform business operations in profound ways. Leading such change can be challenging, as it often involves overcoming resistance, redefining processes, and ensuring all stakeholders are aligned with the change. Deciding where to invest resources for AI initiatives can be difficult. Executives must balance the need for immediate returns with long-term strategic benefits, all while managing risks associated with AI investments.

  • Invest in Education and Training. Executives should seek to improve their understanding of AI through educational opportunities such as seminars, workshops, or online courses tailored for business leaders. This will enable them to make informed decisions about AI strategy, resource allocation, and risk management. Additionally, investing in organization-wide education and training can ensure everyone has a foundational understanding of AI, fostering a culture of learning and innovation.
  • Leverage External Expertise. Engaging with external AI consultants, thought leaders, or specialized firms can help overcome challenges related to technical understanding, talent acquisition, and governance. These experts can provide insights into best practices, upcoming trends, and can assist in forming an AI strategy that aligns with the company's goals.
  • Implement a Strong Governance Framework. Establishing a robust governance structure can mitigate ethical and legal risks associated with AI. This framework should guide data handling, model development, and AI deployment, ensuring they meet ethical standards and regulatory requirements. This requires a multidisciplinary approach, often involving legal, ethical, technical, and business expertise.

Collaboration and Interplay Among the Levels

To guarantee the best outcome for the organization, specialists, generalists, and executives must communicate and collaborate with one another. For example, cross-functional teams and knowledge sharing can ensure that generalists obtain more in-depth knowledge about AI from specialists. They can then use what they've learned to train others, complete jobs, or work toward becoming specialists themselves. 

Specialists can learn how the organization wants to use AI by collaborating with generalists and executives. This knowledge can help specialists create or identify new software and AI tools focused on organizational goals. Specialists can also troubleshoot issues and keep the AI running smoothly. 

Executives, who may or may not have deep knowledge about AI, can learn more about the tools they need, become more knowledgeable, and work to change workplace culture by communicating with generalists and specialists. To implement an effective organization strategy for AI integration, executives must understand the work of specialists and generalists.

To achieve AI readiness, each level of the organization's workforce complements and relies on the others. Without specialists who fully understand AI, generalists who handle day-to-day issues, and executives who encourage the implementation of this technology, organizations won't be prepared to adopt the right AI tools at the right time. 

Nurturing the Growth of AI Readiness

Cross-functional teams encourage everyone to progress from one level to another. Executives or generalists can grow their skills and become specialists with in-depth knowledge of AI processes and platforms. In addition, specialists or generalists can develop skills that allow them to move into an executive role.

To become AI-ready, organizations must provide support, resources, and opportunities for growth at each level. Specialists, generalists, and executives should have opportunities for learning, training, and upskilling, so they can better understand how, when, and to what extent they should implement AI in the organization.

Furthermore, fostering a learning culture will support the overall goal of AI implementation by encouraging data literacy and a foundational understanding of AI across all levels of the organization. 

A diverse, well-rounded AI workforce will be prepared to take on new challenges, work with new AI tools, and overcome common obstacles, such as delayed AI adoption timelines.

AI Readiness Happens at All Levels of an Organization

Fostering an AI-ready culture is a task that requires collaboration, continuous learning, and effective communication at all levels within an organization. It is about empowering AI specialists, generalists, and executives to work harmoniously toward a shared vision, leveraging their unique skill sets and perspectives.

AI specialists provide the technical expertise needed to develop and implement AI technologies, while generalists play a crucial role in bridging the gap between technical AI functionalities and business applications. At the helm, executives provide the strategic vision and create a conducive environment for innovation and learning.

However, as we've seen, each role faces unique challenges, from communication barriers to comprehension gaps, which can be overcome through targeted strategies. Emphasizing data storytelling, prioritizing data literacy, promoting cross-functional collaboration, and establishing clear communication channels are a few steps organizations can take to help bridge these gaps.

At Correlation One, we help business leaders prepare for the challenges of tomorrow. By focusing on data literacy, we've helped finance, health care, manufacturing, and tech companies transform their workforce and achieve AI readiness. Need help to develop a game plan for your organization? Reach out to learn more.