Navigating the AI Frontier: Organizational Implications of Generative AI in EdTech

EdTech companies stand at a crossroads between legacy systems and the frontier of artificial intelligence, navigating a complex dual infrastructure challenge that could redefine the future of education technology.

As such, EdTech companies are pioneering the integration of generative AI technologies to enhance digital education delivery. These innovations span a vast range of applications—from adaptive learning platforms that customize educational content to the unique needs of each student, to AI-driven automation tools that enhance operations for faster delivery, liberating educators from repetitive tasks. AI technologies drive efficiency, boost productivity, and create less friction for students and teachers alike.

While the COVID-19 pandemic accelerated remote learning, leaders in the EdTech industry recognize generative AI as a newly disruptive force and are seizing the opportunity to leverage the technology as a catalyst for net positive change. Sal Khan, CEO of Khan Academy, proclaimed that “we’re at the cusp of using A.I. for probably the biggest positive transformation that education has ever seen.”

To better understand the extent to which leaders are implementing generative AI strategies and what barriers they face with implementation, Russell Reynolds Associates surveyed over 2,500 leaders globally in September of 2023. To contextualize these findings for EdTech leaders who are implementing AI solutions, we interviewed several notable executive leaders in the industry, each of whom are driving innovation within their organizational operations and through their product offerings.

We learned that to effectively harness AI's transformative power, EdTech organizations and their leaders need a proactive AI talent strategy. To develop this:

• Start with leadership consensus to determine how and where AI solutions will be most impactful. AI is most effective when teams are fully aligned. This includes considerations around risks, available budgets, and responsible implementation and growth strategies.
  
  
• Determine who owns AI strategy in your organization’s new structure. There are various AI governance models and factors influencing how companies assign responsibility for their AI roadmap, from technical oversight to strategic integration.
  
  
• Align talent strategy with technology approach. Implementing AI requires EdTech leaders to develop a grounded AI strategy, carefully deciding between leveraging open-source models and investing in proprietary LLMs. This includes ensuring the availability of a robust data infrastructure and recruiting the right executive talent capable of steering the AI strategy.
  
  
• Implement agile and continuous learning cultures. Encourage a company-wide adoption of agile methodologies and continuous learning environments, positioning AI skills as essential for all roles, thereby enhancing the organization's capacity to innovate and adapt in the fast-evolving EdTech sector.
  
  
• Adopt ethical AI approaches. Aim to enhance student experiences and outcomes without compromising equity or privacy. Ensure that the new AI technologies or AI-enabled workflows serve to reduce educational disparities, rather than widen them.

Start with leadership consensus to determine how and where AI solutions will be most impactful

AI implementation requires a behind-the-scenes overhaul that necessitates a robust, forward-thinking leadership strategy. As such, organizations need to determine where they want to focus AI adoption and need full senior leadership alignment and support to be successful.

AI solutions for EdTech companies can be classified into three primary domains: operational infrastructure, educational content enhancement, and student experience optimization.

• Within operations, generative AI can facilitate faster delivery of educational services, streamline administrative tasks, and improve operational efficiency within student information systems.
  
  
• In the educational content domain, adaptive learning technologies, innovative curriculum design, and precise skills assessment tools can support targeted career development and enrich educational content delivery.
  
  
• For student experience, chatbots and conversational AI can provide personalized support to students, while digital assistants embedded in learning platforms can offer real-time, AI-driven guidance.

Prioritizing AI requires not just technological adaptation, but a comprehensive reimagining of operations, go-to-market strategies, and, crucially, the talent that drives these innovations. For example, one executive shared that their company overhauled their entire platform to embed AI into the core of their strategy. This deliberate pivot illustrates EdTech leaders’ seriousness about AI, and that many see embracing the technology as necessary to maintaining their competitive advantage.

Determine who owns AI strategy in your organization’s new structure

As AI solutions are embedded into existing applications or are purposefully deployed to automate, enhance productivity, or completely transform business processes, a corresponding organizational restructuring needs to take place. Different models are emerging, tailored to the unique demands and applications of each company’s core business and intended capabilities for generative AI transformation (Figure 1), embedding AI strategy ownership within:

Existing Tech Roles

• For many EdTech companies, especially those that are already leveraging automation and adaptive learning capabilities, CTOs/CIOs have already embedded AI expertise within their teams. These leaders bring a strategic vision for AI's role as a core competency while navigating the balance between legacy operations and innovation.
  
  

Existing Non-Tech Roles

• Some organizations have brought technology, data, and product teams under the chief operating officer to emphasize operational efficiencies derived from AI and optimize alignment between AI-development and strategic business priorities.
• Others have elevated commercial GMs with technical backgrounds to drive AI as a central element of revenue and growth strategies.

New Roles

• Within the broader educational ecosystem, some institutions – like universities – that are at the early stages of their digital transformation journey have created specialized roles, such as chief AI officer. This reflects the growing recognition of AI's strategic importance and is most common in complex organizations where multi-stakeholders buy-in is needed.

Figure 1: Emerging AI organizational structures within EdTech companies

^{Source: Proprietary research on EdTech organizational structures, Russell Reynolds Associates, 2024}

Amidst these varied options, some companies are still exploring the optimal structure, leading to co-ownership scenarios where executive teams collectively manage AI responsibilities, indicating the evolving nature of leadership in harnessing AI’s potential.

Align talent strategy with technology approach

Implementing AI requires EdTech leaders to develop a grounded AI strategy, carefully deciding between leveraging open-source models and investing in proprietary LLMs. This includes ensuring the availability of a robust data infrastructure and recruiting executive talent who can steer AI strategy.

While ownership of an EdTech company’s AI strategy can vary, the need for tech-savvy talent to support is paramount across every model. And talent needs will depend on the company’s chosen approach—which can range from building their own large language models (LLMs) with their own data lake; buying AI applications from an external vendor; or partnering with other technology companies to customize or augment existing open-source LLMs (Figure 2).

Figure 2: Strategic Approaches for AI Adoption in EdTech Companies

_{Source: Proprietary research on EdTech organizational structures, Russell Reynolds Associates, 2024}

Companies that are building their own LLMs and data lakes are looking to leverage their unique datasets to develop highly tailored AI applications for their specific educational contexts. To do this, these companies need substantial in-house expertise and resources. This route is possible for organizations with significant technical capabilities and a dedicated commitment to long-term AI integration.

More commonly, companies that need to get to market quickly, have clear and attainable AI use cases, or are prioritizing back-office productivity enhancements tend to buy these solutions, rather than build them. These companies may lack the depth of technical resources or are seeking to quickly adopt AI technologies without the lengthy development process.

In the context of scaling new business models and leveraging open-source LLMs, some EdTech companies might prefer to partner with established AI players. This strategy might be most appropriate for organizations aiming to blend innovation with cost-effectiveness, allowing them to customize and augment existing AI solutions without the full burden of developing new technologies from scratch.

Each approach’s appropriateness is implicitly tied to the organization’s strategic goals, resource availability, and desired speed of AI adoption. Additionally, the emphasis on the need for tech-savvy talent across all models underscores the universal requirement for skilled professionals who can navigate the complexities of AI implementation, regardless of the approach taken. Overall, data experts with core machine learning and data architecture experience are the most critical talent pools.

Implement agile and continuous learning cultures

Encourage a company-wide adoption of agile methodologies and continuous learning environments, positioning AI skills as essential for all roles, thereby enhancing the organization's capacity to innovate and adapt in the fast-evolving EdTech sector.

EdTech companies that have adopted “test and try” cultures have accelerated their company’s AI product roadmap. One interviewee shared that “everyone in our organization needs to know prompt engineering,” suggesting a serious dedication to enterprise-wide AI adoption. Another executive proudly claimed that their tech teams participate in bi-monthly hackathons to explore AI use cases. Other interviewees shared that their organizations are developing educational and mentorship programs to address talent shortages and skill gaps in the sector, furthering the focus on learning within their organization’s culture.

Adopt ethical AI approaches

EdTech executives can take a multifaceted approach towards safeguarding the development of AI tools to truly augment and enhance educational experiences. Using student data in large language models is fraught with risk, and it is essential for companies to incorporate ethical AI practices from the very beginning of AI tool development. Adhering to principles like transparency, explainability, fairness, and accountability will ensure that the generative AI tools developed for educational purposes are not only reliable but also free from producing or exacerbating unintended biases or outcomes.

Implementing such principle-driven design necessitates a deep understanding of the diverse stakeholders in the educational ecosystem, the capability to foresee potential unintended consequences, and a thorough awareness of the complex environment in which EdTech companies operate. Moreover, these companies must adopt a measured approach to AI development, carefully navigating the balance between innovation and ethical considerations. Crucially, EdTech firms must vigilantly work to avoid introducing unconscious bias into AI tools, as such biases could significantly harm students’ learning experiences and outcomes downstream.

What’s next for EdTechs leveraging AI?

AI has challenged EdTech companies’ operating models, and will continue to do so. EdTech executives must approach AI strategically, focusing on affordability and applicability within business models. Embracing AI's potential requires R&D and collaboration—both internally and externally. Preparing your workforce for data literacy is vital, and building a strong tech foundation is non-negotiable.

• The proliferation of generative AI is causing leaders to assess how AI capabilities and responsibilities are structured within organizations. Proactively consider what role AI will play in your business as you design future strategies, roles and organizational structures.
  
  
• To successfully implement AI, organizations need the right data infrastructure to embed AI/ML models into tech stacks. Leaders need to recognize the convergence of computing at the storage, edge, network, and infrastructure layers and invest wisely.
  
  
• Big tech companies are able to build massive AI technologies because they can afford the cost of compute. If there are gaps in your infrastructure, industry collaboration is key for rapid AI advancements. Leveraging open source LLMs can accelerate the overall AI development for your company.
  
  
• Experimentation is key. Determine whether your organization’s culture supports experimentation and adaptation, then work to create an environment that supports quick AI wins, as this allows for learning and adaptation. To unlock AI's full potential, substantial R&D is required for greater efficiency and quality.
  
  
• Embedding AI solutions will provoke rethinking traditional role definitions too. While some roles may be eliminated, many more will be enhanced. Some will be created to seize AI’s full potential (e.g., prompt engineers). Above all, EdTechs must develop a data-literate workforce.

Authors

Robert Alexander is a member of Russell Reynolds Associates’ Technology knowledge team. He is based in New York.
Andrew Bauer is a member of Russell Reynolds Associates’ Technology Officers practice. He is based in New York.
Nick Dials is a member of Russell Reynolds Associates’ EdTech practice. He is based in Boston.
Kate Nihill leads Russell Reynolds Associates’ EdTech practice. She is based in New York.