How to empower your university to integrate generative AI using tools and talent you already have

Generative artificial intelligence (GenAI) has rapidly emerged as a transformative technology in higher education. Along with opportunities, AI brings challenges to universities, such as identifying the right tools, securing broad internal adoption and aligning new technologies with existing structures, all while operating within a reasonable budget. 

This guide shares six replicable, scalable and resource-efficient strategies to embed AI across an institution.

1. Start with one role dedicated to AI strategy

Instead of launching a major new unit or hiring consultants to manage AI strategy and implementation, assign it to a single, dedicated staff member. Ideally, this person understands both institutional workflows and digital tools. 

Recognising the complexities of technology adoption, we created the role of business technologist at ESMT Berlin, a position dedicated to understanding and addressing institutional technology needs from both operational and strategic perspectives. This role is embedded within our digital innovation and transformation unit and carries a clear mandate from the management committee to lead the school’s engagement with emerging technologies. 

• Resource collection: Using generative AI in higher education
• Cultivating a responsible innovation mindset among future tech leaders
• Guard rails to mitigate ethical concerns for AI use

Initially, GenAI was a somewhat mysterious technology. At first, I collaborated closely with early enthusiasts. Staff and faculty are naturally curious about the possibilities. We embarked on exploratory projects together, learning through practical experimentation. As these small-scale projects showed tangible success, interest spread throughout the institution.

After demonstrating these early successes, we offered targeted workshops and secured a safe institutional licence option (ChatGPT Teams). From there, I began deeper, focused collaborations with individual departments, carefully aligning the now-maturing technology to meet their practical day-to-day needs. 

My role has evolved organically through collaborative exploration and incremental gains. This positions it as a bridge, translating technological possibilities into practical, everyday solutions. Having a focused, internal advocate dedicated to GenAI integration significantly accelerated adoption, facilitated clear communication and fostered an environment conducive to experimentation and innovation. 

2. Build skills before committing to an enterprise-wide solution

Many institutions rush to procure GenAI platforms, only to find staff lacking the skills or confidence to use them effectively. A better path is to equip faculty and staff with the skills and confidence to apply GenAI before exploring wider platform commitments. 

Through training sessions and workshops on prompt engineering and the functional uses of ChatGPT specifically, we addressed practical institutional needs such as administrative efficiency, course content creation and migration, and daily operational workflows. This hands-on, results-driven approach transformed initial skepticism into growing appreciation and enthusiasm for AI capabilities among staff and faculty. 

3. Empower internal AI champions to seed support

Use AI as a driver of internal staff development. When staff are trained and given exposure to AI, they can become catalysts for broader institutional adoption as they demonstrate the tangible benefits of GenAI to their colleagues. A cornerstone of our strategy was the AI Pioneers initiative, a community-driven programme to nurture internal champions of AI adoption. Rather than relying on external experts, we invited volunteers from across departments to join this peer-led group. They experimented with AI in their roles in structured training and collaborated on experimental processes. This approach encouraged adoption through trusted relationships, not top-down mandates. 

4. Maximise resources through incremental adoption 

Enterprise-level solutions can result in unnecessary costs being incurred without a clear understanding of real institutional needs and use cases. We started small, testing ChatGPT Team licences with enthusiastic departments. This allowed us to securely develop custom GenAI solutions tailored to academic and administrative requirements while using our infrastructure and targeted pilots to demonstrate value before scaling. 

Incremental adoption also enabled us to scale AI use based on real-time organisational readiness and enthusiasm, which helped us avoid committing prematurely to large-scale enterprise licences. Once successful use cases emerged, such as AI-driven role-play simulations or course-level GPTs, we extended more licences. From there, we discovered further custom GPTs that significantly reduced manual administrative workloads, demonstrating AI’s practical benefits with minimal financial investment. 

5. Implement and share clear, flexible, responsive governance 

Responsible AI use requires clear yet adaptable governance guidelines. Policies should emphasise transparency, academic integrity and data protection, allowing for experimentation within a supportive, ethical and flexible framework. We have found that regular communication, feedback loops and proactive governance adaptations help maintain momentum and foster a strong sense of accountability among faculty and staff. 

Documents with clear dos and don’ts for AI use policies should be reviewed regularly. ESMT Legal oversees our governance policy around GenAI use, ensuring compliance with academic integrity, transparency and data protection requirements. These guidelines are centrally documented and shared institution-wide via emails and made accessible to all staff and faculty.

6. Foster a university-wide culture of innovation

The success factor for AI adoption at our institution was cultivation of an innovative, collaborative institutional culture. By encouraging curiosity and experimentation, and celebrating incremental successes, we sustained enthusiasm and improvement. This cultural shift enabled us to achieve impactful, institution-wide adoption, as GenAI became integral to our institutional identity. 

GenAI adoption doesn’t require major new investments. It can begin through institutional strengths. Effective approaches can start with small-scale experimentation, internal know-how and a focus on solving real problems.

To shape a strategy that fits your institution, consider these guiding questions:

• Who within your community is best positioned to champion and drive early adoption?
• What immediate, practical challenges within your institution can GenAI realistically address?
• What governance policies can you implement that support innovation while ensuring responsible use?
• How can you nurture a culture that actively encourages learning, experimentation and collaborative innovation? 

By strategically addressing these considerations – based on our experience at ESMT Berlin – institutions can plan and execute their GenAI integration, achieving sustainable and meaningful outcomes aligned with their broader goals. 

Jon Demiglio is a business technologist at ESMT Berlin.

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