New York, NY – October 26, 2023 – The landscape of higher education is undergoing a seismic shift, propelled by the rapid integration of artificial intelligence (AI). Universities are moving beyond nascent experimentation to embrace AI as a core operational tool, a transition demanding strategic clarity and a focus on tangible impact. This evolution was a central theme at a recent EdgeCon spring session, where Slalom, a global consulting firm, and UCLA Anderson School of Management presented a compelling case study of AI-driven innovation that prioritizes both mission impact and measurable value.
The discussion, led by Slalom Director Alan Wong, underscored that in the current AI-saturated market, true differentiation lies not in the mere adoption of technology, but in its rigorous application towards achieving institutional goals. Wong emphasized that the era of endless pilots is giving way to a demand for demonstrable results, whether those are mission-driven outcomes like enhanced student retention and success, or significant improvements in the efficiency of administrative and service operations.
The Imperative for Rigor and Data Maturity
"Higher education is at a pivotal moment with artificial intelligence, moving rapidly from early experimentation toward full operational deployment," stated Wong. "There are two differentiators that are important, because AI is a noisy space right now. Every person you talk to is going to tell you they do AI and can help you with AI. Successful universities and their partners are focused on rigor around mission impact and business value so that the investment is rationalized."
This focus on "rigor around mission impact and business value" is crucial for navigating the hype surrounding AI. Wong articulated that real success is not measured by the sheer volume of AI projects undertaken, but by the ability to showcase a valuable process that yields measurable outcomes. This can range from improvements in student retention rates and academic achievement to greater financial efficiency in operational areas.
The conversation also highlighted a critical, often overlooked, differentiator: an institution’s underlying data strategy. Wong stressed that a robust, university-wide data foundation is the essential "fuel" for scaling AI initiatives, regardless of whether the technology is generative, agentic, or represents future advancements. He asserted that data maturity is no longer a secondary consideration but an absolute necessity for moving from isolated AI experiments to widespread, impactful deployment.
"In higher education, we don’t ascribe to the belief that all value is measured in dollars and cents," Wong explained. "Value can also be seen in retention rates, student success metrics, and other outcomes that matter deeply to institutions. The second critical differentiator is the strength of an institution’s underlying data strategy. The university-wide data foundation is the fuel for scaling artificial intelligence, whether it’s generative, agentic, or whatever comes next. Data maturity underneath those use cases is no longer optional, it’s essential to moving from pilot to scale."
Partnership as an Extension of the Core Team
At the C-suite level, the value of a strategic consulting partnership extends beyond the completion of individual projects. It is defined by its capacity to accelerate progress, dismantle existing barriers, and provide informed, cross-industry perspectives on complex decision-making. Wong described Slalom’s approach to C-level engagement as fostering a sense of being an integral part of the client’s core team.
"The relationship between Slalom and our C-level clients is about feeling like an extension of the core team," Wong shared. "We’re able to not only accelerate what an institution is already trying to do, but also help unstick things that have gotten stuck. Senior leaders also benefit from access to broader cross-industry insight. We can provide perspective from beyond higher education. There are lessons from other regulated industries dealing with data privacy and security challenges that are very relevant to higher ed as well."
This cross-industry perspective is a significant advantage, allowing universities to learn from the experiences of other sectors facing similar challenges, particularly in areas like data privacy and security, which are paramount in both higher education and other regulated industries.
Slalom’s value proposition, as articulated by Wong, can be distilled into three fundamental pillars: trust, perspective, and embedded execution.
- Trust: The foundational element of any successful partnership is the establishment of a proven track record of reliability and integrity.
- Perspective: Earning the right to offer insights, drawing from both experiences within higher education and broader cross-industry applications.
- Embedded Execution: The ability to seamlessly integrate with client teams and execute solutions collaboratively, acting as a true extension of their capabilities.
"The foundation starts with building a track record of trust," Wong stated. "Then earning the right to bring in insights from other higher education institutions as well as cross-industry experience, and finally being able to execute in a way where we are truly embedded alongside their teams."
Sparking Broader Thinking: The UCLA Anderson AFIA Initiative
A prime example of this integrated approach was the partnership between Slalom and UCLA Anderson School of Management to develop an AI-powered Academic Feedback Intelligent Assistant (AFIA). This innovative solution, built utilizing Amazon Bedrock and integrated with the Canvas Learning Management System (LMS), was designed to significantly streamline the process of providing assignment feedback. The core objectives were to reduce the workload on educators, enhance the consistency of feedback, and crucially, maintain the instructor’s integral role in the review and approval process.
At the EdgeCon spring session, Wong was joined by Mariola Pogacnik, another Slalom director, to delve deeper into the AFIA project and discuss how educational institutions can effectively leverage AI to support teaching and learning while adhering to strict ethical boundaries.
"Edge is an amazing platform for sharing best practices and sharing inspiration," Wong remarked. "What I appreciated about EdgeCon’s spring session was being able to share our recent implementation of generative AI for academic feedback at UCLA’s Anderson School of Management. I think the session resonated with attendees because it moved beyond theory and into a concrete example of how institutions can translate data and AI investments into measurable value."
The session provided attendees with a tangible understanding of the AFIA project, detailing not only the strategic intent but also the essential guardrails and the technical architecture. This included specifics on the large language model (LLM) platform employed and its seamless integration with the university’s LMS. The ability to pair strategic vision with such detailed technical specifics proved invaluable for attendees seeking to grasp the practical realities of AI implementation.
"We were able to describe not just the strategic intent, but also the guardrails and the technical architecture, including the large language model (LLM) platform and how it was integrated with the university LMS. Being able to pair strategy with that level of specificity helps people understand what’s actually possible," Wong elaborated.
Culturally Aware Design for Resonant Solutions
The response from EdgeCon attendees validated the importance of designing AI solutions that are deeply aligned with the unique culture and operational realities of higher education institutions. Wong highlighted that a particularly positive reception was given to the intentional design of the AFIA solution around higher education culture.
"One of the things that received positive feedback was how intentionally the solution was designed around higher education culture," shared Wong. "We were explicit about the current social contract between students and institutions, which is why we kept the instructor as the human in the loop and ensured outputs were fully editable and correctable."
This "human in the loop" approach, ensuring that instructors retain full control over feedback and that AI-generated outputs are editable and correctable, directly addresses common concerns about AI potentially diminishing the role of educators or compromising the quality of academic assessment. It underscores a commitment to augmenting, rather than replacing, human judgment and expertise.
The Edge community, through events like EdgeCon, fosters an environment where such shared insights can catalyze broader thinking and accelerate innovation across the academic sector. The goal is to empower instructors, not to supplant their critical judgment.
"The reactions in the room showed that when solutions are designed with that cultural awareness, they resonate much more deeply than technology alone," Wong observed. "Our partnership with Edge reflects a broader commitment to sharing practical innovation and real-world lessons across the higher education community and ensuring AI is implemented in ways that strengthen human connection, empower educators, and help institutions better serve the people at the center of learning."
Implications for the Future of Higher Education AI Adoption
The Slalom and UCLA Anderson case study offers several critical implications for the broader adoption of AI in higher education:
- Strategic Alignment is Paramount: AI initiatives must be directly linked to an institution’s core mission and demonstrate clear business or academic value. A focus on quantifiable outcomes is essential for justifying investments and driving sustainable adoption.
- Data as the Bedrock: The success of any AI strategy hinges on the maturity and robustness of an institution’s data infrastructure. Universities must prioritize building a strong data foundation before scaling AI applications.
- Partnerships for Acceleration and Insight: Consulting partners can play a vital role in accelerating AI adoption, overcoming implementation hurdles, and providing invaluable cross-industry perspectives that enrich decision-making.
- Ethical Considerations and Human-Centric Design: AI solutions must be developed with a deep understanding of higher education culture and ethical considerations. Maintaining human oversight and ensuring that technology serves to empower, rather than replace, human expertise is crucial for gaining trust and achieving widespread acceptance.
- From Pilots to Scalable Impact: The focus needs to shift from demonstrating feasibility through numerous pilots to achieving demonstrable, measurable impact through strategically deployed AI solutions.
As higher education continues its journey into the AI era, the lessons learned from initiatives like the UCLA Anderson AFIA project will be instrumental in shaping a future where AI serves as a powerful catalyst for enhanced student success, operational efficiency, and a more connected and impactful learning environment. The emphasis on rigor, data, strategic partnership, and culturally aware design provides a robust framework for institutions seeking to harness the transformative potential of artificial intelligence responsibly and effectively.
