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When strategy leads, AI delivers institution-wide value

AI is only as effective as the thinking behind it. Institutions that lead with strategy are the ones seeing meaningful results. That means asking harder questions about culture, outcomes, and what it truly means to be AI-enabled as it relates to your institutional mission.

AI in action, from the classroom to the lab

Our recent survey of higher education leaders shows how AI is taking shape across the institutional mission. Colleges and universities are weaving AI skills into academic programs, preparing for increased adoption across research, and navigating heightened data privacy and security risks.

0%

view data privacy and security vulnerabilities as the top AI-related risk

0%

see AI adoption increasing across research in the next three to five years

0%

have added at least one AI-related skill to academic programs

How can institutions get the most value from AI?

AI's greatest value comes from redesigning how work gets done, not automating existing processes. When integrated into a reshaped operating model that takes institutional priorities, data, and technology into account, AI improves enterprise-wide decision making, workflows, and collaboration.

Decide where AI is worth it

Separate AI uses with real institutional value from those without it.
Build a road map tied to institutional priorities and mission.
Set the policy, governance, and privacy protections that hold up under scrutiny.
Understand the full cost of AI adoption and plan how to fund it.

Get the data ready

Assess whether your data can support the AI you want.
Resolve data quality, access, and ownership challenges that stall AI projects.
Establish data governance built for AI, not just for reporting.
Build the foundation that ensures it serves more than one use case.

Make AI work in your systems

Turn on and configure the AI capabilities already built into your enterprise systems.
Design AI into new platform implementations from the start.
Add AI to systems already live, without restarting the implementation.
Build custom AI solutions where your systems stop short of what the institution needs.

Redesign how the work gets done

Rethink administrative and research administration processes around what AI can do now.
Remove manual effort from high-volume, repetitive work.
Plan the workforce implications, including roles, skills, and staffing.
Measure whether the redesign delivered the capacity or savings it promised.

The most successful institutions are looking and thinking about AI as a part of the process rather than layering it onto broken ones.

Sonia Singh, Managing Director

College and university AI consulting built around your priorities

AI priorities look different across higher education. A tier-one research (R1) university, a four-year institution, and an academic medical center each operate within distinct missions, environments, and constraints. At the same time, the opportunities and challenges vary across functions, from research and workforce development to finance, operations, and technology. The common denominator is a strategy that considers all the factors driving demand for AI.

By institution:

R1 and other research universities: AI benefits include building enterprise-level solutions for sponsored programs management, grant compliance, and research portfolio analytics, with governance designed to hold up under audit.
Academic medical centers: Clinical and research environments carry unique data privacy, regulatory, and compliance requirements that generic AI solutions were not built for. Effective AI governance here requires simultaneous alignment across research, clinical, and administrative functions.
Four-year colleges and universities: Revenue diversification efforts require labor market intelligence, AI-enabled curriculum design, and a strategy built to support institutional capacity.

By function:

Continuing education and workforce development: Succeeding in this space requires AI-enabled program design, real-time market demand analysis, and employer partnership strategies that connect curriculum directly to workforce needs.
Finance and operations: The work here is financial modeling, scenario planning, and operational efficiency analysis that gives leaders the data to make sequenced, defensible investment decisions.
Technology and digital systems: Addressing legacy infrastructure requires governance frameworks, enterprise architecture design, and a modernization sequence that reduces compliance exposure before AI is layered on top.
Boards and senior leadership: Readiness assessments, institutional risk reviews, and benchmarking that surfaces what peers are doing help leaders make better decisions.

Focus on the capabilities you want to build and the outcomes you're pursuing and let the technology be an enabler, not a driver, of that work.

Mark Cianca, Principal

Questions to shape your higher education AI strategy

There is no single road map for AI. The correct approach depends on your mission, strategy, resources, and operating model. The following questions can help you establish a shared starting point for determining where AI fits and shaping a strategy with clarity and confidence.

How could AI and its rapidly advancing capabilities reshape our strategy, competitive position, and brand identity?
What are the business model implications of AI? How will it shape our ability to generate revenue, and how will it change the costs required to earn it?
Given those implications, what resources should we allocate to AI, and where should we prioritize investment?
How should we organize to build AI capabilities and scale them appropriately? 
What governance is needed to guide AI investments, activities, and decision making across the enterprise?
How could these choices shape perceptions of our organization, our role in the community, and our brand, and what might that require us to reconsider?

Questions to shape your higher education AI strategy

Insights and client impact

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Connect with a higher education AI expert

Mark-Cianca

Mark Cianca

Principal

,

Strategy and Operations

Mark Cianca has over 35 years of experience as a higher education leader, with an extensive portfolio of accomplishments in information technology, business transformation initiatives, enterprise resource planning deployments, strategic planning and leadership development.

Geoffrey-Corb

Geoffrey Corb

Managing Director

,

Education AI & Innovation Lead

Geof has over 20 years of experience driving technology-enabled growth and operational excellence, in provider and consumer roles, for higher education and healthcare institutions. From technology implementations to transformed business processes, he helps clients deliver individual, team and organizational success.

Contact Geoffrey

Alexandra-Faklis

Alex Faklis

Managing Director

,

Strategy and Operations

For more than a decade, Alex has helped higher education clients assess, re-imagine, and transform student and alumni experiences.

Fanny-Ip

Fanny Ip

Chief AI Officer

Fanny has over 20 years of experience guiding institutions in many industries through business transformation, customer experience improvement, and automation maturity.

Sonia Singh

Sonia Singh

Managing Director

Sonia has more than 16 years of experience assisting academic medical centers, universities, and hospitals with strategic planning, the organizational alignment of research functions, operational effectiveness, and integrations between institutions to achieve strategic and financial goals.

Laura-Zimmerman

Laura Zimmermann

Managing Director

Laura helps higher education and research institutions advance their missions through enterprise digital transformation, artificial intelligence, technology strategy, and operational improvement. She brings more than 25 years of experience helping institutions align technology investments with strategic priorities, optimize operating models, and accelerate long-term transformation goals.