Key points:
- AI readiness is all about balancing priority areas evenly from day one
- When AI’s architects ask to slow down, education must speak up
- Your institution’s AI strategy needs instructional designers
- For more news on AI readiness, visit eCN’s AI in Education hub
Four years after ChatGPT changed the conversation around AI, many higher education institutions are still asking how they should adopt AI. A more useful question is whether their systems, data, and people are ready to support it.
That distinction matters because AI adoption is already outpacing institutional readiness, with 94% of higher education professionals surveyed reporting that they had used AI tools for work within the past six months, while only 54% were aware of policies or guidelines governing that use.
AI did not create higher education’s fragmentation. It is simply exposing what has been building for decades. Institutions have operated across disconnected systems – but that’s not how AI operates. In response, some leaders may assume they need to replace or upgrade core platforms to support AI. A new SIS. A new ERP. None of those are the case. A cognitive operating layer is the missing piece. A smart data “fabric” that weaves data from multiple systems together so AI can traverse it and reason with it. All without replacing legacy systems like the SIS, ERP and CRM.
Five priorities can help institutions move forward with greater focus.
1. Create Alignment Around a Shared Direction
AI readiness begins with institution-wide agreement about why AI matters and what the institution hopes to accomplish with it. Without that direction, departments will purchase tools or launch experiments independently, adding another layer of complexity to an already fragmented environment.
There has to be crystal clarity on why you are taking on AI, and how you will go about adopting it. Is it teaching and learning first? Or is it the campus operation? It can’t be both. Why start with one? Why is it prudent to wait for the other? Create the direction and alignment first.
2. Establish a Clear Process for AI Initiatives
Traditional institutional decision-making processes may unfold over several semesters or even years. AI raises immediate questions about teaching, learning, operations, and employees’ roles, so institutions need a way to respond thoughtfully without letting every proposal stall.
That starts by clarifying who should participate in evaluating AI initiatives, who has the authority to approve them, and what criteria will guide those decisions. A defined process can also help institutions consistently assess considerations such as privacy, security, accessibility, and potential impact.
The goal is not to bypass shared governance or accelerate decisions at the expense of careful review. It is to make participation and accountability clear enough that promising ideas can move forward while the institution maintains appropriate oversight.
3. Understand the Data Foundation
AI cannot produce reliable results from data an institution cannot locate, interpret, or trust. Leaders do not need to become data engineers, but they should know where critical information lives, which systems are considered authoritative, and whether data can be used securely across institutional boundaries.
This assessment should include the unofficial processes employees rely on every day. Spreadsheets and homegrown tools may contain valuable institutional knowledge that never made it into a central system. Surfacing those practices is a necessary step toward understanding how the institution actually operates. Then figure out how all that structured and unstructured data will come together for AI to reason over. Before you buy a single agent or deploy a single bot, resolve this first.
4. Prioritize a Small Number of Use Cases
If every department launches its own chatbot, automation tool, or pilot, experimentation can quickly become institutional sprawl. Limited resources will be spread across disconnected initiatives, while leaders gain little insight into what creates meaningful value.
A stronger roadmap starts with a few clearly defined problems. The most promising early use cases often share three characteristics:
- The institution has access to reliable and relevant data.
- The institution can understand and manage the potential risk.
- The institution can measure the intended operational or student outcome.
Concentrating on a small number of use cases – ideally just one – gives the institution room to learn. Leaders can evaluate results, identify unintended consequences, and strengthen their implementation approach before expanding into more complex areas. Each initiative should build institutional capability, not simply add another tool.
5. Prepare People For Change
Technology is by far the easiest part of AI adoption. The heavier lift is helping people move beyond entrenched processes, fears, and knowledge gaps – while addressing legitimate concerns about their jobs and the institution’s direction.
Workforce readiness and a modern, human-centric approach to change management must therefore develop alongside the technology roadmap. That includes upskilling IT teams, preparing managers to guide change, and giving employees meaningful opportunities to have their voice heard and contribute their knowledge. People are more likely to support a new direction when they understand that they are part of the solution.
Most institutions will find that they already have some level of execution in several of these areas and targeted gaps in others. Becoming AI-ready is all about balancing these 5 areas evenly from day one.
Joe Abraham is the Co-Founder and CEO of Intellicampus.
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