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Demystifying AI: Practical Ways to Get Started with AI in Data Analytics


Our recent survey shows a lot of interest in AI, but organizations have not progressed beyond the Novice level of implementing AI in data analytics. 

As with any new technology, while there is a lot of potential value, it can be difficult to know where to start. 

In this informative webinar, hear real-world examples from others who are implementing AI at their institutions and get some practical tips on how to get started and be successful with AI.

Attendees will learn about:

  • The current state of AI in Higher Education
  • Key requirements for AI success
  • Lessons from an AI POC at UAB and Cornell College
  • Examples of AI use cases
  • Five steps to getting started with AI

Event Details

Featured Speakers:

  • Scott Sorenson, AVP, Data Operations and Business Transformation, University of Alabama Birmingham
  • Jodi Schafer, Senior Director, Berry Career Institute, Cornell College

Event Title: Demystifying AI: Practical Ways to Get Started with AI in Data Analytics

Date / Time: July 24, 2024, from 2:00 to 3:00 pm CT

Location: Zoom meeting

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AIR Forum 2024: Insights and Takeaways

AIR Forum 2024: Insights and Takeaways


The AIR Forum 2024 was a resounding success, bringing together professionals from the field of higher education analytics. As we engaged with attendees, several key themes emerged, shedding light on the current landscape and future trends in data analytics for higher education.

  1. Carnegie Mellon’s Data Lake
    1. The buzz around Carnegie Mellon University’s presentation was exciting! Attendees couldn’t stop discussing their innovative approach to data analytics. Specifically, they highlighted using Snowflake, a centralized data lake, to unify disparate data sources. This approach resonated strongly with our own Datatelligent Platform for Higher Education. Clearly, there’s a growing need for data-informed decision-making in educational institutions.
  2. AI Awareness vs. Implementation
    1. While everyone acknowledges the potential of artificial intelligence (AI), practical implementation remains cautious. Attendees expressed a desire to leverage existing data and tools effectively rather than diving headlong into AI solutions. Our recent survey on data analytics in higher education confirmed this trend; awareness and interest in AI are high, but adoption remains gradual.
  3. Balancing Choices, Costs, and Flexibility
    1. The data analytics landscape offers an array of solutions, but institutions grapple with trade-offs. Budget constraints drive the need for cost-effective options, while flexibility is crucial for accommodating future growth. Striking the right balance between affordability and scalability is a priority.

The AIR Forum 2024 underscored the importance of data-informed decision-making in higher education. As we navigate this dynamic field, let’s continue to explore innovative solutions, collaborate, and adapt to meet our institutions’ evolving needs.

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Unified Cloud Data Platform in Just 90-Days

Unified Cloud Data Platform in Just 90-Days


In today’s data-informed landscape, educational institutions face a dual challenge. They must manage vast amounts of information while ensuring seamless access and security. The promise of a unified cloud data platform—a centralized hub for data storage, processing, and analytics—holds immense potential. But can it truly be deployed within a tight 90-day window?

This article delves into the intricacies of integrating a unified cloud data platform specifically tailored for higher education. We’ll explore the critical components, address common roadblocks, and provide a roadmap to success. IT administrators, data scientists, and academic leaders must understand the nuances of this transformational journey to ensure a successful implementation.

So, fasten your seatbelt as we embark on a 90-day adventure—a sprint toward data unification that promises efficiency, insights, and a competitive edge. Let’s explore how proper planning, strategic execution, and collaboration can make this ambitious goal a reality.


First, meet with the key academic, administrative, and IT stakeholders and rank-order their priorities, needs, and desired outcomes. For example, one goal may be to improve student retention by 15%. You would then determine the data and data sets required to track each student’s retention.


Once the priorities and desired outcomes have been determined (e.g., improved student retention, analysis of the admission funnel to improve enrollment, a better understanding of enrollment, prediction of when a faculty member or advisor should engage with an at-risk student, etc.), sponsorship will be a critical success factor in this initiative. Most data initiatives fail partly due to a lack of leadership support; enlisting operational leaders to champion the project will help smooth over any obstacles you may face during the project, including obtaining the necessary funds. After you secure funding for the 90-day project, consider requesting funding for the nine months remaining in the year to develop the analytic solutions that will deliver the desired outcomes. Obtaining funding for the full 12 months is ideal to avoid going back to the well for additional dollars.

Next, select the tools and technologies needed to integrate the data and build the data platform. For data pipeline tools, consider Azure Data Factory and Logic Apps, which work well with student information systems (SIS) platforms such as Banner, Jenzabar, Workday, Colleague, Slate, and PowerFAIDS. These tools are also compatible with learning management systems (LMS) like Canvas, Blackboard, and Moodle, and data stored in operational data stores (ODS), Excel, and SharePoint. These platforms accommodate diverse data sources, including dining hall swipe data, to determine if students are socially connected. Third-party sources such as National Student Clearinghouse, IPEDS, student surveys, faculty evaluations, Google Analytics, and social data for recruitment channel analysis can also be integrated.
The number of data sources is not limited, but keep in mind that the goal is to have the data lake live in 90 days, so you will want to limit the number of data sources in this initial 90-day period.
For the cloud data platform, consider Snowflake. It works great with Tableau and Power BI because you can get insights from data sets of all sizes. Additionally, Snowflake’s pay-as-you-go model means you only pay for the storage and compute that you use, making it very cost-effective compared to traditional data warehouses.

Once the tools are connected, set up the data lake in the cloud data platform. To meet your 90-day goal and desired outcomes, take only the necessary tables from the SIS, LMS, CRM, etc., to deliver the desired analytic outcomes. Once the data is in the data lake, you can now perform data transformations, creating the datasets that will drive your analytic solutions.


As you approach the 90-day initiative’s conclusion, reconnecting with the stakeholders and leadership to share the results is crucial. Here’s a summary of the key achievements:

  • Successful Setup
    • Data Pipelines: Establish robust data pipelines.
    • Cloud Data Platform: Implemented a scalable cloud data platform.
    • Data Lake: Created a centralized data lake.
  • Data Integration:
    • Connected 2-3 key data sources,
    • Configured data pipelines to automatically refresh the data lake regularly.
  • Centralized Repository:
    • Developed a single data lake repository for the centralization and collection of data.
  • Preparation for Analytics:
    • Prepared datasets with predefined key metrics that will automatically feed into analytics solutions in the next phase (e.g., admissions funnel, enrollment trends, at-risk students, student success, etc.) running in Tableau or Power BI.

Sharing these accomplishments not only highlights the progress made but also sets the stage for the next phase of development. This will ensure continued momentum and support from leadership and stakeholders.

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Top Data Challenges in Higher Education

Top Data Challenges in Higher Education


I’ve been in my current position at Datatelligent for a little over a year as a business development representative and have talked to many different leaders in higher education. My job is to hear the data challenges that individuals might be facing and discover strategies and solutions for institutions to turn these challenges into strengths. I’ve sat in hours of meetings, webinars, and speeches. Also, I have listened to Directors of Institutional Research, Directors of Institutional Effectiveness, and CIOs spill their challenges to me and strategize what steps they need to take to put a plan in place to improve their data maturity. From my discoveries, I’ll provide the top three data challenges currently in Higher Education for 2024. Also, I want to give some tips on how to avoid these challenges based on what I’ve learned.


For one thing, if you’re currently involved in higher education or have been in the past, I’m sure you experienced data silos or have heard from fellow peers the headache it can cause daily. Here are some of the headaches:

  • Fragmented Decision-making: Data silos hinder collaboration and lead to fragmented decision-making within institutions.
  • Inefficient Reporting: Siloed data is crucial for generating accurate reports, but it is time-consuming and error-prone.
  • Personalization Obstacles: Integrated data is crucial for personalized student experiences, but silos prevent the practical tailoring of services.
  • Strategic Implications: Addressing data silos requires breaking down barriers, investing in infrastructure, and fostering a data-driven culture.

In 2024, we want our day-to-day operation to go smoothly and get all the data we need at our fingertips. Data silos have been throwing a wrench in this for a long time and are a top three challenge, I hear.

Equally as crucial as siloed data, data security and privacy are critical aspects of higher education. As institutions collect and manage vast amounts of student and organizational data, safeguarding this information becomes paramount. Cyber threats can put a lot of stress on schools in order to protect information from being stolen. Data security involves safeguarding institutional assets through access controls, encryption, firewalls, and regular audits. On the other hand, data privacy ensures confidentiality and compliance with regulations like GDPR and FERPA. Transparency and ethical handling are key—like sealing letters in envelopes and treating data responsibly. Together, data security and privacy create a well-guarded digital library, allowing students, faculty, and staff to learn and collaborate without fear.

Lastly, you collect all the data you need, but using it effectively is another challenge. Many schools fail to transform the data into meaningful actions that drive positive outcomes. It requires defining clear objectives, selecting the right analytic tools, and translating insights into actionable steps. Balancing ethical considerations and fostering a data-driven culture are essential for success. Turning data into action is mentioned when talking to many individuals in higher education who don’t know the correct steps to take.


As I’ve mentioned, I have listened to countless meetings and read about many possible solutions for these challenges. Some things I recommend for becoming a more data-informed institution are:

  • Consolidate Data: Bring together data from disparate tools and datasets into a central data warehouse with good reporting tools. We at Datatelligent can help with this and recommend using a cloud-based data warehouse called Snowflake. Snowflake has great elastic scalability, robust security features, and seamless integration with popular BI tools and data services. This allows institutions to gain a more holistic view of learning processes and support mechanisms. How universities can break down data silos and generate new insights | THE Campus Learn, Share, Connect (
  • Advanced Reporting Tools: Advanced reporting tools like Tableau and Power BI can be beneficial for summarizing, visualizing, and comparing data. These tools provide better insights than standard tabular reports. At Datatelligent, we can help with this by having our own Datatelligent Platform for Higher Education that can consolidate data and provide data visualization tools like Tableau and Power BI.
  • Privacy Offices: Establish privacy offices and full-time privacy positions within institutions. Develop and improve privacy and practices.
  • Compliance Management: Manage Compliance across campus operations by understanding and adhering to privacy legislation and regulations.
  • Data-Driven Thinking: Leverage data analytics by encouraging data-driven thinking. Areas where data analytics can be beneficial include personalized learning experiences, academic analytics, and critical care assessments. Towards Evidence-Based, Data-Driven Thinking in Higher Education | SpringerLink
  • Optimize Data Strategy: Focus on data governance, create data leadership, establish actionable data strategies, utilize a cloud data platform to centralize data, create an analytics team, and implement data visualization tools like Power BI and Tableau.
  1. Burns, Sean. “The Evolving Landscape of Data Privacy in Higher Education.” Educause, November 19, 2020.
  2. Komljenovic, Janja. “The future of value in digitalized higher education: why data privacy should not be our biggest concern.”  Springer Link, November 19, 2020.
  3. Gibson, David. “Big Data in Higher Education: Research Methods and Analytics Supporting the Learning Journey.” Springer Link, July 5, 2017.
  4. Florea, Diana and Florea, Silvia.  “Big Data and the Ethical Implications of Data Privacy in Higher Education Research.” MDPI, October 21, 2020.
  5. Times Higher Eduation. “Five actions for data-led transformation in Higher Education.”
  6. Masterson, Douglas, PhD, Davis, Christopher M., PhD, and Carbonaro, Suzanne, MEd. “Transforming Data into Meaningful Information.” The Society for College and University Planning, Jan-Mar 2023.
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Practical Insights from an AI/ML Student Retention Pilot


Join us to explore how Cowley College, in partnership with Datatelligent is integrating machine learning into their established Student-at-Risk/Student Retention solutions.  This session will deliver actionable insights on enhancing student retention strategies through AI.

Attendees will learn:

  • Understand how Datatelligent incorporated machine learning into the existing Student-at-Risk solution, focusing on identifying the key features driving student re-enrollment.
  • How Cowley’s staff and advisors are using ML outputs to prioritize support for students most at risk.
  • Key takeaways and initial lessons from the joint pilot, highlighting both successes and areas for improvement.

Event Details

Event Title: Practical Insights from an ML/AI Student Retention Pilot

Date / Time: May 8, 2024, from 12:00 to 1:00 pm CT

Location: Zoom meeting

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Understanding Trends in Undergraduate Degree Attainment



The pursuit of higher education is a critical milestone for countless individuals around the world. Whether it’s an associate degree, a bachelor’s degree, or a specialized certificate, earning an undergraduate credential opens doors to career opportunities, personal growth, and societal impact. In this blog post, we delve into the latest findings from the National Student Clearinghouse Research Center’s report on undergraduate degree earners for the academic year 2022-23.


The report reveals a concerning trend: the number of undergraduate degree earners has declined for the second consecutive year. In the 2022-23 academic year, there was a 2.8% decrease, resulting in 99,200 fewer graduates compared to the previous year. This decline raises questions about the factors contributing to this downturn.

First-time completers, who represent 73.3% of all graduates, experienced a decline of 73,600 individuals. These are students who successfully complete their degree requirements for the first time. The 2.8% decrease in this group reflects broader challenges in higher education. As institutions adapt to changing demographics, economic shifts, and technological advancements, understanding the needs of first-time completers becomes crucial.

While overall degree attainment declined, there’s a silver lining: the number of students earning certificates reached a ten-year high. Certificates, often shorter and more focused than traditional degrees, provide specialized skills and knowledge. The report attributes this increase to a 6.2% rise in first-time award earners. Whether in fields like healthcare, information technology, or skilled trades, certificates offer a pathway to employment and career advancement.

Despite the surge in certificates, associate and bachelor’s degrees remain foundational. These degrees continue to be valued by employers and serve as stepping stones for further education. However, institutions must address challenges such as affordability, access, and student support to reverse the decline in degree earners.

To combat this trend, educational leaders and policymakers can consider the following strategies:

  • Strengthening Student Support and Flexibility:
    • Support systems: Enhance academic advising, tutoring, and mental health services, and establish mentorship programs to support students throughout their educational and career journeys.
    • Flexible learning options: Expand online and hybrid courses, and offer more classes during evenings and weekends to accommodate non-traditional students and those with additional responsibilities.
  • Improving Educational Pathways and College Readiness:
    • Short-term and stackable credentials: Develop certificate programs aligned with industry needs and offer credentials that can be built upon towards a degree.
    • College readiness initiatives: Collaborate with high schools to ensure students are prepared for college and offer bridge programs to ease the transition to higher education.
  • Enhancing Financial Accessibility:
    • Increase scholarship and grant awareness: Promote the availability of scholarships and grants to help reduce financial barriers for prospective students.
  • Adopting Data-Informed Strategies and Promoting Lifelong Learning:
    • Data-driven approaches: Use analytics to identify at-risk students early and tailor programs to meet diverse needs.
    • Lifelong learning culture: Encourage continuous education for adult learners and partner with businesses to support education benefits and career advancement.

The decline in undergraduate degree earners is a multifaceted issue that requires a collaborative and strategic response. By enhancing financial aid, strengthening support systems, and promoting flexible learning options, we can create a more inclusive and supportive educational environment. Additionally, by fostering a culture of lifelong learning and utilizing data-driven approaches, we can ensure that higher education remains relevant and accessible to all. As we work towards these goals, we can reverse the current trend and pave the way for a brighter future in higher education.

  1. National Student Clearinghouse Research Center. “Undergraduate Degree Earners Report: Academic Year 2022-23.” April 11, 2024. “,by%2073%2C600%20(%2D2.8%25).
  2. Weissman, Sara. “Degrees Earned Fall Again, Certificates Rise.” Inside Higher Ed, April 11. 2024.
  3. Katharine Meyer “The case for college: Promising solutions to reverse college enrollment declines.” Brookings Institution, June 5, 2023.
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Caution: AI Approaching Higher Education



Interest in Artificial Intelligence (AI) is growing across all industries, spurred by daily advancements that showcase its potential to enhance efficiency and predict trends. Higher education institutions, faced with declining enrollments in part due to shifting demographics, are especially interested in using AI to improve their operations around student recruitment and retention. But before colleges and universities start using AI, it is crucial to consider the responsible incorporation of AI, ensuring its use enhances existing processes while mitigating potential pitfalls.

ethical considerations

Using historical data by AI introduces the risk of perpetuating existing biases, a challenge highlighted by Amazon’s reevaluation of an AI recruitment tool biased against female candidates¹. Similarly, the application of AI in risk assessments within the legal system² has faced scrutiny for racial biases. These examples underline the urgent need for comprehensive AI governance frameworks, discussed during the March 2024 Data Analytics Alliance for Higher Education meeting, that prioritize ethical data use and rigorous oversight to combat bias.

AI “Hallucinations” and Misinformation
The phenomenon of AI “hallucinations”³ — baseless but authoritative assertions made by AI systems — has raised significant concerns regarding the use of tools like ChatGPT. Examples such as Google’s Bard AI misrepresenting facts about the James Webb Space Telescope⁴ and Microsoft’s Bing chatbot displaying unpredictable behavior and professing “love” for a New York Times columnist⁵ highlight the risk of misinformation. These incidents reinforce the importance of strong training data curation to mitigate the spread of misinformation in educational settings.

The deployment of AI in analyzing large datasets accentuates privacy and security concerns, particularly around the potential for de-anonymization. AI’s ability to infer sensitive personal information from non-sensitive data⁶ introduces new data protection challenges. Therefore, adopting AI technology requires robust privacy safeguards, including secure platform designs and ethical data handling practices.

A foundational principle for effective AI utilization is the term “Garbage in, garbage out,” emphasizing the critical role of data quality. Higher education institutions often rely on data from student information systems (SIS) and learning management systems (LMS) to train AI models. However, these sources frequently contain incomplete or inaccurate data, potentially leading to unreliable AI outputs.

To navigate these challenges and lay the groundwork for effective AI implementations, the use of a Unified Data Platform (UDP) is vital. A UDP consolidates and harmonizes data from diverse systems, ensuring AI models are trained on high-quality, comprehensive datasets. Key characteristics of an effective UDP include:
  • Centralized Data: Aggregates data from various institutional systems and external sources, providing a complete data ecosystem for accurate AI analysis.
  • Scalability: Offers a scalable infrastructure to accommodate increasing data volumes and complex AI use cases.
  • Robust Security Measures: Incorporates advanced security features to protect sensitive data, ensuring privacy and compliance with data protection laws.
  • AI-Ready Infrastructure: Facilitates the deployment of AI by ensuring the platform and tools are primed for AI applications, supporting advanced analytics, and making data AI-ready.

In response to growing inquiries from our higher education customers interested in AI, Datatelligent recommends that customers consider its Datatelligent Platform for Higher Education, which leverages a UDP to develop standard analytic solutions that most colleges and universities need. Karl Oder, one of the Chief Architects of the platform, talked about what we are doing with the platform. “We’re busy creating several AI prototypes with our partner, Snowflake, using the AI-Ready tools they provide.”

In addition to getting your data “AI-ready” by establishing a UDP, schools should also spend time prepping for the AI Project⁷, starting with selecting the right use case. For higher education institutions, Datatelligent has developed prototypes on our platform that can accelerate this process, including:

  • Admissions and enrollment – predictive factors that will influence admissions and student enrollment projections
  • Student success and retention – identifying student success characteristics and predicting students at risk of leaving.
  • Graduation and program success – predictive factors driving graduation rates and overall program success.

Integrating AI in higher education calls for a balanced, thoughtful approach that acknowledges AI’s transformative potential alongside its challenges. By addressing issues of bias, misinformation, privacy, and ethical governance through strategic planning, institutions can harness AI to enhance educational outcomes and operational efficiency. Central to this endeavor is establishing a Unified Data Platform, ensuring data integrity, and laying a solid foundation for the responsible use of AI technologies.

  1. Dastin, Jeffrey. “Insight – Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women.” Reuters, August 10, 2018.
  2. Angwin, Julia , Surya Mattu, and Lauren Kirchner. “Machine Bias.” Pro Publica, May 23, 2016.
  3. “What Are AI Hallucinations?” IBM.Com. February 1, 2024.
  4. Mihalcik, Carrie. “Google ChatGPT Rival Bard Flubs Fact About NASA’s Webb Space Telescope.” CNET, February 9, 2023.
  5. McMillan, Malcolm. “Bing ChatGPT Goes off the Deep End — And the Latest Examples Are Very Disturbing.” Tom’s Guide, February 17, 2023.
  6. Ahmed, Hafiz. “Challenges of AI and Data Privacy—And How to Solve Them.” @ISACA 32, (2021).
  7. Sassi, Steve. “AI Project Prep for Higher Education.” Datatelligent.Ai. March 26, 2024.
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AI Project-Prep for Higher Education



After almost a year of weighing the pros and cons of AI at your institution, creating an action plan, and cross-collaborating with your peers from other institutions, your team has finally decided that AI is the future for recruiting, retaining, and ensuring your students’ success. Congratulations! Before answering one of the dozen daily emails in your inbox from AI software and services vendors, you should first take some cautious pre-project steps if you are serious about the success of your future AI project. Here are six AI project prep steps you should take before setting up any meeting with an AI software or service provider:

1 – choose and define your use cases

You probably already have the high-level Future Action Roadmap¹ if you’ve come this far, but you can’t, in project terms, “boil the ocean” with a “big bang” AI project covering everything on the map. No Higher Education institution has that kind of time or money. It’s time to pick one to three high-profile use cases where the need is most urgent. Perhaps it’s all about predicting student behavior and outcomes so you can sooner identify students at risk. Or maybe your school would like to personalize student learning and support services to help increase retention. It is very likely your marketing team has been in touch with you about how AI will help them segment and target prospective students for recruitment and deliver personalized and engaging marketing campaigns that can increase awareness, interest, and conversion rates for enrollment.

In March 2024, Scott Sorenson, Executive Director, Data Privacy & Analytics from the University of Alabama at Birmingham, shared how they built a pilot using Salesforce’s AI-powered Tableau Pulse at the Data Analytics Alliance for Higher Education. They focused on use cases for the Marketing and Advancement Departments. Right away, they involved the participating departments, and the team at Tableau helped them build the business case for approval. The Results: Success. The Advancement team liked it and will include it on their IT roadmap, and the Marketing team loved it and wanted it yesterday. Some lessons learned from the UAB pilot:

  • Get the interested teams involved early and define the roles each will play.
  • Develop air-tight use cases founded on strong business reasons.
  • Even if business reasons are solid, leave plenty of time for executive iterations and approval.
  • Developing AI and Data Governance will take twice as long as you think it will.
Lessons learned on the UAB AI pilot segue perfectly into perhaps the most crucial pre-project activity: AI Governance and Security. This should be a strong focus at the beginning of your AI journey, as it is foundational for your institution’s success. Some things to consider:
  • Ethical Considerations: All policy decisions should align with ethical principles and the DNA of who you are as an institution. Ensure transparency, fairness, and equity. Institutional leaders (Chancellor/President, Chief Academic Officer, Chief Information Officer) are pivotal in driving ethical AI practices.
  • Senior Management: Define roles, responsibilities, and accountability related to AI governance and ensure that senior management oversees AI initiatives.
  • Risk Assessment and Iteration: Regularly assess risks associated with AI implementation and adjust policies accordingly.
  • Data Security and Privacy: Data handling and privacy protection will help keep your student and staff data safe. Mistakes made with the mishandling of private data can have serious consequences, so It’s imperative to put guidelines and best practices in place for collecting, storing, and processing data used in AI systems.
  • Transparency and Accountability: Not surprisingly, AI has the same biases as its human counterparts. Make AI algorithms and decision-making processes transparent with regular reviews, carefully define responsibilities, and hold accountable AI system performance and outcomes. 

The legacy systems used by your staff for the past two decades need to be assessed to determine if they are truly ready for AI. Do you have a Unified Data Platform to collect, store, process, analyze, and share your data with data visualization tools? How reliable, relevant, complete, and diverse is your data? Work may need to be done with Data systems before choosing the AI solution. The Data sources you will need will depend heavily on the use case. Here are just some Data systems commonly used for AI:

  • Student Information Systems (SIS) – Holding admissions, enrollment, grades, and financial aid information is often critical for Student Success analysis.
  • Learning Management Systems (LMS) – Platforms like Canvas, Blackboard, or Moodle will facilitate online learning, course management, and distribution of educational content.
  • Human Resource Systems – These systems handle employee data, payroll, benefits administration, and recruitment processes.
  • Vendor-based Systems – Specialized software for recruitment, student success, assessment, space management, and more.
  • External Data Sources – Registers, databases with scientific information, and other external systems that can support enrollment, recruitment, marketing, and student success decisions.
These steps are in this order for a reason. Not until the first three steps have been started and the first draft has been completed can you even begin to make an informed decision about AI technology that will bring your use cases to life. The questions to ask:
  • What type of AI solution best suits your problem or opportunity? The areas with the biggest impact on securing your institution’s future success are typically Student Success, Enrollment, and Retention. The market is catching up quickly with AI offerings to support these initiatives.
  • Do you want to build your own solution from scratch or use an existing solution from a vendor or a partner? The old rule of buy to compete, build to differentiate still applies to AI Projects. Buying off-the-shelf (OTS) AI solutions should be where you start. It is still the lowest-cost entry to AI. Building your own should be for ambitious projects where no other OTS solution exists for an identifiable, mission-critical, market-differentiating AI use case.

At the start of the UAB AI pilot project, after the use cases, governance, data, selected solution, and approvals were in order, Sorenson met with his Marketing and Advancement teams to define what metrics they wanted to see. From there, he determined what data was needed for the metrics. He then asked them what success looked like to them. UAB implemented a pilot, but it’s no different from deploying the actual AI Solution. In fact, it’s more critical.

Defining KPIs will determine how you build the solution. Some common metrics used in higher education include the following:

  • Number of student minutes on a website – Does it lead to a greater conversion percentage to enrollment?
  • Year over Year (YoY) percentage of resources used by at-risk students – Does it correlate to YoY percentage of Student retention?

Setting these KPIs will guide improvements toward success and ensure the Leadership Team that your investment in AI is seeing the hoped-for impact on enrollment, retention, and student success. What are the key performance indicators (KPIs) you will use to track its progress and results?


Before you even select a vendor to implement, how will you deploy your AI solution in your institution? You’ve determined what data from which systems are needed, but now it’s time to consider how you will integrate and unify your data into a usable format for AI Analytic consumption.

Keep in mind, just because the letters A and I are in front of your project, it is still an IT project, and the best practices for this haven’t changed much in the past couple of decades. Bring all the lessons learned at your institution, your institution’s developed best practices, and industry PMO Best Practices² to this project, as you would any project.


1. Jenay Robert and Nicole Muscanell, 2023 Horizon Action Plan: Generative AI (Boulder, CO: EDUCAUSE, 2023) 2023 EDUCAUSE Horizon Action Plan: Generative AI

2. Abudi, G. (2011). Developing a project management best practice. Paper presented at PMI® Global Congress 2011—North America, Dallas, TX. Newtown Square, PA: Project Management Institute.

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