The Relational University: A Vision for AI in Higher Education

Universities have a set of deep, structural problems that long predate artificial intelligence. Student engagement is thin. Bureaucratic barriers are thick. Advising is reactive and understaffed. Instruction runs on a lecture-midterm-lecture-final cycle that compresses all assessment into a few high-stakes moments. Faculty who entered the profession to work with ideas and people spend most of their time on mechanical tasks. Students navigate systems designed for institutional convenience, not for learning. These are not new complaints. They are defining failures that decades of reform have failed to resolve.

AI offers a way to resolve them without reducing the workforce. The vision is direct: universities become more relational, not more automated. Faculty gain time for personal contact with students because machines absorb the mechanical parts of teaching. Instruction improves because AI makes genuine formative assessment possible for the first time, using results of previous assignments to design better subsequent ones, something the theory always called for but practice never had capacity to deliver. Advising shifts from reactive to proactive. Administrative barriers dissolve for routine matters, freeing human staff to handle complex ones. The university does not shrink. It refocuses.

Advising is limited by labor. An advisor with a caseload of 800 students cannot know any of them. The result is a pull model: students must identify their own problems and seek help. Those who most need support are least equipped to ask for it. AI can change this by detecting risk patterns across enrollment, academic, and financial data, flagging students who are drifting before they reach crisis. But the flag is not the intervention. The intervention is a human advisor who calls, meets, and listens. If we build this well, AI handles the surveillance and triage; advisors handle the relationship. The same constraint limits course scheduling. We know that certain combinations of courses and workloads predict failure. We know that work schedules, commute patterns, and academic preparation all interact with course selection. But no human advisor can model these interactions across hundreds of students each semester. AI could make risk visible at the point of decision, so that advisors and students choose schedules with open eyes. The underlying problem is the same: knowledge exists that could prevent failure, but the labor required to apply it at scale has always exceeded institutional capacity.

Instruction faces the same bottleneck. The lecture-midterm-lecture-final model persists because faculty lack time for anything better. Substantive weekly feedback on student work is physically impossible at current teaching loads. So we compress assessment into a few high-stakes moments and call it evaluation. AI could absorb the mechanical first pass of formative feedback, freeing faculty to review, personalize, and respond to individual learning trajectories. More than that: formative assessment theory has always held that results of one assignment should inform the design of the next. No instructor teaching over a hundred students has time to analyze patterns across a set of papers and redesign the subsequent task accordingly. AI could close that loop, identifying collective strengths and weaknesses and drafting assessments calibrated to what students actually need next. The instructor reviews, adjusts, and teaches. If we get this right, the feedback cycle that formative assessment always promised becomes operational for the first time.

The downstream effect on faculty time could be substantial. Time recovered from mechanical grading and routine administration could go toward direct contact with students: structured individual meetings, small-group conversations about ideas rather than grades. The labor has always been there. It has just been allocated to the wrong tasks. Similarly, administrative staff currently spend large portions of their time answering routine questions with definitive answers. AI can handle those instantly, not to eliminate positions, but to redirect human attention toward complex cases that require judgment.

None of this is guaranteed. The technology exists, but institutional habits are strong. Universities could just as easily use AI to cut costs, reduce headcount, and further depersonalize the student experience. The reinvented university, more relational, more responsive, more human, will only emerge if we deliberately choose to augment human labor rather than replace it. The defining question is not what AI can do. It is what we decide to do with the capacity it creates. If we play our cards right, universities could become what they have always claimed to be: places where education happens between people, supported by systems that finally serve that purpose rather than obstructing it.

Custom Bot Segregation and the Problem with a Hobbled Product

CSU’s adoption of ChatGPT Edu is, in many ways, a welcome move. The System has recognized that generative AI is no longer optional or experimental. It is part of the work students, researchers, and educators do across disciplines. Providing a dedicated version of the platform with institutional controls makes sense. But the way it has been implemented has led to a diminished version of what could have been a powerful tool.

The most immediate concern is the complete ban on third-party custom bots. Students and faculty cannot use them, and even more frustrating, they cannot share the ones they create beyond their own campus. The motivation is likely grounded in cybersecurity and privacy concerns. But the result is a flawed solution that restricts access to useful tools and blocks opportunities for creativity and professional development.

Some of the most valuable GPTs in use today come from third-party developers who specialize in specific domains. Bots that incorporate Wolfram, for instance, have become essential in areas like physics, engineering, and data science. ScholarAI and ScholarGPT are very useful in research, and not easy to replicate. There are hundreds more potentially useful tools. Not having access to those tools on the CSU platform is not just a minor technical gap. It is an educational limitation.

The problem becomes even clearer when considering what students are allowed to do with their own work. If someone builds a custom GPT in a course project, they cannot share it publicly. There is no way to include it in a digital portfolio or present it to a potential employer. The result is that their work remains trapped inside the university’s system, unable to circulate or generate value beyond the classroom.

This limitation also weakens CSU’s ability to serve the public. Take, for example, an admissions advisor who wants to create a Custom bot to help prospective or transfer students explore majors or understand credit transfers. The bot cannot be shared with anyone outside the CSU environment. In practice, the people who most need that information are blocked from using it. This cuts against the mission of outreach and access that most universities claim to support.

Faced with these limits, faculty and staff are left to find workarounds. Some are like me and now juggle two accounts, one tied to CSU’s system and another personal one that allows access to third-party tools. We have to pay for our personal accounts out of pocket. This is not sustainable, and it introduces friction into the very work the platform was meant to support.

Higher education functions best when it remains open to the world. It thrives on collaboration across institutions, partnerships with industry, and the free exchange of ideas and tools. When platforms are locked down and creativity is siloed, that spirit is lost. We are left with a version of academic life that is narrower, more cautious, and less connected.

Of course, privacy and security matter. But so does trust in the people who make the university what it is. By preventing sharing and disabling custom bots, the policy sends a message that students and faculty cannot be trusted to use these tools responsibly. It puts caution ahead of creativity and treats containment as a form of care.

The solution is not difficult. Other platforms already support safer modes of sharing, such as read-only access, limited-time links, or approval systems. CSU could adopt similar measures and preserve both privacy and openness. What is needed is not better technology, but a shift in priorities.

Custom GPTs are not distractions. They are how people are beginning to build, explain, and share knowledge. If we expect students to thrive in that environment, they need access to the real tools of the present, not a constrained version from the past.

The Invasive Innovation, a Case of AI in Education

Disruptive innovations, as defined by Clayton Christensen, typically emerge at the margins before gradually overtaking established players. In contrast, artificial intelligence in education is not a slow-moving force of competitive displacement. It is an invasive innovation—spreading far beyond the predictions of Everett Rogers’ diffusion model, propelled not as much by institutions as by students themselves. This is not a technology waiting to be adopted. It is a reality forcing adaptation. Treating it as an optional innovation mises the point entirely. 

Educational leaders, policymakers, and institutions may wish to evaluate AI’s impact, consider best practices, and implement careful frameworks. But they are not in control of the timeline. The adoption is not up for debate. It is already happening, driven by students who see AI not as a tool of the future but as an everyday utility, as natural as search engines or calculators. The question is not when to integrate AI into education, but how to manage an environment where AI is already integrated—often in ways institutions neither designed nor anticipated.

This is why traditional approaches to educational innovations are inadequate. Normally, innovations are assessed, piloted, and then scaled. But AI in education is unfolding more like a crisis than an initiative. It requires a response more akin to a public health emergency than a strategic rollout. When COVID-19 disrupted education, schools did not spend years analyzing whether remote learning was effective. They pivoted overnight, creating imperfect but necessary solutions. AI invasion demands a similar level of urgency.

Institutional response to invasive AI must be comprehensive rather than defensive. How do we accommodate this invasive innovation? Putting AI tools directly in the hands of faculty represents a critical first step, ensuring they engage with the same technologies their students already access freely. This must be paired with robust professional development—not merely technical training but genuine pedagogical exploration that helps educators reimagine their disciplinary approaches in an AI-augmented world.

Beyond tools and training, institutions must invest in creating dedicated time and space for disciplinary teams to collaboratively revise their assignments. The goal is not to create "cheat-proof" assessments but to develop authentic tasks that meaningfully incorporate AI as a partner in learning. This demands protected time away from everyday teaching pressures, where faculty can experiment, fail, refine, and document new approaches that acknowledge AI as a permanent fixture in their educational landscape rather than an external threat to be neutralized.

The AI Recruiter Will See You Now

The tidy world of job applications, carefully curated CVs and anxious cover letters may soon become a relic. Every professional now leaves digital traces across the internet - their work, opinions, and achievements create detailed patterns of their capabilities. Artificial Intelligence agents will soon navigate these digital landscapes, transforming how organizations find talent.

Unlike current recruitment tools that passively wait for queries, these AI agents will actively explore the internet, following leads and making connections. They will analyze not just LinkedIn profiles, but candidates' entire digital footprint. The approach promises to solve a persistent problem in recruitment: finding qualified people who are not actively job-hunting.

The matching process will extend beyond technical qualifications. Digital footprints reveal working styles and professional values. A cybersecurity position might require someone who demonstrates consistent risk awareness; an innovation officer role might suit someone comfortable with uncertainty. AI agents could assess such traits by analyzing candidates' professional communications and public activities.

Yet this technological advance brings fresh concerns. Privacy considerations demand attention - while AI agents would analyze public information, organizations must establish clear ethical guidelines about data usage. More fundamentally, AI agents must remain sophisticated talent scouts rather than final decision makers. They can gather evidence and make recommendations, but human recruiters must evaluate suggestions within their understanding of organizational needs.

The transformation suggests a future where talent discovery becomes more equitable. AI agents could help overcome human biases by focusing on demonstrated capabilities rather than credentials or connections. The winners will be organizations that master this partnership between artificial intelligence and human judgment. The losers may be traditional recruitment agencies - unless they swiftly adapt to the new reality.

The Subtle Art of Monopolizing New Technology

Monopolizing new technology is rarely the result of some grand, sinister plan. More often, it quietly emerges from self-interest. People do not set out to dominate a market; they simply recognize an opportunity to position themselves between groundbreaking technology and everyday users. The most effective tactic? Convince people that the technology is far too complex or risky to handle on their own.

It starts subtly. As soon as a new tool gains attention, industry insiders begin highlighting its technical challenges—security risks, integration headaches, operational difficulties. Some of these concerns may be valid, but they also serve a convenient purpose: You need us to make this work for you.

Startups are particularly skilled at this. Many offer what are essentially "skins"—polished interfaces built on top of more complex systems like AI models. Occasionally, these tools improve workflows. More often, they simply act as unnecessary middlemen, offering little more than a sleek dashboard while quietly extracting value. By positioning their products as essential, these startups slide themselves between the technology and the user, profiting from the role they have created. 

Technical language only deepens this divide. Buzzwords like API, tokenization, and retrieval-augmented generation (RAG) are tossed around casually. The average user may not understand these terms. The result is predictable: the more confusing the language, the more necessary the “expert.” This kind of jargon-laden gatekeeping turns complexity into a very comfortable business model.

Large organizations play this game just as well. Within corporate structures, IT departments often lean into the story of complexity to justify larger budgets and expanded teams. Every new tool must be assessed for “security vulnerabilities,” “legacy system compatibility,” and “sustainability challenges.” These concerns are not fabricated, but they are often exaggerated—conveniently making the IT department look indispensable.

None of this is to say that all intermediaries are acting in bad faith. New technology can, at times, require expert guidance. But the line between providing help and fostering dependence is razor-thin. One must ask: are these gatekeepers empowering users, or simply reinforcing their own relevance?

History offers no shortage of examples. In the early days of personal computing, jargon like RAM, BIOS, and DOS made computers feel inaccessible. It was not until companies like Apple focused on simplicity that the average person felt confident using technology unaided. And yet, here we are again—with artificial intelligence, blockchain, and other innovations—watching the same pattern unfold.

Ironically, the true allies of the everyday user are not the flashy startups or corporate tech teams, but the very tech giants so often criticized. Sometimes that criticism is justified, other times it is little more than fashionable outrage. Yet these giants, locked in fierce competition for dominance, have every incentive to simplify access. Their business depends on millions of users engaging directly with their products, not through layers of consultants and third-party tools. The more accessible their technology, the more users they attract. These are the unlikely allies of a non-techy person. 

For users, the best strategy is simple: do not be intimidated by the flood of technical jargon or the endless parade of “essential” tools. Always ask: Who benefits from me feeling overwhelmed? Whenever possible, go straight to the source—OpenAI, Anthropic, Google. If you truly cannot figure something out, seek help when you need it, not when it is aggressively sold to you.

Technology should empower, not confuse. The real challenge is knowing when complexity is genuine and when it is merely someone else’s business model.

Why Parallel Integration Is the Sensible Strategy of AI Adoption in the Workplace

Artificial intelligence promises to revolutionize the way we work, offering efficiency gains and new capabilities. Yet, adopting AI is not without its challenges. One prudent approach is to integrate AI into existing workflows in parallel with human processes. This strategy minimizes risk, builds confidence, and allows organizations to understand where AI excels and where it stumbles before fully committing. I have described the problem of AI output validation before; it is a serious impediment to AI integration. Here is how to solve it.

Consider a professor grading student essays. Traditionally, this is a manual task that relies on the educator's expertise. Introducing AI into this process does not mean handing over the red pen entirely. Instead, the professor continues grading as usual but also runs the essays through an AI system. Comparing results highlights discrepancies and agreements, offering insights into the AI's reliability. Over time, the professor may find that the AI is adept at spotting grammatical errors but less so at evaluating nuanced arguments.

In human resources, screening job applications is a time-consuming task. An HR professional might continue their usual screening while also employing an AI tool to assess the same applications. This dual approach ensures that no suitable candidate is overlooked due to an AI's potential bias or error. It also helps the HR team understand how the AI makes decisions, which is crucial for transparency and fairness.

Accountants auditing receipts can apply the same method. They perform their standard checks while an AI system does the same in the background. Any discrepancies can be investigated, and patterns emerge over time about where the AI is most and least effective.

This strategy aligns with the concept of "double-loop learning" from organizational theory, introduced by Chris Argyris. Double-loop learning involves not just correcting errors but examining and adjusting the underlying processes that lead to those errors. By running human and AI processes in parallel, organizations engage in a form of double-loop learning—continually refining both human and AI methods. Note, it is not only about catching and understanding AI errors; the parallel process will also find human errors through the use of AI. The overall error level will decrease. 

Yes, running parallel processes takes some extra time and resources. However, this investment is modest compared to the potential costs of errors, compliance issues, or damaged reputation from an AI mishap. People need to trust technology they use, and bulding such trust takes time. 

The medical field offers a pertinent analogy. Doctors do not immediately rely on AI diagnoses without validation. They might consult AI as a second opinion, especially in complex cases. This practice enhances diagnostic accuracy while maintaining professional responsibility. Similarly, in business processes, AI can serve as a valuable second set of eyes. 

As confidence in the AI system grows, organizations can adjust the role of human workers. Humans might shift from doing the task to verifying AI results, focusing their expertise where it's most needed. This gradual transition helps maintain quality and trust, both internally and with clients or stakeholders.

In short, parallel integration of AI into work processes is a sensible path that balances innovation with caution. It allows organizations to harness the benefits of AI while managing risks effectively. By building confidence through experience and evidence, businesses can make informed decisions about when and how to rely more heavily on AI.

Navigating the AI Gold Rush: Skins, Security, and the Real Value Proposition

 The economic battle surrounding artificial intelligence is intensifying at an unprecedented pace. Major AI players like OpenAI, Google, Meta, and Anthropic are leading this technological revolution. Tech giants such as Microsoft, Amazon, and Apple, along with thousands of startups, are vying for a stake in this burgeoning market without being able to develop their own competitive models. Amidst this frenzy, a critical question arises: what exactly is being sold?

Two primary value propositions have emerged in this landscape: skins and security mongers. Skins are interfaces or applications that overlay major AI models, aiming to simplify user interaction. They cater to individuals lacking advanced prompting skills, offering a more user-friendly experience. Security mongers, on the other hand, emphasize heightened privacy and security, often exaggerating potential risks to entice users.

While both propositions seem valuable on the surface, a deeper examination reveals significant shortcomings. Skins promise to streamline interactions with AI models by providing preset prompts or simplified interfaces. For instance, a startup might offer a chatbot specialized in drafting business emails, claiming it saves users the hassle of formulating prompts themselves. However, is this convenience truly worth it?

Major AI models are increasingly user-friendly. ChatGPT, for example, has an intuitive interface that caters to both novices and experts. Users often find they can achieve the same or better results without intermediary platforms. Additionally, skins often come with subscription fees or hidden costs, meaning users are essentially paying extra for a service the primary AI model already provides. There is also the issue of limited functionality; skins may restrict access to the full capabilities of the AI model, offering a narrow set of functions that might not meet all user needs.

The second proposition taps into growing concerns over data privacy and security. Vendors claim to offer AI solutions with superior security measures, assuring users their data is safer compared to using mainstream models directly. But does this claim hold up under scrutiny?

Most of these intermediaries still rely on API connections to major AI models like ChatGPT. Your data passes through their servers before reaching the AI model, effectively adding another point of vulnerability. Introducing additional servers and transactions inherently increases the risk of data breaches. More touchpoints mean more opportunities for data to be intercepted or mishandled. Furthermore, major AI providers invest heavily in security and compliance, adhering to stringent international standards. Smaller vendors may lack the resources to match these safeguards.

For example, a startup might advertise an AI-powered financial advisor with enhanced security features. However, if they are routing data through their servers to access a model like GPT-4, your sensitive financial data is exposed to additional risk without any tangible security benefit. The promise of enhanced security becomes questionable when the underlying infrastructure depends on the same major models.

AI platforms have not introduced new risks to privacy or security beyond what exists with other online services like banks or credit bureaus. They employ advanced encryption and security protocols to protect user data. While no system is infallible, major AI models are on par with, if not superior to, other industries in terms of security measures. They use end-to-end encryption to protect data in transit and at rest, implement strict authentication measures to prevent unauthorized access, and conduct regular security assessments to identify and mitigate vulnerabilities. It is easy to opt out of providing your data to train new models. It is much more difficult to know what your vendors are going to do with your data.

In a market flooded with AI offerings, it is crucial to approach vendors' claims with a healthy dose of skepticism. Validate the functionality by testing whether the convenience offered by skins genuinely enhances your experience or merely repackages what is already available. Assess the security measures by inquiring about the specific protocols in place and how they differ from those used by major AI providers. Transparency is key; reputable vendors should be open about how your data is used, stored, and protected.

As the AI gold rush continues, distinguishing between genuine innovation and superficial value propositions becomes essential. Skins and security mongers may offer appealing pitches, but often they add little to no value while potentially increasing costs and risks. It is wise to try using major AI models directly before opting for third-party solutions. Research the backgrounds of vendors to determine their credibility and reliability. Seek reviews and testimonials from other users to gauge the actual benefits and drawbacks.

In the end, the most powerful tool at your disposal is due diligence. By critically evaluating what is being sold, you can make informed decisions that truly benefit you in the rapidly evolving world of AI. Beware of vendors selling either convenience or security without substantial evidence of their value. At the very least, take the time to validate their claims before making an investment.

 

The Stealth AI Adoption

In modern workplaces, a quiet trend is taking hold: employees are secretly adopting artificial intelligence tools to enhance their work. Whether it is writing, designing, coding, or creating content, many are leveraging AI without informing their bosses. This “stealth AI adoption” is likely more widespread than managers realize.

Consider Alex, a software developer at a bustling tech firm. To streamline his coding process, Alex uses an AI assistant that can generate snippets of code in seconds. This tool not only saves him hours each week but also allows him to tackle more complex projects. However, Alex keeps this AI helper under wraps. Why? He has two choices: use the extra time for personal activities or take on additional work to appear more productive than his peers. There is no actual incentive to admit the use of AI. In some shops, cybersecurity people will come after you, if you confess. 

This hidden use of AI offers clear benefits for employees. Saving a few hours each week is tempting, whether for personal pursuits or to discreetly boost one’s workload. As a result, many organizations might be underestimating how extensively AI is being integrated into daily tasks.

Productivity can be measured in two ways: doing the same work with fewer people or doing more with the same number. The latter is a healthier, more sustainable approach. To achieve true success, organizations should aim to do more with their existing workforce rather than cutting staff. However, the stealth adoption of AI complicates this goal.

When employees use AI tools without disclosure, organizations miss out on opportunities to harness these technologies strategically. Without knowing how AI is being utilized, companies can not provide proper training or integrate AI into their workflows effectively. This fragmented approach can lead to missed efficiency gains and a lack of cohesive progress.

To foster a productive and innovative environment, companies need to build trust with their employees. Here is how:

1. Reassure Employees: Let your team know that adopting AI will not lead to layoffs. Emphasize that AI is a tool to help them do their jobs better, not a replacement for their roles. In unionized environments, a conversation with labor leaders would be wise. 

2. Create Incentives for Disclosure: Encourage employees to share the AI tools they are using by offering rewards or recognition. This transparency can help management understand how AI is being integrated and identify best practices.

3. Do More with the Same People: Focus on expanding the scope of work and fostering innovation rather than cutting positions. This approach not only boosts morale but also drives the organization forward.

By building trust and creating a supportive environment, organizations can turn stealth AI adoption into a strategic advantage. Employees will feel comfortable sharing their AI discoveries, allowing organizations to implement these tools effectively and sustainably.

As we move further into the AI-driven era, organizations must address this hidden trend. Encouraging transparency about AI tools and developing clear strategies for their use can ensure that productivity gains are real and sustainable. Until then, the silent spread of AI will keep reshaping workplaces, one undisclosed tool at a time. 

Meet Jinni, a Universal Assistant Bot

In a busy campus with 30,000 students, hundreds of faculty, and staff, managing everyday tasks and emergencies can be tricky. Imagine a universal bot, named Jinni, designed to assist everyone regardless of what they want and need to happen. Here’s a glimpse into how this could transform daily life on campus.

Take Dr. Nguyen, for instance. A junior professor with a packed schedule, she was just invited to present at a conference in Milan but wasn't sure how to get funding. She turned to Jinni.
"Good afternoon, Professor Nguyen. What do you need today?" Jinni asked.
"I want to attend a conference in Milan. Can I get support?" she inquired.

Jinni quickly scanned the institutional website and the financial data wharehouse and responded, "In your College, it takes a request from your Associate Dean. There is still some travel budget left, but you need to hurry. However, if it’s not a peer-reviewed conference and you’re not presenting, I wouldn't bother—the College's policy does not allow for this."

It added, "If you’d rather tell me the details about the conference and upload the invitation letter, I can file the request for you. Or, you can follow the link and do it yourself."

Professor Nguyen appreciated the options and the clarity, and chose to upload her details, letting Jinni handle the rest. Within a minute, Jinni said "Done, you shuold hear from the dean's office within a week. I alrready checked your eligibility, and recommended the Associate Dean to approve."

Then there was Mr. Thompson, a new staff member who discovered a puddle in the lobby after a rainy night. He pulled out his phone and described the situation to Jinni.

"You need to file an urgent facilities request. Here’s the link. Would you like me to file one for you? If yes, take a picture of the puddle," Jinni offered. "But if it’s really bad, you may want to call them. Do you want me to dial?"

Mr. Thompson opted for the latter, and within moments, Jinni had connected him to the facilities team.

Finally, there was Jose, a student who had missed the course drop deadline because of a bad flu. Anxious and unsure what to do, he asked Jinni for help. 

"Sorry to hear you’ve been sick. Jose. Yes, there is a petition you can file with the Registrar," Jinni replied. "I can do it for you, but I need a few more details. Do you have a note from your doctor? If not, you should get it first, then take a picture of it for me. If you used the Campus Health Center, I can contact them for you to request documentation. I will then write and submit the petition on your behalf. I will also need a few details - which class, the instructore's name, when you got sick, etc." Jose was relieved to find a straightforward solution to his problem and began to answer Jinni's questions one by one. 

The technology to create a universal agent bot like Jinni is not yet on the open market, but all elements do already exist as prototypes. More advanced customizable AI models, trained on extensive and diverse datasets, are essential to handle such tasks. More active, agentic AI also does exist. It can file and submit forms, not just find them. But even if we could to simply find and interpret policy and procedures, and point users to the right forms, it would alredy be a huge step forward. 

Simplifying and streamlining hundreds of procedures that any complex organization develops is definitely possible, but we know few examples of successful transformations like that. The next best thing is to use AI to help people navigate those procedures. This will lower barriers for all and reduce transactional costs. 

The Three Wave Strategy of AI Implementation

Whether it's a university, a tech giant, a manufacturing company, a public utility, or a government agency, the complexity of operations can be overwhelming. To illustrate this point, Claude and I have generated a list of over 1,150 workflows typical for a large university, many of which can be further broken down into smaller, more specific processes.

Given this complexity, the question arises: Where do we start with AI implementation? The answer lies in a strategic, phased approach that considers the unique characteristics of each workflow and the organization's readiness for AI adoption.

The First Wave: Low-Hanging Fruit

The initial phase of AI implementation should focus on what we call the "low-hanging fruit" - workflows that meet three crucial criteria:

1. Self-evident quality: The output quality is immediately obvious and doesn't require complex evaluation.
2. Single-person control: The workflow is typically managed or executed by one individual.
3. Ready-made AI tools: The process can be enhanced using existing AI tools without requiring specialized development. It is either using one of the primary LLM's or building a custom bot.

These criteria help identify areas where AI can quickly and effectively augment human efforts, improving efficiency and potentially enhancing the quality of service provided. Based on these criteria, here's a priority list of workflows that could be considered for the first wave of AI implementation. These are just examples:

1. Student services
• Student and prospective student advising of all kinds
2. Resume and Cover Letter Review (Career Services)
   • Offering individual resume critiques
   • Assisting with cover letter development
3. Academic Policy Development and Enforcement (Academic Affairs)
   • Drafting and revising academic policies
4. Health Education and Outreach (Health and Wellness Services)
   • Creating and distributing health education materials
5. Sustainability Education and Outreach (Sustainability and Environmental Initiatives)
   • Creating sustainability guides and resources for campus community
6. Digital Marketing and Social Media Management (University Communications and Marketing)
   • Creating and curating content for various platforms
7. Grant Proposal Development and Submission (Research and Innovation)
   • Assisting faculty with proposal writing
8. Financial Aid Counseling (Financial Aid and Scholarships)
   • Providing one-on-one counseling sessions
   • Offering debt management and financial literacy education
9. Alumni Communications (Alumni Relations and Development)
   • Producing alumni magazines and newsletters
10. Scholarly Communications (Library Services)
    • Supporting faculty in publishing and copyright issues
    • Providing guidance on research impact metrics
11. International Student and Scholar Services (International Programs and Global Engagement)
    • Providing immigration advising and document processing

This first wave serves multiple purposes. It demonstrates the proof of principle, making more stakeholders comfortable with AI integration. It also helps build internal expertise and confidence in working with AI technologies. These early successes can pave the way for more ambitious implementations in the future.

The Second Wave: Tackling Costly Workflows

Once the organization has gained experience and confidence from the first wave, it can move on to more complex and costly workflows. These are typically processes that involve significant labor, occur frequently, and have a broad scope of impact on the organization. However, it is crucial to narrow down this list based on feasibility and readiness for AI implementation.

For instance, while teaching is undoubtedly one of the most labor-intensive and impactful processes in a university, we do not yet have sufficient knowledge on how to make it significantly more efficient through AI. Some processes, like teaching, may never be fully optimized by AI because to their inherently relational nature. 

Note, this is also an opportunity to review major workflows; they often evolved over the years, and are far from ideal efficiency. AI can help review these workflows, and recommend streamlining. And of course, AI can be integrated into actually doing the work. 

The Third Wave: Enterprise-Level Solutions

Only after successfully navigating the first two waves should an organization consider enterprise-level AI solutions. These solutions have the potential to radically redefine the organization's core operations, placing AI at the center of its processes. This level of integration requires a deep understanding of AI capabilities, a clear vision of the organization's future, and a robust infrastructure to support AI-driven operations. Most importantly, it requires specialized tools and high level of security. 

The Timeline and Exceptions

This phased approach to AI implementation is not a quick process. For most large, complex organizations, it could take a couple of decades to fully realize the potential of AI across all workflows. However, there are exceptions. Some businesses with simpler and fewer workflows, such as narrowly specialized customer service operations, may be able to leapfrog straight into the third wave, especially if they have prior experience with AI technologies.

But these are the exceptions rather than the rule. For the majority of organizations, the path to comprehensive AI implementation requires a well-thought-out strategy, clear priorities, and a focus on building confidence and expertise over time.

Integrating AI into a complex organization's workflows is a marathon, not a sprint. It asks for patience, strategic thinking, and a willingness to learn and adapt. The key is to approach this journey with a clear strategy, well-defined priorities, and a commitment to building internal AI expertise. 

AI is not going to implement itself, but governments can help

The AI hype has passed, and the overexcited futurists' voices are mercifully fading away. We're now entering a practical era where AI is leveraged to boost productivity in businesses, non-profit, and public organizations. This shift brings a sobering realization: AI integration requires a meticulous, pragmatic approach to build reliable and trustworthy systems. It's a lot of work and requires some strategy.

When a single person manages a well-defined workflow, integrating AI is relatively straightforward. It's easy to incorporate AI tools like ChatGPT or Claude to assist with ad copy, reports, or applications. The beauty of these scenarios lies in their simplicity - the user acts as both operator and quality controller, immediately judging the output's effectiveness.

However, the story changes dramatically when we shift to multi-user workflows or more complex processes, where both inputs and outputs are more of a collective responsibility. I recently spoke with an Accounts Payable team who posed a challenging question: "Yes, we can see that AI can help review travel claims, but can you guarantee it's going to be 100% accurate?" I couldn't provide that guarantee; I don't have time to conduct a hundred tests, and I don't even have access to a hundred travel reports. They emphasized their need for completely audit-proof outcomes. This conversation highlighted the trust issues that arise when moving from AI enthusiasts to skeptics in larger organizations. And organizations should have a healthy group of skeptics to remain viable.

I've also recently been a fly on the wall during discussions between healthcare executives and a U.S. lawmaker. The executives explained that each AI-assisted medical procedure needs validation, which is expensive and often duplicated across multiple hospital systems. This challenge extends beyond healthcare. For instance, when using AI to crunch data in all organizations, we need to understand its reliability in analyzing large datasets, cleaning them, and handling outliers.

The problem is that no private institution can conduct the kind of comprehensive testing and validation needed to establish trust in AI systems across various industries. We cannot seriously trust claims of startups who are trying to sell a specialized product to an industry or a government organization. It's not clear how a hypothetical validation private service would monetize such an endeavor.

This is where I believe government involvement becomes crucial. Instead of obsessing with deep fakes and ethics, that's what governments should be doing. Governments can collaborate with industry experts to develop standardized benchmarks for AI reliability and performance. They could establish certification programs that act as quality marks, assuring users that AI systems have undergone rigorous testing. Moreover, government funding could support businesses, NGOs, and government agencies in conducting extensive AI testing, especially benefiting smaller organizations lacking the necessary resources.

In my view, public-private partnerships are key to navigating these challenges. By leveraging expertise from both sectors, we can develop robust testing frameworks and create dependable AI systems. This approach would pave the way for more efficient and innovative workflows across industries, ensuring that the benefits of AI are realized while maintaining trust and reliability. 

Why doing nothing with AI is not an option

In the business of technology adoption, the prudent path often lies in inaction. Education, in particular, has a natural proclivity for sifting through the chaff of technological fads, embracing only those innovations that truly enhance learning outcomes or make educators' lives easier. This organic process of selection has served the sector well, allowing it to evolve at a measured pace without succumbing to the allure of every shiny new tool. However, the emergence of AI presents a singular challenge, one that makes doing nothing all but impossible.

The disruptive potential of AI in education cannot be overstated. For centuries, the cornerstone of our pedagogical approach has been the written word – assignments and assessments that serve as both a means of developing and gauging understanding. The AI-powered tools capable of generating human-like responses threaten to undermine this foundational element of education. Inaction in the face of this shift is not merely ill-advised; it is a recipe for curricular erosion and a potential deskilling of an entire generation. Most educators intuitively understand the threat, hence the tinge of moral panic surrounding the AI invasion of education. 

Moreover, a passive approach to AI in education risks exacerbating existing inequities. As Leon Furze, a prominent voice in the field, has vividly described, policing student use of AI tools will inevitably lead to a new digital divide. Access to these technologies, even at the seemingly modest price point of $20 per month, can serve as a significant barrier for many students. The solution lies not in restriction, but in universal training – ensuring that all students are equipped with the skills to harness AI efficiently, thus leveling the playing field.

The stakes extend beyond the classroom. Higher education and K-12 institutions that fail to adapt to the AI revolution risk further straining their already tenuous relationships with employers. In an era where the relevance of traditional education is increasingly questioned, ignoring the clear signals from the labor market is a perilous path. It leaves educational institutions vulnerable to political attacks and diminishes their ability to prepare students for the realities of the modern workforce.

The imperative, then, is clear: embrace the bots. This is not a call for wholesale abandonment of traditional pedagogy, but rather a recognition that AI must be woven into the fabric of our educational approach. Curriculum must be revised, assignments and assessments reimagined to not only incorporate but require the use of AI. Every student, regardless of background or discipline, should be exposed to and ideally proficient in leveraging these tools.

Such a transformation is no small undertaking. It demands resources, commitment, visionary leadership, and a comprehensive institutional strategy. But the alternative – a slow, painful descent into irrelevance – is far more daunting. The question is not whether education can afford to embrace AI, but whether it can afford not to. In this particular case, inaction is the riskiest action of all.

Why am I obsessed with custom bots?

Policies are meant to cover a wide range of cases, but when you're faced with a specific situation, wading through all that information can be a real pain. It's like trying to find a needle in a haystack. You just want to know what applies to your case, but you're forced to read through pages and pages of stuff that doesn't matter to you. No wonder people don't bother reading policies at all.

And that's where the real problem lies. When people don't read policies, they end up doing things without knowing if they're compliant or not. They hope that if they make a mistake, someone will catch it down the line. But that's a risky game to play. It's why we have all these layers of control, multiple signatures, and quality checks in place. We're trying to catch all those errors that happen when people don't follow the rules.

But what if we could flip the script? What if we could make it easy for people to find the information they need, when they need it? That's where AI-powered bots come in. These bots can bridge the gap between broad policies and specific cases. They can take a person's situation, analyze the relevant policies, and give them the exact information they need to move forward.

Imagine how much time and effort that could save. No more reading through endless pages of policies, no more guesswork, no more hoping you got it right. Just clear, concise guidance that helps you get things done quickly and correctly.

And here's the kicker: if everyone used these bots and followed the policies correctly, we could start to relax some of those strict controls. We wouldn't need as many signatures, as many quality checks, as many layers of oversight. We could trust that people are doing things the right way, because they have the tools to do so.

That's the power of AI-powered bots. They can help us move from a culture of control to a culture of empowerment. They can give people the information they need to make good decisions, without bogging them down in unnecessary details.

Of course, it's not a silver bullet. We'll still need policies, and we'll still need some level of oversight. But AI-powered bots can help us strike a better balance. They can help us create a system that's more efficient, more effective, and more user-friendly.

So if you're struggling with the gap between policies and specific cases, it's time to start exploring AI-powered bots. They might just be the key to unlocking a better way of working. And if you need help getting started, well, that's what people like me are here for. Let's work together to build something that makes a real difference.