Blood in the Instructional Design Machine?
The reality of AI, job degradation & the likely future of Instructional Design
Hey folks!
For the past two years, a question I’ve repeatedly been asked simple yet existential question: "Will AI take my job?" As the dust begins to settle, we're slowly realising this this may have been the wrong question all along.
The more sophisticated and arguably more urgent questions we must now ask are: "How will AI change my job?”. What we’re really asking here is, will AI degrade our skills? Will it automate the creative, satisfying parts of our work and - contrary to the promise of AI - leave us with the administrative drudgery?
This is the new reality check for the Instructional Design profession and education professionals more broadly. The greatest risk to our roles may not be a future of unemployment, but a future of profound professional dissatisfaction.
The data and testimonials emerging from across the knowledge economy suggest we are at a pivotal crossroads, facing two divergent paths for human-AI collaboration. One path sees AI as a tireless assistant, a tool that handles the toil and frees us to focus on the strategic and creative work we love. The other, more insidious path, sees the machine doing the "fun stuff" while the human is left with the functional drudgery of feeding it source files and managing its output.
This is the "inverted centaur" model—where the human becomes the machine's hands, not the other way around. This is not a distant fear; it is a choice being made right now in the design of our tools and the structure of our teams. It is the central fight for the soul of our work.
Let’s dive in.
A Market in Flux: Growth, Volatility, and a Strategic Shift
The current job market for instructional designers is a complex tapestry of contradictory signals. The long-term outlook is overwhelmingly positive. Data from Recruiter.com forecasts that 29,390 new instructional design jobs will be filled in the U.S. by 2029 , and other analyses project a 6% to 9% job growth through 2029. This growth is underpinned by the unstoppable expansion of the global eLearning industry, which is projected to surpass $400 billion by the end of this year.
However, this growth is paired with a strategic realignment in corporate spending. While the ATD's 2024 State of the Industry report shows per-employee spending on learning is up, reaching an average of $1,283. Training Magazine's 2024 Industry Report reveals that internal L&D payroll spending is down 4%5. The key to this paradox lies in a crucial data point: spending on outside products and services has jumped a dramatic 23% to $12.4 billion.
This signals a fundamental shift: companies are reallocating funds from large internal teams toward specialised consultants and advanced learning technologies like AI. L&D is not being de-funded; it is being re-engineered.
The AI "Barbell Effect": Two Futures for the Price of One
This re-engineering is creating what the latest market analysis calls the "barbell effect" within the ID profession. By automating and therefore devaluing routine, production-focused tasks, AI is threatening roles centered on basic content development. At the same time, AI is also creating immense demand for high-level strategic skills. The middle ground is becoming increasingly precarious. These two ends of the barbell represent the two competing "centaur" models for our future: one where the fun stuff is delegated to humans, and other where the fun stuff is delegated to AI.
On one end of the barbell, we see the "inverted centaur"—the human as the machine's assistant. This is the world of job degradation, captured in the stark testimonials from Brian Merchant's journalistic project, "AI Killed My Job." An EdTech writer describes her role being reduced to generating "complete slop" with ChatGPT. A programmer finds his job is now the dull, janitorial task of reviewing AI-generated code.
This reality is confirmed by data from the software industry. In an episode of the Pragmatic Engineer podcast published yesterday, DX CTO Laura Tacho cited DORA research showing that developers' job satisfaction sometimes decreased after AI adoption because the tools accelerated the parts they enjoy (creative coding), leaving them with a higher proportion of the toil they dislike. In this model, the AI performs the interesting work, and the human is left to manage, edit, and validate the output.
On the other, more hopeful end of the barbell, is the "augmented human". This is the vision where AI handles the drudgery, freeing the instructional designer to evolve into what is often described as a "Learning Ecosystem Architect". In this model, the ID focuses on deep domain expertise (pedagogy, instructional strategy) and, perhaps, the more so-called “human skills” of empathy and strategic thinking. This is the future where we remain the strategic and creative heart of the process.
Perhaps reassuringly, right now the market seems to be placing its bets on the latter model. A 2024 report found that 71.3% of hiring professionals now consider the application of ID theory and learning science to be a top-three skill—placing it above proficiency with specific tools. But will this still be true a year on from now? The answer to this question is TBC.
The Tools Tell a Story
The abstract "barbell effect" becomes concrete when we analyse the new generation of AI tools being built specifically for L&D. Their very design reveals a bet on one future over the other.
A primary focus among many new AI tools aimed at Instructional Designers and L&D is on “democratising the first draft” - i.e. empowering non experts to create content. Platforms like Arist, Easygenerator, and CourseboxAI are built to automate instructional design decision-making: users to upload a document and have the AI instantly generate a structured course design.
This raises a very important, perhaps even existential question for our profession: do these tools free a designer from the mind-numbing drudgery of content conversion (the "augmented human")? Or do they automate the core expertise of the learning professional’s role, e.g. selecting instructional startegies, structuring narratives and designing a learning flow, in the process reducing the ID's role to simply finding the source file and pushing a button (the "inverted centaur")?
The stated aspiration of these tool builders seems to be a future where AI means that the instructional designer's value shifts decisively from production to strategy. Their stated goal is to handle the heavy lifting of content generation, allowing the human ID to provide the indispensable context, creativity, and pedagogical judgment that AI cannot replicate.
However, the risk of these tools lies in how we use them, and the "inverted centaur" model remains deeply potent and possible. In an organisation that prioritises cost above all, these same tools can be used to justify reducing the ID role to the functional drudgery of inputting a PDF and supervising the machine.
As is often the case when it comes to AI, the impact of the machine lies ultimately in the hands on the humans and how they decide to use it.
Emerging Trends are Positive
The "barbell effect" is directly reflected in compensation. While the average salary for a traditional instructional designer in the U.S. (which produces most data) is a solid $83,000 - $90,000, a significant salary premium is emerging for the strategic skills that define the "Augmented Architect" role.
Across all industries, L&D job postings requiring AI skills offer a salary premium between 21% and 28%. Applying this to the average corporate ID salary of ~$87,000 projects a potential average salary for an AI-proficient ID in the range of $105,000 to $111,000.
Meanwhile, a 2024 report found that 71.3% of hiring professionals now consider the ability to apply instructional design theory and learning science to be a top-three skill—placing it above AI tool proficiency.
Alongside this, the ability to strategically orchestrate AI is emerging as a new key new competency. This includes skills in prompt engineering, curating and validating AI outputs, and designing AI-powered learning experiences. The market is rewarding the "T-shaped" professional who combines deep human-centric design expertise with a broad capability in technology and data
TLDR: the incentive to become the strategic human in the loop, rather than only a functional operator, is exceptionally strong. For now at least, the signals are positive for the future of the profession.
Designing the Future of Our Profession
To me, the message is clear: the future of our profession is not a predetermined outcome of technological advancement; it is a choice that we - the humans - actively make. The "inverted centaur" model, where our professional expertise is devalued and we are left with the drudgery of supervising machines, is not a hypothetical risk—it is a reality for many.
As a profession, we must consciously and strategically fight for the other path. Ans this inevitably raises the question: what does a better future look like, and how do we build it?
This is a question I’ve been exploring for the last 2 years or so as co-founder of Epiphany. The core research question driving this work is: what would AI tooling look like if we designed it to enhance - not automate - the most fulfilling parts of the Instructional Design role?
Here’s a hint of what that could look like in practice:
AI for Deeper Analysis: Imagine an AI that transcribes and thematically analyses 20 stakeholder interviews, highlighting contradictions in their statements and surfacing the true, underlying performance gaps. This automates the data-gathering toil, freeing the ID to focus on the fun and high-value work of strategic diagnosis and creative problem-solving.
AI for Instructional Excellence: Imagine an AI that analyses your target learner, business goal and learning outcome and makes smart recommendations for the optimal mode of delivery, length and instructional strategy based on what we know about how humans learn and what works on the ground.
AI as a Creative Muse: Imagine an AI that acts as a Socratic partner. You feed it your learning objectives and learner context, and it suggests five wildly different, creative treatment ideas—a branching scenario, a collaborative simulation, a narrative-based game—complete with potential risks and benefits for each. It augments your creativity, not replaces it.
AI for Seamless Adaptivity: Imagine an AI that allows you to visually design complex, adaptive learning paths based on learner performance, without writing a single line of code. You focus on the art of designing the choices, consequences, and remedial loops, while the AI handles the technical implementation.
Conclusion & Call to Action
This is a call to action for every instructional designer, L&D leader, and educator. We must refuse to let our roles be degraded into mere "AI janitors." We must advocate for and champion a vision of AI as a co-pilot, not an autopilot.
As Practitioners, we must become discerning and demanding customers. We must use our voices and our budgets to reward tool builders who focus on augmenting creativity and analysis. We must champion tools that make us better designers, not just faster producers.
As Leaders, we must shift our focus when evaluating technology. Do not just ask, "How much time will this save?" Ask, "What kind of work will this free my team up to do?" Prioritise AI investments that hit strategic KPIs beyond speed and cost by asking: how does this make us faster, cheaper and better at what we do?
In the age of AI, the goal is not simply to stay employed — it’s to preserve, protect, and enhance our craft and profession.
Keep innovating!
Phil 👋
PS: If you want to get future-ready and hone both your pedagogical and prompting expertise, check out my AI & Learning Design Bootcamp.
References
Career Outlook and Job Vacancies for Instructional Designers and Technologists, Recruiter.com, 2025
Top 10 Instructional Design Careers [+Salary Guide], University of San Diego Online Degrees, 2025.
Becoming a Successful Instructional Designer in 2024: Key Strategies and Skills, Next Software Solutions, 2025
2024 State of the Industry Report, Association for Talent Development (ATD), 2024.
2024 Training Industry Report, Training Magazine, 2024.
AI Killed My Job, blog post series by Brian Merchant, 2025
Measuring the impact of AI on software engineering – with Laura Tacho, The Pragmatic Engineer Podcast, July 2025
The Top 15 Instructional Design Skills You Need in 2025, Devlin Peck, 2025
Instructional Designer Salary Report 2024, Devlin Peck, 2024
New Lightcast Report: AI Skills Command 28% Salary Premium, Lightcast, 2025