AI
What we’re seeing The market numbers are strong. For example, the global market for AI in HR was valued at USD ~$8.16 billion in 2025 and is projected to reach ~USD 30.77 billion by 2034, with a CAGR of ~15.9%. In recruiting specifically, AI is already widely used: one study says ~89% of HR professRead more
What we’re seeing
-
The market numbers are strong. For example, the global market for AI in HR was valued at USD ~$8.16 billion in 2025 and is projected to reach ~USD 30.77 billion by 2034, with a CAGR of ~15.9%.
-
In recruiting specifically, AI is already widely used: one study says ~89% of HR professionals whose org uses AI in recruiting say it saves time/increases efficiency.
-
In terms of function and capability: AI is no longer just “nice to have” for HR—according to Gartner, Gen-AI adoption in HR jumped from 19% in June 2023 to 61% by January 2025.
-
The kinds of tools: AI in HR/Recruiting is being deployed for resume screening, candidate matching, chatbot-based initial interviews, predictive analytics for attrition/retention, onboarding automation, etc.
So all signs point to a transformative wave of digital tools automating parts of the HR/tracking/talent space, and platforms that embed those tools becoming more valuable.
Why that transformation matters
From your point of view as a senior web/mobile dev, someone working in automation, dashboards, data → here’s why this trend is especially worth noting:
-
Efficiency & scale
Automation brings huge scale: tasks that used to be manual (screening 1000 resumes, scheduling interviews, tracking candidate flows) are now increasingly handled by AI-powered platforms. That opens up new architecture and UI/UX problems to solve (how to integrate AI agents, how human + machine workflows coexist). -
Data + predictive insight
HR tech is turning into a data business: not just “post job, get applications” but “predict which candidates will succeed, where skills gaps are, how retention will trend”. That means developers and data people are needed to build frameworks, dashboards, and pipelines for talent intelligence. -
Platform and ecosystem opportunity
Because the market is growing fast and valuations are strong (investors are backing niche HR/Recruiting AI companies), there’s space for new entrants, integration layers, niche tools (e.g., skill-matching engines, bias detection, candidate experience optimisation). For someone like you with varied tech skills (cloud, APIs, automation), that’s relevant. -
UX + human-machine collaboration
One of the key shifts is the interplay of humans + AI: HR teams must move from doing everything manually to designing workflows where AI handles repetitive tasks and humans handle the nuanced, human-centric ones. For developers and product teams, this means designing systems where the “machine part” is obvious, transparent, and trustworthy, especially in something as sensitive as hiring.
But it’s not all smooth sailing.
As with any rapid shift, there are important caveats and risks worth being aware of, as they highlight areas where you can add value or where things might go off course.
-
Ethical, fairness, and trust issues: When AI is used in hiring, concerns around bias, transparency, candidate perception, and fairness become critical. If a system filters resumes or interviews candidates with minimal human oversight, how do we know it’s fair?
-
Tech maturity and integration challenges: Some organisations adopt tools, but the full suite (data, process, culture) may not be ready. For example, just plugging in an AI screening tool doesn’t fix poorly defined hiring workflows. As one report notes, many organisations are not yet well prepared for the impact of AI in recruiting.
-
Human+machine balance: There’s a risk of automation overshooting. While many tasks can be automated, human judgment, cultural fit, and team dynamics remain hard to codify. That means platforms need to enable humans, rather than entirely replace them.
-
Valuation versus real value: High valuations signal investor excitement, but they also raise the question—are all parts of this business going to deliver sustainable value, or will there be consolidation, failures of models? Growth is strong, but execution matters.
What this could mean for you
Given your expertise (web/mobile dev, API work, automation, dashboard/data), here are some concrete reflections:
-
If you’re exploring side-projects or startups, a niche HR/Recruiting tool is a viable area: e.g., developing integrations that pull hiring data into dashboards, building predictive analytics for talent acquisition, or creating better UX for candidate matching.
-
In your work with dashboards/reporting (you mentioned working with state health dashboards, etc), the “talent intelligence” side of HR tech could borrow similar patterns—large data, pipeline visualisation, KPI tracking — and you could apply your skills there.
-
From a product architecture viewpoint, these systems require robust pipelines (data ingestion from ATS/CRM, AI screening module, human review workflow, feedback loops). Your background in API development and automation is relevant.
-
Because the space is moving quickly, staying current on the tech stack (for example, how generative AI is being used in recruiting, how candidate-matching algorithms are evolving) is useful; you might anticipate where companies will invest.
-
If you are advising organisations (like you do in consulting contexts), you could help frame how they adopt HR tech: not just “we’ll buy a tool” but “how do we redesign our hiring workflow, train our HR team, integrate with our IT landscape, ensure fairness and data governance”.
My bottom line
Yes—it absolutely signals a transformation: the speed, scale, and investment show that the industry of recruiting/HR is being re-imagined through digital tools and automation. But it’s not a magic bullet. For it to be truly effective, organisations must pair the technology with new workflows, human-centric design, ethical frameworks, and smart integration.
For you, as someone who bridges tech, automation, and strategic systems, this is a ripe area. The transformation isn’t just about “someone pressing a button and hiring happens,” it’s about building platforms, designing workflows, and enabling humans and machines to work together in smarter ways.
See less
1. The Simple Idea: Machines Taught to "Think" Artificial Intelligence is the design of making computers perform intelligent things — not just by following instructions, but actually learning from information and improving with time. In regular programming, humans teach computers to accomplish thingRead more
1. The Simple Idea: Machines Taught to “Think”
Artificial Intelligence is the design of making computers perform intelligent things — not just by following instructions, but actually learning from information and improving with time.
In regular programming, humans teach computers to accomplish things step by step.
In AI, computers learn to resolve things on their own by gaining expertise on patterns in information.
For example
When Siri quotes back the weather to you, it is not reading from a script. It is recognizing your voice, interpreting your question, accessing the right information, and responding in its own words — all driven by AI.
2. How AI “Learns” — The Power of Data and Algorithms
Computers are instructed with so-called machine learning —inferring catalogs of vast amounts of data so that they may learn patterns.
That’s how machines can now identify faces, translate text, or compose music.
3. Examples of AI in Your Daily Life
You probably interact with AI dozens of times a day — maybe without even realizing it.
AI isn’t science fiction anymore — it’s present in our reality.
4. AI types
AI isn’t one entity — there are levels:
We already have Narrow AI, mostly, but it is already incredibly powerful.
5. The Human Side — Pros and Cons
AI is full of promise and also challenges our minds to do the hard thinking.
Advantages:
Disadvantages:
The emergence of AI presses us to redefine what it means to be human in an intelligent machine-shared world.
6. The Future of AI — Collaboration, Not Competition
The future of AI is not one of machines becoming human, but humans and AI cooperating. Consider physicians making diagnoses earlier with AI technology, educators adapting lessons to each student, or cities becoming intelligent and green with AI planning.
AI will progress, yet it will never cease needing human imagination, empathy, and morals to steer it.
Last Thought
Artificial Intelligence is not a technology — it’s a demonstration of humans of the necessity to understand intelligence itself. It’s a matter of projecting our minds beyond biology. The more we advance in AI, the more the question shifts from “What can AI do?” to “How do we use it well to empower all?”
See less