Executive Summary
The market has moved from "how do we build AI?" to "how do we build our business with AI?" This fundamental shift creates massive opportunities for recruiters who understand the new landscape. While competitors chase "AI developers" with generic searches, smart recruiters are quietly placing implementation-focused candidates at 18% salary premiums.
Key Market Intelligence:
- 36% of tech job postings now require AI skills
- Implementation skills exploded year over year: AI Agents (+2,043%), RAG (+475%), Edge Intelligence (+608%) while research skills decline
- Texas outpaces California in AI hiring growth (149% vs 108% year-over-year)
- Traditional industries (manufacturing, healthcare, aerospace) are hiring AI talent faster than pure tech companies
- Consulting firms dominate: Four of the top 25 AI hirers are Deloitte, Accenture, PwC, and KPMG
Organizations increasingly need people who can implement, scale, and govern AI capabilities alongside traditional AI development. The fastest growth is in AI Orchestrators (prompt engineers, implementation specialists) and AI-Enhanced Professionals (traditional tech roles using AI tools). Understanding the distinct AI talent tiers and their market dynamics is essential for successful AI placements.
Report Methodology
To present the insights in this report, Dice used job posting data provided by Dice's partner, Lightcast, which has a database of more than 1 billion current and historical job postings worldwide. Dice pulled data on June 5, 2025 and analyzed tech job postings in the U.S. using Lightcast's skills category taxonomy specific to "Artificial Intelligence and Machine Learning (AI/ML)" and "Natural Language Processing (NLP)". The AI/ML subcategory contains 301 skills (up from 120 in 2024) and the NLP subcategory contains 65 skills (up from 44 in 2024). The information in this report is a snapshot of tech job posting data as of June 6, 2025 and backward revisions to prior month's data may occur from the sources used in this report.
Table of Contents
Chapter 1: Mapping the AI Skills Economy
- Understanding the Three Tiers of AI Talent
- The Impact of Market Forces on AI Skills
- Action Items for Recruiters
Chapter 2: Where are AI Jobs Right Now?
- Who is Hiring AI Talent?
- Where are AI Jobs Located?
- Which AI Skills are Hot Right Now?
Chapter 3: Turning Market Intelligence into Competitive Advantage
- Converting Insights into Client Conversations
- Red Flag Identification Guide
- Overcoming Objections with Market Reality
Chapter 1: Mapping the AI Skills Economy
Your competitors think AI hiring is about finding data scientists, and they are struggling with a limited talent pool. Meanwhile, smart recruiters are quietly placing a broad range of AI candidates at 18% salary premiums. The artificial intelligence job market has evolved into something completely different from what anyone expected. The firms that come in with the right strategy are building massive competitive advantages while everyone else chases the wrong talent.
In May 2025, over one third of all tech job postings required AI skills, a new peak to a trend that has shown no sign of slowing since it took off in early 2023. Does this mean more demand for tech professionals who can build and implement AI? Or does it mean that AI skills are now required even in more traditional tech roles? In this chapter, we look at the various ways AI skills are being integrated into new jobs on the market, and how you can communicate better with hiring managers on the subject.
As of May 2025, 36% of all tech jobs require AI skills.
The Three-Tiers of AI Talent
AI has gone from emerging trend to essential skillset in record time. Today, professionals across tech are expected to understand and apply AI in their roles. However, AI roles are as broad and varied as general tech roles ever have been. Knowing what kind of AI expertise a role really demands can save hours of wasted sourcing and open up entirely new talent pools.
To make some sense of this, we have split this group into three distinct categories of AI talent.
AI Builders: The Technical Backbone
The first group of AI roles are filled by the people who can build AI systems, typically carrying job titles such as “machine learning engineer,” “AI researcher,” “data scientist,” etc. These experts could be focused on model development, or they possess specialized skills around training and deploying AI models. These professionals likely have advanced degrees in computer science, mathematics, or related fields, and their work involves the mathematical and computational challenges of making AI systems function.
The talent pool here is relatively small and highly concentrated. AI Builders typically came up through academic research programs or have spent time at major tech companies working on AI infrastructure. They speak in terms of algorithms, training datasets, and model architectures. When they evaluate opportunities, they're often going to weigh factors like research freedom and the technical complexity of the problems they'll be solving.
What's interesting about hiring experts from this segment is how it resembles academic hiring. Publications, conference presentations, and open-source contributions may matter more than traditional software development portfolios. Compensation packages frequently include conference budgets and continuing education allowances. These AI Builders may even have expectations around intellectual property rights that would be unusual in other technical roles.
Our AI Orchestrators group consists of people who understand the tech behind AI, but focus on the implementation and business integration of an existing model. In other words, they deploy, govern, and scale AI to meet the needs of a business. Jobs within this category include prompt engineers, AI product managers, implementation specialists, and the various "AI strategist" positions that have emerged over the past two years.
Many of these AI Orchestrators come from traditional tech or business roles but are working to angle their career into the AI space. They're the people who can evaluate when an AI solution makes sense for a business problem, manage the integration of AI tools into customer facing products, and communicate between AI Builders and other stakeholders.
\Organizations need people who can think strategically about AI applications while understanding enough about the tech to make realistic implementation decisions. With all that is changing in AI, you can see why this doesn't map neatly into traditional career paths.
Compensation in this segment varies widely depending on industry and company size, but these roles often command premiums because they're critical to successful AI adoption. Companies have learned that having great AI technology without someone who can orchestrate it results in failed implementations.
AI Orchestrators: The Business-Technical Bridge
AI-Enhanced Professionals: The Business Accelerators
Hiring AI-enhanced Professionals to fill traditional roles is just breaking the surface, but it is looking more and more like the future every day. This group represents traditional tech professionals who've integrated AI into their workflow to improve their own quality and efficiency. Software developers using GitHub Copilot, data analysts leveraging automated insights, QA engineers with AI-powered testing suites all fall into this category; they might seem like “vibe coders,” but they have the provable technical experience to leverage AI into a usable product.
This category is where most of the actual hiring volume exists, though it's often not recognized as "AI hiring" in the traditional sense. These professionals typically don't have experience building AI systems, or the ability to implement models directly into products, but they've developed marketable fluency with AI tools by using it in their day-to-day tasks.
Some clients might not be sure they need “AI-enhanced professionals” quite yet, and you can demonstrate expertise by helping them get here. When they say "AI software engineer," they mean "developer who can work with our AI tools." Getting this distinction right will open your candidate search up to some of the most innovative, adaptable talent out there.
Prompt engineering skills grew 219% year-over-year, but "Prompt Engineer" job postings only increased 77%. What's happening? The skill is becoming a baseline requirement across multiple AI roles rather than a standalone position.
What this means for recruiters:
Don't search for dedicated "Prompt Engineers". Instead, look for traditional tech professionals—product managers, developers, implementation specialists—who've added prompt engineering to their toolkit. The market has moved from hiring prompt engineering specialists to expecting prompt engineering competency in broader tech roles. You can read more about specific AI skills on the rise in Chapter 2.
Market Forces Driving Skill Premiums
AI skills command premium rates, and budgets are following. According to our 2025 Tech Salary Report, this premium can be up to 18%. As a result, AI hiring has become competitive and expensive.
Supply and demand imbalances are the most obvious factor. The number of organizations trying to implement AI solutions has grown faster than the number of professionals with relevant experience. This is particularly acute in the AI Orchestrator category, where the required skill combination is new enough that there aren't established educational pathways or strong networks of circulating candidates.
Geographic distribution also plays a role. AI talent tends to be concentrated in major tech hubs such as Silicon Valley and New York City, but demand has become more geographically distributed as organizations across different industries adopt AI solutions that need to be implemented.
Competing Market Pressures
This comes at a time where unemployment and remote work are contentious subjects in the tech community. On one hand, companies are implementing return-to-office mandates and layoffs among their tech workforces. On the other hand, they are fighting to attain the AI talent needed to compete with their competitors and implement solutions that excite shareholders.
Perhaps most significantly, the integration complexity of AI implementations has made organizations willing to pay premiums for professionals who can help them avoid costly mistakes. Failed AI projects are expensive in terms of both direct costs and opportunity costs, so organizations have become willing to invest more in talent who can increase their chances of successful implementation.
The result is a market where AI-related skills command significant premiums across all three talent tiers, but where the specific premiums and their drivers vary depending on the role type and implementation context.
The Shifting AI Hiring Market
While the overall share of job postings that require AI skills continues to grow, we have noted an interesting shift in trend in our 2025 data that demonstrates that the AI hiring market has already matured quite a bit since last year.
The experimental phase of AI adoption—roughly 2020 to 2023—required companies to build custom solutions and figure out what AI could do for their business. This drove demand for AI Builders who could create models from scratch, conduct research, and develop bespoke AI systems. But in 2025, we are seeing a much stronger market preference for Orchestrators and tech professionals who are using AI to enhance their skills.
Notably, roles requiring expertise in the following skills are experiencing year-over-year growth of over 100%:
- Agentic AI & Multi-Agent Systems — coordinating AI systems that can work together and make decisions
- Enterprise AI Infrastructure — platforms like LangChain, MLflow, and Azure AI Studio that help deploy AI at scale
- RAG & Vector Databases — systems that help AI access and use company-specific data
- AI Safety & RLHF — ensuring AI systems behave reliably and align with business requirements
- Cloud Architecture & AI-specific Infrastructure — the underlying systems that make enterprise AI possible
"In 2025, we are seeing a much stronger market preference for Orchestrators and tech professionals who are using AI to enhance their skills."
Notice what these skills have in common: they're all about implementing, scaling, and governing AI rather than building it from scratch. As the market matures, companies seem to be deciding that they don't need to create their own large language models or foundational AI systems. The pretrained models from OpenAI, Anthropic, Meta, and Google are good enough for most enterprise use cases, and the cost of hiring deep AI scientists versus buying API access or using open-source models is prohibitive for many organizations.
This shift explains why we're seeing slightly less growth in traditional AI Builder roles even as AI adoption accelerates. The market has moved from "how do we build AI?" to "how do we build our business with AI?" Companies need people who can integrate existing AI capabilities into their operations, ensure those systems work reliably at scale, and manage the organizational changes that come with AI adoption.
It is important to note that, although AI Builder roles are not exploding to the same degree as other AI role types, demand is still solid (and growing!)
Your Competitive Edge
Recruiters will have to surf waves of both challenge and opportunity in this market. For one thing, clients may still think they need AI Builders to solve problems better suited for AI Orchestrators or AI-Enhanced Professionals. Not only does this open up a much larger talent pool to source from, but recruiters have the opportunity to source promising tech professionals who are actively upskilling themselves. This presents a great opportunity for recruiters and staffing agencies to deliver big wins for their clients.
It is likely that AI hiring strategies will increasingly focus on finding people who can make AI useful, safe, and scalable. Understanding the varied ways tech professionals can solve these problems, and helping clients understand these nuances, becomes a significant competitive advantage in a market where many organizations are still figuring out what kind of AI talent they actually need.
In the next chapter, we'll examine which traditional tech skills are actually declining, which new categories are emerging fastest, and where the geographic and industry hotspots are creating the most opportunity for recruiters sourcing AI talent.
Chapter 2: Where are AI Jobs Right Now?
Understanding the three-tier AI talent framework gives you the vocabulary to navigate client conversations. Now you need to know where the actual opportunities exist. In this section, we break down the geographic hotspots, industry patterns, and company-specific hiring trends that determine where you should focus your sourcing efforts.
The data reveals some surprises: for example, why Texas is outpacing California for growth, which traditional industries are outspending tech companies, and why consulting firms represent your best placement opportunities. Use these insights to identify underserved markets, position yourself in high-growth regions, and target the clients most likely to pay premium rates for quality AI talent.
Who is Hiring AI Talent?
Amazon leads AI hiring with when it comes to sheer volume of open roles, but the real story is consulting dominance. Four of the top 25 hirers, Deloitte, Accenture, PwC, and KPMG, are consulting firms implementing AI for clients who either can't hire internally or need to cycle up AI teams as fast as possible.
Traditional industries such as manufacturing, aerospace, and healthcare are hiring AI talent faster than “pure” tech companies. These sectors often struggle to compete for Silicon Valley candidates.
Recruiter Opportunities:
- Consulting firms pay placement fees and hire quickly. They need candidates who can work on client sites and communicate effectively with non-technical stakeholders.
- Help traditional companies structure competitive offers with the insights you gain from regularly working the AI hiring market. They have budget but lack AI hiring experience.
Where are AI Jobs Located?
While California has the most AI jobs (63,993), Texas shows 149% year-over-year growth versus California's 108%. Austin and Houston metros each grew 143%.
New York, Boston, and Washington DC metros combine high volume with established tech ecosystems (and in the case of DC, the federal government and various contractors in need of AI expertise). Additionally, these markets have candidates familiar with enterprise AI implementations.
Recruiter Opportunities:
- Texas offers lower competition for candidates and cost-conscious clients. Remote work makes location less critical for placement success.
- Many organizations are moving back to an in-person workplace culture. Dense metro areas reduce candidate travel requirements and increase placement efficiency for firms managing multiple client relationships.
Which AI Skills are Hot Right Now?
AI Agents leads skill demand with 2043% growth year over year, an exponential growth metric that likely comes from the fact that AI agents are relatively new to the market this year alongside the potential of that technology to radically change and streamline businesses. Alongside Edge Intelligence (608%) and Retrieval Augmented Generation (475%), we note that these skills aren’t research skills, but implementation technologies that integrate AI models into existing business environments.
Market Insight:
The highest-growth skills focus on making AI practical: agent orchestration, data retrieval, workflow automation, and cloud deployment. This confirms the shift from AI experimentation to AI implementation we saw in our job title trend analysis in Chapter 1.
Recruiter Opportunities:
- Candidates with LangChain, MLflow, or Vector Database experience are rare but essential for enterprise AI deployments. These skills command significant premiums and shorter hiring cycles because they directly solve business problems rather than theoretical challenges.
- Look for candidates with enterprise AI infrastructure experience rather than general programming or model research backgrounds. Implementation skills command better results in the modern AI market.
Chapter 3: Turning Market Intelligence into Competitive Advantage
The disconnect between what executives think AI hiring looks like and what actually happens in the market has created some interesting chatter. While C-suite leaders talk about "transforming our workforce with AI," the ground-level reality involves a lot more complexity and some very granular market understanding. As it happens, tech recruiters in the trenches with these AI professionals are perfectly primed to deliver value here.
Positioning Through Market Expertise
The most successful firms have stopped leading with their candidate pipeline and started leading with their market understanding. When they engage with potential clients, the conversation begins with insights about AI talent availability, compensation trends, and realistic timelines.
This approach works because it addresses the fundamental problem most organizations face with AI hiring: they don't know what they don't know. A manufacturing company that's decided to implement AI in their quality control processes typically understands their business problem, but has limited insight into the talent market for AI professionals who can solve it. A staffing firm that can explain the difference between hiring a machine learning engineer versus an AI implementation specialist immediately demonstrates value beyond candidate sourcing.
The positioning conversation changes from "we can find you AI talent" to "here's what we're seeing in the AI talent market and how it applies to your situation." The firm becomes a source of market intelligence rather than just a vendor providing candidates. This shift often leads to longer-term client relationships because the value extends beyond individual placements.
Firms that have made this transition successfully tend to invest more time in market research and industry analysis. They track compensation trends, monitor skill demand patterns, and maintain relationships with AI professionals even when they're not actively placing them. This investment in market intelligence becomes a competitive differentiator that's difficult for other firms to replicate quickly.
Putting It Into Practice
The following toolkit provides the tactical frameworks needed to position your firm as the AI hiring authority while managing client expectations and winning competitive searches through market expertise rather than just candidate volume.
Converting Insights into Client Conversations
The conversation structure changes significantly when market intelligence becomes the foundation. Instead of starting with capability presentations, you can stand out from competition by sharing relevant market observations and asking informed questions about the client's specific situation. Here are some examples:
Quick Links:
Framework for Managing Objections
Client Assessment Scripts
The conversation structure changes significantly when market intelligence becomes the foundation. Instead of starting with capability presentations, you can stand out from competition by sharing relevant market observations and asking informed questions about the client's specific situation. Here are some examples:
Opening Positioning Statement:
"Before we discuss specific roles, I'd like to share what we're seeing in the AI talent market and how it might impact your timeline and approach."
Market Reality Framing:
"What we're seeing across our client base is a 60-90 day average time-to-fill for specialized AI roles, with compensation premiums of 18% above traditional tech roles. Here's why..."
AI Maturity Quick Assessment:
Use these questions in discovery calls to quickly gauge where clients stand:
- "When you say 'AI talent,' are you looking to build AI systems or implement existing AI tools?"
- "What's driving the urgency—competitive pressure, a specific project deadline, or strategic planning?"
- "Who on your current team has experience evaluating AI solutions?"
- "What's your timeline expectation, and how flexible is that based on market conditions?"
Framework for Managing Objections
"AI talent is too expensive."
To respond, don't argue cost. Reframe around ROI and risk.
Try something like this:
"You're right that AI talent commands a premium. Our clients who've been most successful think about it this way: the cost of hiring the wrong person or taking six months longer to implement is typically much higher than the compensation premium. Would it help if I shared some examples of how other organizations in [industry] have structured their AI hiring investments?"
"We need someone who knows everything about AI."
This is an opportunity to educate on specialization reality.
Try something like this:
That can be a red flag for experienced AI professionals. The field has become highly specialized—someone who builds neural networks from scratch typically isn't the same person you want managing AI implementation across business units. Can we talk about what specific problems you're trying to solve? That'll help us identify whether you need an AI Builder, an AI Orchestrator, or someone who can use AI tools to work more efficiently in their own role."
"Can't we just train our current team?"
Here you can acknowledge validity while presenting alternatives that are more likely to be successful.
Try something like this:
"Training existing team members is often part of a successful AI strategy, but it typically works best alongside hiring specialized expertise. The learning curve for complex AI implementation is steep, and there isn’t a lot of findable information out there about this emerging tech. Most organizations benefit from having someone who's already navigated those challenges. Would a hybrid approach make sense, bringing in an AI specialist, either contract or full-time, while developing internal capabilities?"
"Junior developers with AI skills should be easier to find."
Address the junior talent paradox.
Try something like this:
"This is actually one of the most challenging segments right now. Junior developers often have AI tool familiarity but lack the depth to troubleshoot when things go wrong or adapt tools to complex business requirements. The sweet spot we're seeing is mid-level professionals with strong fundamentals who've developed AI competencies through practical application."
Client Education Conversation Starters
The conversation structure changes significantly when market intelligence becomes the foundation. Instead of starting with capability presentations, you can stand out from competition by sharing relevant market observations and asking informed questions about the client's specific situation. Here are some examples:
Industry-Specific Market Intelligence:
- "In financial services, we're seeing the highest demand for AI professionals who understand regulatory compliance..."
- "Healthcare organizations are prioritizing candidates with experience in data privacy and clinical workflows..."
- "Manufacturing clients are finding success with AI professionals who have operational experience..."
Geographic Reality Check:
"AI talent is concentrated in major tech hubs, but remote work has changed the game. Here's what that means for your search strategy..."
Competition Context:
"You're competing against [tech companies/startups/consulting firms] for this talent. Here's how successful clients differentiate their opportunities..."
Scripts for Positioning your Expertise
You have one key advantage your clients don’t have: you have seen successful AI placements and have experience communicating with candidates with niche skillsets related to AI. Offer some of the insight you have gained to your customers and potential clients when you can to showcase what you know. For example:
"We've been tracking AI hiring trends across [X] placements this year, and we're seeing some interesting patterns that might impact your search..."
"Based on our recent placements, here's what's working for organizations similar to yours..."
"Other staffing firms might promise quick AI hires, but we've found that taking time upfront to understand your specific AI implementation needs leads to much better long-term outcomes."
Overcoming Objections with Market Reality
The AI hiring market rewards expertise over volume. While your competitors chase "AI developers" with generic searches, you now have the framework to identify whether clients need Builders, Orchestrators, or Enhanced Professionals—and the tools to source each type effectively. The geographic shifts, industry patterns, and skill evolution outlined here represent immediate opportunities for firms that can act on market intelligence.
Organizations will pay premium rates for recruiters who can explain why their AI hiring timeline should be 90 days instead of 30, why their budget needs to account for compensation premiums, and why the person they really need isn't the person they're asking for. The firms still treating AI hiring like traditional tech placement will struggle. Those who master these market dynamics will build sustainable competitive advantages that compound over time.
Your next move: audit your talent database using the AI-adjacent skills framework, then start leading client conversations with market insights instead of candidate availability. The AI skills economy is reshaping tech recruiting so make sure you're positioned to profit from it.