Basic proficiency in generative AI tools isn’t enough
The majority of professionals today know how to use ChatGPT, Google Gemini, and the usual set of AI productivity tools. What separates one applicant from another is how they use AI to solve real problems, in real workflows. If you’re hiring today, you want thinkers, people who see AI as a partner, not a shortcut.
This is about showing your team understands what’s beneath it. For example, they should know what data goes in, what process happens in the middle, and what the outcome really tells us. That’s the core of AI fluency now.
According to staffing firm ManpowerGroup, job ads referencing “AI skills” rose by 5% in 2023. That’s not just a hiring trend, it’s a signal. More companies want people who can meaningfully apply these tools, not just play with them. Carter Busse, CIO at Workato, said it plainly: “If an agent can look ahead to help a human, that will be game changing.” That’s the level of value we’re aiming for, forward-thinking collaboration between human decision-making and machine logic.
From a leadership standpoint, there’s clear ROI in building teams who treat AI this way. You’re no longer just reacting, you’re shaping outcomes based on provable intelligence. Data becomes a lever, not a report. If you want speed, precision, and scalability, this is the level of engagement with AI that drives all three.
Context engineering surpasses traditional prompt engineering
Forward motion in AI doesn’t slow, it compounds. Prompt engineering was a necessary step, a good one, but an early one. The real leap is context engineering. This is where work begins to scale.
Put simply, context engineering means designing your AI setup in a way that delivers consistent, reliable answers, even as models evolve behind the scenes. Unlike classic prompt use, which can produce unpredictable outputs from the same input, context engineering adds stability. It tells the AI what matters, where the boundaries are, and what the user needs, every time.
Bekir Atahan, VP at Experis Services, explains it well: “Humans are moving from operators to policy designers.” In practical terms, this means your subject-matter experts, whether in legal, finance, marketing, or logistics, need to define how AI behaves under different scenarios. This goes beyond knowing the tool. It demands people who understand the rules of your business, the risks of machine hallucinations, and how to spot flawed assumptions.
From a C-suite perspective, this shift is important. It means quality control at scale. When you’ve got consistent output, you have trust. When your AI tools reflect your domain logic, you have relevance. The future of work in AI isn’t about having the fastest model. It’s about having the most dependable one, worked into processes that actually support your strategy. You don’t need to micromanage the output, you need to guide the patterns.
Your edge will come from your people, not just coders, but decision-makers who know their space and can teach machines to think with precision. That’s where the long-term value is.
AI governance and trust building are critical competencies
AI is moving fast. The real challenge now isn’t building new models, it’s managing them responsibly. Governance is no longer optional. It’s central to whether AI delivers reliable value or becomes a liability.
What does good AI governance look like? It’s a framework that ensures decisions made by AI are traceable, explainable, and aligned with business priorities. It means your team knows where data comes from, how it’s processed, and how outputs are validated. It’s not about paranoia, it’s about precision and ownership.
Deepak Seth, Director Analyst at Gartner, put it clearly: “Beyond algorithms and coding, the next wave of AI talent must bridge technology, governance, and organizational change.” He’s right. The real skill isn’t coding. It’s building systems of trust that support innovation without compromise. That includes defining accountability, ensuring compliance, and maintaining the credibility of your AI tools across business units.
For executives, this matters. If your AI isn’t trusted by users or stakeholders, it won’t scale. And if it fails under scrutiny, it will set you back more than it helped. Trust is limited by how well your governance systems work, especially when AI outputs influence customer service, financial decisions, or internal operations.
Gartner’s outlook is clear: governance will be among the most valuable AI competencies in the short term. It’s not something you add later. It needs to be part of the foundational approach as you expand AI use. Skip it, and you’re betting everything on blind automation.
Demonstrable experiential learning is key to standing out
What separates an average AI user from one who can drive strategic innovation is simple: experience. Not credentials. Not theory. It’s the ability to show where a tool was used, what outcome it delivered, what failed, and what was learned. That kind of thinking isn’t just useful. It’s scarce, and valuable.
Matthew Blackford, VP of Engineering at RWS, called out a key hiring signal: “Strong candidates can talk honestly about something they tried, what did not work, and what they learned.” It’s about building teams that improve by doing, not theorizing. Hiring managers are starting to lean into this, asking candidates how they’d rethink standard workflows using generative AI. The answers reveal more than any résumé can.
From a C-suite perspective, this gives you a meaningful filter. When you’re hiring product managers, engineers, or team leads, focus on people who’ve applied AI in ways that solve real problems. People who test, learn, and adjust fast. They won’t waste time guessing because they’ve already faced the friction and know what works.
This mindset also scales. Teams built on curiosity and execution adapt quickly. They spend less time chasing trends and more time building results. If your goal is to make AI a core capability across business units, experience-driven hiring is not a recommendation, it’s a requirement. The best results in any fast-moving environment come from people who’ve actually done the work.
Internal upskilling and governance initiatives enhance organizational value
Smart companies aren’t just hiring for AI talent, they’re building it internally. Upskilling programs have become essential for organizations that want to stay technically relevant and strategically agile. When employees are trained to use AI tools, not just for function execution but for problem-solving across teams, it reduces dependency on external hires and accelerates innovation from within.
These programs work best when paired with structured governance. At Ivanti, for instance, the AI Governance Council gives employees a framework to test and propose AI tools while maintaining oversight and accountability. This approach encourages internal experimentation without sacrificing control. It also reinforces a culture where innovation is encouraged, and risk is managed, not avoided.
Brooke Johnson, SVP and Chief Legal Counsel at Ivanti, made the goal clear: “The council allows employees to submit tools for review, fostering collaboration and innovation while maintaining oversight and accountability across all departments.” That balance, between flexibility and discipline, is what makes these initiatives impactful.
For executive teams, the outcomes are tangible. You create talent pipelines tailored to your own operations. You strengthen institutional knowledge with people who already understand your business. And you embed AI capabilities across departments without losing track of compliance, data integrity, or strategic alignment. Upskilling isn’t a short-term fix, it’s a long-term asset, and governance ensures it scales without going off track.
Adaptability and continuous learning outweigh fixed AI skillsets
In AI, whatever skill you think is critical today might be outdated in a year, or less. That’s not a barrier. It’s the reality of working in a rapidly shifting environment. The people who succeed aren’t the ones holding onto current tools, they’re the ones ready to absorb the next one. Fast.
According to Deepak Seth from Gartner, the pace of evolution in AI tech makes static skills obsolete. What matters more is someone’s ability to learn quickly, adapt, and experiment under new conditions. “There will be no perfect set of skills,” he said. “Attitudes are more important.” That mindset, agility over rigidity, is what executive teams need to look for and reinforce.
Matthew Blackford at RWS echoed this, pointing out that genuine interest and responsiveness to change are now more crucial than having one narrow specialization. That doesn’t mean technical grounding doesn’t matter, but it means it must be paired with curiosity and learning velocity.
This adaptability has bottom-line impact. It lets your teams keep pace as tools evolve, as regulations shift, and as new use cases emerge. It also prevents stagnation, teams that are encouraged to explore new capabilities bring those advantages back into their everyday roles. You build internal resilience, not just technical compliance.
As AI moves from assistive tech to core infrastructure, static roles won’t drive transformation. People who can move with change, and lead through it, will. That’s how you protect and grow your competitive position in an environment that won’t slow down.
Key takeaways for decision-makers
- Basic AI usage isn’t enough: Leaders should prioritize hiring professionals who can apply AI to real business problems and demonstrate strategic thinking beyond tool operation.
- Context engineering is the new standard: Organizations should invest in training domain experts to design context-aware prompts that deliver accurate, consistent AI outputs across evolving models.
- Governance powers scalable AI: AI strategy must be built on a foundation of trust, making governance, accountability, and risk mitigation core competencies.
- Experience proves capability: Prioritize candidates and teams with hands-on AI problem-solving experience, as experimentation and iteration reveal practical value far beyond academic understanding.
- Internal upskilling drives sustainable innovation: Companies should establish cross-functional AI upskilling programs paired with governance councils to foster innovation while preserving control.
- Adaptability beats static skillsets: The most valuable hires will be those who learn fast and adapt to new tools, ensuring your organization stays competitive as AI tech rapidly evolves.
