The AI-Readiness Crisis: Why Businesses Can't Wait for Universities
More than half of recent college graduates don't know if their colleges have prepared them for "the use of generative AI," according to a Cengage Group cited by Higher Ed Dive, while about 66% of employers feel that potential job candidates should have "foundational knowledge" of generative AI tools.
This disconnect is causing problems for the hiring process—especially, in my experience, for recent computer science grads.
In my role, I've spoken with dozens of bright developers who have never used GitHub Copilot, have no idea how machine-learning pipelines or observability frameworks work and don't comprehend how SHAP or LIME describe models. Let me be clear: This is a reflection of how quickly industry changes and how slowly academia adjusts, not of these developers' abilities.
Having managed international AI engineering teams, I've seen how these gaps manifest locally as longer onboarding times, a lack of trust in AI tools and lost chances to streamline development cycles. To solve this challenge, companies can't wait for universities to catch up to AI. They need to find ways to train engineers to adapt to this ever-changing technology.
According to a 2024 survey from Kyndryl, 71% of business leaders feel their workforce is not yet ready to leverage AI, with many citing the lack of "skilled talent needed to manage AI" as a major reason. Companies without an AI-savvy workforce are compelled to postpone deployments, contract out critical functions or deal with poor product quality.
The good news is that, according to McKinsey, nearly half of employees want more formal AI training, but other research shows that only 31% of employers are providing AI training. When AI talent can't be hired fast enough, it must be developed from within.
Big Tech is already working on this, with Microsoft, Google, IBM, Intel, SAP and Cisco collectively planning to train over 100 million workers.
I've seen the success of these types of programs at my own company, where we set up an internal AI bootcamp, started hands-on labs that were directly related to real-world projects and matched junior engineers with mentors who had experience with AI.
To promote practical upskilling, we also host project-focused webinars, arrange hackathons with an AI focus and assign structured learning paths on sites like Udemy to guarantee ongoing improvement.
Based on these experiences, here are five ways to bridge the AI skills gap at your organization:
1. Launch an internal AI learning program. Instead of using pre-made tutorials, create learning tracks centered on actual issues that your engineers encounter, such as using AI for CI/CD optimization, auto-generating test cases or enhancing search relevance with natural language processing.
2. Make AI a core part of DevOps. AI is not an "optional add-on." Tools like Amazon CodeWhisperer and GitHub Copilot are quickly taking over as the standard. Integrate them into documentation procedures, deployment flows and code reviews.
3. Promote peer mentorship. While formal training has its place, one-on-one, contextual mentoring frequently works better. Establish "AI champion" positions and facilitate team members' real-time shadowing and learning.
4. Measure AI tool adoption. Keep track of how often engineers use AI tools for backlog grooming, testing, debugging and code commits. Organize frequent hackathons or internal demonstrations centered on AI-enhanced engineering.
5. Partner with academic institutions. Talk to the faculty at the schools you hire a lot from. Provide real-world problem statements, fund student projects with an AI theme or collaborate on developing modular course materials. It helps your brand and the talent pipeline.
There is no longer any room for speculation regarding the move toward AI-native development. It has already arrived. In addition to writing code, developers are now expected to work with machines to direct and verify the output of AI. Businesses that don't facilitate this change will experience increased turnover, higher training expenses and decreased developer productivity.
On the other hand, companies will gain a compounding advantage if they make AI fluency a strategic capability for all engineers, not just data scientists. They will attract top talent who wish to build for the future rather than the past, ship more quickly and adapt better.
Don't wait for the AI gap to be filled by higher education. Begin within your organization. Invest in mentorship, align tooling with learning and cultivate an internal culture of AI fluency. The ability to code with AI is more important than simply knowing how to code in the future of software engineering.
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