The career stories enterprise IT pros actually read.
Volume 342, Issue 2 · 6 articles from 2 issues
TechTarget highlights 10 AI certifications and courses, from Stanford and MIT programs to IBM's Coursera certificate, for AI careers.
With Gartner forecasting worldwide AI spending of $2.52 trillion in 2026, TechTarget rounds up 10 diverse AI certification and course programs.
Picks include Stanford's Artificial Intelligence Graduate Certificate, MIT's Professional Certificate Program in Machine Learning and AI, UC Berkeley's AI business strategies program for executives, and IBM's AI Professional Certificate on Coursera.
The guide details each program's key elements, costs and prerequisites. It notes certifications can differentiate job candidates in a field where many AI job postings require advanced degrees.
A photo guide to the skills computer engineers need as AI reshapes software work, from AI literacy to cloud and security.
AI is becoming part of everyday software development, making AI literacy essential for computer engineers.
Strong programming in languages such as Python, Java and JavaScript still matters for building systems and catching errors in AI-generated code.
Data skills, cloud computing and deployment knowledge are increasingly core as AI applications demand significant resources.
Engineers also need stronger cybersecurity skills, the ability to work with and verify multi-step AI agents, and critical thinking to evaluate AI outputs for errors and security risks.
A roundup of career development sites for US professionals, led by LinkedIn, Coursera, CareerOneStop, Indeed, Glassdoor and FlexJobs.
Career growth now requires tools for skills development, job research, salary checks and networking, and different sites serve different needs. LinkedIn leads for all-around use, combining more than 1 billion members with LinkedIn Learning's 26,200-plus courses.
Coursera offers more than 10,000 programs from universities and companies such as Google, Microsoft and Meta, suited to career changers.
CareerOneStop, funded by the US Department of Labor, provides reliable labor-market data on more than 900 occupations, while Indeed, Glassdoor and FlexJobs help assess job demand, salaries, employers and remote work.
AI, data science and machine learning overlap but require distinct skills and lead to different roles; here's how to choose.
Employers often treat AI, data science and machine learning as interchangeable, but each solves different problems and rewards different strengths.
AI is the broad field of systems that reason or decide, machine learning is the set of techniques for learning patterns from data, and data science extracts insight using statistics and business context.
Data science leans on statistics, SQL, visualization and storytelling, while machine learning demands stronger math and frameworks like TensorFlow or PyTorch. The article offers a decision framework to help learners match their aptitude to a specialization rather than chase every trend.
Survey finds AI fatigue among knowledge workers is a change-management failure, not a technology one.
A survey finds 65% of knowledge workers would prefer to reverse workplace AI adoption, citing a steep learning curve and an absence of clear guidance.
The research points to poor change management, thin training and unaddressed employee concerns as the cause of widespread AI fatigue, with staff spending more time verifying outputs than gaining from them. Notably, 67% of the same respondents still want their organisation to use more AI, suggesting the objection is to how it was rolled out.
The takeaway for IT leaders is that transparent communication, real training and deliberate change management are what convert resistance into adoption.
Self-assessment data shows rising IT leaders are strong technically but weak on influence and relationships.
Chris Boyd reports on rising technology leaders at a CIO100 event who self-assessed across five dimensions: enterprise leadership, business acumen, influence, relationships and technology stewardship.
The group averaged 3.4 out of 5 overall, and 64% rated themselves advanced or proficient on technical judgement. But roughly 65% scored below proficient on influence and relationship-building, the very capabilities that separate a strong technologist from a credible CIO.
Boyd recommends closing those gaps deliberately, pairing structured goals with specific measurable tactics on six-month horizons rather than waiting for exposure to happen.