How AI is changing the HR function
AI is automating significant portions of HR work, from resume screening to employee onboarding to workforce planning analysis. The change is uneven across different HR activities, and the organizations navigating it well are distinguishing clearly between what AI handles reliably and where human judgment remains necessary.
How AI is changing the accounting profession
AI is automating significant portions of accounting work that used to require human time and expertise. The accounting profession is adapting, but the change is uneven across different types of work, different firm sizes, and different segments of the market.
What AI means for the outsourcing industry
Business process outsourcing has been one of the most durable cost-management strategies in enterprise operations. AI is changing the economics that made outsourcing attractive, and the change is faster and more structural than most enterprise buyers and outsourcing providers have prepared for.
How the AI vendor market is consolidating and what it means for buyers
The AI vendor market is undergoing structural consolidation. The number of viable foundation model providers is narrowing, platform layers are absorbing point solutions, and enterprise buyers who made early AI procurement decisions are renegotiating or reconsidering them. Understanding the consolidation forces helps buyers make better decisions now.
How AI is changing what companies buy from consultants
The traditional consulting model sells access to knowledge that clients do not have: frameworks, benchmarks, best practices, research. AI has made much of that knowledge accessible directly. The consulting market is not disappearing, but what clients are willing to pay for is shifting, and both buyers and sellers need to understand the change.
What enterprise AI buyers get wrong about build versus buy
The build-versus-buy decision for AI is genuinely different from the same decision for traditional software. The frameworks that worked for ERP or CRM do not transfer cleanly, and the mistakes companies make are predictable enough that they are worth understanding before you make them.
Why AI changes how companies think about data ownership
Data has always mattered, but AI changes what it means to own it, what it is worth, and what obligations come with it. The companies working through these questions now are ahead of regulatory and competitive pressure that will arrive whether they are ready or not.
What AI means for software pricing
AI is changing the economics of software in ways that are starting to show up in pricing models. Usage-based pricing, outcome-based contracts, and AI-specific cost structures are challenging how software gets bought and sold. What buyers and vendors need to understand about where this is heading.
The AI vendor due diligence checklist
Buying AI software is different from buying traditional software. The evaluation criteria are different, the failure modes are different, and the questions vendors are used to answering are not always the ones that matter most. A practical guide to what to ask and how to verify the answers.
How AI is changing the software vendor landscape
The software vendor market is reorganizing around AI in ways that will matter to every buyer. Some changes are already visible; others are still working through the system. Understanding what is happening makes for better purchasing decisions.
The AI skeptic's guide to getting value anyway
Healthy skepticism about AI is well-founded. A lot of what gets claimed about AI does not hold up. But wholesale skepticism is also a trap: a few specific AI applications genuinely change what is possible, and dismissing everything because some things are overhyped means missing those.
AI in regulated industries: what actually changes
The conversation about AI in regulated industries is usually framed as a conflict between innovation and compliance. That framing is wrong. The real constraint is not regulation but the specific requirements that regulation imposes, which are more tractable than they appear and sometimes work in AI's favor.
The AI talent market: what companies are actually competing for
The AI talent shortage most companies experience has almost nothing to do with AI researchers and everything to do with engineers who can ship AI products reliably. Understanding the actual shape of the talent market changes how you hire, how you retain, and where you invest in developing internal capability.
How AI is reshaping competitive strategy
The competitive advantages that have held for decades are being stress-tested by AI. Speed of implementation is no longer a durable moat. The organizations rethinking where their real advantages lie are better positioned than those optimizing harder for advantages that are eroding.
What AI means for technical documentation
Technical documentation has a new audience: AI systems that consume it to answer questions, generate code, and assist with operations. That changes what good documentation looks like, which parts of the investment pay off, and where human writing still has no substitute.
How AI is changing software testing
AI tools are reshaping software testing in ways that go beyond generating test boilerplate. The more interesting changes are in what gets tested, who finds the gaps, and how teams decide what 'enough coverage' means.
How AI changes hiring in technical roles
The skills that distinguish strong technical candidates are shifting. Hiring processes that optimize for what candidates can build from scratch are increasingly misaligned with what makes a technical professional valuable when AI tools are available.
The state of AI in 2026: what changed and what did not
2026 was a year of real progress in AI capability and significant noise about what that progress means. Here is an honest accounting of what actually shifted, what stayed stuck, and what that implies for the year ahead.
The real cost of AI technical debt
AI technical debt accumulates differently than traditional technical debt and is harder to see until the costs become unavoidable. The shortcuts that look expedient in early AI deployments create compounding costs that most organizations are underestimating.
The AI vendor due diligence checklist
Evaluating AI vendors with traditional software procurement criteria misses the risks that matter most. Here is what to ask about production reliability, data handling, model versioning, and vendor lock-in before you commit.
The AI skills gap is not what you think it is
The conventional narrative says companies need more ML engineers and data scientists. The actual shortage is different: domain experts who can evaluate AI outputs, and organizations that know what they are trying to accomplish before they start hiring.