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.
What makes an AI capability defensible
Most AI implementations are easier for competitors to replicate than the teams building them realize. Foundation models are available to everyone. APIs are the same. The question of what creates genuine competitive advantage from AI investment is worth answering before committing substantial resources.
How to communicate AI progress to leadership
AI teams often struggle to communicate progress in terms leadership finds meaningful. Technical metrics like model accuracy and latency tell part of the story, but they do not answer the questions leaders are actually asking. The gap between what AI teams measure and what leadership needs to know creates unnecessary friction.
Why your AI strategy needs a portfolio view
Most organizations evaluate AI investments one project at a time, asking whether each initiative will deliver value. Portfolio thinking asks a different question: what mix of AI investments, taken together, gives us the best expected outcome? The difference in framing produces substantially different decisions.
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 to decide which AI investments to stop
Most organizations have a process for starting AI investments and almost none have a process for stopping them. The result is a portfolio that accumulates underperforming projects indefinitely, consuming resources that could go to initiatives that actually work. Deciding what to stop is as important as deciding what to start.
Why AI projects need sponsors, not just champions
Most AI projects have champions. The engineer who believes in the technology, the team lead who pushed for the pilot, the individual contributor who made it work. What they often lack is a sponsor: someone with organizational authority who has committed the project's success to their own outcomes. That gap is why so many AI pilots succeed and so few scale.
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 most AI strategies are technology plans in disguise
When companies say they have an AI strategy, they usually mean they have a plan to acquire and deploy AI technology. That is not a strategy. The difference matters more than it seems, and the companies that confuse the two end up with expensive infrastructure and no competitive advantage.
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.
The AI reporting problem
Executives want to know how AI investments are performing. Most organizations cannot tell them. The metrics being tracked measure activity, not value, and the reporting structures that work for traditional software do not transfer to AI. Here is what better AI reporting looks like.
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 case for slowing down your AI roadmap
The pressure to move fast on AI is real and the costs of moving too fast are underappreciated. The organizations that build durable AI capability tend to spend more time than their peers on evaluation, integration, and the organizational work that determines whether AI actually changes how things get done.
The AI strategy question most companies avoid
Most organizations building AI strategy answer the questions about what to build and how to implement it. The question that gets avoided is the harder one: what will you stop doing because AI changes the economics? Avoiding it produces AI strategies that add cost rather than change the business.