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.
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.
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 to do when your AI project loses momentum
Most AI projects do not fail with a dramatic announcement. They slow down gradually, lose visibility on the roadmap, and eventually stop without a clear decision being made. Understanding the patterns that cause AI projects to stall is the first step to recovering them.
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.
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 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.
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 to structure an AI center of excellence
An AI center of excellence can accelerate adoption and build durable capability, or it can become a bottleneck that slows everything down. The difference is almost entirely structural. Here is what the effective ones do differently.
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 second act of enterprise AI: what separates pilots from platforms
Most organizations have successfully run AI pilots. Far fewer have converted them into production platforms that deliver compounding value. The gap between pilot success and platform capability is not a technology problem.
What good AI governance looks like
AI governance is not primarily a compliance exercise. It is the set of decisions, processes, and accountability structures that determine whether AI systems produce outcomes the organization can stand behind. Most organizations have less of it than they think.
How to make the business case for AI investment
Most AI investment proposals fail not because the technology does not work, but because the proposal is framed around capability rather than outcome. Here is how to build a case that finance and leadership will approve.
The AI vendor landscape is consolidating: what it means for buyers
The number of credible AI infrastructure vendors is shrinking. For enterprise buyers, that changes the procurement calculus in ways that are not yet reflected in most vendor evaluation frameworks.
What to ask before buying an AI platform
Most AI platform evaluations focus on benchmark scores and feature checklists. The questions that predict whether a platform will work in production are different ones.