Accounting firms rushed to adopt point AI tools and now face a fragmented, expensive stack. Short-term gains from features are real; long-term competitive advantage comes from a firm-level AI foundation that connects models to firm data, workflows, permissions, and governance.
Buying isolated AI features produces short-term productivity but creates integration, governance, and maintenance costs that multiply with each tool.
Many organizations fail to scale AI because they lack accessible data foundations, governance for agents, and integration capability.
Without foundational work, agentic AI projects face high cancellation risk and escalating costs despite large planned AI budgets.
Firms adopted AI one use case at a time: a tool for general productivity, another for professional research, another for audit and document work, and increasingly, AI embedded in individual applications and workflows. Firms wanted immediate efficiency gains, and employees adopted the tools that helped them work faster. As these AI features accumulated, each became its own technology decision, with its own cost, integration requirements, data considerations, workflow implications, and governance questions.
It lets AI capabilities scale across the organization instead of being deployed as isolated point solutions, and it creates a common base on which new capabilities can be added, governed, measured, and improved over time. Buy ten features and you have paid that bill ten times over, because nothing built for one is reused by the next. It predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, and inadequate risk controls.
The result is that AI adoption has outpaced firm-wide strategy and control. AI is already in use across the organization, but not through a coordinated, centrally governed approach. This is why buying the latest AI feature is unlikely to create a lasting competitive advantage.
Ask yourself one question before you build another workflow around a cheap AI tool: who's actually paying for how cheap it is? Not you, not fully. Nvidia, Microsoft, and Amazon are absorbing enormous losses right now to make AI compute and API pricing look like a bargain, because winning the platform war matters more t
AI makes software development faster and cheaper, but speed alone does not create better products.
At DevSparks Chennai, Intel Director Aditi Nanda said developers may need to spend less time memorizing languages and frameworks, but engineering fundamentals, system knowledge, and the ability to understand user needs will remain essential.
The entrepreneur says he is cynical about frontier model makers suddenly calling for slowing AI progress
When it comes to “buy vs. build, the consensus is moving towards neither—and both. Law firms have adeptly found a middle ground to a question that, from the start, belied the complexity of legal AI era.
Nearly every conversation about AI right now includes some version of the same claim: “Because of AI, we can build this ourselves.” It’s a sentiment shared by private equity firms, investment banks, and professional services organizations across the board. It’s an understandable thesis. AI has made it genuinely easier
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