Where do we truly see the AI landscape evolving? Artificial intelligence has moved from research labs to the heart of business operations. Tools like ChatGPT or Copilot can write, summarize, and generate ideas but they are fundamentally processing engines, not automatic transformers of business.
The AI market is evolving. Large language models LLMs are volume driven, capital intensive products. They require massive computing power, huge datasets, and billions in investment, meaning this base layer will be dominated by a few players monetizing access through usage based pricing. These foundational models are essential but commoditized. The real differentiation comes higher up the pyramid, where AI is applied to solve industry specific problems.
Many companies start small using AI to automate routine tasks or slightly boost productivity. While useful, that is just the beginning. The true advantage comes when AI is embedded in core processes, enabling smarter decisions and measurable outcomes.
The financial sector demonstrates that AI is more than a productivity enhancer, it can be a true strategic partner. While chatbots handle routine tasks, machine learning enables banks to make smarter operational and strategic decisions: forecasting customer demand, optimizing staffing, monitoring account activity, and adjusting credit limits in real time, all while maintaining compliance. Leveraging machine learning effectively, banks could, for instance, save costs by allocating the right staff to customer contacts, predict staffing needs when opening a new branch, or assess whether a new office is truly necessary. Yet only 25 percent of banks have embedded AI into their strategy, according to BCG. Treating AI merely as a tool leaves its potential untapped. With strong data foundations and a unified approach, AI can guide smarter decisions, drive efficiency, and create sustainable competitive advantage.
The next phase shifts AI from a passive assistant to an active decision partner, combining human judgment with machine precision. Beyond staff allocation, it can optimize portfolios, forecast liquidity, or model financial outcomes. Retailers can adjust inventory and marketing spend in real time. The key is linking AI to decision making and strategy, not just task automation.
Organizations that thrive will embed AI into their core, rethink incentives, and align leadership around a bold vision. Think of AI as a pyramid: the base LLMs is capital intensive and concentrated among a few; the middle automation delivers measurable efficiency; and the top, industry-specific applications, creates true differentiation by shaping decisions, not just executing them.
While the base is consolidating, the real fight is at the top. This is where AI will fundamentally change how businesses operate, as SaaS companies and industry specialists leverage foundational models to create high-value, tailored solutions. Success goes to those who combine domain knowledge, strategy, and technology to apply AI effectively and transform industries.
Sara thank you for sharing. Your post captures the evolving AI landscape perfectly—moving from broad, commoditized LLM infrastructure to high-impact, domain-specific applications. It’s a compelling reminder that true value isn't just in automating tasks but in transforming decision-making across industries. The financial sector example shows how AI can shift from assistant to strategic advisor when deeply integrated into operations.
As AI matures, competitive advantage will belong to those who don’t just use AI—but who embed it into the DNA of their business strategy. Every company should answer this question, What will it take for your organization to move beyond experimentation and fully commit to AI as a decision-making partner?