Enterprise AI: Build or Buy?
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As we look toward 2026, it’s clear that enterprise AI is entering a deeper, more strategic phase. The conversation is shifting away from experimentation, toward systems that are reliable, governable, and tightly aligned with real business outcomes. Here are some of the enterprise AI trends I believe will define how organizations build, buy, and scale AI over the next year and beyond.
Enterprises that spent years building internal AI platforms will realize those efforts didn’t produce meaningful outcomes. They’ll turn to external partners who can deliver business outcomes fast and at scale, while keeping only their real core IP in-house. Building the entire AI stack themselves doesn’t create value and pulls focus away from what actually moves the business forward.
Predictions from Forbes and SAP point to rapid consolidation: enterprises are moving away from fragmented AI point tools and toward unified AI platforms that combine knowledge retrieval, reasoning, workflow orchestration, governance, and observability into a single system.
A full-stack AI company isn’t just selling tools, it’s building an entire business around AI and competes directly with incumbents. The real advantage comes from owning the full workflow. That’s why legacy players can’t afford to move slowly: if they don’t ramp up their AI adoption fast, they risk getting outpaced by AI-native challengers that are leaner, faster, and built to win in this new landscape.
Fast, no-code app generators definitely grab attention and create a lot of buzz, but most of what they produce isn’t truly production-ready, let alone enterprise-grade. Teams experiment with them, hit the limits pretty quickly, and then move on in search of something that can actually scale.
Models will keep getting better, but only incrementally - there’s no massive change coming anytime soon. That means the real advantage is shifting to the application layer: taking the models we already have and using them to solve real, everyday work problems, rather than waiting around for the next big breakthrough.
Expect more regulatory frameworks globally in 2026, including EU AI Act phases, NIST, and sector-specific regulations.
Enterprise requirements will emphasize:
Enterprises are shifting away from one “big agent” toward coordinated multi-agent systems with role separation, shared context, and cooperative task execution. Multi-agent ecosystems will emerge as enterprise AI adoption increases.
Gartner predicts agents will be embedded in 40% of enterprise applications by 2026. At the same time, McKinsey outlines the rise of multi-step, goal-driven agents that begin to for e.g. mimic junior analyst roles - breaking down tasks, executing multi-step workflows, integrating across systems, and operating under enterprise policies.
Agents that:
Context layers finally go mainstream. Enterprises adopt semantic layers, knowledge graphs, and knowledge fabrics to support agents, explainability, and high data quality.
Here’s why:
As there is a push for measurable ROI, enterprises shift from usage-based pricing (“pay per token”) toward outcome-based pricing models tied to business metrics. Research by BCG demonstrates that AI increasingly maps to value delivered, not compute consumed.
Enterprises are tired of AI projects failing at a 95 % rate, so they’re pushing for pricing tied to real business outcomes instead of raw consumption. Currently, most vendors can’t deliver this well, but the pressure will keep growing because buyers see outcome-based models as the closest thing to guaranteed value.
Enterprises embrace domain models, smaller specialized LLMs (SLMs), and industry-specific knowledge layers. The trend is not new, but by 2026 it will become mainstream and expected. Enterprises must choose LLM-agnostic, future-proof solutions, even allowing them to bring their own models (BYOM).
Following Anthropic’s disclosure of the first AI-orchestrated cyber-espionage campaign, enterprises recognize the need for full observability across AI agents. Monitoring models is no longer enough - organizations require real-time agent behavior tracking, anomaly detection, drift alerting, and action logs, especially in regulated or mission-critical workflows.
Enterprises demand:
What stands out across all of these trends is a shared direction: enterprise AI is becoming more structured, more contextual, and more outcome-driven. The winners in 2026 won’t be those chasing the newest model, but those building resilient AI foundations that balance autonomy with control. This is the moment for leaders to think long-term and design AI systems not just for what’s possible today, but for what will be necessary tomorrow.
