Enterprise AI reality check: 4 lessons we learned in 2025 and 4 predictions for 2026

2025 was a step-change year for enterprise AI. For many teams, it was the year AI moved from experimentation to shipping. We saw real progress, but also a consistent pattern: moving from pilot to scale is still hard. McKinsey’s 2025 survey puts numbers behind this: 88% of organizations use AI in at least one business function, but only 7% have fully scaled AI across the organization.

In this post, we will share the biggest lessons our team at Gruve learned from 2025, and our predictions for what will matter most in 2026.

Lessons learned in 2025

Lesson 1: Define ROI early, avoid the R&D trap

The biggest lesson we learned in 2025 is that enterprise AI projects without ROI defined upfront are the most likely to fail later. If the outcome isn’t clear from day 0, the work can easily drift into an open-

ended R&D effort with scope creeps and stretched timelines. Sooner or later, the project gets questioned or shut down.

Before teams even pick AI models, they need to align on success criteria: what workflow we are improving exactly and how we will measure “better.” Demos are useful signals, but they are not milestones. If ROI is genuinely hard to quantify, time-box the effort: if there is no ROI within X months, simply pause and re-allocate resources. If you do need real exploration, separate it from delivery and set up a small R&D track, so the core delivery team’s timeline and resources stay protected.

Lesson 2: Siloed data is one of the biggest value killer

In 2025, we saw many teams deploy AI and then complain the ROI just wasn’t there. Beyond adoption challenges, the biggest blocker was often much simpler: the data wasn’t ready.

It’s easy to expect LLMs (Large Language Models) to infer missing context, but general-purpose models are trained on public internet data. Without access to the right internal context, they can’t deliver reliable, high-value answers. Even when companies connect AI to their Google Drive or SharePoint, results can still feel limited, because the most important knowledge usually lives across many systems, not just one folder.

If enterprises want AI to work for specific teams, such as sales, marketing, and engineering, they almost always need team-specific data sources wired in: CRM, IT tickets, product docs, analytics, and more. So before concluding “AI has no ROI,” we learned to ask a simpler question first: do we actually have the right data connected?

Lesson 4: Deployment is where enterprise AI succeeds or dies

If 2024 was about prototyping, 2025 was about taking those prototypes into production. Across our projects, we learned a simple truth: model quality gets you a compelling demo, but deployment quality gets you real adoption.

Teams are often excited to stand up an AI prototype and only later run into the hard challenges, such as usage controls, caching and routing, data integration, latency, and security issues. None of this is as fun as demos, but it’s exactly what determines whether an AI solution survives in an enterprise.

That’s why it’s critical to align traditional engineering teams with AI engineering early, both on ownership and on architecture, so deployment realities don’t become last-minute blockers.

Our predictions for 2026

Prediction 1: AI governance becomes mandatory

In 2025, many enterprise teams, especially in less regulated industries, were still comfortable shipping “quick AI” without stringent governance. That won’t be the case in 2026.

As AI moves into core workflows and starts triggering real business actions, enterprises will require governance as part of deployment. Many will align to frameworks like NIST AI RMF and standards like ISO/IEC 42001 to formalize controls.

In practice, if you can’t show who had access, what data was used for AI (and where it lives), what the system produced, which parts were generated by AI versus humans, and how it’s monitored and auditable, your AI solution may never make it to production. Security, legal, and procurement will have to stop it. That’s why governance obligations need to be part of planning from day one.

Prediction 2: Inference engineering is coming

While many teams are still building AI prototypes, in 2026 there will be more teams trying to scale, especially for workloads with heavy inference, such as multi-step agent flows, audio processing, and image/video pipelines. For those teams, inference engineering will become a top priority.

At scale, the bottleneck shifts from “can the model do it?” to “can we run it predictably under real traffic and constraints?” Inference engineering is about keeping cost-per-run and P95 latency under control without compromising security. The teams that succeed will treat inference like a production system: track unit cost, batch and cache aggressively, route work across a model fleet, manage latency, and build guardrails into the serving path.

Prediction 3: Multi-agent orchestration becomes the default architecture

In 2025, we saw many teams build agents in silos and postpone orchestration. In 2026, we believe multi-agent orchestration will become the default architecture for enterprise AI.

As organizations assemble more specialized agents, an orchestration layer needs to be designed from the start, especially in large enterprises where ownership and tooling quickly get fragmented. In most cases, this won’t be owned by any single app team. It needs a centralized group to set standards and keep the system consistent.

At a minimum, a strong orchestration layer should include three things: a workflow engine that manages steps and handoffs, a permission layer that controls what each agent can access and do, and an observability layer that keeps an end-to-end audit trail and helps teams debug failures.

Prediction 4: Each industry requires more specialized AI solutions

In 2025, many frontier AI labs push generic LLMs forward at an incredible pace. But there’s only so much generic LLMs can do. Multiple researchers have warned about a coming “data wall,” where additional gains from training on more internet text get harder and noisier, because high-quality public text is finite.

That’s why our bet for 2026 is simple: the next real gains will come from domain and industry-specific AI. Healthcare, finance, manufacturing, media, and the public sector all need solutions designed for their specific workflows and constraints.

“Specialized” doesn’t just mean training on more domain data. Teams first have to define what good means. For example, it’s great to have lab notes for healthcare, but a useful output might need to follow a clean SOAP (Subjective, Objective, Assessment, Plan) format, cite the right note sections, and handle uncertainty safely. On top of that, every industry carries its own control requirements, such as HIPAA in healthcare or SOX in financial reporting, which must be built into the solution from the start.

Looking ahead

2025 was about proving AI can work. 2026 will be about running it well. While there is plenty of debate about whether we are living through an AI bubble, the way to stay grounded is simple: focus on real outcomes. Build systems that solve specific problems, hold up under scrutiny, and create value that teams can actually feel in their day-to-day work.