It’s 2025, and AI is everywhere — or at least, that’s the promise. Executive teams talk about it, budget cycles are aligned to it, and most enterprise roadmaps include it in some form. But here’s the uncomfortable truth: for all the pilot projects and headlines, many organizations are still not seeing material ROI from AI.
At Remarkable Advisors, we’ve heard the same frustration repeated across industries: “We’ve implemented AI, but we’re not seeing the impact.” And they’re not alone. A Boston Consulting Group executive recently summed it up well — companies are spreading themselves across too many projects and ignoring the foundational workflow redesign required to make AI stick. In other words, they’re throwing AI at problems that don’t yet have the operational discipline to benefit from it.
So what should change?
The Illusion of Progress: What’s Really Going Wrong
Let’s start with what’s going wrong in 2025. Despite the rapid maturity of generative AI models and enterprise tooling, most AI initiatives stall at one of three points:
- Too many initiatives, too little focus: Companies are launching five, ten, even fifty AI pilots across departments. But few of these efforts are truly resourced for success, and even fewer are aligned with enterprise-wide priorities.
- Workflow misalignment: AI doesn’t just automate work — it reshapes how work gets done. If you plug AI into a broken or manual workflow, you amplify inefficiency, not solve it.
- No embedded accountability: Many AI programs operate on the margins of business operations — run by innovation teams or labs with little operational ownership. When things break, there’s no clear line of responsibility.
Add to this the ongoing uncertainty around regulation, explainability, and security, and it’s no surprise that many AI efforts look good on paper but fizzle in production.
Step One: Align AI to Business Priorities
The companies seeing value from AI in 2025 aren’t necessarily spending more. They’re just more strategic and disciplined. They begin by asking:
- What are the top 2–3 business outcomes we’re prioritizing this year?
- How can AI support or accelerate those specific outcomes?
- What’s already working manually that AI could enhance?
AI should never be the starting point. Start with the business goal, then ask where AI makes a difference — not where it fits.
Example: A medtech firm wanted to boost digital lead conversion. Instead of jumping straight into AI chatbot vendors, they first mapped the entire customer journey. Only after identifying friction points did they prototype a narrowly scoped AI tool that could offer content recommendations to HCPs based on prior behavior. That tool now runs in production with a measurable lift in engagement.
Step Two: Rethink Workflow Before Deploying AI
Here’s the part most teams miss: AI requires new ways of working.
Too often, we see AI layered onto legacy workflows — or worse, used as a patch for broken handoffs, approvals, or manual checkpoints. But generative tools don’t thrive in that environment. They require:
- Fewer handoffs
- Clear inputs and feedback loops
- Defined ownership of exceptions or edge cases
If you’re building a content generation tool but haven’t updated your review or compliance process, your output will stall. If your AI is summarizing support tickets but your agents don’t trust the summaries, usage drops.
Tip: For each AI use case, map the before and after workflow. Who’s responsible at each step? What happens if the AI fails? What approval gates change or disappear?
This isn’t just about UX — it’s about org design. AI shifts where decisions are made, how long tasks take, and what level of trust teams place in software. You must be deliberate in managing that transition.
Step Three: Don’t Just Prototype — Operationalize
It’s tempting to stay in the “AI pilot” comfort zone. The demo works, the team is excited, and it’s easy to declare success. But unless you operationalize — meaning it’s fully integrated into your systems and workflows — you won’t get sustained value.
To operationalize AI:
- Integrate outputs directly into existing tools (CRM, ticketing, analytics)
- Set clear SLAs for performance and escalation
- Monitor for drift or hallucination, and refresh models or prompts as needed
- Establish product ownership — not just technical maintenance, but business accountability
This is where most AI efforts stall. Who owns prompt updates? Who checks model accuracy each month? Treat your AI tool like any other business system: assign it owners, metrics, and a review cadence.
Key idea: AI systems need post-launch care just like SaaS products. Without maintenance and iteration, your launch ROI degrades within months.
Step Four: Train Teams for Human-AI Collaboration
Here’s the truth many execs don’t want to hear: AI won’t replace most of your team — but your team needs to learn how to work with AI.
That means:
- Training teams to ask better prompts, not just use the UI
- Building muscle around interpreting and correcting AI output
- Defining new roles (e.g. Prompt Librarian, Output Validator, Workflow Designer)
- Shifting KPIs to reflect AI-augmented performance
The skill gap here is real. Many knowledge workers are either overconfident (trusting AI too much) or underconfident (avoiding it entirely). The middle ground — effective, empowered use — comes from training, policy, and cultural reinforcement.
Step Five: Score Use Cases by Feasibility, Not Novelty
Lastly, remember this: the best AI use cases are often boring.
Think document summarization, call log classification, or automated QA reports. These may not win headlines, but they drive real savings and time-to-value.
When prioritizing what to build next:
- Score ideas on feasibility (data availability, integration ease, model readiness)
- Consider change management cost — what’s the organizational lift?
- Focus on use cases with clear measurable impact
We recommend maintaining a living backlog of AI opportunities across departments. Use a simple scoring framework (value, effort, readiness) to guide quarterly prioritization.
Framework Tip: Use the RICE model (Reach, Impact, Confidence, Effort) to evaluate.
Final Thought: AI Strategy = Operational Strategy
In 2025, AI isn’t a side project. It’s not an innovation showcase. It’s a lens through which operational work gets redesigned.
If your AI investments aren’t producing value, it’s likely not the model’s fault — it’s the organizational scaffolding around it that needs to evolve.
The firms winning with AI this year are doing less, but executing with clarity. They’re aligning use cases to real outcomes, redesigning workflows to support them, and embedding accountability for ongoing maintenance.
That’s the roadmap. The rest is just noise.