Most companies don’t need more AI. They need AI that ships.
Here’s what we hear most often from the executives we talk to at Perceptive Analytics: “We’ve done the pilots. Nothing is in production.”
The data says they’re not alone. According to McKinsey’s State of AI 2025, 88 percent of organizations regularly use AI in at least one business function, up from 78 percent a year earlier. Yet only about one-third have begun to scale AI across the enterprise. And just 39 percent can attribute any EBIT impact to AI, with most of those reporting less than 5 percent.
That gap between adoption and impact is exactly why the AI consulting market has changed. Five years ago, firms sold strategy decks. Today, a strategy deck without a production path is close to worthless.
The Perceptive POV: We judge an AI consulting engagement by one question: is something running in production, used by real people, and moving a number the CFO cares about? If not, it’s research, and research should be priced and scoped that way.
The five things a serious AI consulting company delivers
- A use-case decision, not a use-case list.
Most roadmaps list 30 ideas. That’s the easy part. The hard part is killing 28 of them. At Perceptive Analytics, we score every candidate on three axes: financial value, data availability, and cost of being wrong. Then we recommend one or two. An internal knowledge assistant with a clean document base will beat an ambitious customer-facing agent built on messy data almost every time. - An honest read on data readiness.
Data is where AI projects quietly die. Gartner predicted that at least 30% of GenAI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs or unclear business value. Perceptive’s roots are in data engineering and analytics, which is why every Perceptive AI engagement starts with the data. If your data isn’t ready, we’ll say so and scope the fix first. Selling you a pilot that can’t scale isn’t something we’ll do. - The build: classic ML, GenAI, or agents.
The right architecture follows the problem. Forecasting and churn usually call for classic machine learning. Contract review and policy Q&A usually call for retrieval-augmented generation (RAG) on a model hosted in your own cloud, whether that’s Azure OpenAI, Amazon Bedrock, or Google Vertex AI. Multi-step workflows may justify agents, but go carefully. Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027. - Production engineering (the part most firms underprice).
A system that runs perfectly on 50 test records in a notebook can buckle at real volume. Production engineering covers monitoring, evaluation, cost controls on LLM usage, access controls, and rollback. This is where Perceptive Analytics puts its most senior engineers. - Adoption and governance.
Who owns the model? How is it audited? What happens when it’s wrong? We align governance to recognized frameworks like the NIST AI Risk Management Framework, so your risk and compliance teams have something concrete to sign off on.
What this looks like in practice
Case study: A financial services client engaged Perceptive Analytics to build an AI-powered document intelligence system that automated contract review, reducing manual processing time by 75%. [CASE STUDY LINK: financial services document intelligence]
Case study: Perceptive built custom machine learning pipelines that cut customer acquisition cost by 20% for a leading financial institution. [CASE STUDY LINK: ML pipelines CAC reduction]
Neither project started with a model. Both started with a narrow business problem, a baseline metric, and a data audit.
Engagement models, and which one fits
| Model | Best for | Watch out for |
| Advisory only | Boards and leadership teams setting direction | No one is accountable for delivery |
| Fixed-scope project | A first use case with a clear KPI | Scope creep if discovery is skipped |
| Dedicated pod | An ongoing AI roadmap | Needs strong internal product ownership |
| Managed AI services | Keeping live systems healthy | Make sure you still own the IP |
For most mid-market companies, Perceptive Analytics recommends a fixed-scope first project that converts into a small dedicated pod once value is proven. You limit risk early without losing momentum later.
Why mid-market companies need a different kind of AI partner
Enterprise playbooks assume a 40-person data science team and a platform budget to match. Mid-market companies rarely have either. What they need is a senior team that has shipped before, a scope sized to their budget, and no six-month discovery phase.
That’s the gap Perceptive Analytics was built to fill. We’ve been a data and analytics consulting firm since 2013, and our AI consulting services carry that data-first discipline into GenAI, machine learning, and AI governance.
Executive takeaway: Don’t ask an AI consulting company what it can build. Ask what it has put into production, what broke, and who fixed it.
Find out what your data can support today. The Perceptive Analytics AI Readiness Assessment gives you a scored view of your top AI use cases and what’s standing between you and production.
Frequently Asked Questions
What does an AI consulting company do?
It identifies high-value AI use cases, assesses data readiness, builds and deploys AI systems, and sets up the governance and adoption needed for those systems to deliver measurable results.
Why do so many AI projects fail to reach production?
Poor data quality, weak risk controls, rising costs, and unclear business value are the most common causes, according to Gartner. A data-first discovery phase catches most of them early.
What makes Perceptive Analytics different from larger AI consultancies?
Perceptive Analytics focuses on mid-market and enterprise companies that need senior engineers, fixed scope, and a fast path to production, backed by data engineering depth built since 2013.
Should our first AI project be customer-facing?
Usually not. Internal, knowledge-heavy use cases carry less risk and prove value faster.