Nexgits Joins the Databricks Partner Programme for AI

Most companies that come to us asking for AI do not have an AI problem. They have a data problem that has been sitting quietly for years, and AI is simply the first project big enough to expose it.

The pattern is always similar. Sales figures live in one system, stock levels in another, customer history in a third, and nobody can say which one is correct. Then someone asks for an AI assistant that answers across all three, and the project stops before it starts.

Nexgits is now registered in the Databricks Consulting and System Integrator (C&SI) Partner Programme. Below is what that actually means, why we chose this direction, and what it changes for businesses trying to get real value out of their data.

Why most AI projects stall before they reach production

The numbers here are worth sitting with, because they explain the decision better than any pitch could.

MIT’s Project NANDA study, The GenAI Divide: State of AI in Business, found that roughly 95% of enterprise generative AI pilots produced no measurable impact on profit and loss, against 30 to 40 billion dollars of spending. Separate analysis puts the share of proofs of concept that never reach wide deployment at around 88%.

The important detail gets lost in the headline. MIT’s own diagnosis was organisational rather than technical. The models were not the failure point. The failure was that nobody connected them properly to the business.

A 2026 Deloitte survey of 501 senior leaders found the gap plainly. 72% said they lack unified, accessible data, and only 42% considered their data foundation genuinely prepared for AI agents.

A Fivetran benchmark of 500 senior data and technology leaders reported that nearly 97% had seen data pipeline failures slow their analytics or AI programmes.

Read those together and a clear picture forms. A pilot runs on a small, clean, hand-picked dataset. Production runs on the messy live estate that nobody has fully mapped. That handover is where projects die.

What the Databricks Data Intelligence Platform actually does

Stripped of the marketing language, Databricks gives an organisation one place to do four things that usually live in four separate tools:

• Store all company data, including the untidy kinds like documents, logs and images
• Clean it and move it on a schedule so reports are not built on stale numbers
• Analyse it and build dashboards from it
• Build AI features on top of the same data, without copying it somewhere else first

The part that matters most to a business owner is the fourth point. When AI runs on the same governed data as the reporting, answers come from one version of the truth rather than a copy that drifted out of date three months ago.

Two terms are worth defining, because you will meet them in any Databricks conversation.

Lakehouse simply means the data stays in open file formats that you own and can move elsewhere, rather than being locked inside one vendor’s proprietary system. If you ever want to leave, your data is still yours in a usable form.

Governance here means one central set of rules about who can see which data. Instead of managing permissions separately in the warehouse, the dashboard tool and the AI tool, you set them once.

For context on adoption, Databricks reports more than 20,000 organisations use the platform, including over 60% of the Fortune 500.

Genie: asking questions of your data in plain English

Of everything on the platform, Genie is the part non-technical teams notice first. It became generally available this year, and it is worth understanding because it changes who in a business can get an answer without asking someone else.

Genie is a chat box for your company data. Someone types an ordinary question, such as which ten customers brought in the most revenue last quarter, and gets back a table or a chart. Behind the scenes it converts that question into SQL, the standard language used to pull information out of a database, runs it and shows the result.

Two things separate this from a general chatbot.

First, the answers come from your own governed data rather than from the open internet, so the numbers match the numbers in your reports. Second, it respects the permission rules already set on that data. A regional manager asking about revenue sees their region, not everyone’s.

Setting it up is not automatic. An analyst configures what Databricks calls a Genie space, choosing which datasets are in scope and writing example questions and short notes explaining what the business terms mean. When Genie cannot answer something confidently, it asks a clarifying question rather than guessing.

That last detail matters more than it sounds. Genie’s accuracy depends almost entirely on how well your tables and columns are described and how clearly your business terms are defined. Which brings the argument back to where it started. If four departments still define “active customer” differently, Genie will inherit that confusion and hand it to a manager who does not know to question it.

What being a registered Databricks partner actually means

Honesty is more useful than positioning here, so a plain explanation.

A Consulting and System Integrator partner is an implementation team, not the software vendor. Databricks builds and sells the platform. A C&SI partner is the team that plans the work, moves the data, sets up the permissions and builds what sits on top.

Registration in the programme gives us structured access to Databricks training and certification paths, reference architectures for common problems, and direct technical channels when something unusual comes up. It shortens the distance between a client question and a correct answer.

It is also worth saying what it does not mean. It does not mean Databricks is the right platform for every business. A company running one clean system with modest data volumes does not need a lakehouse, and we will say so.

The wider market context explains why implementation capacity matters. In March 2026, Accenture launched a dedicated Databricks Business Group backed by more than 25,000 Databricks-trained professionals. When firms of that size are building teams specifically around one platform, the bottleneck in the industry is clearly no longer the software. It is the number of people who can implement it properly.

What a full data and AI implementation involves

Our work in this area covers the whole path rather than one stage of it, across industries rather than one vertical. In practice it runs in four phases.

Assessment. Find out what data exists, where it lives, who owns it and how much of it is trustworthy. This phase is unglamorous and it is the one that decides whether everything after it works.

Consolidation. Bring the data together on the platform with reliable, monitored pipelines, so a failure at 2am is caught rather than discovered in a board meeting.

Governance. Set the permission rules once, centrally, and document what each dataset means. AI cannot answer a question about “active customers” if four departments define that phrase differently.

Build. Only now do the dashboards, the reports and the AI features get built, on data that can actually support them. This is also the point where something like Genie starts giving trustworthy answers rather than confident wrong ones.

Most of the value arrives in the first three phases. That is uncomfortable for anyone who wanted the AI demo in week two, but it is the difference between the 5% and the 95%.

How to know if you are ready to start

Four questions worth answering honestly before any platform decision:

1. Can you name the single source of truth for your most important business number, and does everyone agree on it?
2. How long does it take to answer a question that needs data from two different systems? If the answer is days, that is your real problem.
3. Do you know who has access to what, or would an audit be a scramble?
4. Have you defined what success looks like in a number, before the project starts?

If those answers are shaky, fix them first. The platform will not fix them for you, and no amount of AI will paper over a definition nobody agrees on.

Where this leaves things

The gap between companies getting value from AI and companies burning money on pilots is not a gap in talent or in technology access. It is a gap in preparation. The 5% did the boring data work first.

Working through the Databricks programme is how we make that preparation faster and less risky for the businesses we work with. Teams that have taken a project from scattered spreadsheets through to a working AI feature tend to know which shortcuts are safe and which ones cost six months later, and that experience is usually what separates a project that reaches production from one that quietly stops after the pilot.