The Buyer's Guide for Data and AI in 2027

Business intelligence looks different heading into 2027. Almost every team has tried pasting spreadsheets into Claude or ChatGPT for a quick summary, but that approach breaks down the moment the numbers stop matching and executive decisions are on the line.

Buying an AI subscription is easy. Building reliable agentic analytics, AI that answers questions, recommends next steps and acts on them, is not. It needs a data foundation underneath: connected data across systems, shared metric definitions, and clear governance over who sees what. Most 2027 budgets will include a line for AI. Very few will include one for the data it reads from.

This guide is for owners, executives, technology leaders and PE operating partners, especially in multi-location businesses like healthcare groups. It covers where your organization sits today, what to build versus buy, and how to budget for the data AI depends on.

Three trends reshaping analytics in 2027

Three shifts are changing how businesses get answers from their data, and each one affects what you should buy.

AI is already part of how reporting gets done. Your front desk, your marketing lead and probably your CFO are pasting numbers into ChatGPT or Claude to build reports. So the 2027 question isn't whether to bring AI into reporting. It's whether that use can scale: one setup the whole organization works from, on the same numbers, with customer and patient data kept in enterprise tools covered by the right agreements (like a BAA, if you handle patient data) instead of personal accounts.

Every kind of analytics tool is adding AI, each in its own way. BI vendors are building copilots into tools like Power BI and Tableau. Data platforms like Snowflake and Databricks are adding agents of their own. Some teams skip BI altogether and build their own setup with Claude or ChatGPT, and more analytics platforms are being built for a single industry. They all call it AI analytics, but each covers a different part of the data stack: the infrastructure underneath, querying and visualization, or the AI agents on top. Few cover all of it. Because they share a label, buyers often treat them as like for like, or aren't sure how to compare them at all.

Most teams aren't equipped for agentic analytics yet. Companies are spending on agentic AI well ahead of the data it needs, as Fivetran's 2026 Agentic AI Readiness Index shows below. Only 15% of companies surveyed are fully prepared to run agentic AI. That gap shows up in the accuracy of analytics as well. When Anthropic tested its own analytics agent, Claude alone scored 21% accuracy on Anthropic's internal analytics evals, and above 95% only with a governed data stack underneath. We break down those numbers here. The AI model is rarely the weak point. The data it reads from usually is.

"Most companies aren't failing at AI because of the models, they're failing because their data isn't ready."

George Fraser, CEO, Fivetran

Where are you today?

Most organizations sit in one of three stages, from manual reporting to fully connected, governed data. The stages aren't new. Large enterprises have run at the top stage for years, with a warehouse, cleaned and modeled data, a reporting layer, and a team of engineers and analysts to run it. What's changed is that AI can now do much of the work those tools and people did, which changes what it costs and puts the most sophisticated stage within reach of businesses that couldn't build and staff it before. That said, it also tempts teams to bolt AI onto manual reporting and skip the foundation, which is a trap: the answers come fast, but can be unreliable.

Here's what each stage looks like today, with AI in the picture. Use this to assess the current standing of your organization and understand what you could be missing.

If you're at stage 1, AI on top of spreadsheets may be enough, but only in specific scenarios. There are teams that are skipping BI tools entirely and doing their analysis in Claude or ChatGPT, which can now build dashboards too. Sometimes that's the right call, and you may not need to move up a stage at all. If your spreadsheets are already clean, one person owns them, and what you want is faster charts and quicker analysis, go do that. It costs a subscription, not a project.

It stops working when the question changes from "can I chart this faster?" to "can everyone ask questions of our data and trust the answer?" The usual signs:

• More than a handful of people need answers, and they're getting different numbers.
• The data comes from more than two or three systems, and someone has to export from each one every time, so the AI is always working from an out-of-date snapshot.
• Leadership, a board or a PE sponsor needs numbers they can trace back to the source. An AI answer built on hand-edited exports can't show its work the same way twice.
• Customer or patient data is involved, and the AI tool isn't covered by the right agreements, like a BAA if you handle patient data.
• You want AI to act on the data, not just describe it.

If what you're missing affects decision-making, revenue opportunities, is a compliance risk, or reporting is too slow, it's time to move up. If it isn't, stay put.

Plan for the stage you're moving into, not the one you're in. That tells you what to buy, and what to budget.

If you're a PE operating partner, run the check per platform company and look at the gap between the strongest and weakest. That gap is usually where the 2027 budget should go. It pays off at exit, too: a company that can show clean revenue by location, provider or staff member, and service line, tied back to its source systems, gets through diligence faster than one rebuilding the numbers in spreadsheets.

Moving up means building the data foundation covered in the next section.

What you need to run agentic analytics

Getting from manual reporting to accurate agentic analytics takes a data stack with six layers on top of your source systems. You need all of them whether you buy one product or five. The budget question is who provides each layer and who keeps it running. If your last analytics project was a BI rollout, expect fewer tools, fewer specialists, and answers on demand instead of a report queue.

Skipping straight to AI is where businesses get burned. Buy an AI analytics tool before you have standard metric definitions or a data strategy, and the AI has nothing consistent to reason from, so nobody trusts its answers. That groundwork has to come first, whether your own team does it, a strategy partner does, or it comes with a platform's onboarding.

Most tools cover one layer well. Here's how the common categories compare:

Breadth isn't the same as depth. Point tools usually go deeper in their own layer: Fivetran has more prebuilt connectors than any all-in-one platform, and Tableau has more ways to visualize data. A managed platform is built to make the layers work together instead, so you get one vendor to manage, one set of metric definitions that everything uses, and no engineering work on your side. Which fits better, several point tools or one managed platform, depends on whether you have a data team to run the point tools.

What changes in your day-to-day

Take a regional director who sees revenue dip at one location.

The old way. She spots the dip in Tuesday's report and emails the analyst. The request waits behind the board deck. By Friday, the analyst finds that cancellations jumped for two providers. The team plans outreach the following week, about 10 days after the dip started.

With agentic analytics. Overnight, the agent flags the dip and traces it to the jump in cancellations for those two providers. When she opens her inbox the next morning, a rebooking list and a draft outreach message are waiting for her review. Same data, same definitions, and the fix starts that morning.

Build vs. buy: the real numbers

There are two ways to put that foundation in place: build the data stack yourself, or buy it as a managed platform. Below, both are priced for an example group with 10 locations and 25 people who need access to the numbers. Year 1 includes setup, and the ranges come from public salary and pricing data.

See the appendix for line-by-line build costs and where each number comes from.

What building it actually involves. Each source system is its own project. Your EMR or POS, CRM, phone system and accounting software each need a pipeline with its own access, rate limits and data model, and each one has to be watched and fixed when the vendor updates its system. Once the data lands, someone has to match customers or patients, staff and locations across systems before any two reports agree. Then come the metric definitions, the access rules and the dashboards. One engineer can carry that for a while. It usually stretches thin at the second or third system, or the first acquisition.

Why some companies still build their own. It's the most expensive path, and for some it's still the right one: when they already have a data team and a warehouse, when IT policy requires data to stay in their own cloud account, or when they have niche sources no vendor supports.

When a managed platform fits better. It's usually the right call when you run several locations on the same core systems, don't have a data team and don't want to hire one, and need trusted numbers in weeks rather than months, for a fraction of what a build costs. It also fits groups that are growing by acquisition, since each new location connects to data models that already exist, and teams that handle patient or customer data and want one vendor to sign the agreements and own the security.

What to ask before you sign

Whichever path you choose, these questions separate a vendor who has done this in your industry from one who is learning on your data. Ask us too.

1. Which parts of the data stack does your platform handle (data ingestion, warehousing, data modeling, dashboards, AI), and what would we need to buy or build separately? Some tools are a warehouse with AI agents, some sit on top of a warehouse you already own, and some cover the whole stack. Map each vendor to the six layers above before comparing anything else.
2. Do you have native integrations with our systems, and are they live with other customers today? Name your core operating system (EMR, PMS, POS or ERP), CRM, phone system and accounting software. "We integrate with everything" is not an answer. Ask what has already run in production.
3. How are metric definitions set up and maintained, and can we see and edit the logic behind them? You should be able to read how "new customer" or "location gross margin" is calculated, and change it when your business changes.
4. When we acquire or open a location, how long does it take to add it to reporting, what does it cost, and who does the setup: your team or ours? For a growing group, this matters more than the price on day one.
5. Who owns our data, and can we export it or query it directly if we leave? Your data should live somewhere you can reach directly, not only through the vendor's screens.
6. What data does your AI query, and how do you keep its answers consistent with our reports? If two people ask about revenue, they should get the same number. Ask how the vendor makes sure of that.
7. What's the all-in annual cost for a business our size, including onboarding, AI usage and any third-party tools we'd still need? Ask about seats, integrations and any API fees your software vendors charge, too. Usage-based, seat-based and per-location pricing don't compare directly, so price each option for a full year at your size.
8. What's your typical implementation timeline, from kickoff to reports our team relies on? Ask for a realistic timeline for your stack, not a best case.

If you handle patient data, see our guide to what HIPAA actually requires when AI touches patient data.

Making the call for 2027

The businesses that pull ahead in 2027 won't be the ones with the most tools. They'll be the ones whose team, and whose AI, trusts the same numbers. That starts with what you decide to buy, and budget for, this quarter.

Start by placing yourself on the three stages above, compare build and buy at your own size, and take the vendor questions into every demo you book, including ours.

Where CorralData fits

CorralData is built for businesses that want the data foundation and agentic AI without building it all themselves. It's one HIPAA-compliant data and AI platform that covers all six parts of the stack. Here's how we answer the questions above:

• Which parts of the data stack? All six: ingestion, warehouse, semantic layer, dashboards, AI agents and activation, run by our team. There are no separate data tools to buy.
• Integrations? 600+, covering EMRs and practice management systems, POS systems, CRMs, ad platforms, phone systems and accounting. No engineering required on your side.
• Metric definitions? We start from standard definitions for your industry, build them with your team, and check every number against your source systems. You can see the logic behind each one.
• New locations? Our team connects them to the same data models, and pricing grows by location, not by headcount.
• Data ownership? Your data stays yours. We can run on a warehouse you already own, or host it for you.
• AI? AskCorral, or Claude and ChatGPT through MCP, all query the same governed data models, so AI answers match your reports.
• Cost? $300 per location per month billed annually, with unlimited users, a BAA, a named technical account manager and 40 hours of guided onboarding included.
• Timeline? Onboarding typically takes four to six weeks.

Appendix: what building your own costs, line by line

Building your own is the path with the most moving parts, so here it is line by line. Each range runs from a lean setup to a fuller one, for the same 10-location example, priced for a group that handles patient data and needs HIPAA-ready tools.

Most builds land between $250K and $410K a year to run, which is the range in the main table. Enterprise-grade tools can push it past $600K. Not included: recruiting and turnover, and data observability tools.