What shapes the cost of agentic BI
Agentic BI on certified metrics, not a chatbot on a dashboard
Is agentic BI the right fit?
Start here when
Consider another path when
The person who owns this is not always the person who signs.
Agentic BI puts an AI layer on top of your certified metrics so people ask a question in plain language, get the right number, and see a suggested next action. It isn't a chatbot bolted onto a dashboard. It reads the semantic layer, respects your access controls, cites the source, and escalates anything that needs a human. Built right, it turns a static report into an assistant your team trusts.
We implement agentic BI on top of your certified metrics: natural-language questions, trusted answers, suggested actions, and governance. Vendor-neutral, grounded in your data.
Agentic BI is business intelligence with an AI layer that does more than visualize data. It answers natural-language questions against a certified metric layer, suggests the next action, and can trigger governed workflows, while keeping a human in the loop on anything that matters.
Agentic BI puts an AI layer on top of your certified metrics so people ask a question in plain language, get the right number, and see a suggested next action. Thinklytics builds it to read the semantic layer, respect your access controls, cite the source, and escalate anything that needs a human. It isn't a bolted-on chatbot.
An AI layer that reads your certified semantic layer, so its answers match the dashboard instead of contradicting it.
Natural-language questions in, trusted numbers and a suggested next step out.
Access-aware. It only returns what the asking user is allowed to see.
Grounded and cited. Every answer traces back to a source the user can open.
A chatbot pasted on top of a dashboard. Those guess, and the guesses look confident.
A replacement for your BI tool. It sits on top of Tableau, Power BI, or the warehouse.
An ungoverned text-to-SQL toy. We don't ship a tool that runs arbitrary queries against production with no semantic layer underneath.
Magic. If the metric layer underneath is messy, the agent inherits the mess. We fix that first.
A scoped agentic BI capability built on your certified metrics and access model, not a generic demo.
Natural-language query, answer grounding, source citation, and a suggested-action surface for the questions your team actually asks.
Approval gates and audit logs on any action the agent can trigger beyond read-only answers.
Evals, a quality bar, and an enablement transfer so your team can extend the agent after launch.
It had no semantic layer to read, so it guessed at definitions and returned confident wrong answers.
Execs still ask an analyst to pull the same numbers every week.
There's no trusted answer surface, so people route questions through a person.
It was wired without the access model, so it answered without checking permissions.
Answers weren't grounded or cited, so users can't verify them against a source.
Agentic BI is business intelligence with an AI layer that does more than visualize. It answers natural-language questions against a certified metric layer, suggests a next action, and can trigger governed workflows, while keeping a human in the loop on anything that matters.
A chatbot pasted on a dashboard guesses at what your terms mean. Agentic BI reads your certified semantic layer, returns the same number the dashboard shows, cites the source, and respects who is allowed to see what.
Effectively, yes. An agent pointed at raw tables inherits the mess and produces confident wrong answers. If you don't have a certified metric layer, we build that first, then put the agent on top of it.
Is it tied to one vendor like Tableau Pulse or Power BI Copilot?
No. We implement vendor-specific tools when they fit, but this engagement is vendor-neutral. We build the agentic layer on top of whatever stack you run and the certified metrics underneath it.
Yes, with guardrails. Read-only answers need no approval. Anything that writes to a system of record or touches a customer goes through an approval gate and an audit log.
A scoped capability on an existing certified metric set typically ships in 8 to 12 weeks. If the metric layer needs to be built first, that comes before the agent.
A dashboard is right when the questions are known and repeat, because a well-built one answers them instantly and cheaply and never hallucinates. Agentic BI earns its cost when the questions vary, when the follow-up matters more than the first answer, and when people who can't write SQL need to keep asking. The failure mode to avoid is bolting a chat box onto an ungoverned warehouse, where the agent confidently returns a number no two teams define the same way.
The model spend is minor and predictable. The real cost is the certified semantic layer underneath, because an agent is only as trustworthy as the metric definitions it reads. Teams that already have a governed layer are looking at a 6 to 10 week implementation. Teams that don't are really buying a metric certification project with an agent at the end, which is a longer engagement and worth scoping up front rather than discovering in month three.
Agentic BI reads your certified metrics. These are the factors that move the effort.
Agentic BI reads a semantic layer; if metrics aren't certified yet, that foundation comes first.
The range of questions users ask sets how much grounding and testing is needed.
Read-only answers differ from agents that can trigger actions, which need approval gates and audit logs.
Row- and object-level access so each user sees only what they should adds setup.
Agentic BI on certified metrics, not a chatbot on a dashboard
A chatbot bolted onto a dashboard guesses. Agentic BI reads the certified definition. Here is the difference.
Reads the semantic layer, so answers match the certified number.
You've certified metrics and want people to ask questions in plain language.
You want answers grounded in your data with source citations, not a guessing chatbot.
You want a suggested next action, with approval gates on anything beyond read-only.
Your metrics aren't defined once yet: start with a Semantic Layer.
You want generated commentary on existing dashboards: see AI Reporting Automation.
You need a task agent, not a question interface: see AI Agent Consulting.
Agents that do scoped jobs across your stack, beyond answering.
120 to 14: Ad-hoc report requests per week after self-service rollout to 340 clinical staff. $890K in analyst labor saved annually. (Community Health Network)
$1.4M: Revenue discrepancy resolved. Six conflicting metrics reconciled to one certified ARR definition. The data foundation that makes sales AI work. (Enterprise SaaS)
75% to 94%: Member match accuracy on three previously stalled ML pilots. Addressed $4.8M a year of misrouted claims. (Express Scripts)
The people who normally own this problem are CFO, FP&A director, COO. They are the ones who start the search, and they are not always the person who signs for the work.
A buyer usually describes the situation this way: My team spends days assembling the monthly pack, and most of it is reconciliation rather than analysis.
The spend is normally gated by the CFO or executive sponsor, whose question is: Is the improvement worth the investment? What that person needs to approve it is a business case with stated assumptions and measurable outcomes.