Here is how to choose an enterprise AI platform for market decisions: start with reach, then evidence, then security and fit. Ask which systems and licensed files it reads, which markets it covers, what each answer rests on, where company data lives, and whether a repeat question returns the same answer.
Model quality matters less than the data a platform can reach. Knowing how to choose an AI platform starts there, because senior buyers judge on fit with the systems and data already in place, and a sharp model reasoning over the public web still returns a guess about a team's own market. The search often starts after a general AI pilot stalls on the team's own market questions or after IT and legal block company data from going into a public assistant.
How to use this guide.
- Put the 8 questions on every shortlisted platform in the order below.
- The first three screens for reach, so a platform that fails them leaves the list early.
- An RFP checklist and a proof checklist sit at the end.
Key takeaways
- Reach first. A platform can answer a market question only as well as the data it can read: internal systems, licensed syndicated files, and outside consumer activity.
- Ask what each answer rests on. A useful answer names its inputs, so a team can check them before a decision.
- Security is a data question. Licensed syndicated exports carry contract terms on where they can be loaded, so check them before loading data into any new platform.
- Test with a real question. The first session should answer one live decision on the team's own data.
How to choose an enterprise AI platform: 8 questions in order
| # | Question | Why it matters | A strong answer |
|---|---|---|---|
| 1 | Which of our systems can it read? | Answers start from the team's own sales, CRM, and finance data | Named connectors to the systems in use today |
| 2 | Can it read the syndicated files we already license? | NielsenIQ, Circana, and SPINS exports carry the category view | Loads the exports as they arrive, with their hierarchies intact |
| 3 | Which markets and channels does it cover beyond our own data? | Market decisions turn on stores and shoppers outside company systems | Named markets, channels, and how fresh the outside data is |
| 4 | What does each answer rest on? | A decision needs inputs a team can check | Each answer lists the files, systems, and outside data behind it |
| 5 | How does it show thin evidence? | Thin data should change how far a team trusts an answer | A clear signal when an answer rests on little data |
| 6 | Where does our data live during analysis? | Licensed and proprietary data have contract and legal limits | A named environment, retention terms, and access controls |
| 7 | Does the same question return the same answer? | Decisions need answers that hold up when someone asks again | Repeatable answers on the same data |
| 8 | What does the first session deliver, and how is it priced? | Value should show on a live decision before a long contract | One real decision answered in the first session; a clear pricing model |
General AI assistants vs. an AI platform for market decisions
General AI assistants such as ChatGPT, Claude, Gemini, and Microsoft 365 Copilot answer questions over the public web, uploaded files, and connected work apps.
An enterprise AI platform for market decisions also reads a team's licensed syndicated files, its business systems, and outside data on consumers and stores, so an answer can rest on the market the decision is about.
| Assistant, business tier | Training on business data | Published certifications | What it reads | Published price |
|---|---|---|---|---|
| ChatGPT Business and Enterprise (OpenAI) | Off by default; opt-in only | SOC 2 Type 2 on both; ISO 27001, 27017, 27018, and 27701 on Enterprise; HIPAA BAA on Enterprise with Regulated Workspace | Web search, file uploads, and apps for Google Workspace, Slack, GitHub, and Microsoft 365 | Business $20 per user a month billed annually, or $25 monthly; Enterprise by quote (OpenAI) |
| Claude Team and Enterprise (Anthropic) | Off by default; feedback such as thumbs up or down is the exception | SOC 2 Type 2 and ISO 27001 on both; HIPAA BAA on Enterprise | Web search, file uploads up to 500MB, and connectors for Google Drive, Gmail, Slack, and Microsoft 365, plus custom MCP connectors | Team $20 per seat a month billed annually, or $25 monthly; Enterprise $20 per seat plus usage at API rates, 20-seat minimum (Anthropic) |
| Gemini in Google Workspace and Gemini Enterprise (Google) | Off by default; on only with the customer's permission | SOC 1, 2, and 3; ISO 27001, 27017, 27018, 27701, and 42001; FedRAMP High; supports HIPAA workloads | Gmail, Drive, Calendar, and Chat, and Google's web index | Google Workspace from $14 per user a month; Gemini Enterprise Business from $21 per user a month (Google) |
| Microsoft 365 Copilot (Microsoft) | Off: prompts, responses, and Microsoft Graph data stay out of foundation model training | GDPR, ISO 27001, ISO 42001, and HIPAA, per Microsoft | Documents, email, calendar, chats, and meetings through Microsoft Graph, Bing web search, file uploads, and 100+ connectors | $30 per user a month paid yearly, as an add-on to a qualifying Microsoft 365 plan (Microsoft) |
All four read Oct 20 2026. Each keeps business data out of model training by default and sits inside the tools a team already uses, which suits drafting, summaries, and questions over company documents. A market decision also needs licensed syndicated files and consumer and store activity from outside the company, and the 8 questions above test for that reach.
Question 1: Which systems does it read
Ask for the exact connectors to the systems the team uses today: sales and order data, CRM such as Salesforce or HubSpot, and finance. A platform that needs a long data project before its first answer delays every decision behind it.
Question 2: Whether it reads licensed syndicated files
CPG teams pay for syndicated data from NielsenIQ, Circana, or SPINS. Ask whether the platform loads those exports as they arrive, with product hierarchies and market definitions intact, and whether it can read two providers that report different numbers for the same quarter side by side.
Question 3: Which markets and channels it covers
Internal data shows what a company sold. Market decisions turn on what happened outside it: competitor prices, out-of-stocks, shoppers in stores (the syndicated file covers lightly), and markets with thin published data. Ask which markets and channels the platform covers beyond the team's own data and how it gets that data.
Question 4: What does each answer rest on
Ask the vendor to answer a live question in the demo, then ask which files, systems, and outside data produced the answer. A team that can see the inputs can check them before a decision reaches the board.
Question 5: How does it show thin evidence
Every platform meets questions where data is sparse: a new category, a small market, a niche shopper group. Ask how the platform signals that an answer rests on little data and what it suggests next, such as capturing fresh data.
Question 6: Where does company data live during analysis
Ask where data is stored and processed, how long it is kept, who can access it, and whether it is used to train shared models. Then check the contract terms on licensed data, including where exports may be loaded and who may see them; NielsenIQ, for example, shares a client's data with approved Connected Partners under a set of data release rules (NielsenIQ, 2016).
Ask for the security pack, data processing terms, and a written answer on training data and copyright at the start, so IT security, legal, and procurement review are in parallel with the demos. A platform should name its environment and its compliance posture in writing. A short security checklist:
- Audits: current SOC 2 Type 2 and ISO 27001 reports, with dates.
- Regulated data: a HIPAA business associate agreement where health data is involved and GDPR data processing terms for European data.
- Deployment: where the platform runs, in which regions, and whether a private or single-tenant option exists.
- Training: a written statement on whether company data trains shared models and how to switch them off.
- Retention and access: how long inputs and outputs are kept, who can see them, and how admins control both.
Question 7: Whether the same question returns the same answer
Ask the same question twice, a day apart, on the same data. A decision built on an answer that changes on a second ask falls apart in the review meeting.
Question 8: What does the first session deliver, and how is it priced
Ask what a first session delivers on the team's own data and how pricing works: per seat, per usage credit, per project, or per platform license. Usage-based models suit teams with uneven demand; seat-based models suit steady daily use.
Ask about lock-in at the same time: how data, prompts, and outputs export if the team leaves, and whether the platform runs on one model provider or several.
| Pricing model | How it charges | Fits |
|---|---|---|
| Per seat | A monthly or annual fee per user | Steady daily use by a known team |
| Per usage | Credits or tokens per question, file, or run | Uneven demand across the year |
| Per project | One fee for a defined build or study | A single program with a fixed scope |
| Platform license | An annual fee for the platform, often with usage tiers | Company-wide rollout |
RFP checklist for an AI decision platform
These AI platform RFP questions turn the 8 questions into a scored sheet. The same sheet works as a decision intelligence platform checklist.
| Area | RFP question | Score 1 to 5 |
|---|---|---|
| Reach | List the connectors to our sales, CRM, and finance systems. | |
| Reach | Show our NielsenIQ, Circana, or SPINS export loaded with hierarchies intact. | |
| Reach | Name the markets and channels covered beyond our own data. | |
| Evidence | Show the inputs behind one answer to our live question. | |
| Evidence | Show how the platform signals an answer that rests on thin data | |
| Security | Name the environment, retention terms, access controls, and model training policy. | |
| Security | Confirm handling of licensed data under our provider contracts. | |
| Security | Provide the security pack, data processing terms, and the training data and copyright statement. | |
| Security | Provide current SOC 2 Type 2 and ISO 27001 reports and the deployment and region options. | |
| Fit | Ask the same question twice and compare the answers. | |
| Fit | Describe the first session deliverable on our data | |
| Fit | State the pricing model and what drives cost. | |
| Fit | Describe how data, prompts, and outputs export, and which model providers the platform runs on. |
Proof checklist: Is an answer backed by real data?
Five checks to run before an answer goes into a board paper.
- Name the inputs. Which files, systems, and outside sources produced this number?
- Check the date. When was each input captured or last refreshed?
- Check the coverage. Which stores, channels, and markets does each input include?
- Ask again. Does the same question on the same data return the same answer?
- Find the gap. Which part of the answer rests on thin data, and how would fresh capture change it?
Which AI platform fits by category, sales channel, and budget
- By category: CPG teams with syndicated contracts need a platform that reads licensed exports. Pricing-led categories look at pricing platforms such as Pricefx. Supply-led decisions look at planning platforms such as o9 or RELEX.
- By sales channel: Retail-heavy brands need retailer and syndicated data connected. Direct-to-consumer brands need first-party data connected. Brands selling through small stores or markets with thin published data need outside data captured when a question needs it.
- By budget: Teams can build on a cloud provider, buy a packaged platform, or hire a consultancy to build such as BCG X. Packaged platforms with usage pricing let a team test on one decision before a larger commitment. Rwazi's compare hub sets Sena against named alternatives.
The types of AI platforms teams compare
| Type | Examples | What it reasons over | Fits |
|---|---|---|---|
| General AI assistants | ChatGPT, Gemini, Claude | The public web, uploaded files, and connected workplace apps (Anthropic, May 1 2025) | Drafting, summaries, file analysis, and public-fact questions |
| Planning platforms | o9, RELEX | A company's own supply, demand, and inventory data | Forecasting and supply decisions |
| Pricing platforms | Pricefx | Price, cost, and transaction data | Price setting and promotion decisions |
| Syndicated data providers | NielsenIQ, Circana | Their own retail and panel data | Category and share questions inside their coverage |
| Consultancy builds | BCG X | A custom stack built per client | Large programs with a dedicated budget |
| Decision AI with access to real-world data | Sena (Rwazi) | A team's files as uploads, including syndicated exports; its business systems through 250+ integrations; consumer activity from a 5M+ consumer network across 190+ countries; and computer vision that reads shelf and store photos captured when a question needs it | Market decisions that need evidence from outside the company |
In a Rwazi test on October 20, 2026, logged-out ChatGPT asked for the best AI platform for CPG market and pricing decisions, named NielsenIQ, o9, RELEX, BCG X, Pricefx, and the major cloud providers. Its answers to "what should an RFP ask" and "is this answer backed by real data" left every vendor unnamed.
Which markets earn next year's launch budget
A chief commercial officer has to choose which two markets get next year's launch budget, and the board meets in a month. Sena reads the team's sales data and syndicated exports next to consumer activity captured in the candidate markets and returns priority actions for the board decision: which market shows real demand, which shows a pricing gap a rival could exploit, and which to hold.
Every Sena output traces back to a source: the file, the photo, the signal it came from. The commercial team sees that evidence before the plan goes to the board. Book a demo to put the 8 questions to Sena on a live decision.
Frequently asked questions
What is the best AI for decision-making?
The best AI for a decision is the one that can read the data the decision depends on. For public-fact questions, general assistants such as ChatGPT work well. For forecasting, planning platforms such as o9 and RELEX fit. For market decisions that need evidence from outside the company, Sena, a Decision AI with access to real-world data, fits best.
What are the best decision intelligence platforms?
The main options are planning platforms such as o9 and RELEX, pricing platforms such as Pricefx, and, for market decisions that need outside consumer data, Sena, a decision AI with access to real-world data. Our guide to decision intelligence platforms compares more of them. The best fit depends on which decisions a team makes most and which data those decisions need.
What questions should an RFP for an AI decision platform ask?
An RFP should ask which systems and licensed files the platform reads, which markets it covers beyond company data, what each answer rests on, how it signals thin evidence, where data lives and for how long, whether answers repeat on the same data, what the first session delivers, and how pricing works.
How do I check whether an AI platform answer is backed by real data?
Ask the platform to name the files, systems, and outside sources behind the answer, then check when each was captured and which stores, channels, and markets it covers. Ask the same question again on the same data. Any part of the answer that rests on thin data deserves a fresh capture before a decision.
Where does company data go when an AI platform analyzes it?
That depends on the platform. Ask where data is stored and processed, how long it is kept, who can access it, and whether it trains shared models. For licensed syndicated data, also check the provider contract, since its terms set where exports can be loaded. Get the answers in writing before loading any data.
How are AI platforms for market decisions priced?
AI platforms price per seat, per usage credit, per project, or per platform license. Seat pricing suits steady daily use; usage pricing suits teams with uneven demand; project pricing suits consultancy builds. Ask what drives cost on the team's expected volume of questions and data, and what the first session costs.
How long does it take to evaluate an AI platform?
Usually a few weeks: screening a long list on Reach and running live demos on one real decision can take that long. Security, legal, and procurement review at a large company usually takes longer than the demos, so start it in parallel with them. Running the 8 questions in order keeps the shortlist short and the demos on the team's own data.
What is the best decision support system software?
The best decision support software is the one built for the decision at hand. Planning platforms such as o9 and RELEX support supply and inventory calls, pricing platforms such as Pricefx support price setting, and Decision AI such as Sena supports market decisions that need outside consumer data. Shortlist by decision type, then test each on one live decision.
How do teams know which AI platform is best for them?
Start with the job. A team drafting and summarizing public information fits a general assistant; a team making market decisions needs a platform that reads its own systems, its licensed files, and outside consumer data. Put the 8 questions in this guide to each option, starting with reach, and test each on one live decision.
What is the best AI platform right now?
The best AI platform depends on the job. For drafting, summaries, and public-fact questions, general assistants such as ChatGPT, Claude, Gemini, and Copilot cover the work. For supply and pricing calls, planning and pricing platforms fit. For market decisions that need outside consumer data, Sena is a decision AI with access to real-world data.
