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Choosing an AI platform is one of the most consequential technology decisions your organization will make this year. The market is crowded, the claims are big, and the demos are always impressive. But enterprise AI deployments fail not with crashes, but with eroded trust, leaked data governance, and workflows that never get adopted.
We've worked through this evaluation process with some of the world's leading media agencies and brands. Here are the ten questions that separate platforms worth deploying from ones that look good in a pitch.
This should be the first question you ask — and the answer should be unambiguous.
Many AI platforms require you to move data into their cloud, create new data pipelines, or sign updated data processing agreements before you can do anything useful. For organizations managing sensitive client data, that's a non-starter.
The right answer: your AI platform should run inside your existing cloud infrastructure (AWS, GCP, or Azure), read data at rest directly from your warehouse, and write outputs back into datasets you own. No data should leave your environment. No new DPAs required.
What to ask: Where does our data live during processing? Does the vendor access our data? What changes to our data agreements are required?
Fluent-sounding wrong answers are more dangerous than obvious errors. An AI that confidently produces incorrect campaign data, fabricated audience insights, or invented performance metrics can cause real business damage before anyone notices.
Look for platforms that use Retrieval-Augmented Generation (RAG) architecture, meaning the AI grounds every response in your actual data, not in what the model "knows" from training. Every output should be traceable back to a source, with citations and the ability to "show the work."
What to ask: How does the platform ground responses in our data? Can users see which data sources were used to generate an answer? What happens when the model is uncertain? What kind of guardrails or logging is kept to double check LLM responses?
AI should accelerate human decisions, not replace them. In practice, this means the platform should present recommendations for review and approval, not take autonomous action on your data or campaigns without a person signing off.
This is especially important for high-stakes outputs: audience segments pushed to activation, budget allocations sent to buying platforms, or reports delivered to clients. A good platform makes human review easy and auditable, not an afterthought.
What to ask: At which steps does the platform require human approval before proceeding? Can users edit, override, or reject AI recommendations? Is there a full audit trail of what was approved, by whom, and when?
ISO 27001, SOC 2, GDPR, and HIPAA certifications matter, but the details matter more. Ask specifically about role-based access controls (who can see what data), how the platform handles multi-market data residency requirements, and whether compliance is enforced at the data layer or just in policy documents.
For global organizations, you'll also need to know how the platform handles market-specific regulations, not just blanket statements about GDPR compliance, but concrete answers about data storage locations, deletion workflows, and cross-border data handling.
What to ask: What certifications does the vendor hold and when were they last audited? How is RBAC implemented – at the application level or the data layer? How do you handle data residency requirements across different markets?
The biggest failure mode in enterprise AI isn't technical; it's adoption. Platforms built for data engineers get used by data engineers. If your media planners, strategists, and account managers can't get value from the tool without filing an IT ticket, the investment won't compound.
Look for a natural language interface that lets business users ask questions, build audiences, generate reports, and explore data in plain language, without writing SQL, configuring pipelines, or understanding the underlying data model.
What to ask: Can you show us a workflow completed entirely by a non-technical business user? What does onboarding look like for someone who has never used a data platform before?
AI platforms that require you to rip and replace existing infrastructure rarely get deployed. The better question is whether the platform integrates with what you already have: your data warehouse, your infrastructure, your SSO provider, your activation tools.
Look for support for standard authentication protocols (SAML, OIDC), direct connections to your warehouse (BigQuery, Snowflake, Databricks), and API-based integration with downstream tools like DSPs, CRMs, and buying platforms.
What to ask: What does the integration architecture look like with our current data warehouse? How does the platform handle SSO and existing user management systems? What activation platforms and downstream tools are supported out of the box?
Generic AI platforms don't know client KPIs, what "attention-weighted CPM" means, or how your agency structures a media plan. They produce fluent outputs that make no sense to anyone who actually works in the industry or on an account.
Purpose-built platforms solve this through a context layer: a proprietary system trained on your agency's data, client KPIs, terminology, and workflows. This is what separates an AI that sounds smart in a demo from one that's actually useful in production.
What to ask: How does the platform learn our specific terminology, workflows, and client context? Is this customization done once at setup, or does it improve continuously? Can we add our own context without retraining the model?
AI vendor timelines are notoriously optimistic. "Deploy in days" often means "deploy a demo in days, fully configured production environment in six months."
Push for specifics: what are the dependencies on your team, what data access is required from day one, and what's the realistic timeline to first value for a real user in a real workflow. Ask for references from organizations with similar data complexity and team size.
What to ask: What does week-one look like? What do you need from us to get started? What's the average time to first production workflow for a client similar to us? Can we speak to a reference customer?
For global organizations, regional deployment is where AI platforms frequently fall apart. A tool that works in English in the US may produce unreliable results in French or Portuguese in other markets, especially when the underlying data models, terminology, and regulatory requirements differ.
Look for multi-language support in the user interface, market-specific context ingestion, and compliance with local data storage and compute laws, not just a promise that "international support is on the roadmap."
What to ask: Which languages are supported today, not on the roadmap? How does the platform adapt to market-specific data providers and terminology? Where is data stored for each market, and how does that comply with local regulations? Do you have experience with international deployments?
A platform that performs the same way in month twelve as it did in month one isn't an AI investment; it's a software license. The compounding value of AI comes from a system that gets smarter with every campaign, every user interaction, and every feedback loop.
Look for long-term memory architecture, feedback mechanisms that let users rate and correct outputs, and a context layer that continuously incorporates new performance data, client learnings, and market insights into future recommendations.
What to ask: How does the platform incorporate feedback from users? How does past campaign performance influence future recommendations? Can we see how the platform has improved over time?
Akkio is purpose-built for media agencies and enterprise marketing teams and meets every criterion in this list. From data-in-place architecture and ISO 27001 compliance to natural language workflows built for non-technical users, Akkio was designed to answer the hardest questions buyers ask.
To see how Akkio approaches each of these considerations in depth, visit our workflows page.
Transform your campaign workflows with powerful AI that delivers measurable results.