Evaluating AI presentation software in 2026 comes down to seven areas: the processing chain, model training, permissions, output format, brand and protected content, auditability, and the contract. Everything else is a feature comparison, and feature comparisons are the part of this decision that ages fastest.
I sit on both sides of these evaluations. I sell into them, and as a certified data protection officer I have run them. So let me tell you what I have learned from the buyer's chair: the vendors who pass are not the ones with the best answers. They are the ones who can show you the configuration behind the answer.
Below is the checklist I would use. It is long on purpose. Skip the sections that do not apply to your risk profile rather than skipping the depth.
Before you evaluate anything, define the use case
The most common failure in these procurements is evaluating a tool without a use case. "AI for presentations" cannot be assessed. "Draft quarterly account review slides from the approved CRM export, for relationship managers in Germany" can.
Write down, in one page: the business purpose, the categories of people and data involved, the source systems, the recipients, the classification level of the material, the prohibited inputs, and whether output influences decisions about individuals. Every question below becomes answerable once that page exists, and unanswerable while it does not.
Section 1: the processing chain
This is where most evaluations stop too early. Data location for the main application is usually documented. The rest of the chain often is not.
- Where is data processed, for every component including retrieval, generation and rendering?
- Which subprocessors are involved, where are they located, and what is the change notification process?
- Where do support engineers sit, and what can they access?
- What telemetry is collected, and does it include content or only metadata?
- Where are backups stored, and for how long?
- If data leaves the EEA at any point, what transfer mechanism applies and what assessment sits behind it?
- Is there a documented data flow diagram you can review, rather than a marketing claim?
- What happens to data in transit between the application and any model provider?
The question I would not let go of: where do support engineers sit and what can they see. It is part of the same processing chain and it is the answer most frequently missing from a trust page.
Section 2: model training and data use
Short section, high stakes. Ask for the contractual position, not the blog post.
- Are customer inputs used to train, fine tune or evaluate models, by the vendor or by any model provider?
- Is that a default setting, a contractual commitment, or a configuration you must enable?
- Are outputs used for product improvement, quality review or human evaluation?
- Is there a human review queue, and if so what enters it and who sees it?
- Does the answer change for different tiers, regions or model providers?
- Is the commitment written into the agreement, or only stated on a website that can change?
A commitment that exists only on a website is not a commitment. It is a preference.
Section 3: permissions and isolation
For presentation tools this matters more than people expect, because the whole value proposition involves reaching into company knowledge.
- Is tenant isolation absolute, with no shared retrieval index across customers?
- Can workspaces separate clients, mandates, projects or confidentiality tiers?
- Does retrieval respect document level permissions, including inherited restrictions?
- Do AI features run with the invoking user's permissions rather than a service identity?
- Are administration, content ownership, creation, review and publishing separable roles?
- Does the tool support enterprise identity, single sign on and automated deprovisioning?
- Do exports, share links, search and any API respect the same boundaries as the main interface?
- Can you test the boundary yourself with a deliberately narrow test account?
That last item is the one I would insist on. Ask to create a restricted user, request out of scope content, and confirm the system reports nothing found. If a vendor will not let you run that test before signing, you have learned something important.
Section 4: output format
I want to make the case that this belongs in a procurement checklist rather than a design review.
- Does the tool produce native PowerPoint with editable text, shapes, tables and charts?
- Are charts real chart objects with underlying data, or images?
- Does output use your master, layouts, theme colours and fonts?
- Do source references and record identifiers survive into the file?
- Can a reviewer inspect and correct the file without the vendor's application?
- If the contract ends, what happens to your content, and in what format do you get it back?
- Is the output accessible, with reading order, alt text support and selectable text?
Two of those are exit questions in disguise. Native output means your material is not trapped in a proprietary format when the relationship ends. Flattened or viewer only output means it is.
Section 5: brand and protected content
- Can the master, layouts and theme be enforced during generation, not just checked afterwards?
- Can specific elements be locked so neither a user nor an agent can alter them?
- Are legal notices, disclaimers and regulatory statements protected by configuration?
- Can approved wording be retrieved from records rather than generated?
- Can rules vary by market, entity and language?
- Is an attempt to modify protected content reported rather than silently applied?
Section 6: auditability
An audit trail should answer a question somebody will actually ask, and it should not itself become a liability.
- What is logged: user, task, sources selected, objects changed, validation results, reviewer, approval, export?
- Are logs tamper evident and retained for a period you can configure?
- Can you retrieve the evidence for a specific deck after the fact?
- Are prompt and content logs bounded, or unlimited by default?
- Who at the vendor can read the logs, and is that access itself logged?
- Can logs be exported into your own SIEM or archive?
An unbounded prompt log is a data protection problem you paid for. Ask what is retained and make it configurable.
Section 7: the contract and the exit
- Is there an Article 28 data processing agreement, and does the role analysis actually fit the data flows?
- Are retention periods configurable for uploads, prompts, drafts, outputs, logs and backups?
- Are deletion commitments technically supported, and can the vendor demonstrate a deletion?
- What is the incident notification timeline and process?
- What support does the vendor provide for rights requests, audits and impact assessments?
- What are the availability commitments, and what happens when they are missed?
- What is the exit process: data export format, timeline, assistance and deletion confirmation?
- How are subprocessor changes notified, and can you object?
- How does the vendor handle law enforcement or government access requests?
What certification does and does not tell you
Certifications help. They are not conclusions.
An ISO 27001 certificate concerns an information security management system and its defined scope. It tells you the vendor has a managed approach to security. It does not tell you whether your specific configuration, data flow or intended use is appropriate. Always ask what the scope statement covers, because scope is where certificates get interesting.
The same logic applies to SOC 2 reports. Read the exceptions. A report with no exceptions and a narrow scope tells you less than a report with a few honest exceptions and broad coverage.
Where the EU AI Act fits in a 2026 evaluation
Be precise here rather than repeating headlines, because the timeline changed.
The AI literacy obligation under Article 4 has applied since 2 February 2025 to deployers of any AI system. That includes you, once you roll out a slide drafting assistant. Ask the vendor what training material and system documentation they provide to support it.
The transparency obligations under Article 50 have applied since 2 August 2026.
Obligations for Annex III high risk systems were deferred to 2 December 2027 by Regulation (EU) 2026/1744, the Digital Omnibus on AI, which entered into force on 27 July 2026. Systems embedded in products under Annex I follow on 2 August 2028.
Most presentation drafting tools are not high risk systems. Reach that conclusion by assessing your use rather than assuming it, and document the assessment. And note what the deferral did: it moved a date. It did not remove a direction.
Run a real pilot before you sign
Demos are built to succeed. Pilots are where you learn.
Choose one repeatable workflow. Not the most impressive one. The most frequent one.
Use non sensitive or synthetic material first. You are testing the system, not risking your data.
Test the failure cases deliberately. Permission boundaries. Requests that cannot be satisfied from approved sources. Conflicting instructions. Attempts to modify protected content. Very long text and awkward characters. A file that a human edited between runs.
Measure review effort, not just drafting speed. The pattern to watch for is generation getting faster while review gets longer. That is cost moving, not cost disappearing.
Then convene the reviewers. Security, privacy, compliance, procurement, brand and the business owner evaluate the evidence from the pilot together, including the failures. A pilot report that contains only successful examples is a sales document.
The five questions that decide it
If you strip this checklist down to what actually determines the answer:
- Can the vendor show you the full processing chain, including support access and backups?
- Is the training and retention position contractual rather than promotional?
- Does the agent run with the user's permissions, and can you test that yourself?
- Is the output native, editable and yours to take with you?
- Can you reconstruct, after the fact, what went in, what changed, who checked it and what was released?
A vendor who answers all five with evidence is worth a pilot. A vendor who answers them with adjectives is not, however good the interface looks.
Where offgen fits
We publish the material that supports this review rather than asking you to take it on trust. Our trust center, security overview and data processing agreement cover the processing chain, subprocessors, retention and the contractual position on training.
On the product side: retrieval runs inside existing tenant, workspace, role and document permissions. Output is native, editable PowerPoint using your master. Lockable elements and brand governance enforce protected content during generation.
Read those documents the way I would read a competitor's, which is to say looking for the parts that get specific. Specificity is the signal. Everything else is positioning, including ours.
Frequently asked questions
What should you evaluate when buying AI presentation software?
Seven areas: the data processing chain including subprocessors and transfers, whether inputs are used for model training, how permissions and tenant isolation work, whether output is native and editable, whether brand and protected content can be enforced, what is auditable, and the contractual position on retention, deletion, incidents and exit.
Is an ISO 27001 certificate enough to approve an AI presentation tool?
No. ISO 27001 certifies an information security management system and its defined scope. It does not tell you whether a specific product configuration, data flow or intended use is appropriate for your organisation. Use it as an input to the assessment, never as a substitute.
Does EU hosting make an AI presentation tool GDPR compliant?
No. Data location is one factor. You also need the controller and processor analysis, a lawful basis, an Article 28 agreement where applicable, the subprocessor list, transfer mechanisms including remote support access, retention and deletion, security controls and the actual product behaviour at runtime.
What is the most commonly missed question in these evaluations?
Where support engineers sit and what they can access. Data residency for the main application is usually documented. Remote support access, telemetry and backup locations frequently are not, and they are part of the same processing chain.
Why does native PowerPoint output matter for procurement?
Because it determines whether review is possible and whether you can leave. Native output lets reviewers inspect and correct the real artifact, and it means your content is not trapped in a proprietary format when the contract ends. Treat it as an exit consideration, not a design preference.
How should you pilot AI presentation software before signing?
Run one repeatable workflow with non sensitive or synthetic material. Test the failure cases deliberately: permission boundaries, missing sources, conflicting instructions, protected content and manual edit recovery. Measure review effort as well as drafting speed. Then have security, privacy, procurement and brand evaluate the evidence.
Sources
- 01Regulation (EU) 2016/679 (General Data Protection Regulation) — EUR-Lex, 2016-04-27. Accessed 26 August 2026.
- 02Regulation (EU) 2024/1689 (Artificial Intelligence Act) — EUR-Lex, 2024-07-12. Accessed 26 August 2026.
- 03Opinion 28/2024 on data protection aspects of AI models — European Data Protection Board, 2024-12-17. Accessed 26 August 2026.
- 04Data Processing Agreement — offgen. Accessed 26 August 2026.
- 05Trust Center — offgen. Accessed 26 August 2026.
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About the author
Maximilian Betz
Co-Founder and CEO, MD
Max writes about management consulting, enterprise adoption, data protection, and the operating controls required for AI in regulated organisations.