Preservica has launched a targeted practitioner workshop series to teach archivists, records managers and information professionals how to apply human‑centric AI to archival enrichment, capture, long‑term preservation and discovery — a hands‑on program that promises practical skills, Microsoft integration examples and direct interaction with Preservica’s product team.
Background
Preservica is positioning the new series as a practical bridge between archival practice and contemporary AI tooling, delivered under the company’s Active Digital Preservation™ umbrella and tied into Preserve365®, its embedded archiving suite for Microsoft 365. The company’s recent product moves — including a Microsoft‑verified Power Automate connector and deeper Preserve365 integrations with SharePoint and Outlook — provide the technical context and vendor ecosystem the workshops will use as live examples.The workshops are scheduled to run from late October through early December 2025, beginning on October 23, 2025. Sessions are organized around specific, practical use cases — PII detection, OCR for digitized materials, large‑scale image classification, AV transcription and captioning, metadata cleanup, and AI‑driven capture and discovery using Microsoft Copilot. Early registration interest, the company says, has been strong.
Why this matters now
Digital archives are growing in volume and complexity. Long‑term preservation is no longer purely about bit‑level storage and format migration; it increasingly requires scalable indexing, enrichment and governance to make content findable, legally defensible and usable by AI systems in the future.- Scale problem: Organizations face massive backlogs of unprocessed digital content and ongoing influxes from email, SharePoint, OneDrive, Teams and other sources. Preservica frames its Preserve365 approach as an embedded path for Microsoft 365 customers to make archiving part of day‑to‑day workflows rather than a separate, manual task.
- AI opportunity: Modern AI can speed previously manual tasks — OCR, speech‑to‑text, PII detection, image tagging — while improving discovery and reducing routine workload through automation and Copilot‑style assistants. The workshops are designed to put these specific capabilities into the hands of practitioners so they can evaluate and govern them.
- Governance imperative: Applying AI in archives brings legal, ethical and provenance concerns — from PII exposure to audit trail requirements — which is why Preservica emphasizes human oversight and practitioner control in the workshop messaging.
What Preservica is offering: Workshop structure and content
Series format and schedule
The announced lineup runs across four practical workshops with distinct focuses:- Workshop #1 — PII detection: Comply with legal mandates by identifying files containing Personally Identifiable Information (PII). (October 23, 2025).
- Workshop #2 — OCR for faster discovery: Using OCR to extract and surface text from digitized materials (November 6, 2025).
- Workshop #3 — Image analysis: Categorizing and describing image collections at scale (November 20, 2025).
- Workshop #4 — AV transcription & metadata QC: Transcription, captioning and metadata quality control best practices (December 4, 2025).
Tools and vendors on show
The workshops will demonstrate both Preservica’s own tools (notably Preserve365®) and third‑party AI services including Microsoft Copilot, plus OCR and AV transcription stacks. Preservica’s recent work to create a Power Automate connector and expand Preserve365’s reach into Microsoft 365 workflows is part of the technical backbone for these sessions.What the announcement actually claims — and what is marketing
Preservica frames the series under several assertive claims: that AI assistance can accelerate discovery, improve metadata quality, and make long‑term archives “AI‑ready.” Those are plausible outcomes where properly configured tooling and governance exist. It is important to differentiate between verifiable capabilities and vendor positioning:- Verifiable technical integrations exist: Preserve365 integrates with Microsoft 365, has a Power Automate connector and has recently expanded support for SharePoint and Outlook archiving workflows. Those product milestones have been publicly documented by Preservica.
- Vendor claims about being “the leader in AI‑powered Active Digital Preservation™” are marketing language and should be treated as a company positioning statement rather than an objective, independently validated ranking. Workshop outcomes depend heavily on configuration, underlying AI model selection and institutional governance; results will vary by customer. This distinction matters when assessing ROI and compliance.
Critical analysis — strengths and practical benefits
1. Practical, use‑case driven training
The workshop agenda focuses on discrete, high‑value tasks: PII detection, OCR, image tagging and AV transcription. That design reduces abstraction and provides immediate takeaways for practitioners who must justify budget and staffing decisions.- Practical benefits include faster discovery of digitized text, scalable image curation, and automating repetitive metadata cleanup. Early adopters often report substantial time savings on routine triage and indexing tasks when supervised AI is well‑integrated.
2. Microsoft ecosystem integration
Preservica’s Preserve365 is explicitly embedded into Microsoft 365 workflows and supports Power Automate connectors. For organizations already invested in Microsoft cloud‑first stacks, the technical integration reduces friction and leverages existing Purview, Power Platform and Copilot investments. That lowers the operational cost of deployment and simplifies automation of archive capture from SharePoint, Outlook and Teams.3. Human‑in‑the‑loop emphasis
The company repeatedly states the series is about human‑centric AI and applying AI with human oversight. This is consistent with modern best practice: maintain human review for sensitive decisions, use AI for draft enrichment and keep full audit trails for provenance and compliance. Those safeguards are essential for FOI, legal discovery and regulatory audits.4. Opportunity to influence emerging tooling
By inviting practitioners to experience tools and provide feedback, the series could help shape responsible feature design — for example, improving explainability in automated metadata edits, or refining PII detection thresholds to suit different legal regimes. Engaging practitioners early is an important governance step that can improve practical outcomes.Critical analysis — risks, limitations and unknowns
1. Model performance and error rates (OCR, PII, image analysis)
AI models carry measurable error rates that vary by data quality, language, image resolution and domain specificity. For OCR and PII detection, false negatives (missed sensitive data) can create legal exposure, while false positives can introduce unnecessary workload and over‑redaction.- Archivists must validate model recall and precision on representative samples before broad application and document thresholds for acceptance and escalation. Off‑the‑shelf models may need fine‑tuning with domain data. This is a technical requirement not fully addressed in marketing materials. Workshop sessions may demonstrate capabilities, but organizations should treat those demonstrations as initial proof‑of‑concepts, not production guarantees.
2. Explainability and auditability
Many AI systems, especially those built on large foundation models, are opaque. For legal and regulatory scrutiny, archives must retain provenance metadata — what model processed the content, at what time, what thresholds were used, and who approved changes.- The press materials emphasize human oversight but do not detail how explainability, model versioning, or immutable audit trails will be implemented. Organizations should insist on clear logging, deterministic reproduction of results and the ability to export audit records for compliance reviews.
3. Data residency, privacy and vendor dependencies
Using cloud AI services and Microsoft Copilot implies routing data to third‑party processors. For regulated records or sensitive cultural heritage materials, organizations must map data flows, evaluate contractual protections, and consider on‑prem or private‑cloud alternatives.- Preserve365’s integration with Microsoft 365 is a strategic advantage for Microsoft customers, but it also ties long‑term preservation workflows to Microsoft’s service model. Institutions should model vendor lock‑in risks and export options before deep operational dependency.
4. Metadata integrity and downstream AI use
Automated metadata cleanup and quality control are powerful, but automated changes must be reversible and provenance‑aware. Poorly standardized or incorrectly normalized metadata can be propagated to discovery systems, causing long‑term harm to archival integrity.- The workshops promise metadata QC techniques, but teams must implement policies for review, rollback and version control. This is crucial when archives are later used to train AI models or when records are subject to legal scrutiny.
5. Resource and skills gap
Adopting AI in archival practice requires new skills: understanding model behaviour, validating outputs, setting governance thresholds and integrating tools into existing workflows. A one‑day or short workshop is a start, but institutional adoption requires sustained training, technical staffing and governance support.Practical checklist: What archivists should do before joining the workshops
- Inventory priority collections that will benefit from AI (digitized newspapers, email collections, image archives, audiovisual holdings).
- Prepare representative sample sets (100–1,000 items) that reflect real data quality and variety for evaluation exercises.
- Document legal and regulatory constraints: PII categories, retention schedules, FOI obligations and any local data residency rules.
- Identify current Microsoft 365 architecture: SharePoint layouts, teams structure, Purview/retention policies and the identity model.
- Define success metrics: acceptable OCR accuracy, PII detection recall/precision, image classification F1 score targets, and SLA expectations for human review.
- Plan governance: who signs off on automated metadata edits, who reviews redaction decisions, and how audit logs are stored.
How to evaluate AI outputs practically (a short protocol)
- Step 1: Baseline measurement — run AI tools on the sample set and measure initial precision and recall for the task.
- Step 2: Error analysis — categorize common error types (e.g., OCR misreads due to low contrast, PII false positives from numeric strings, image mislabels for historical costumes).
- Step 3: Threshold tuning — adjust confidence thresholds and post‑processing rules to balance human review workload versus missed detections.
- Step 4: Human review loop — define a human verification sample rate (for example, 10% random + all items below confidence threshold).
- Step 5: Audit and version control — store model version, prompts (if applicable), configuration, and reviewer decisions as preservation metadata.
Governance and policy considerations
Privacy and PII handling
Automated PII scanning must be integrated with retention and access rules. Detection alone is not disposal: it triggers a governance workflow that includes legal review, possible redaction, and recorded decisions. Policies must specify retention for derived enrichment metadata and whether enrichment is allowed on restricted materials.Record authenticity and chains of custody
When AI alters or annotates digital objects, the original file must be preserved unchanged and the derived artifacts stored as separate, provenance‑tagged resources. Any automated normalization (e.g., metadata cleanup) must be reversible or retain the original values for future audit.Openness and reproducibility
Archive professionals should insist on:- Clear identification of models and services used (provider, version, configuration).
- Exportable audit logs and the ability to reproduce or reprocess assets if better models become available.
- Contracts that allow export of preserved content and associated enrichment metadata in standard formats.
Technology interoperability: Preserve365 and Microsoft Copilot
Preservica’s Preserve365 has been incrementally strengthened to work inside Microsoft 365 workflows, including Power Automate connectors and support for SharePoint and Outlook archiving scenarios. These integrations make it easier to automate capture and to expose archived content for discovery inside Microsoft experiences — a pragmatic path for organizations already standardized on Microsoft technologies.Copilot-style AI can be used to automate transfer, enrich discovery metadata, and help users craft effective search queries against long‑term archives. However, reliance on Copilot also inherits Microsoft’s service model and data flow considerations, such as where prompts are processed and how model outputs are stored. Institutions should map these flows and validate contractual protections for sensitive records.
Recommendations for organizations considering Preservica’s workshop and AI adoption
- Treat the workshop as a discovery and governance checkpoint, not an immediate production rollout. Use it to define pilot scope, success metrics and governance rules.
- Bring technical staff plus policy owners to the sessions: adoption requires both product configuration and organisational buy‑in.
- Insist on auditable, repeatable workflows: require preservation metadata that records AI model, version and configuration for every enrichment action.
- Pilot on non‑sensitive collections first, then expand to regulated assets after you’ve validated models, thresholds and review processes.
- Evaluate long‑term portability and export formats to mitigate vendor lock‑in risk. Ask vendors for explicit export guarantees and data retention clauses in contracts.
What to expect from a vendor workshop — realistic outcomes
- Immediate: hands‑on experience with OCR, PII scanning, and image/audio tools plus sample workflows and a chance to ask product teams detailed questions.
- Short term (2–3 months): a defined pilot based on workshop learnings, representative performance metrics and a governance plan.
- Medium term (6–12 months): integration into day‑to‑day workflows for targeted collections, with documented audit trails and a plan for scaling or vendor diversification.
Cautionary notes and unverifiable claims
- Preservica’s marketing claims about market leadership and the long‑term effectiveness of AI in preservation are company positions and should be validated against procurement‑grade evaluations and independent third‑party benchmarks when possible. Treat vendor demonstrations as informed previews rather than guarantees of performance in your environment.
- Any statements about specific model accuracy or legal compliance outcomes should be independently tested with local datasets and legal counsel; the press materials do not provide independent accuracy figures or formal compliance certifications. This is an area where the organization, not the vendor, must do the final validation.
Final assessment
Preservica’s new AI in Archiving & Digital Preservation workshop series is a timely and pragmatic offering. It couples hands‑on skill building with real product examples tied into Microsoft 365 — a useful combination for any archival team operating in that ecosystem. The sessions should help practitioners separate plausible automations from marketing claims, design human‑centred review workflows, and begin defensible pilots that balance efficiency gains with compliance needs.However, the real work begins after the workshops: validating model performance on representative collections, documenting governance and audit trails, ensuring reversible enrichment, and managing vendor and data residency risks. For organizations that take these follow‑up steps seriously, AI assistance can be transformative; for those that treat demos as production guarantees, the risks — legal exposure, metadata corruption and locked‑in workflows — can outweigh the short‑term benefits.
Practical next steps (quick action list)
- Register for the workshop most relevant to your role and data types.
- Assemble a cross‑functional pilot team that includes archivists, IT, legal/compliance and a data steward.
- Prepare representative sample datasets and legal constraints before the session.
- Use the workshop to define pilot scope, success metrics and governance rules.
- Insist on auditability, exportable metadata, and contractual clarity on third‑party AI processing.
The Preservica workshop series signals acceleration in practical, vendor‑led support for AI in archiving; the community value will be determined by how practitioners use those sessions to demand transparency, measurable performance and governance that protects both the public interest and the integrity of the archival record.
Source: PA Media Preservica Launches New AI in Archiving & Digital Preservation Workshop Series