As we enter 2026, artificial intelligence is no longer a curiosity — it’s becoming a strategic force shaping how enterprises create, manage, and scale video content. For highly regulated industries, such as financial services, insurance, legal, and healthcare, the question isn’t whether to use AI in video creation, but which approaches actually work at scale without introducing unacceptable risk.
There are four major AI trends shaping video creation. Here’s the impact regulated organizations can expect.
Trend 1 — Traditional outsourced models using AI tools
What traditional outsourced models look like
Agencies and production partners applying AI to enhance editing, cleanup, or translation in isolated project workflows.
Why it still matters
- Good for high-production, flagship content
- Agencies can deliver better post-production results with AI assistance
The real limitation
- Costs scale with volume
- Workflows remain fragmented
- Compliance and review happen after production, not as part of the process
McKinsey proves planning is critical
This trend represents incremental improvements, not transformation, and as McKinsey & Company’s enterprise AI research shows, most organizations have not moved beyond pilots into scaled use because the underlying workflows weren’t redesigned for AI impact.
Trend 2 — Standalone generative AI video tools
What are Generative AI video tools
Text-to-video, avatars, and prompt-based creation tools that generate content quickly.
Where they work
- Rapid ideation and concepting
- Internal, low-risk content
Where GenAI fails for regulated enterprises
- Hard to govern or audit outputs
- Difficult to ensure consistent brand, regulatory, or compliance controls
- Minimal support for enterprise security
McKinsey shows GenAI needs to be integrated
McKinsey’s latest surveys show that while 88% of enterprises use AI in at least one function and generative AI experimentation is widespread, most organizations have not yet scaled these efforts or embedded them into core workflows.
This means you can generate faster, but without the right systems in place, you cannot operationalize at the enterprise level.
Trend 3 — Embedded, cloud-based AI across the full workflow
What embedded AI workflows look like
AI capabilities built directly into end-to-end video platforms — from capture through review, approval, and publishing — rather than as separate add-on tools.
Why embedded workflows matter for regulated industries
- Keeps governance, audit trails, and permissions in one system
- Reduces risk and compliance burden by design
- Turns AI from an adjunct tool into an embedded workhorse
Gartner predicts increased AI deployment
Industry analysts are pointing to this shift as critical. Gartner predicted that more than 80% of enterprises will have deployed GenAI-enabled applications in production by the end of the year, reflecting a move from experiments toward practical, embedded AI models.
Moreover, in the coming years, we’ll see task-specific AI agents deeply integrated into enterprise software, rather than as isolated pilots.
This trend aligns with broader AI adoption research showing that the highest ROI comes when AI is built into business processes and workflows, not bolted on
Trend 4 — AI-driven localization & accessibility built in
What built-in AI localization looks like
AI translation, transcription, and accessibility that is part of the core workflow — not a separate step.
Why it works
- One video asset reaches global audiences
- Meets regulatory and accessibility requirements with fewer handoffs
- Reduces reliance on external vendors and lowers cost
Why built-in localization is key
Unlike manual or outsourced models, this trend treats localization as default, not optional — an operational capability, not a special project.
The key distinction in 2026
The biggest evolution isn’t smarter AI models — it’s where AI lives and how it’s managed.
Outsourced + AI-enhanced tools help individual projects. Embedded AI in cloud workflows changes how video is created, reviewed, and scaled.
Multiple industry analysts are pointing to this shift:
- McKinsey reports most organizations are still experimenting with AI and have not yet captured broad enterprise-level impact — largely because use isn’t embedded into workflows.
- Gartner forecasts widespread GenAI adoption, but warns that many early AI projects — especially standalone agentic or hype-driven ones — fail to deliver real ROI without strategic planning and integration.
In other words, AI that is integrated, governed, and workflow-native will outperform AI treated as a point tool.
What this means for regulated enterprises
For organizations where compliance, brand integrity, and auditability matter:
✅ Build AI inside core systems — not beside them
✅ Prioritize solutions with governance, security, and audit baked in
✅ Treat video creation as an operational capability, not a boutique deliverable
✅ Measure success with business impact — not just experimental outputs
By 2026, the safe, scalable application of AI will be a competitive necessity, not a fringe advantage — but only for enterprises that embed it into end-to-end workflows, not just into isolated projects.
In video creation, tools like Socialive’s AISuite that are built directly into powerful video creation workflows truly stand out. Regulated industries will get the most out of AI in 2026 by using applied, embedded AI to supercharge workflows that are built to scale.
