Smart Communications is expanding AI-assisted migration capabilities designed to help regulated enterprises replace legacy customer-communications systems with modern, reusable templates. The company says its approach uses AI to analyze existing communications, identify consolidation opportunities and convert legacy documents, while keeping human review and auditability in the process.
For banks, insurers, healthcare organisations and government agencies, modernising customer communications has an awkward problem: the technology may be old, but replacing it can be expensive, risky and time-consuming.
Smart Communications is betting that artificial intelligence can make that transition less painful.
The company, which specializes in customer engagement technology for regulated industries, announced expanded AI-enhanced migration capabilities on Oct. 8. The offering is intended to help enterprises consolidate fragmented communications environments, reduce reliance on legacy systems and create a more governed foundation for future AI applications.
The underlying problem is broader than document conversion. Large regulated organizations can accumulate thousands of communications templates through acquisitions, departmental silos and decades of technology changes. Moving those assets to a new customer communications management platform traditionally requires extensive manual analysis, reconstruction and testing.
Smart says its new migration process starts with AI analyzing an organization’s existing communications to identify duplication and opportunities for consolidation. Those findings are organized into what the company calls a “Migration Blueprint,” essentially a prioritized modernization plan.
An AI conversion engine then reconstructs legacy documents as modern, reusable templates. Human reviewers remain involved, with customers and Smart’s migration specialists validating conversions before deployment, according to the company. That human-in-the-loop approach is particularly relevant in regulated sectors, where communications can carry compliance, disclosure and audit requirements.
The timing reflects a larger shift in enterprise AI. Companies are moving beyond experiments with large language models and looking for ways to embed AI into operational systems without creating another layer of technology debt.
McKinsey’s 2025 State of AI research found that 88% of respondents said their organizations regularly used AI in at least one business function, but most organizations remained in experimentation or pilot stages when it came to scaling AI across the enterprise.
That gap helps explain why modernization of underlying platforms matters. Generative AI technologies can produce personalized content, summarize information and automate workflows, but they still depend on reliable enterprise data, controlled processes and systems that can expose information in consistent formats.
For communications teams, consolidating templates could therefore become an AI infrastructure issue as much as a cost-saving exercise.
The competitive landscape is already moving in that direction. Microsoft, Google, Amazon, Salesforce and Adobe are embedding AI capabilities into enterprise software, while specialized vendors are competing around customer engagement, workflow automation and governed content. In this environment, the ability to migrate existing enterprise assets quickly can influence how readily customers adopt a new AI platform.
Smart’s pitch is that migration itself can become an AI-enabled workflow rather than a one-time implementation project.
IDC Research Director Amy Machado said in the company’s announcement that AI-assisted template reconstruction is reducing migration timelines and potentially weakening switching costs that have historically protected incumbents. The observation points to an important competitive consequence: if customers can move communications assets more efficiently, vendor lock-in may become less durable.
The broader AI market is also putting pressure on enterprises to make these decisions faster. Gartner forecasts worldwide spending on AI platforms and models will reach $64.3 billion in 2026, up 63.4% from 2025. Gartner also expects spending on AI platforms for data science and machine learning to reach $26.4 billion this year.
Yet infrastructure modernization is unlikely to disappear simply because AI is involved. Converting a template does not automatically resolve data-quality problems, integration dependencies, regulatory requirements or organizational resistance. Nor does it guarantee that subsequent AI applications will produce measurable business value.
That distinction matters for enterprises evaluating AI development frameworks, AI cloud platforms and AI automation platforms. The more AI moves into customer-facing workflows, the more important it becomes to establish governed sources of information and repeatable processes underneath those applications.
Smart says it has supported hundreds of large enterprises across insurance, financial services and healthcare. Aptia, a pension administration and consulting provider, is cited as one customer that completed a complex implementation in a few months. Such customer examples provide evidence of deployment activity, but they do not independently establish how much time or cost the new AI capabilities save across the market.
The bigger story is therefore not simply that another enterprise software vendor has added AI to migration. It is that legacy modernization is increasingly being reframed as part of the enterprise AI stack.
For regulated companies, that could make the ability to clean up, consolidate and govern decades of communications assets a prerequisite for deploying more sophisticated AI agents, automation and personalized customer experiences.
Market Landscape
The enterprise AI market is shifting from isolated generative AI pilots toward governed, workflow-level deployments. Gartner projects $64.3 billion in 2026 spending on AI models and platforms, with AI platform spending growing 36.9%.
At the same time, McKinsey reports that 88% of surveyed organizations use AI in at least one business function, while only a minority have reached enterprise-scale deployment.
For regulated industries, this creates an important infrastructure challenge. AI applications, LLMs and autonomous systems need reliable data and controlled workflows. Modernizing legacy communications platforms can therefore become a prerequisite for enterprise AI adoption rather than a separate IT modernization project.
The competitive field includes horizontal enterprise platforms from Microsoft, Google, Amazon, Salesforce and Adobe, alongside specialized customer communications and workflow providers. Vendors that can combine migration, governance, automation and AI capabilities may have an advantage as enterprises seek fewer technology silos.
Top Insights
- AI-assisted migration: Smart Communications is using AI to analyze legacy communications, identify duplication and reconstruct documents as reusable modern templates.
- Legacy modernization: The announcement targets a persistent enterprise problem where outdated communications infrastructure increases technical complexity, migration costs and vendor switching barriers.
- AI readiness: Consolidated, governed communications assets can provide a cleaner foundation for enterprise AI, personalization, automation and future AI-agent workflows.
- Regulated industries: Human validation and audit trails remain important when AI is used to transform customer communications in financial services, insurance and healthcare.
- Competitive pressure: Faster AI-assisted migration could reduce switching costs and make legacy technology less effective as a long-term vendor-retention mechanism.
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