Banks and credit unions have spent years assembling separate systems for customer data, business intelligence, audience segmentation and digital engagement. Digital Onboarding is betting that AI agents can collapse much of that workflow into a single interface, announcing the completion of its transition to an AI-native platform and the launch of DOBI, or Digital Onboarding Business Intelligence.
For financial institutions, the problem is rarely a lack of data. It is what happens between having the data and turning it into an action.
A bank can have millions of transactions spread across thousands or millions of accounts, alongside customer profiles, campaign histories and product information. Yet extracting a useful business insight, identifying the right audience and launching a personalized engagement campaign can require several teams and disconnected technology systems.
Digital Onboarding, which says its platform is used by more than 170 banks and credit unions, is attempting to reduce that complexity with DOBI, Digital Onboarding Business Intelligence, an AI agent embedded within its lifecycle engagement platform.
The company describes the move as the completion of its evolution into an AI-native platform. Rather than positioning AI as another feature layered onto an existing analytics or marketing system, Digital Onboarding is putting a conversational interface between financial-services teams and the data, audiences and engagement workflows they already use.
In practical terms, DOBI allows users to make requests in natural language. The system can build an audience, generate campaign content, optimize performance and report on outcomes without requiring users to move between a business-intelligence dashboard, segmentation tool, content platform and campaign system.
That puts Digital Onboarding into a broader shift occurring across enterprise software: the move from AI assistants that answer questions toward AI agents that can execute multi-step workflows.
McKinsey has identified multiagent systems as an emerging component of the AI-first bank, with agents potentially able to plan actions, use tools and coordinate across complex workflows. The consultancy estimates that generative AI could create $200 billion to $340 billion in annual value for the global banking industry, largely through productivity improvements.
DOBI’s initial capabilities are relatively focused. The platform currently offers Content Creation and Audience Building. Content Creation can generate emails, text messages and landing pages for review, while Audience Building works with Digital Onboarding’s Ready-to-Launch Audiences to translate a natural-language request into a targeted, compliance-reviewed audience.
Digital Onboarding says two additional capabilities are also being demonstrated: end-to-end Campaign Creation and the ability to build, track and optimize custom Outcomes. Those capabilities are expected to become available in the coming months.
The distinction matters because many enterprise AI deployments still stop at insight generation. A model might tell a marketing team that a particular customer segment is under-engaged, for example, but another system is needed to build the audience and a separate platform is required to launch the campaign.
DOBI is designed to connect those steps.
“You tell it what you’re trying to accomplish, in plain language, and it builds the audience, drafts the content, optimizes performance, and shows you what it’s worth,” said Ted Brown, co-founder and CEO of Digital Onboarding.
That approach also reflects a larger industry challenge. Banks are under pressure to improve digital experiences while controlling the cost and complexity of technology infrastructure. McKinsey has noted that banking technology spending has grown faster than revenue in recent years, increasing pressure on institutions to find higher-return uses for technology and AI.
For smaller banks and credit unions, the argument for consolidation may be particularly significant. A fragmented engagement stack can require data warehouses, BI platforms, campaign software, analytics tools and external agencies. The challenge is not simply the licensing expense. It is the organizational effort required to keep those systems synchronized.
AI agents could change that equation if they can reliably operate across those functions.
The caveat is governance.
Financial institutions cannot treat an AI interface like a generic consumer chatbot. Customer information, campaign decisions and compliance requirements create a substantially higher bar for security, explainability and auditability.
Digital Onboarding says every action taken by DOBI is logged for audit and compliance review. It also says personally identifiable information is not sent to third-party large language models. Institutions can optionally allow DOBI to use aggregated data from Digital Onboarding’s network of more than 170 financial institutions for benchmarking.
That last capability could become an important differentiator. An individual bank’s data can show what happened inside its own customer base. Aggregated cross-institution data can potentially provide a reference point for determining whether a campaign’s performance is actually strong relative to peers.
The competitive landscape, however, is much larger than traditional banking engagement software. Microsoft, Salesforce and Adobe are all pushing AI deeper into enterprise workflows, while cloud providers such as Amazon Web Services and Google Cloud are building infrastructure for organizations developing their own AI applications. In financial services, the competitive question increasingly is not whether an institution can access generative AI, but how deeply AI can be embedded into operational systems without creating another disconnected layer.
That makes DOBI’s most important proposition less about generating marketing copy and more about reducing the distance between data, decisions and execution.
For enterprise teams considering the technology, the key questions will be practical: how well the agent performs against existing BI and campaign systems, how transparent its recommendations are, what controls administrators can impose, and whether consolidation actually produces measurable savings and revenue gains.
Gartner’s 2025 research illustrates the broader challenge. It found that 59% of finance leaders surveyed were already using AI in their finance functions, while data quality and availability and technical skills remained major barriers to adoption.
In that environment, the next generation of enterprise AI platforms may be judged less by how impressive their models sound and more by how effectively they turn existing organizational data into governed, measurable action.
DOBI is an early example of that transition in financial-services engagement: from dashboards that explain what happened to AI systems designed to help determine what should happen next.
Market Landscape
The banking AI market is moving from experimentation toward workflow integration. Financial institutions are increasingly looking beyond isolated chatbots and copilots toward systems capable of connecting data analysis, decision-making and execution.
That creates an opening for platforms such as DOBI, but also puts them in competition with much larger enterprise ecosystems.
Salesforce is embedding AI into CRM and customer engagement workflows. Adobe is combining generative AI with marketing, analytics and customer-experience tools. Microsoft is positioning Copilot and Azure AI across enterprise workflows, while Google Cloud and Amazon Web Services provide infrastructure and models for institutions building their own AI applications.
Digital Onboarding’s narrower strategy is to combine AI with financial-institution engagement data and campaign execution. Its potential advantage is therefore specialization rather than model scale.
The larger market question is whether banks will prefer specialized AI-native applications or increasingly assemble AI capabilities from their existing cloud, CRM, data and marketing platforms.
For enterprise buyers, integration, governance, auditability and measurable business outcomes are likely to matter as much as the underlying model.
Top Insights
- Digital Onboarding launched DOBI, an AI agent connecting audience building, content creation and performance intelligence for banks and credit unions seeking fewer engagement tools.
- DOBI uses natural-language prompts to turn business requests into audiences and campaign content, potentially reducing reliance on separate BI, analytics and marketing workflows.
- Compliance controls are central to the platform, with Digital Onboarding saying DOBI logs actions and keeps PII away from third-party large language models.
- Aggregated benchmarking could differentiate DOBI, allowing financial institutions to compare campaign performance against data from Digital Onboarding’s broader customer network.
- The launch reflects a broader banking AI shift from conversational assistants toward agentic systems that connect analysis, decision-making and operational execution.
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