ServisFirst Bank, a US-based regional wholesale bank, has selected Covecta to deploy agentic AI across its operations. The engagement will embed AI agents into the bank's workflows, starting with manual tasks performed outside core systems, to drive efficiency gains while minimising operational risk.
Starting outside the core
The initial deployment targets manual work completed outside mission-critical systems. Scott Wilson, Founder and CEO of Covecta, said the approach is designed to innovate "from the outside-in" to reduce disruption. ServisFirst already operates with strong efficiency metrics, Wilson noted, so the programme also aims to reshape how teams work alongside AI.
Michael Lindsey, Chief Information and Operations Officer at ServisFirst Bank, explained the selection criteria. "We selected Covecta because their agents bring deep banking domain expertise out of the box," Lindsey said. The bank evaluated vendors specifically for their understanding of banking operations rather than generic AI capabilities.
Beyond time savings
Lindsey framed the initiative as more than a cost-cutting exercise. "This is about more than efficiency. The time savings matter, but the real value is building an agentic mindset across the bank, capturing our institutional knowledge, refining it, and deploying it consistently through agents across the organization."
That emphasis on institutional knowledge signals a longer-term shift. Rather than simply automating rote tasks, the bank intends to codify how experienced staff make decisions and distribute that reasoning through AI agents. For operations teams, this changes the nature of process improvement work - from documenting procedures to training AI models that can execute them.
How the rollout is structured
The programme will support multiple teams, with a phased approach that Covecta and ServisFirst designed to avoid disrupting existing systems. By embedding agents into workflows that currently happen outside software - spreadsheets, email chains, manual reconciliation - the partners aim to prove value before touching core banking platforms.
This outside-in method also reduces the governance burden that typically slows AI adoption in regulated financial institutions. Agents operate at the workflow layer rather than deep inside transaction processing engines, which keeps compliance and risk teams closer to the deployment.
Why this matters for operations professionals
When a bank known for best-in-class efficiency still finds room to deploy agentic AI, the technology is moving beyond hype into operational reality. The emphasis on capturing institutional knowledge - not just automating tasks - suggests operations roles will shift toward curating and refining the decision logic that agents use. Professionals who understand how to structure that knowledge for AI systems, through paths like AI Learning Path for Operations Managers, will be positioned to lead these transitions rather than react to them.
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