Thread Bank’s chief digital officer breaks down AI strategy
$1B bank ties AI efforts to efficiency ratio to quantify results
Thread Bank is gearing up for its next stage of AI initiatives while working to create a blueprint for how smaller FIs should innovate.
The roughly $1 billion bank has broken its AI strategy into three pillars as it takes a disciplined approach to innovation, Chief Digital Officer Marty Miracle told FinAi News. They are:
- User productivity, comprising tools such as LLMs, to improve the quality of a report, extrapolate data faster or enhance overall logic in day-to-day tasks;
- Business automation platforms, which streamline and accelerate manual, repetitive processes; and
- Business intelligence, using AI-driven analytics tools or agentic AI, to convert datasets into actionable insights.
Understanding how AI must be governed in a highly-regulated industry is key to executing AI at a large scale, Miracle said. Thus, Thread Bank has created an internal user portal that manages access to its AI solutions and ensures data is shared securely and other guardrails are in place, he said.
Path for smaller FIs
Nashville-based Thread Bank hopes its own AI strategy can provide a framework for smaller FIs, Miracle said.
Roughly 40% of banks with under $10 billion in assets have generative AI tools live or in the pipeline, according to a survey conducted by Hanover Research for bank software provider Temenos. That compares with 79% at banks with over $250 billion in assets and 75% at banks with assets between $50 and 250 billion.
Gen AI Adoption By Bank Asset Size
Smaller FIs are lagging in AI adoption partly because they’re hesitant to overhaul their operations, Miracle said. These FIs must overcome this apprehension while being selective in how they allocate resources to build internal expertise, he said.
Smaller banks also struggle with AI deployment because their data isn’t clean or organized, Miracle said.
“So, we are going to make sure that we have all the data in one place, that it is valid and clean and trustworthy, and then build that expertise little by little going forward,” he said.
Quantifying results
Thread Bank is working to quantify its AI efforts by tying them directly to financial performance, particularly its efficiency ratio, which measures noninterest expense relative to revenue, Miracle said.
“Then, we can start a project where we actually go to every functional area here, we set a target of reducing noninterest expense, and we find the processes that aren’t very efficient solutions,” he said.
Thread Bank is also tracking adoption and time savings from productivity tools, with each employee setting their own AI automation goals, Miracle said.
“We measure that through how much it improves their data or how many agents they may build on the platform that we have,” he said. “We start making it the expectation — we’re going to use AI to make everyone more efficient or to meet revenue goals better than we’re doing now.”
Register here for the inaugural FinAi Banking Summit, taking place March 2-3 in Denver. View the full event agenda here.
