In-house Financial Services global Company newsroom

Bank of America

Customer Service Automation

Bank of America wanted to help customer service representatives resolve client needs faster, navigate complex topics more easily, and deliver more personalized guidance during live client conversations without removing human judgment from the service process.

EricaAssist reduces average call times by nearly one minute per interaction.
More than 18,000 employees use EricaAssist as a human-assisted AI agent to help serve clients.
New Generative AI (Gen AI) capabilities deliver contextual guidance in under three seconds.

Reported impact

Business outcome

The enhanced EricaAssist helps employees resolve client needs faster, supports decision making in real time, reduces call times, and improves efficiency and client experience while maintaining human oversight.

  • Customer Experience
  • Productivity Improvement

Solution

Bank of America enhanced EricaAssist, its in-house human-assisted AI agent for customer service representatives, by adding generative AI capabilities that operate during live client calls. In the workflow described, EricaAssist ingests the immediate conversation context along with relationship-specific context such as the employee’s role and the client’s relationship with the bank. It then processes that context in real time to summarize why the client is calling, pull together relevant information, and recommend next steps without interrupting the conversation flow. The output is served directly to employees as contextual guidance in under three seconds, so representatives can keep listening, explain solutions, and make decisions faster. The page does not name separate storage or model-serving products, but it does emphasize governance: the system is deployed with human oversight, transparency, accountability, and the employee kept at the center of each outcome.

Data flow

During live client calls, EricaAssist takes in conversation context plus employee-role and client-relationship context, uses generative AI to summarize why the client is calling and assemble relevant information, then returns recommended next steps and guidance to customer service representatives in under three seconds so they can act without interrupting the conversation.

Solution architecture

1 components · 1 layer
  1. Uncategorized
    • EricaAssist A human-assisted AI agent that supports employees during client conversations by summarizing calls, surfacing relevant guidance, and recommending next steps in real time.

Architecture clues

  • Governance emphasizes transparency, accountability, and responsible AI.
  • Human oversight remains central to outcomes.
  • Human-assisted AI agent used by more than 18,000 employees.
  • Planned expansion to additional servicing scenarios and business lines later in 2026.
  • Real-time guidance delivered in under three seconds during calls.

Evidence from the source

"Bank of America (B of A) today announced enhancements to EricaAssist, its human assisted AI agent that supports employees during client conversations, delivering real time insights that help resolve client needs faster while keeping the employee at the center of the experience."
"Bank of America customer service representatives use generative AI capabilities within EricaAssist to summarize why a client is calling, pull together relevant information, and recommend next steps based on the employee’s role and the client’s relationship with the bank."
"EricaAssist reduces average call times by nearly one minute per interaction, improving efficiency and client experience."
"New Generative AI (Gen AI) capabilities deliver contextual guidance in under three seconds, helping resolve client needs faster and supporting decision making by customer service representatives."
"This technology helps our teammates deliver relevant insights in seconds, while operating with strong governance, transparency, and accountability."
"Used by more than 18,000 customer service representatives, EricaAssist works alongside employees during calls – summarizing and surfacing relevant guidance in real time."
Transparency Evidence & methodology 75/100 reference value · 92% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Bank of America

    Bank of America customer story

    First-party
    https://newsroom.bankofamerica.com//content/newsroom/press-releases/2026/07/bank-of-america-enhances-ericaassist-with-generative-ai-to-help-.html
    Source type
    Customer-published story
    Published
    Jul 21, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 21, 2026 · Bank of America · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.