In-house Financial Services global Company newsroom

Bank of America

Customer Service Automation · Document Processing · Enterprise Knowledge Search · Sales Automation

Bank of America needed to improve employee productivity and client service at scale across a global workforce of 213,000 people, while reducing routine support demand, accelerating employee access to information, standardizing client preparation, and helping service teams respond more effectively.

20 million clients now actively using the virtual assistant
Employees completed over 1 million simulations last year
clients have interacted with Erica more than 2.5 billion times
efficiency gains of over twenty percent

Reported impact

Business outcome

Bank of America reports higher employee efficiency and better client service from broad internal AI adoption, including lower IT service desk demand, faster information curation for wealth teams, improved employee coaching, software-development productivity gains, faster meeting preparation, and more personalized contact-center support.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Bank of America describes a broad in-house AI operating layer embedded across technology, operations, training, and client-facing support workflows. Erica for Employees is the primary internal virtual assistant, serving employee requests such as password resets, device activation, benefits questions, and payroll or tax-form lookups. The bank also reuses the Erica technology foundation in ask MERRILL and PRIVATE BANK so Merrill and Private Bank teams can curate information faster and escalate harder requests to human experts through chat. The Academy applies AI conversation simulators to onboarding and coaching, generating real-time feedback on practice client interactions. Additional generative AI services support code writing and optimization, drafting client-meeting materials, summarizing research and market commentary, guiding contact-center specialists, and summarizing call recordings. Inputs come from employee questions, training conversations, research content, service interactions, and call audio; the source does not name a separate storage layer, but it does state these AI and GenAI services deliver answers, summaries, drafts, and guided assistance directly into employee workflows under human oversight.

Data flow

Employee questions, practice conversations, research content, meeting-preparation inputs, and call recordings are fed into Bank of America’s in-house assistants and GenAI tools. Erica for Employees, ask MERRILL, PRIVATE BANK, The Academy, and the internally-developed GenAI platform process those inputs and return answers, curated information, coaching feedback, summaries, and guided assistance directly to employees; complex chat requests can be escalated to bank experts.

Solution architecture

6 components · 2 layers
  1. Compute
    • internally-developed GenAI platform Lets the Global Markets sales and trading team search, summarize, and synthesize research and market commentary more quickly and efficiently.
  2. Serving
    • Erica Provides the underlying AI assistant technology that Bank of America says it extended into employee and wealth-management workflows.
    • Erica for Employees Acts as an internal virtual assistant for employee technology and HR-related questions such as password resets, device activation, benefits, and payroll forms.
    • ask MERRILL Helps Merrill teams curate information efficiently and connect with bank experts through chat for more complex requests.
    • PRIVATE BANK Helps Private Bank teams curate information efficiently and connect with bank experts through chat for more complex requests.
    • The Academy Uses AI conversation simulators to coach employees on client interactions with real-time feedback.

Architecture clues

  • AI and machine learning are embedded across global technology, operations, and training.
  • Erica for Employees was expanded in 2023 to cover benefits, payroll, and tax-form questions.
  • Erica launched in 2018 and Erica for Employees launched in 2020.
  • The bank says future Erica for Employees capabilities will add enhanced search and broader AI and GenAI assistance.
  • The deployment supports 213,000 employees globally.
  • The source emphasizes human oversight, transparency, and accountability for AI outcomes.

Evidence from the source

"A modernized desktop tool leveraging AI proactively provides guided assistance to customer service specialists in Bank of America contact centers, enabling them to deliver a more personalized client experience, increase client satisfaction, and reduce call handling times."
"An internally-developed GenAI platform enables the Global Markets sales and trading team to search, summarize, and synthesize our industry-leading research and market commentary more quickly and efficiently."
"Bank of America software developers are using a GenAI-based tool to assist with code writing and optimization, through which they have experienced efficiency gains of over twenty percent."
"Bank of America’s onboarding, education, and professional development organization uses AI to provide interactive coaching through conversation simulators... through real-time feedback."
"Building upon Erica’s success, in 2020, Bank of America launched Erica for Employees, an AI-driven internal virtual assistant."
"Leveraging the technology behind Erica, these tools help Merrill and Bank of America Private Bank teams efficiently curate information to deliver superior client experiences."
"Today, over 90% of employees use Erica for Employees, with the virtual assistant having reduced calls into the IT service desk by more than 50%."
Transparency Evidence & methodology 100/100 reference value · 94% 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 value100/100
  • 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 impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • 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
    7 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 specificity10/10
    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/2025/04/ai-adoption-by-bofa-s-global-workforce-improves-productivity--cl.html
    Source type
    Customer-published story
    Published
    Apr 8, 2025
    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 Apr 8, 2025 · 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.