IBM Commercial and Professional Services United States

BWF

Sales Automation

BWF needed to modernize how it helps nonprofit organizations identify donor opportunities and plan campaigns, because new AI-driven competitors were emerging and its customers needed faster, more specific fundraising insights than generic machine-learning models could deliver.

30% reduction in resource investment
USD 1.5 million new donor gift investments

Reported impact

Business outcome

BWF reduced resource investment, helped clients identify new donor opportunities, improved fundraising ROI, and created a more differentiated AI-based offer for nonprofit organizations.

  • Productivity Improvement
  • Revenue Growth

Solution

BWF partnered with IBM Client Engineering and Converge Technology Solutions to build a fundraising-focused AI platform on watsonx. IBM watsonx.ai provides the modeling environment for validating, preparing, and processing nonprofit fundraising data, while watsonx Orchestrate coordinates adaptive modeling, forecasting, simulation, and dashboard delivery so results are available in a single interactive experience. Instead of piecing together separate steps and tools, BWF’s data-science team can now generate scenario-based fundraising insight from one platform, giving nonprofit customers a faster way to identify donor prospects, evaluate engagement strategies, and allocate campaign resources. The platform is tuned for the nonprofit domain rather than generic propensity scoring, which helps BWF give customers insights tied to specific organizational contexts in the USD 500 billion philanthropy market. By combining domain expertise with watsonx automation, BWF turned AI into a differentiating service layer that protects its customer base while increasing the value customers can derive from their own donor data.

Data flow

Nonprofit fundraising data is validated and prepared in watsonx.ai, watsonx Orchestrate executes the modeling and simulation workflows, and the resulting forecasts and prospect recommendations are delivered through dashboards for campaign planning and resource allocation.

Solution architecture

3 components · 2 layers
  1. Compute
    • IBM watsonx.ai Runs the AI models and forecasting logic that identify donor prospects and campaign insights.
  2. Orchestration
    • IBM Client Engineering Worked with BWF to co-develop the fundraising AI solution and align the platform to nonprofit use cases.
    • IBM watsonx Orchestrate Coordinates adaptive modeling, forecasting, and simulation workflows inside the fundraising solution.

Architecture clues

  • BWF previously needed multiple steps, teams, and tools to produce similar output.
  • The platform combines adaptive AI modeling with forecasting, simulation, and interactive dashboards.
  • The solution is specifically tuned for nonprofit fundraising rather than generic philanthropy scoring.

Evidence from the source

“BWF used IBM watsonx Orchestrate technology, coupled with the IBM watsonx.ai AI studio, to facilitate adaptive AI modeling that would quickly and accurately provide forecasting and simulation information”
“BWF’s nonprofit customers will now be able to use real-time data to better determine where to focus their fundraising efforts”
“New donor gift investments”
“Reduction in resource investment”
“What previously took several steps, teams and tools, now takes BWF’s data science team much less time and is coordinated in one platform.”
Transparency Evidence & methodology 90/100 reference value · 89% 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 value90/100
  • 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 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. IBM

    BWF customer story

    First-party
    https://www.ibm.com/case-studies/bwf
    Source type
    Vendor-published customer story
    Published
    May 29, 2024
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.