IBM’s marketing teams needed a faster, more standardized way to create brand-compliant assets at scale because content drafting, research, editing, and template preparation were consuming too much of marketers’ time and reducing capacity for strategy and major market moments.
Reported impact
Creative Assistant accelerated content production, improved consistency, reduced drafting time for high-volume programs like client stories, and freed marketing teams to focus more on strategy and higher-impact creative work.
IBM built Creative Assistant as a purpose-built generative AI platform for marketing content production rather than a generic chat tool. A user prompt triggers microservices, APIs, and task-oriented agents that use retrieval-augmented generation with Elasticsearch to combine user-supplied context with trusted IBM sources such as IBM.com, product documentation, brand guidelines, RedBooks, Seismic, and competitive research. After reranking selects the most relevant evidence, an LLM hosted on IBM watsonx.ai—often Granite, but not always—generates content in prebuilt templates for emails, blog posts, client stories, presentations, and product pages. Indexed data is refreshed through automated storage and retrieval workflows using IBM Cloud Object Storage, Elasticsearch, Milvus, and watsonx.data, while Instana observes the next-generation chat experience. This gives marketers a governed content-production stack that accelerates drafting, standardizes brand voice, and democratizes access to product knowledge without removing writers and editors from review and refinement.
A marketer submits a prompt, Creative Assistant enriches it with trusted IBM content retrieved from Elasticsearch, Milvus, and watsonx.data-backed stores, watsonx.ai and Granite generate a templated draft, and the resulting asset moves to writers and editors for brand-compliant review and refinement.
“IBM Granite foundation models are commonly used”
“The enriched input was passed to a large language model (LLM) trained and hosted on IBM watsonx.ai”
“The system uses retrieval augmented generation (RAG) with Elasticsearch”
“review-ready client story drafts ... can now be completed in just about five days, down from the previous 10-day timeline”
“the tool onboarded over 1010 active users who generated over 7000 drafts of assets across 10 different content formats.”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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