Information is vital: Effective generative AI marketing plan

Marketing teams can scale personalisation and satisfy high client expectations with generative AI powered creative, tailored messaging. This powerful new technology may improve internal communications, productivity, customer-facing channels, and product support across the marketing process. In a May 2023 IBM and Momentive.ai survey, 67% of CMOs planned to adopt generative AI within 12 months and 86% within 24 months.

AI for business has long provided seamless communication across platforms and devices, fast problem resolution, and location and purchase history-based personalization. Generative AI technologies can help marketing teams personalize at scale and increase staff skills and performance.

Generative AI will benefit enterprise marketing teams, but it will require new skills and methods. The IBM poll found that CMOs’ main worries in implementing generative AI were managing implementation complexity, creating the data collection and brand, and IP risk.

Using generative AI, marketers may reduce these issues. The journey begins with good data.

Generative AI needs the correct data

As with any AI implementations, generative AI needs data sourcing and maintenance. As the IT saying goes, “garbage in, garbage out,” and high-quality data is necessary for a good result. If training data is biased or incomplete, algorithms may produce erroneous content.

Generative AI aids marketing content creation and audience targeting. Data curation, guardrails, and supervision are essential to address bias and assure brand voice and product and service accuracy.

A retail clothing company may utilize generative AI to design email or online interactions for distinct consumer profiles. Generative AI for text, images, and video could customize and engage. This might feature a virtual model wearing clothes that match the customer’s body type, fashion preferences, and interests. The generative AI tool can also consider weather, events, and the shopper’s location.

What if the generative AI tool suggests a swimming suit in winter or a snow parka in summer? Because they are educated on enormous amounts of data, generative AI solutions can misinterpret existing data. Thus, the technique may yield unexpected results.

AI foundation models hallucinate when they generate off-topic or erroneous material. Teams must customize their models with private datasets instead of open-source internet data to avoid this scenario.

Make a data-driven generative AI marketing plan

Before your marketing company can execute effective generative AI solutions, you need an AI foundation model plan. Given the huge amount of external and internal data, it’s crucial to define use cases before sourcing and training models. Understanding the benefits and risks of each use case will help develop a model training-focused step-by-step route.

Marketers and IT must collaborate on data architecture to safely design and deploy foundation models while protecting intellectual property and confidential data. The right usage guardrails will protect your IP and brand.

Generative AI needs human marketers

After deployment, your generative AI data journey continues. Customer interactions refine foundation models by collecting more data, which improves their capabilities. Generative AI programs running on foundation models need human supervision (such as supervised fine-tuning using human annotations and reinforcement learning from human input) to be helpful, ethical, and dependable.

Even though generative AI can produce human-like customer-facing work, it still needs a human guide with data usage ethics and law experience. Human reviewers can also spot and fix bias or delusion in the content.

Add generative AI to your marketing arsenal

CMOs named content generation and editing, SEO, and social media marketing as the top B2B uses for generative AI in the IBM poll. B2B marketing leaders cited lead generation and sales nurturing as top use cases.Leaders worried most about data accuracy, privacy control, and having the competent resources to construct generative AI. Adopting generative AI technology demands a realistic approach to design, test, and learn its capabilities. The marketing process will be streamlined and cost-effective, proprietary data secured, and consumer experiences relevant and fulfilling.

IBM has pioneered commercial AI for decades. Our products and services help marketers properly and successfully use generative AI. IBM Watsonx, an enterprise-ready AI and data platform, helps marketers and other business professionals confidently adopt generative AI. The platform has three powerful parts:

• watsonx.ai: A corporate studio for generative AI training, validation, tuning, and deployment.
• watsonx.data: An open hybrid data storage with an open lakehouse architecture to scale generative AI workloads.
• WatsonX Governance: A framework that speeds responsible, transparent, and explainable AI workflows.

IBM Consulting and its worldwide team of over 20,000 AI professionals assist marketing firms in designing and scaling AI and automation efficiently. We supply any AI model on any cloud, led by ethics and trust, with IBM Watsonx technology and an open community of partners.

Start generative AI with the correct data sources and architecture to support brand access, quality, richness, and protection.

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