5 Ways to Scale Generative AI for Marketing

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scienz ai 5 Ways to Scale Generative AI for Marketing

Generative AI transformation is well underway across many industries as marketing, sales, and customer service departments look to harness the power and leverage the efficiency it brings. 

For one, pharma companies are confident of its value with 40 percent of executives saying they are incorporating expected savings into their 2024 budget from GenAI, according to a Bain survey. In the public sector, potential productivity enhancements from GenAI are estimated to be worth $1.75 trillion annually worldwide, across all levels of government.  

Among the many advantages of GenAI over conventional AI, which companies and organizations already were incorporating before GenAI’s boom and watershed year in 2023, is it can scale more quickly.  

Here are five ways that generative AI marketing can rapidly scale, according to PwC, which created these tips

Start with trust and a focus on data governance and security 

Lay the groundwork for trust in GenAI’s design, function, and use of its outputs. The first step to a sound approach to trusted AI is governance and considerations around security. A secure GenAI system is a private system, with guardrails and responsible practices around it to protect data and intellectual property.  

In marketing as in other areas of the enterprise, look beyond the systems to add security to GenAI – assess the marketing strategy, policies, campaign and data governance, and overall frameworks in light of GenAI’s risks. Once you’re done assessing, close existing gaps.  

Craft a GenAI strategy to quickly realize ROI 

Think big – look for GenAI “patterns” that can scale as well as deliver ROI across the marketing space. For instance, generative AI can deliver more than modest value through personalization or customizing the customer experience, where the ROI can be spectacular.  

The strategy, of course, should consider the readiness of your marketing processes, data, and people – roll out a plan based on these considerations and be ready to close gaps. Don’t think in terms of months when putting this strategy together; think in terms of weeks, as GenAI is fast-evolving.  

Commit to use cases and launch 

Ensure that your initial pilot program should align with your strategy, concentrating on core marketing processes, team readiness, and repeatability. Use cases of moderate value but are highly replicable may deliver more ROI than high-value yet one-and-done use cases. GenAI models are already pre-trained, so your marketing team should be able to use initial use cases with a 90-day sprint.  

Get your generative AI marketing launches off the ground with a GenAI “team” that involves not just specialists in the technology, but also marketing analysts and leaders themselves. These people know what the company needs and your key differentiators in the market, and they have the customer data and insights needed to customize GenAI models. This way, GenAI can produce accurate and relevant outputs.  

Create an AI factory for fast, repeatable, verifiable results 

It would be best if you had a series of pods focused on a specific domain within marketing, from marketing analysts to content managers to GenAI-specific roles, if possible, to customize the inner workings of the model. These pods can scale to form GenAI toolkits to support tasks like content creation, research, personalized customer experiences, email campaigns, and more.  

Transform your workforce and grow ROI 

Congratulations: Your GenAI factory is already operating. The next step for you is to integrate more of your marketing data into GenAI and transform your processes using it. Your marketing team should benefit from GenAI as it becomes an integral part of your major processes. This, however, requires upskilling your team members on responsible GenAI use.  

Continue to assess GenAI’s outputs as well as performance, costs, and alignment with your company’s risks and objectives. You may also compare current data with historical data to validate your marketing efforts. This is also one way to know if your organization’s trust goals are getting realized.  

While brands and companies need to move quickly to meet customer and stakeholder expectations, it’s best to move with caution so they do not violate regulations or ethical guidelines around data privacy and bias when using GenAI. For some enterprises, this might mean changing the workforce and partnering more with tech to design safe, effective, highly reliable genAI for their marketing ambitions.  

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