The ever-increasing accessibility and simplicity of artificial intelligence are creating ample opportunities for enterprise marketing teams to get ahead and find success in their digital ads, email campaigns, and overall marketing game plan. Both free and paid-for AI plans can now be seamlessly integrated across e-commerce platforms, content creation tools, and existing marketing platforms.
In this sense, there’s a level playing field for marketing teams to navigate.
Leveling the Marketing Field With AI
On the enterprise level, IT leaders are seeking ways to democratize AI and put it into the hands of users who don’t have specialized or technical knowledge. One example is extending low- and no-code tools into AI, enabling non-developers to build and deploy software into it. Another is building data literacy across the enterprise.
In enterprise, adopting AI in marketing can even the playing field, according to Jess Dickenson, COO of digital marketing agency Precis.
- Using paid AI technology – These are generally cost-effective tools for enterprise marketing teams to harness. Code Interpreter is a ChatGPT plug-in that makes data analysis and visualization easy, while Canva’s host of generative AI features is also vastly beneficial, letting businesses create new assets without previous design experience.
- Sparking creativity – Instead of investing in GenAI to slash costs, enterprise marketing teams can use it to unlock creativity: Midjourney for creative testing and evolving innovation, for instance. GenAI creates a form of agility not often seen with more traditional firms, Dickenson said, allowing companies that use it to easily ride on cultural trends or adjust their strategy or execution based on performance insights.
- Activating smarter data – Enterprise marketing teams can spend their marketing dollars better when they bid for what matters, meaning they optimize for business outcomes. Those who are in the subscription space can optimize for customer lifetime value or users staying beyond free trials. This lets them bid for the right users more competitively, outperforming peers who focus only on short-term acquisitions.
- Knowing the customer base – With predictive modeling via AI, marketing strategies can deploy out-of-the-box solutions to understand the customer base better and enhance customer lifetime value. Investing in data science is key, as this will capture more nuanced audience signals as well as micro-conversions – insights that feed into the entire user journey.
Bright Spots for Democratizing AI Ahead
The good news is that the global democratized GenAI market is forecasted to grow at a compound annual growth rate of 33.4 percent from 2024 to 2029. Primarily driving the market growth is the increasing availability of large datasets along with the rising adoption of AI in various industries such as healthcare, IT, finance, and manufacturing.
The key trends in democratizing GenAI are:
- Widespread accessibility – GenAI tools are now more accessible to a broader audience, leading to the development of more user-friendly interfaces and platforms to give individuals with varying levels of technical expertise the ability to use GenAI. In enterprise marketing, this means better adoption among teams with a more traditional approach.
- Collaborative innovation –Communities within and across industries are sharing insights, best practices, and GenAI applications in their space. This collaborative approach breeds innovation and expands the potential applications of GenAI.
- Customization and personalization – Enterprise marketing teams can tailor generative models to suit their specific needs, may it be in content generation, design, or strategic execution. When the use of GenAI applications is user-centric, the ensuing solutions align precisely with the unique requirements.
It’s crucial to note that challenges to democratizing the use of AI across marketing, sales, customer experience, and other areas of enterprises can wipe out the potential benefits. Experts advise deploying new systems like GenAI with proper guidance and conducting proper training and implementation to refrain from making decisions (or problem-solving) based on inaccurate data or bias.
