Enabling the generative AI tools within your existing marketing tech (martech) stack might be more effective than purchasing new products. Salesforce, in particular, has many new generative AI features that are already available within its marketing technology platforms.
At this point, it shouldn’t be if you are going to use generative AI in your marketing efforts — it’s when and how.
You can justify the use and any necessary initial investment in generative AI tools by pointing to the increase in efficiency and the resulting time and resource savings. The real question is how to incorporate generative AI into your marketing tools.
Using generative AI tools available within your existing marketing technology stack is often more efficient, secure, and expedient than incorporating new solutions. But is it the right choice?
The answer isn’t the same for every organization. That said, adopting and using generative AI capabilities within your current marketing technology infrastructure can often be more beneficial than integrating new products.
In this blog post, we’ll explore how you can embrace Salesforce’s marketing platforms that are already equipped with multifaceted generative AI functionality.
But first, let’s explore why you should use your current marketing platforms’ generative AI capabilities in the first place.
4 Benefits of Using Your Existing Platforms’ Generative AI Capabilities
Using your current martech stack’s generative AI capabilities instead of trying to integrate completely new AI products has several benefits. Here are a few to consider:
1. Cost-effective
Advancing your current AI capabilities will require investment regardless of your route. It is essential to look beyond the software or license cost of AI tools. Although enabling the functionality within your current platform could have a higher initial price, it could be more cost-effective in the long run than investing in a completely new product for your martech stack.
Consider the total cost of ownership: implementation, adoption, operation, and scaling. The more applications you integrate into your martech, the more challenging and costly the maintenance and operations may be. Introducing a new product or platform comes with the associated costs of adoption, security, support, and governance.
Depending on the AI product and its integration capabilities, it may introduce an additional new user interface (UI) to your users and require them to switch products. This reduces users’ efficiency and creates a more significant learning curve. It may also require additional training and adoption support compared to utilizing generative AI capabilities in your existing platforms.
2. Easier integration
If AI implementation is time-sensitive for your company, you may have to reprioritize internal projects, which could result in deferring other planned tasks or projects. This can impact other functional groups across the company and may cause you to question if it’s really worth it.
Alternatively, your existing platforms may already have AI capabilities (or will soon) to meet your initial and immediate needs. Accessing this functionality typically only requires working with your platform vendor to enable the functionality, often without any integrations and less technical effort.
3. Lower risks
When you introduce a new component into your martech stack, you also introduce new risks, including vendor stability, the ability to meet your industry’s and business model’s compliance needs, and data security.
Compliance and security go hand in hand. Any new technology component that you add to your martech stack needs to be carefully evaluated because it will likely be handling sensitive data, including customers’ personally identifiable information (PII). The risks of using generative AI for marketing are based on your planned usage, the data it will access or use, and its security architecture for data usage and sharing. For instance, a prominent concern with using generative AI is whether the AI platform will use your data to train large language models (LLMs).
The AI vendor landscape is still developing and, as such, constantly changing. It could be risky to use a new technology vendor when you don’t know its long-term viability.
For example, a risk to the long-term viability of the vendor is that it could be a niche company whose success makes it an acquisition target, and it disappears into the acquiring company, along with the products and integrations it had provided. It could also be a company that is more susceptible to changes in legislation or regulatory compliance relating to AI. The company may fail due to high market competition. Or changes in leadership and key personnel could impact a niche AI company’s long-term existence.
Using an established platform vendor with AI solutions embedded in their products — such as Salesforce, Microsoft, and Adobe — decreases those long-term viability and security risks. These companies do not have the viability concerns mentioned. And security is a foremost consideration embedded across their platforms and designed to work across the products.
Finally, your existing vendors have already passed your vendor qualifications processes that should cover risks regarding security, viability, industry-specific regulations, and compliance.
4. Quicker employee adoption
Implementing AI features within your organization’s existing products can make adoption easier and faster.
From the user perspective, these new features are embedded in existing products using a familiar UI. Employees will not need separate login credentials, and they won’t have to switch to a different product to gain access to AI functionality, which can slow them down.
If your martech stack is built around a single or primary technology provider, you benefit from their regularly scheduled product releases and updates. These releases have been tested and are proven to work across the product suite, alleviating fears of technical or integration issues.
With the more prominent platform vendors, you can likely find robust documentation and communities of users to assist with any implementation or use questions you may encounter. Plus, you have access to established customer support infrastructure and processes from the vendor.
Understand Your Current Martech Stack and Its AI Capabilities
Your current tools likely already have generative AI features that can enhance your marketing strategy and operations. It’s important to understand those capabilities and whether they align with valuable use cases for your organization. Just because the products have generative AI functionality doesn’t mean they are ideally suited for your use cases.
Understanding the current and future AI features in your platforms will help you effectively implement generative AI within your martech stack. Ask your vendors about their product AI road maps and release schedules, as well as when and if the features will be available in your product edition. You can also stay abreast of AI advances in your martech products by monitoring online vendor communities and signing up for vendor marketing communications.
For example, marketers whose organizations have Salesforce within their martech stack can use currently released generative AI marketing capacities available in some of those products, and more will be rolled out soon.
By the end of 2023, there were almost a dozen generative AI capabilities in Salesforce Marketing Cloud and eight more in Commerce Cloud that were available on Saleforce’s core Data Cloud platform. More than 25 additional offerings are coming in 2024.
Here are some of the highlights of Salesforce generative AI capabilities now in place:
Salesforce Marketing Cloud
- Marketing in Starter provides small business functionality in basic sales, service, and marketing.
- DC + Account Engagement personalizes marketing assets such as emails, landing pages, forms, and your website. It also suggests content based on prospect data, including someone’s previous purchases.
- Data Spaces in Marketing Cloud segregates data, metadata, and processes into categories, like brand, region, or department, and lets users look at and work on data that’s only pertinent to their category.
- Advertising Audiences in Marketing Cloud empowers you to reach your existing customers with targeted, specific messaging; acquire new customers by creating seed audiences that appear as lookalike audiences on destination networks; and reconnect with unengaged email subscribers.
- Email Copy Creation generates emails more quickly, and the emails it produces reflect what has successfully advanced previous marketing campaigns.
- Subject Line Creation lets marketers quickly edit and test the style and attitude of subject lines with suggested variations that draw upon top-performing emails. Or, if they prefer, they can do an A/B test.
Other Marketing Cloud generative AI features create lookalike audiences, develop the best audience segments for increasing campaign engagement, generate images and layouts specific to brand guidelines, and clarify how effective campaigns are with particular audience segments.
Align Your AI Use Cases With Your Enterprise’s AI Adoption
Understanding the generative AI marketing capabilities you have and how you can apply them to your marketing efforts is only a small part of the path to AI success.
You must also ensure your marketing efforts align with your organization’s larger AI vision and strategy. This includes understanding your organization’s usage and security policies, governance practices, generative AI capabilities under evaluation or used in other parts of the organization, and data readiness.
Your marketing team must understand what high-value use cases you would like support for. This will vary based on the marketing tactics and processes.
Once you define your use cases, align them with other AI products being used or evaluated. Your marketing department may benefit from AI in a platform you don’t own or use. Take a holistic look across your organization at the generative AI offerings within existing tools and how other departments use them. You might obtain invaluable data and insights that might not otherwise be available.
There are potentially negative repercussions if marketing divisions — or, really, any functional divisions — take a go-it-alone approach to implementing generative AI instead of doing this within an organizationwide strategy. Unfortunately, according to a recent survey conducted by Forrester Consulting on behalf of Grammarly, 72 percent of corporate technology decision-makers reported that various departments within their companies are implementing AI without an organizational strategy.
Those separate adoption plans raise the possibility of future technical debt, where IT teams must struggle to make mismatched codes achieve the desired business objectives.
Taking such a disjointed approach to implementing generative AI can also restrict its scalability — and, hence, its transformative impact — within the enterprise.
Conversely, the Forrester research found that companies with a whole-enterprise generative AI strategy were 2.6 times more likely to grow or upgrade their implementation than those with siloed, line-of-business strategies.
Integrate Generative AI into Your Martech Stack: A Composable Architecture Approach
You can introduce generative AI into your martech stack in less disruptive ways, including using it for new capabilities. This is most easily done with products that are singularly focused on their capabilities (e.g., chatbots). But for them to have the greatest impact and highest value, you will still need to integrate these into your existing stack and enterprise data.
This approach also aligns with having, or moving toward, a composable architecture for your martech stack. It’s possible now to connect data with composable technologies. This eliminates data silos and allows for a more streamlined and integrated workflow.
Integrating this workflow with generative AI benefits you in several ways. You get access to personalized content generation for different audiences, predictive profile enrichment, content modeling based on your existing content, and A/B testing variants.
The Bottom Line
Adopting generative AI in marketing departments involves much more than deciding to use a generative AI feature. It’s a process that should include developing a strategy, governance, security, and an adoption plan. It’s more than a technology decision and should include people, processes, business values, and risks.
Using your existing marketing technology infrastructure, whether it’s Salesforce or something else, may help you proceed through this process significantly quicker and mature your generative AI usage. Exploring this option while planning your AI strategy is time well spent.
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