At this point, it shouldn\u2019t be if you are going to use generative AI in your marketing efforts \u2014 it\u2019s when and how.<\/p>\n
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.<\/p>\n
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?<\/p>\n
The answer isn\u2019t the same for every organization. That said, adopting and using generative AI capabilities<\/a> within your current marketing technology infrastructure can often be more beneficial than integrating new products.<\/strong><\/p>\n In this blog post, we\u2019ll explore how you can embrace Salesforce\u2019s marketing platforms that are already equipped with multifaceted generative AI functionality.<\/p>\n But first, let\u2019s explore why you should use your current marketing platforms\u2019 generative AI capabilities in the first place.<\/p>\n Using your current martech stack\u2019s generative AI capabilities instead of trying to integrate completely new AI products has several benefits. Here are a few to consider:<\/strong><\/p>\n Advancing your current AI capabilities<\/a> 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.<\/p>\n 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.<\/strong> Introducing a new product or platform comes with the associated costs of adoption, security, support, and governance.<\/p>\n 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\u2019 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.<\/p>\n 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\u2019s really worth it.<\/p>\n Alternatively, your existing platforms may already have AI capabilities (or will soon) to meet your initial and immediate needs.<\/strong> Accessing this functionality typically only requires working with your platform vendor to enable the functionality, often without any integrations and less technical effort.<\/p>\n When you introduce a new component into your martech stack, you also introduce new risks, including vendor stability, the ability to meet your industry\u2019s and business model\u2019s compliance needs, and data security.<\/p>\n 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\u2019 personally identifiable information (PII). The risks of using generative AI for marketing<\/a> are based on your planned usage, the data it will access or use, and its security architecture for data usage and sharing.<\/strong> For instance, a prominent concern with using generative AI is whether the AI platform will use your data to train large language models (LLMs).<\/p>\n 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\u2019t know its long-term viability.<\/p>\n 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\u2019s long-term existence.<\/p>\n Using an established platform vendor with AI solutions embedded in their products \u2014 such as Salesforce, Microsoft, and Adobe \u2014 decreases those long-term viability and security risks.<\/strong> 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.<\/p>\n Finally, your existing vendors have already passed your vendor qualifications processes that should cover risks regarding security, viability, industry-specific regulations, and compliance.<\/p>\n Implementing AI features within your organization\u2019s existing products can make adoption easier and faster.<\/p>\n 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\u2019t have to switch to a different product to gain access to AI functionality, which can slow them down.<\/p>\n If your martech stack is built around a single or primary technology provider, you benefit from their regularly scheduled product releases and updates.<\/strong> These releases have been tested and are proven to work across the product suite, alleviating fears of technical or integration issues.<\/p>\n 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.<\/p>\n Your current tools likely already have generative AI features that can enhance your marketing strategy and operations<\/a>. It\u2019s 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\u2019t mean they are ideally suited for your use cases.<\/p>\n 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.<\/strong><\/p>\n 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.<\/p>\n By the end of 2023, there were almost a dozen generative AI capabilities in Salesforce Marketing Cloud<\/a> and eight more in Commerce Cloud that were available on Saleforce\u2019s core Data Cloud<\/a> platform. More than 25 additional offerings are coming in 2024.<\/p>\n Here are some of the highlights of Salesforce generative AI capabilities now in place:<\/p>\n 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.<\/p>\n 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.<\/p>\n You must also ensure your marketing efforts align with your organization\u2019s larger AI vision and strategy. This includes understanding your organization\u2019s usage and security policies, governance practices, generative AI capabilities under evaluation or used in other parts of the organization, and data readiness<\/a>.<\/p>\n 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.<\/strong><\/p>\n 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\u2019t 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.<\/p>\n There are potentially negative repercussions if marketing divisions \u2014 or, really, any functional divisions \u2014 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<\/a> 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.<\/p>\n 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.<\/p>\n Taking such a disjointed approach to implementing generative AI can also restrict its scalability \u2014 and, hence, its transformative impact \u2014 within the enterprise.<\/p>\n 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.<\/p>\n4 Benefits of Using Your Existing Platforms\u2019 Generative AI Capabilities<\/h2>\n
1. Cost-effective<\/h3>\n
2. Easier integration<\/h3>\n
3. Lower risks<\/h3>\n
4. Quicker employee adoption<\/h3>\n
Understand Your Current Martech Stack and Its AI Capabilities<\/h2>\n
Salesforce Marketing Cloud<\/h3>\n
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Align Your AI Use Cases With Your Enterprise\u2019s AI Adoption<\/h2>\n
Integrate Generative AI into Your Martech Stack: A Composable Architecture Approach<\/h2>\n