Salesforce AI: Why independent AI platforms are better than Einstein and Agentforce – Hybrid approach beats vendor lock-in!

The strategic importance of independent AI platforms in Salesforce: An analysis beyond Einstein

Salesforce prominently positions its native artificial intelligence (AI) as an integral part of its Customer 360 platform, promoting it as the “#1 AI for CRM.” The core message emphasizes the seamless integration of AI capabilities like Einstein, Agentforce, and the broader AI Cloud into existing Salesforce workflows to boost productivity and personalize customer experiences. This promise of easy implementation and use within a familiar environment resonates with many businesses.
However, Salesforce customers increasingly face a strategic decision: Should they rely solely on Salesforce’s native AI suite or consider integrating independent, potentially more specialized AI platforms? The AI ​​market is evolving rapidly, with third-party vendors continuously introducing highly specialized models and innovative solutions that may exceed the capabilities of an all-in-one platform.

This article analyzes the strategic advantages of using independent AI platforms within the Salesforce environment. It critically examines the capabilities and limitations of native Salesforce AI, highlights integration paths and challenges, and addresses key aspects such as flexibility, cost, data privacy, and vendor lock-in. The goal is to provide a sound basis for deciding whether a more open AI strategy might be more beneficial for Salesforce users than relying solely on Salesforce's own solutions.

The core question revolves around the trade-off between the convenience of a deeply integrated solution and the potential power and specialization of external AI tools. While Salesforce emphasizes the benefits of its integrated AI, the high degree of specialization and rapid pace of innovation in the AI ​​field necessitates a more nuanced approach. A single platform provider may not be able to deliver excellence across all AI domains, compared to providers that focus on specific areas. This tension between integration and best-of-breed solutions forms the core of the strategic considerations explored in this report.

Suitable for:

• AI integration of an independent and cross-data source-wide AI platform for all company matters

Understanding Salesforce's native AI suite (Einstein, Agentforce, AI Cloud)

Salesforce offers a wide range of AI capabilities deeply integrated into its various cloud products, grouped under the brand names Einstein, Agentforce, and AI Cloud. This suite aims to optimize everyday business processes through automation, prediction, and personalized interactions.

Functional overview by cloud

• Sales Cloud: Core features include scoring leads and opportunities based on their likelihood of closing (Einstein Lead/Opportunity Scoring), more accurate revenue forecasts (Einstein Forecasting), automatic creation of personalized sales emails (Sales Emails), summaries of sales calls (Call Summaries), and automatic activity capture from emails and calendars (Einstein Activity Capture). The Einstein Copilot also provides context-aware actions and support throughout the sales process.
• Service Cloud: Here, AI supports the automatic classification of customer cases (Case Classification), recommends suitable knowledge articles or pre-made answers (Article/Reply Recommendations), creates summaries of completed cases (Work Summaries) and enables the use of chatbots to automate standard requests.
• Marketing Cloud: AI features help with the creation and automatic tagging of marketing content (Content Generation/Tagging), assess the likelihood of interaction from contacts (Engagement Scoring), optimize sending times for maximum open rates (Send Time Optimization), and enable in-depth personalization of campaigns and customer experiences.
• Commerce Cloud: In this area, AI focuses on personalized product recommendations, optimizing search results, and providing insights into purchasing behavior to increase conversions.
• Cross-platform/General: Tools like the Einstein Prediction Builder allow administrators to create custom predictive models without writing code. Einstein Discovery helps find patterns and insights in data. Einstein Next Best Action provides context-aware recommendations. Agentforce represents autonomous AI agents that can perform tasks independently. Prompt Builder and Copilot Studio allow the customization and creation of AI-powered assistants and prompts.

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• Get out of the US cloud: Sovereign SaaS offers at overview + recommendations for action

Underlying architecture

The functionality of Salesforce AI is based on two essential pillars: the Data Cloud and the Einstein Trust Layer.

Data Cloud dependency

The Salesforce Data Cloud acts as a central data foundation. It unifies customer data from various sources (both internal and external to Salesforce) into a 360-degree view. This harmonized data forms the basis for many AI applications, especially generative AI and personalization. Importantly, certain generative AI capabilities and the Trust Layer's audit trail require Data Cloud provisioning, even if it is not heavily used for data harmonization. This creates an architectural dependency and can introduce additional complexity and potential costs, particularly for companies that already have established data warehouses or data lakes. The need for Data Cloud can therefore increase the total cost of ownership (TCO) and represents a potential bottleneck if not carefully managed.

Einstein Trust Layer

This security framework is designed to ensure the trustworthy use of generative AI. It comprises several components:

• Secure data querying: Accesses Salesforce data to enrich prompts with relevant context, taking into account the access rights of the respective user.
• Prompt Defense: System policies are intended to reduce hallucinations and harmful output from Language Models (LLMs).
• Data masking: Sensitive data such as personally identifiable information (PII) or payment information (PCI) is masked before being sent to external LLMs.
• Toxicity assessment: The generated responses are checked and evaluated for potentially harmful content.
• Zero-Data Retention Policy: Salesforce has agreements with partners such as OpenAI and Azure OpenAI to ensure that submitted company data is neither stored by these third-party providers nor used to train their models.

A closer look at the architecture reveals that Salesforce relies on external Large Language Models (LLMs) from providers like OpenAI, Anthropic, or Google for many of its generative AI capabilities. These models are often integrated via cloud services such as AWS Bedrock, with the Einstein Trust Layer acting as a secure gateway. This means that Salesforce primarily acts as an integrator and security intermediary, rather than solely developing its own core generative models. While this provides access to powerful models, it creates dependencies and raises the question of how the core AI technology differs from directly using these models via other platforms. Customers are essentially paying Salesforce for the integration, security layer, and embedding in workflows based on largely externally available AI models. This strengthens the case for evaluating direct integration with these external models or platforms.

Recognized strengths of the native solution

Despite the points mentioned, the native Salesforce AI suite offers undeniable advantages:

• Seamless integration: The AI ​​features are deeply embedded in the Salesforce user interface and workflows, enabling smooth use.
• Ease of use and familiarity: Existing Salesforce users and administrators typically find their way around quickly, which reduces onboarding time. Low-code tools also enable non-technical users to create AI-powered experiences.
• Leveraging existing CRM data: The AI ​​is designed to work directly with customer data stored in Salesforce, which can simplify data preparation.

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Arguments for independent AI platforms in Salesforce

While native integration of Salesforce AI offers advantages, several compelling reasons to seriously consider incorporating independent AI platforms. These external solutions can be superior in areas such as flexibility, specialization, adaptability, and potential cost advantages.

Flexibility and model specialization

The AI ​​market is characterized by high dynamism and specialization. Independent AI providers often focus on specific domains or technologies, enabling them to offer more advanced or tailored solutions in certain areas than a generalist platform like Salesforce.

Access to best-of-breed models

External vendors often develop highly specialized algorithms for areas such as natural language processing (NLP), computer vision, or industry-specific analytics. Examples include specialized AI for legal documents like ContractPodAi or industry-specific diagnostic tools like Aquant. Such specialized models can outperform the more general models built into Salesforce.

Faster innovation cycles

Dedicated AI companies can often develop and release new models and features faster than a large platform provider like Salesforce, whose AI roadmap is tied to broader release cycles. This allows companies to benefit more quickly from the latest AI advancements.

Greater model variety

Independent platforms or marketplaces offer access to a wider range of models, including niche solutions, open-source options, or models from vendors that are not directly available through Salesforce's "Bring Your Own Model" (BYOM) feature.

Suitable for:

• Independent AI platforms as a strategic alternative for European companies

This specialization of external providers contrasts with Salesforce's broader approach, which aims to provide basic AI capabilities across its entire CRM suite. While this broad approach ensures AI is available in many areas, it can come at the expense of depth. A specialized fraud detection AI or a medical image analysis tool will likely outperform a general CRM-integrated model for those specific tasks. Organizations with critical requirements in specialized AI domains might find that native Salesforce AI falls short. Independent platforms allow them to select the best tool for the job, rather than settling for the potentially only adequate native solution.

Adaptation and control

Independent AI platforms often offer a higher level of control over the entire AI lifecycle, from data preparation to model implementation and monitoring.

In-depth model fine-tuning

External platforms are often designed for machine learning engineers and offer granular control over training and fine-tuning models. This goes beyond the capabilities of Salesforce's more abstract low-code tools like the Einstein Prediction Builder or the limitations of fine-tuning imported models (BYOM) within Salesforce.

Algorithm selection and transparency

Users have more freedom in selecting specific algorithms and potentially gain more transparency into how the models function (explainability) than through Salesforce's abstraction layers. Although Salesforce offers tools like the Model Inspector, external MLOps tools are often more comprehensive.

Control over the AI ​​stack

Managing the entire AI pipeline (data preparation, training, deployment, monitoring) on ​​platforms like AWS or Google Cloud offers more control than relying on Salesforce's managed environment.

Salesforce customization limits

While Salesforce offers a low-code builder for easy customization, external platforms often allow for deeper, code-based customization. There are also specific functional limitations with Salesforce AI features, such as complex requirements or when customizing Einstein Activity Capture, as well as general platform limitations.

Potential cost advantages

The cost structures for AI solutions can vary considerably, and a simple comparison of license fees is often insufficient.

Different pricing models

Salesforce often licenses its AI capabilities per user per month as an add-on to existing cloud licenses. In contrast, pricing for standalone AI platforms is often based on actual usage (computing time, memory, API calls). Standalone AI providers, in turn, may have their own, potentially more flexible pricing models. While the BYOM option in Salesforce can reduce the cost of Einstein Requests, the underlying costs of the external model provider still apply.

Total Cost of Ownership (TCO)

A comprehensive TCO analysis is crucial. While native integration of Salesforce AI can reduce initial integration costs, other factors can increase the overall cost: the potential need for Data Cloud licenses or usage, the relatively high per-user costs for AI add-ons, and the possibility of paying a premium for AI models that would be available more cheaply externally. The TCO for standalone AI must include integration costs but can benefit from lower core AI usage costs and the use of existing cloud infrastructure. Agentforce is also described as potentially expensive to use ($2 per conversation).

Avoiding redundancy

The use of independent AI can enable companies to leverage existing investments in other cloud platforms or their own data infrastructures, thereby avoiding redundant spending within the Salesforce ecosystem.

Salesforce Native AI vs. Independent AI: A Comparison of Features and Flexibility

Salesforce Native AI vs. Independent AI: A comparison of features and flexibility – Image: Xpert.Digital

Salesforce native AI, such as Einstein or Agentforce, and independent AI platforms, which often use specialized or open models, differ significantly in their features and flexibility. While Salesforce native AI focuses on generalist approaches and CRM applications, independent platforms often offer specialized models and a wider selection, including open-source options. Access to the latest models with Salesforce depends on release cycles and partnerships, whereas specialized providers potentially offer faster updates. Regarding fine-tuning, native Salesforce models are often limited and abstract, for example, through tools like the Prediction Builder, whereas independent platforms offer more granular control over the training process. The choice of specific algorithms is restricted with Salesforce, as these are usually predefined or sourced through partners, while independent platforms offer more freedom in this regard. Furthermore, Salesforce fully manages the infrastructure, often based on AWS or GCP, whereas independent platforms allow direct access to hosting environments, whether in the company's own cloud or on-premises. Integration effort with Salesforce is low because its solutions are natively integrated, while external platforms require more development and configuration work. Regarding costs, Salesforce often uses a user-based monthly pricing model as an add-on, while independent platforms often use consumption-based pricing, such as based on compute power or API calls, or vendor-specific models.

Integration navigation: Connecting independent AI with Salesforce

Choosing an independent AI platform requires careful planning for its integration into the existing Salesforce environment. Several methods exist for establishing this connection, each with its own advantages and challenges.

Integration methods

AppExchange / AgentExchange

The Salesforce AppExchange offers a wide variety of third-party applications, including AI solutions, which often provide pre-built integrations. AgentExchange is a newer marketplace specifically focused on AI agent skills, themes, and templates from partners, aiming to accelerate AI agent deployment. This is often the simplest approach but requires a suitable partner to offer a solution.

APIs (REST/SOAP/Bulk/Streaming)

Directly using the Salesforce APIs enables customized integration. Developers can exchange data, trigger processes in Salesforce, or feed back results from external AI models. The Composite API can help efficiently bundle multiple operations. This method offers maximum flexibility but requires significant development effort.

Middleware platforms (e.g. MuleSoft)

Integration platforms like MuleSoft (Salesforce's own solution) or others can act as intermediaries. They handle tasks such as data transformation, orchestration of complex workflows, and managing connectivity between Salesforce and external AI services.

Cloud platform connectors (AWS/GCP)

Large cloud providers are increasingly offering specific services to facilitate integration with Salesforce. Examples include AWS Private Connect for secure network connections, AWS Event Relay for real-time event transmission, the AWS Glue Salesforce Connector, and the SageMaker Data Wrangler Connector for data preparation. Google Vertex AI can be integrated into Salesforce Data Cloud via Model Builder. While these connectors can simplify integration, they do tie users to the ecosystem of their respective cloud providers.

BYOM via Einstein Studio

As mentioned previously, this feature allows you to integrate externally hosted models into the Salesforce environment via the Model Builder. Requests still route through the Salesforce infrastructure and utilize the trust layer, which simplifies integration but also creates a certain dependency.

Suitable for:

• Integration of AI and machine learning in warehouse logistics - global developments in Germany, EU, USA and Japan

Common integration challenges

Integrating external systems with Salesforce is not trivial and presents specific challenges:

API limits

Salesforce limits the number of API calls per organization and time period (e.g., daily, concurrently). Data-intensive AI processes that frequently synchronize or query data can quickly reach these limits. This necessitates careful design (e.g., throttling, batch processing, caching) or may require purchasing higher Salesforce editions or additional API quotas. The limits of the Streaming API are particularly relevant for real-time use cases.

Data synchronization

Ensuring data consistency between Salesforce and the external AI platform is critical. Challenges include handling large data volumes (LDV), deciding between real-time and batch updates, managing latency, and avoiding data inconsistencies. Approaches like zero-copy integrations aim to mitigate these issues but may not always be applicable.

Data mapping and transformation

Different data models, formats, and field semantics must be aligned. This can require complex transformation logic to ensure correct data interpretation.
Security and authentication: Secure management of access credentials (API keys, tokens), implementation of robust authentication methods (e.g., OAuth 2.0, named credentials), and ensuring secure data transmission (encryption) are essential. Misconfigurations can lead to security vulnerabilities.

Error handling and data consistency

Integrations must be resilient to errors (network problems, system failures, data errors). Robust mechanisms for logging, monitoring, and automatic retry logic are necessary to ensure data integrity and minimize downtime.

Complexity and maintenance

Custom integrations require continuous maintenance and adjustments, especially as Salesforce or the external AI platform evolves. This ties up resources and requires technical expertise.

Integration complexity is an often underestimated cost factor. While standalone AI platforms may offer lower core costs or superior features, the costs and effort of integration—including development time, potential middleware licenses, and ongoing maintenance—must be factored into the total cost of ownership (TCO) calculation. Salesforce's native AI benefits from pre-built integration. API limitations can further increase complexity and costs if cumbersome workarounds or more expensive licenses are required. Therefore, the decision to use standalone AI must consider the organization's technical capabilities and resources to manage this integration complexity. A poorly planned integration can negate the benefits of the external platform.

Successful integration patterns

Despite the challenges, established patterns and tools exist for successful integrations. Case studies demonstrate the successful integration of AWS SageMaker with Salesforce, often leveraging specific AWS services to optimize performance and costs. Similar integrations are possible with Google Vertex AI, particularly through the Model Builder. Tools like Zapier can be used for simpler, code-free integrations to move data between systems, such as between Google Sheets and Vertex AI as a proxy for Salesforce data. Utilizing cloud-native connectors and services like AWS Glue, EventBridge, or Private Connect can also significantly simplify and secure the integration process.

Independent AI platform: Integration methods & challenges at a glance

Independent AI platform: Integration methods & challenges at a glance – Image: Xpert.Digital

The independent AI platform offers a variety of integration methods, each with its own specific advantages and challenges. AppExchange or AgentExchange apps allow for the easy installation of pre-built applications or components from partners with minimal development effort and often certified quality. However, customizability is limited, and there is a dependency on partner offerings and potential costs. Direct API integration, which enables custom development using Salesforce APIs such as REST, SOAP, Bulk, and Streaming, offers maximum flexibility and full control over data flow and logic. However, it requires significant development effort, API limit management, thorough security audits, and ongoing maintenance. Using middleware like MuleSoft simplifies complex integrations through connectivity, data transformation, and orchestration. It offers centralized management and reusability but requires additional licensing costs and extensive platform training. Cloud connectors like AWS or GCP optimize integrations through specific, sometimes low-code, services such as Glue, Event Relay, or Private Connect. These are usually powerful, secure, and perfectly suited to their respective cloud ecosystems, but require specialized configurations and tie the user to the provider. With BYOM via Einstein Studio, externally hosted models can be easily integrated into Salesforce workflows, leveraging the trust layer and simplifying the integration process. However, limitations exist regarding model support compared to direct use, fine-tuning, and dependence on the Salesforce platform.

Integration of an independent and cross-data source-wide AI platform for all company matters-Image: Xpert.digital

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• Use across a wide variety of company data sources
• Choice of your own or various AI models (DE, EU, USA, CN)

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Critical considerations: Risk management in independent AI

The decision for or against independent AI platforms must also include a careful assessment of potential risks, particularly in the areas of data protection, vendor dependency, and data sovereignty.

Privacy and security

While Salesforce positions the Einstein Trust Layer as a guarantee for secure AI use, closer inspection reveals practical limitations that must be weighed against independent solutions.

Einstein Trust Layer limitations:

Disabled Data Masking for Agentforce: A key point is the explicit statement that data masking is disabled for Agentforce workflows. The justification given is that masking would impair the contextual accuracy and relevance of the results, for example, when searching for similar accounts where the details of the reference account are needed. This poses a significant data privacy risk, as potentially sensitive customer data could be sent unmasked to external LLMs, which is particularly problematic in regulated industries and contradicts the "trust" promise.
Alternative Mitigation (Anthropic): Salesforce plans to offer Anthropic models as an alternative, running within a "Salesforce Trusted Boundary" (hosted on AWS Bedrock). Although the data does not leave Salesforce's control sphere with this approach, data masking remains disabled. It is questionable whether this adequately addresses data privacy concerns compared to functioning masking.
General Trust Layer functionality: Core functions such as zero-retention with partners and toxicity checks remain in place. However, the exception for Agentforce is a significant limitation.

Potential advantages of independent platforms:

Dedicated data residency options: Independent cloud providers or specialized platforms may offer more granular control over where data is stored and processed. This may be necessary to comply with strict regional data privacy laws (such as GDPR or specific national regulations) that go beyond the general assurances of Salesforce Hyperforce.
Alternative security architectures: Organizations can choose architectures that better suit their specific security requirements, such as dedicated encryption, stricter access controls, or data isolation mechanisms.
Direct vendor accountability: Working directly with an AI vendor creates clearer accountability for data handling, without Salesforce as an intermediary.

The gap between the marketing promise of the Trust Layer and its technical reality, particularly the disabled masking for Agentforce, is crucial for risk assessment. Decision-makers cannot rely solely on marketing claims but must examine the specific implementation for their use cases and compare it to the potentially more consistent or configurable controls of independent platforms.

Suitable for:

• Trusty AI: Europe's trump card and the chance of taking on a leading role in artificial intelligence

Data protection and security aspects: Einstein Trust Layer vs. independent platforms

Data protection and security aspects: Einstein Trust Layer vs. independent platforms – Image: Xpert.Digital

Data privacy and security are paramount for both Salesforce's Einstein Trust Layer and independent platforms. Regarding data masking, the Trust Layer offers support for specific regions and languages, though with limitations for Agentforce. Independent platforms, on the other hand, can provide configurable and customizable rules and supported data types. Data masking is disabled for agent-based workflows in the Trust Layer, while it is often possible with independent platforms, depending on the implementation, if performance degradation is acceptable. Zero data retention with third-party providers is ensured through contractual agreements, such as with OpenAI; independent platforms allow direct contracts or hosting on the customer's own infrastructure to completely avoid third parties. Audit trails are logged in the Trust Layer by the Data Cloud, including toxic content and masking, while independent platforms often offer detailed logging and monitoring capabilities such as MLOps tools. When controlling data residency, the Trust Layer depends on the Hyperforce region and provisioning, whereas independent platforms typically allow for a more granular selection of data center regions. Salesforce's hosting options range from vendor-managed hosting to BYOM (Bring Your Own Host) via the SF Gateway with hosting on partners like AWS or GCP, with Anthropic also planned for the SF area. Independent platforms, on the other hand, allow hosting in a dedicated cloud instance, on-premises, or in the vendor's cloud. Regarding the granularity of controls, the Trust Layer offers configurable options, such as defining masking rules, while the basic architecture is fixed; independent platforms can often provide more comprehensive configurability of security measures.

Avoiding Vendor Lock-In

The deep integration of Salesforce services carries the risk of a strong dependency on the provider.

Risk of ecosystem dependency

Relying solely on Salesforce for CRM and AI creates a significant dependency. This can weaken your negotiating position when it comes to price adjustments and limit your flexibility to use other technologies in the future.

Strategic diversification

Using independent AI platforms diversifies the technology stack. Companies can leverage innovations from across the market and more easily switch providers if needed. This maintains their strategic flexibility.

Salesforce’s “Open Ecosystem” paradox

While Salesforce promotes an open ecosystem, for example through BYOM (Bring Your Own Machine), the practical reality of deep integration often leads to a de facto lock-in. Even with BYOM, management and deployment are handled through the Salesforce platform, making switching difficult. The convenience of the integrated solution can thus lead to "soft lock-in," as the underlying dependencies are obscured, and switching to a different management or deployment strategy causes friction.

More about it here:

• The dangers of Vendor Lock-in: Why companies should avoid dependencies

Data sovereignty and portability

Control over one's own data and the ability to migrate models or data as needed are important strategic aspects.

Concerns regarding Einstein Activity Capture (EAC)

One specific problem concerns EAC. The captured email and calendar data is not stored as standard activity records in Salesforce, but externally on AWS. This data is subject to a limited retention period (6 months by default, up to 24 months with a paid license) and is lost if EAC is deactivated. This raises significant questions regarding data sovereignty, long-term access, and backup options. In this case, you do not fully own your data.

Model portability

Models built natively with Salesforce tools like the Einstein Prediction Builder are tied to the platform and not easily portable. While the underlying data can be exported, the trained model itself is not transferable. In contrast, models developed on external platforms (AWS, GCP, etc.) are inherently more portable, even if they are temporarily integrated with Salesforce.

Data portability in independent AI

When using external AI platforms, core data processing and model artifacts often remain outside of Salesforce. This potentially offers better data and model portability if the relationship with Salesforce or the strategy changes.

Strategic recommendations for decision-makers

Choosing the right AI strategy in the Salesforce context requires a nuanced evaluation that goes beyond a simple comparison of features. The following recommendations can help decision-makers:

Critically evaluate use cases

Don't rely on native Salesforce AI by default. Evaluate each AI use case individually based on:

• Required specialization: Does the task require deep, specialized AI capabilities (e.g., complex scientific analysis, niche industry predictions) that are likely to be better served by a dedicated platform?
• Adaptation needs: How much control over the model, training data, and algorithms is necessary? Is Salesforce's level of abstraction sufficient?
• Performance requirements: Are there strict latency or throughput requirements that might be better met by optimized external infrastructure?
• Data sensitivity & compliance: Does the use case involve highly sensitive data where the limitations of the trust layer (especially the lack of masking in Agentforce) pose unacceptable risks? Are specific data residency requirements better met externally?

to pursue a hybrid approach

Consider a strategy that leverages native Salesforce AI for simpler, highly integrated tasks where it excels (e.g., basic lead scoring, email drafting in Sales Cloud). Simultaneously, integrate independent platforms for high-value, specialized, or highly sensitive use cases.

Consider integration readiness

Realistically assess the organization's technical resources and know-how to handle the complexity of integrating and maintaining external AI solutions. Start with well-supported integrations (e.g., AppExchange, established cloud connectors) before tackling complex in-house developments.

Calculate the complete TCO

Conduct a thorough TCO analysis that compares the total cost of native Salesforce AI (licenses, data cloud usage, potential functional limitations) with that of independent AI (core AI costs + integration development/maintenance + middleware).

Total Cost of Ownership (TCO) analysis is a method for assessing the total costs associated with acquiring and operating a technology throughout its entire life cycle – including not only acquisition costs, but also ongoing operating costs, maintenance, training, upgrades, etc.

Why external AI platforms can be more cost-efficient:

• Economies of scale: Providers spread infrastructure costs across many customers.
• Lower investment: No need to build your own infrastructure.
• Faster deployment: Faster time-to-market reduces indirect costs.
• Maintenance & updates included: No effort required on your part for IT operations.
• Pay-as-you-go: Costs adjust to demand.

A TCO analysis often shows that external AI platforms are cheaper and more flexible in the long run than in-house solutions.

Prioritize strategic flexibility

Weigh the convenience of the integrated Salesforce ecosystem against the long-term strategic risks of vendor lock-in (see section VB). Incorporate portability considerations into the AI ​​strategy from the outset.

Demand transparency

Demand clear documentation from all vendors (including Salesforce and independent providers) regarding model capabilities, limitations, data processing practices, security measures, and pricing models. Critically examine marketing claims and compare them with the technical realities.

Suitable for:

• Data management systems in change: Strategies for the company's success in the Age of AI

A plea for an open AI strategy within Salesforce

The analysis clearly shows that while relying solely on Salesforce's native AI suite offers convenience and seamless integration with existing CRM processes, it is not necessarily the optimal strategy for every company. Strategically considering independent AI platforms offers significant advantages: access to highly specialized and potentially more powerful models, greater flexibility and control over the AI ​​stack, potential cost efficiencies through alternative pricing models and the use of existing infrastructure, and crucial risk mitigation regarding vendor lock-in and data sovereignty.

The identified limitations of the Einstein Trust Layer are particularly critical, especially the disabled data masking for Agentforce workflows. This underscores the need to look beyond marketing promises and carefully examine the technical realities, particularly when processing sensitive data. Concerns regarding data portability, as illustrated by the example of Einstein Activity Capture, also serve as a warning against excessive reliance on proprietary storage and processing mechanisms.

At the same time, the role of Salesforce AI should not be underestimated. It offers a valuable, well-integrated solution for many standard CRM tasks. Despite its limitations, the Einstein Trust Layer represents an important governance and security layer. Furthermore, the low-code tools enable broader democratization of AI adoption within organizations.

The most compelling strategy for many companies is likely to be an open, hybrid approach. Such a strategy leverages the strengths of native Salesforce AI for everyday, integrated tasks, but doesn't shy away from selectively integrating external, best-of-breed AI solutions for specific, highly demanding, or strategically critical use cases. This requires moving away from the default approach of using only native tools and instead conducting a rigorous, use-case-based evaluation.

Decision-makers are urged to carefully determine the right mix of native and standalone AI solutions. This decision should be guided by specific business requirements, existing technical capabilities, risk tolerance, and long-term strategic goals to fully leverage the potential of AI within the Salesforce ecosystem without creating unnecessary dependencies or risks.

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