Salesforce Einstein and AI Cloud: A CIO’s Playbook for Adoption
Overview of Salesforce’s AI Strategy
Salesforce has invested heavily in AI across its Customer 360 platform, positioning Einstein as the intelligence layer for all products. The strategy centres on embedding generative AI (Einstein GPT) into every cloud – Sales, Service, Marketing, Commerce, Slack, and Platform – under the umbrella of Salesforce AI Cloud.
AI Cloud is essentially a suite of AI capabilities (not a single product) that brings together Salesforce’s AI models, the Einstein GPT Trust Layer, Data Cloud, analytics (Tableau), automation (Flow), and integrations (MuleSoft) to deliver AI features in a trusted, enterprise-ready way.
In practice, this means:
- Sales GPT: Integrated into Sales Cloud for sellers. It can auto-generate sales emails, meeting scheduling prompts, opportunity updates, and even call summaries. For example, a sales representative can have Einstein GPT draft a personalized follow-up email to a prospect in seconds, using CRM data, which frees the rep to focus on selling.
- Service GPT: Embedded in Service Cloud for support teams. It can generate knowledgeable agent responses to customer queries (based on case context), summarize lengthy case interactions into concise wrap-ups, and even draft knowledge-base articles from past cases. Early adopters have found that this reduces after-call wrap-up time significantly (some reports cite a 50% reduction in case summary effort).
- Marketing GPT: Available for Marketing Cloud users to auto-generate campaign content. It can suggest email subject lines, write marketing copy tailored to segments, and even generate images or ad text. This helps marketers produce content faster while maintaining personalization.
- Platform/Developer AI (Einstein for Devs): Salesforce is also incorporating AI into its platform. Developers can use Einstein GPT in tools like Salesforce’s IDE or Flow Builder – for instance, to generate Apex code snippets, formulas, or even entire Flow workflows from natural language prompts. This “Einstein for Developers’ can scan code for bugs or suggest improvements, which accelerates development cycles.
- Data Cloud and Analytics: Data Cloud (Salesforce’s customer data platform) plays a pivotal role by unifying real-time data from multiple sources, which Einstein GPT can leverage for more contextually relevant answers. New features, such as a built-in vector database and over 50 connectors, enable AI to tap into both structured and unstructured data (including text, audio, and video) to enrich its outputs. Tableau and CRM Analytics can then consume AI-driven insights to visualize trends, forecasts, and more.
- Einstein GPT Trust Layer: To address enterprise concerns, such as data privacy and security, Salesforce’s strategy includes a robust governance layer. The Trust Layer ensures the underlying LLM providers retain no sensitive CRM data – prompts and responses stay within Salesforce’s security boundary. It provides encryption of data in transit to AI models, checks user permissions before including data in a prompt (to enforce data access controls), and logs all AI interactions for auditing. This lets CIOs leverage powerful third-party LLMs (like from OpenAI or Anthropic) without the data governance nightmares – a key differentiator of Salesforce’s approach to “trusted AI.” It also enables an “open” AI ecosystem: Salesforce can dynamically choose or swap out the best large language model for a task (and even allows customers to integrate their models), all while applying this trust and compliance layer uniformly.
Overall, Salesforce’s AI strategy is to integrate generative AI into the everyday workflows of CRM users, boosting productivity (Salesforce touts gains such as sellers saving hours per week on emails and data entry) and enhancing customer experiences with more personalized and immediate responses.
The strategy emphasizes trust and integration: Salesforce wants enterprises to adopt AI confidently by building it natively into the platform they already use rather than relying on external AI tools. It’s a fast-evolving roadmap, with dozens of Einstein GPT features rolling out – from AI-assisted email composition to autonomous agents.
Salesforce has even previewed “Agentforce” AI agents that can execute multi-step tasks on behalf of users. CIOs should view Salesforce’s AI Cloud as an accelerator that can make every employee more effective, provided it’s adopted with clear goals and guardrails.
Recommendations for CIOs:
- Align AI with CRM Goals: Ensure Salesforce’s AI capabilities map to your business objectives. For example, if improving customer support efficiency is a top goal, prioritize Service GPT’s case summarization and reply suggestions. Always ask: How will this AI feature make a measurable impact (e.g., faster sales cycle, higher customer satisfaction)?
- Leverage the Trust Layer: Engage your security and compliance teams early to review the Einstein GPT Trust Layer. Understand how it safeguards data. This builds internal confidence that generative AI can be used without regulatory or privacy breaches – a critical step before broad rollout.
- Stay Informed on the Roadmap: Salesforce’s AI feature set is expanding rapidly across sales, service, marketing, and more. Regularly review Salesforce’s AI Cloud roadmap and pilot new features in sandboxes. Being aware of upcoming capabilities, such as AI agents or new Data Cloud integrations, lets you plan future use cases and stay ahead of your competitors.
- Champion an AI-Ready Culture: As with any advanced tool, success depends on user adoption. Prepare your organization by communicating the purpose of Einstein GPT features and providing training. Encourage teams to treat Einstein’s suggestions as assistive, to be reviewed and refined by humans, so they trust the tool and avoid relying blindly on it. A culture that is excited but cautious about AI will extract the most value from it.
Licensing Structures: Einstein GPT and AI Cloud
Salesforce’s generative AI capabilities are add-ons to existing Salesforce clouds, and understanding the licensing model is crucial for budgeting. For Sales and Service Cloud customers, Einstein GPT is packaged as an add-on SKU, often referred to as “Sales Cloud Einstein” or “Service Cloud Einstein.” These terms were historically used for the Einstein analytics and prediction add-ons, which have now been expanded to include GPT.
The headline pricing is $50 per user per month for each Sales GPT or Service GPT. In practical terms, if you want Einstein GPT features for 100 Sales Cloud users, you would purchase 100 licenses of Sales Cloud Einstein at ~$50/user/month on top of their base Sales Cloud licenses. Similarly, Service GPT is an add-on to Service Cloud, priced at $50 per user.
These prices are at the list rate and include a certain amount of generative AI usage credits, as discussed below. Notably, suppose you already subscribe to Salesforce’s Unlimited Edition for Sales or Service Cloud. In that case, top-tier packages already include Einstein add-on licenses at no extra cost per user, making GPT capabilities automatically available (Salesforce’s strategy to encourage uptake of the Unlimited Edition).
For other clouds and products:
- Marketing GPT: Salesforce has been introducing Einstein GPT features for Marketing Cloud, including content creation in Journey Builder and email studios. Marketing Cloud’s pricing is not user-based, but rather based on the number of contacts and editions. Therefore, GPT features may be included in specific tiers or offered as a capacity-based add-on. For instance, high-end Marketing Cloud packages might bundle some AI capabilities, but expect that extensive use of generative text or image creation in marketing campaigns could incur additional costs or credit usage. Clarify with Salesforce whether Marketing GPT is included in your edition or requires an add-on license (this may change as products mature).
- Commerce GPT: In the Commerce Cloud, a B2C e-commerce platform, Einstein GPT can generate product descriptions and provide AI-driven recommendations. Commerce Cloud licensing is typically based on gross merchandise value (GMV) or the number of transactions, rather than the number of seats. Salesforce may include Commerce GPT in certain editions or as an optional module. Ensure you understand if it’s an extra SKU.
- Platform/Analytics AI: If you use Salesforce Platform licenses or Tableau/CRM Analytics, some AI features (like Einstein Discovery’s predictive models or GPT in Slack/Analytics) might be packaged differently. For example, Slack GPT (generative AI in Slack for creating summaries or drafting messages) is likely included with Slack’s existing plans for now, but could be tied to Salesforce AI Cloud licensing if it is deeply integrated with CRM data. Also, Einstein Discovery (predictive analytics) has historically been included in Analytics Plus or CRM Analytics licenses, rather than being usage-based. However, as AI Cloud converges, Salesforce might unify how these AI features are sold.
AI Cloud is presented as a whole, combining these capabilities (Einstein GPT, Data Cloud, Analytics, etc.). Enterprises interested in a broad AI-enabled Salesforce stack might negotiate an “AI Cloud bundle” in their contract. In essence, this would mean purchasing multiple components together – for example, Einstein GPT add-ons for various clouds, a Data Cloud license to unify data, perhaps Tableau for visualization, and even MuleSoft for connecting external data – all in one package.
Salesforce has branded this concept as “AI Cloud” to signal a comprehensive solution. While there isn’t a single magic SKU that gives everything by default, you can work with Salesforce to structure a bundle deal.
A bundle could offer better value if you plan to deploy AI across multiple Salesforce products (such as Sales, Service, and Marketing) and need the supporting infrastructure, including a data platform and integration. The pricing in a bundle will depend on your scale and negotiated discounts, but the key is to articulate which pieces you need and ensure you’re not paying for components you won’t use.
One constraint to note: in the initial rollout (circa 2023), Salesforce limited Einstein GPT access to customers with the Unlimited Edition or those part of specific pilot programs. By now (2025), these features are generally available to all, but Salesforce might still require certain prerequisite licenses.
For example, you typically cannot buy Sales GPT for a user who doesn’t have at least a Sales Cloud license, and you may need the latest Salesforce releases enabled. Always check the eligibility requirements; some AI features may require enabling Salesforce Winter ’24 or later features, specific data storage settings, etc.
Recommendations for CIOs:
- Map Licensing to Use Cases: Don’t assume every user needs the AI add-on. Identify which roles will benefit (e.g., sales reps drafting emails, support agents handling cases) and license those users. You can start with a subset of power users or a specific department, rather than rolling out to the entire organization. This targeted licensing avoids overspending on users who may never use the AI features.
- Understand Included vs. Paid: Ask Salesforce exactly what’s included in the base product versus the AI add-on. For instance, some basic AI features, such as simple lead scoring or basic chatbots, may already be included in your edition, whereas generative text capabilities are available as a paid add-on. Knowing this prevents you from paying for features you might already have and sets expectations on what $50 per user per month buys (e.g., which AI features are unlocked).
- Budget for Data and Platform Needs: If generative AI is only as good as the data it has, many enterprises end up investing in a Data Cloud (for real-time data unification) or additional storage, among other things. Be aware of these adjacent costs. You may need to allocate a budget for a data integration project or Analytics tools, in addition to the AI licenses. It may be wise to negotiate those together (for example, if you foresee needing a Customer Data Platform to feed Einstein GPT, bundle it in your agreement).
- Keep Licensing Flexible: The AI field is shifting quickly, and Salesforce’s pricing models might change in the coming year. Avoid committing to a fixed high cost per user in the long term until you have more usage experience. If possible, seek annual re-evaluation clauses or pilot-period pricing. CIOs should negotiate the ability to adjust the number of AI user licenses up or down each year rather than locking into a huge quantity upfront. This flexibility is crucial as you learn the true adoption rate and value.
Generative AI Credits: How They Work
A unique aspect of Einstein GPT licensing is the concept of generative AI credits. Unlike a standard software feature, generative AI involves ongoing computing costs.
For example, using large language models on the back end incurs usage fees from providers and requires a significant amount of processing power, as is the case with Salesforce. Salesforce has abstracted these costs into a credit system.
Here’s how it works in principle:
- When you purchase an Einstein GPT add-on (e.g., Sales GPT for $50/user/month), your organization receives a pool of Einstein GPT credits included with that purchase. Salesforce has deliberately not widely publicized the exact number of credits included per user – it might be a fixed amount per license or an org-wide pool based on the number of licenses. (For example, it could be something like X thousand generative characters per user per month, aggregated across the org – but the exact formula is proprietary and subject to change).
- Consuming credits: Every time a user invokes a generative AI feature – say, they click “Generate Email Draft” or an agent asks the GPT to summarize a case – credits are consumed from the pool. The amount of credit used typically depends on the complexity of the request. A simple output (like a two-sentence email reply or a summary) might consume one credit. In contrast, a longer, more complex output, such as a multi-paragraph product description or a detailed report, could consume multiple credits. In essence, the more tokens (words) the AI has to process and produce, the more credits it costs. This is analogous to how cloud AI providers charge by usage (e.g., by characters or by computing time), but Salesforce wraps it into a credit for simplicity.
- Metrics examples: In traditional Salesforce Einstein (pre-GPT), some services were metered by outcomes or predictions – for instance, predictive scores might have been charged per 1,000 predictions, or Einstein Vision image recognition was charged per image processed. With Einstein GPT, think in terms of a generative response. Each AI-generated outcome (an email, a chat reply, a piece of code) deducts credits. If an agent engages in an AI-assisted chat that has the AI generate five responses during a session, that could be five separate credit-consuming events, assuming one credit is used for each response, if they are short. If a marketer generates an entire email campaign draft, that single prompt might use several credits due to its length. The key is that credits correspond to usage – heavier usage or more complex outputs will burn through credits faster.
- Included vs. Overage: The included credits with your license are intended to cover a baseline of usage. In normal, day-to-day use by a typical user, you should stay within the allotment. However, if your organization’s usage exceeds those included credits – say your service team is using far more AI responses than average – you will need to purchase additional credits. Salesforce offers “Enterprise Expansion Packs” for AI credits, essentially blocks of extra generative capacity you can buy. This can work like purchasing a bundle of extra API calls. Depending on your contract, it may be a fixed bundle (e.g., pay $X for an extra Y credits per month) or a true pay-as-you-go plan, where overages are billed at a rate (e.g., $N per additional credit beyond the free allotment). Salesforce’s messaging suggests a pay-as-you-go model “to democratize pricing,” meaning you pay for what you use beyond the free portion, rather than having unlimited use for a flat fee, which could encourage misuse.
- Tracking usage: Salesforce knows that a consumption-based model can cause “bill shock” if not managed, similar to cloud usage overages. Therefore, they provide ways to monitor your usage of generative AI credit. Admins can likely view usage statistics in the Salesforce setup. For example, there may be a dashboard or metrics for Einstein GPT usage, similar to how you monitor API call usage or Einstein Analytics credits. The Einstein GPT Trust Layer also logs all prompts and responses, which indirectly provides a log of usage frequency. In practice, you should track how quickly your credit pool is depleting. For instance, if you’ve used 80% of your credits by mid-month, that’s a red flag that you may incur overages by month-end. Salesforce support or your account team can help provide detailed usage reports if needed.
One challenge is that credit definitions can change over time. As of its initial launch, Salesforce had not firmly pinned down “what equals one Einstein GPT credit” in public documentation – they were likely fine-tuning it as real usage data came in. CIOs should demand clarity in the contract on how credits are measured and what happens if you run out.
Ensure you understand the unit: is one credit equivalent to one prompt or response under certain token limits? How are extremely long outputs handled? Is there any cap where the system will refuse a too-large request to protect your budget? These details help avoid surprises.
Recommendations for CIOs:
- Insist on Transparency: When negotiating, ask Salesforce for the specifics on generative AI credits for your deployment. Even if they don’t publish it on a website, your sales rep should tell you, for example, “With 50 users on Sales GPT, you’ll get approximately Z00 responses per month included; beyond that, the cost is $___ per additional 100 responses.” Push for those numbers. It’s essential to have a ballpark to plan usage.
- Monitor from Day 1: Treat AI credits like a utility meter. Upon enabling Einstein GPT, set up a process to monitor credit consumption on a weekly basis (or daily, if initially). Many CIOs set up internal dashboards that pull data from Salesforce’s usage metrics. If such metrics aren’t easily available in the UI, leverage Salesforce’s APIs or talk to your account team – you might get access to a usage report. Early monitoring will tell you if, for example, a particular team or feature is consuming a lot of credits.
- Establish Usage Policies: To prevent accidental overuse, you may need internal policies. For instance, you might disable very high-volume use cases initially (e.g., don’t auto-generate summaries for every record in a batch job or prevent a single user from spamming the generate button). Educate users that the AI is a shared resource with a cost – e.g., an agent should use the AI for genuine needs, not out of curiosity or for every trivial reply. Sometimes, simply awareness helps: if reps know each AI-generated email “costs” a bit, they’ll use it when it adds value, not for every single sentence.
- Plan for Overage or Limits: Decide in advance how you will handle reaching the credit limit. One approach is to use hard limits: configure the organization to stop AI generation once credits are exhausted (if Salesforce allows this setting), thereby avoiding unplanned charges. However, this could disrupt users who suddenly lose access to the AI mid-cycle. Alternatively, soft limits: allow overage but set a budget cap (e.g., you inform Salesforce “don’t bill more than $X in overages without approval”). Weigh the trade-off between cost control and user experience. Communicate this plan to stakeholders so everyone knows what to expect if usage spikes (e.g., “If we run out of credits, further AI suggestions will be unavailable until next month’s reset, unless we authorize more budget.”).
Pilot Deployment Strategies
Rolling out generative AI in a large enterprise Salesforce environment is best done gradually and strategically. A pilot program allows you to test the waters, capture value, and learn about usage patterns before launching on a full scale.
Here’s how CIOs can introduce Einstein GPT in a cost-controlled, value-focused way:
Start with High-Impact, Low-Risk Use Cases:
Not all AI applications are equal – some deliver quick wins with minimal downside. Two prime candidates to pilot: sales email generation and service case summarization. These are relatively contained tasks:
- Sales email generation (Sales GPT): Select a small group of sales representatives, such as a regional sales team or a handful of early adopters, and enable Sales GPT for them. They can use it to draft prospecting emails, follow-ups, or reminders for renewals. Sales emails are a great pilot because reps still review and edit the content, so the risk of incorrect AI output is low (they won’t send something nonsensical; they can fix it). Meanwhile, you can measure time saved per email and possibly an uptick in outreach volume. It directly tackles a known time sink for reps (writing emails), so if the AI is decent, the benefit will be evident.
- Service case summarization (Service GPT): Similarly, pick a subset of support agents (maybe one support tier or one product line’s support team) to pilot Service GPT features like Work Summaries. After a customer calls or chats, the agent can click to have Einstein GPT generate a summary of the interaction and the next steps. The agent can refine it and save it as a ‘case closed’ note or send it to the customer as a recap. This use case is ideal because it shortens after-call work, resulting in a measurable efficiency gain. The agent can also catch any errors in the summary before it is used. Early pilots have shown a significant reduction in wrap-up time, which is quantifiable in the pilot results.
- Other pilot ideas: If those two don’t apply, consider knowledge article drafting (have AI draft articles based on case solutions for internal knowledge bases) or lead qualification notes (AI summarizes key points from a lead or account into call prep notes). The key is to pick something that users do frequently and that AI can handle with moderate complexity – avoid mission-critical or highly sensitive tasks in the pilot phase. For example, using AI to auto-recommend financial advice to clients might be too high-stakes initially; save complex scenarios for later when trust in the AI has grown.
Limit the Scope:
Define clear boundaries for the pilot. Perhaps 50 users for 2 months, focusing on one or two specific AI features. This controlled scope keeps credit usage predictable (since you roughly know how often those 50 users might use the feature in their daily work) and contains any potential negative surprises.
It also makes it easier to gather feedback: you know exactly who’s using it and for what purpose. Make sure these pilot users are aware that they’re part of a trial and are encouraged to provide candid feedback, as well as report any issues or “strange AI behavior.”
Gather Baseline Metrics:
Before the pilot starts, measure the current state of those users or use cases. E.g., for how long does it currently take an average sales rep to write a proposal email? How many support cases can an agent close per day, and how much time is spent on documentation? This baseline will be crucial for evaluating ROI later. During the pilot, also track the same metrics with AI assistance.
Cost Control during Pilot:
Since you may be unsure how quickly users will consume credits, consider setting a credit usage alert or threshold. If, for example, 80% of the included credits are used in the first month, pause and assess why.
During a pilot, it’s acceptable to impose some manual limits – for instance, instruct the pilot group, “Don’t use the AI for more than 20 prompts a day each” initially, and see if that’s sufficient or if they bump against that limit. Because the group is small, even heavy usage won’t break the bank, but you’re learning the upper bounds. If you provided the pilot group with a certain budget of extra credits, monitor that it doesn’t get exhausted too soon.
Iterate and Refine:
Use the pilot to learn not just about the technology but how your people interact with it. You might discover, for example, that sales reps love the email generation but find the suggested tone too formal, so you adjust the prompt templates or fine-tune settings (Salesforce allows some customization of prompts or tone).
Or support agents might report that the summaries sometimes miss critical details from the case, which tells you the AI might need more data fields as input, or agents need to phrase things differently.
Capture these insights to refine processes. It might even be that the AI feature isn’t used as much as expected. Find out if it’s due to awareness, ease of use, or something else, and address the issue (perhaps with more training or a UI tweak).
Finally, expand gradually. If the pilot is successful, don’t jump to enterprise-wide overnight. Maybe expand to another group or add another use case and continue to monitor. This phased approach prevents unforeseen budget overruns and allows your organization to absorb the changes.
Recommendations for CIOs:
- Define Success Criteria Upfront: Before the pilot, determine what outcomes would make it a success. For example, “If email generation saves each rep 5 hours per week, we consider that a win worth expanding,g” or “If case handles time drops by 10% with equal or better customer satisfaction”. Having these targets helps evaluate the pilot objectively.
- Choose AI Champions: Within the pilot users, identify a few “super users” or champions. They can be trained a bit more deeply and serve as go-to resources for their peers during the pilot. They often help drive adoption (through peer encouragement) and can quickly surface issues. A tech-savvy sales manager or support team lead makes a great champion for this.
- Solicit Feedback Actively: Don’t wait until the end of the pilot to hear from users. Have weekly check-ins or a shared chat channel for pilot participants. Quick feedback loops will let you adjust mid-pilot. For instance, if nobody is using the feature because they forgot it was there, you can send tips or have the champion do a quick demo again. This ensures the pilot truly tests the value and doesn’t fizzle out due to practical hiccups.
- Communicate Pilot Learnings: Keep stakeholders (and Salesforce, if applicable) informed about pilot progress. Share wins (e.g., “Rep Jane closed a deal citing an AI-generated email that impressed the client”) and challenges. This not only builds momentum internally but also gives you leverage with Salesforce – if the pilot shows promise but reveals that cost is a concern, you can take those data points into a negotiation (e.g., “We’d like to roll this out to 500 users, but at the current credit burn rate, that’s cost-prohibitive – how can you help us?”).
Monitoring and Governance
Once Einstein GPT features are enabled, strong governance and monitoring are crucial to maintain value and prevent misuse or cost overruns. This encompasses tracking usage, setting policies, and governing the content output by AI:
Usage Monitoring:
As discussed under credits, you should continuously monitor how the AI is being used. This is both a cost issue and an adoption issue. From a cost perspective, keep an eye on that credit consumption dashboard.
Identify patterns – e.g., is there a particular team or user generating an unusually high number of AI outputs? That could indicate either a power user (who might inspire others or need some limits if they’re overusing it) or perhaps an unintended use (maybe someone has set up an automated process that calls the AI repeatedly).
From an adoption perspective, monitoring might show some departments hardly using the AI at all. That’s a flag to investigate: do those teams need training? Are the features not relevant to their work?
Early in the deployment, it’s worth producing a simple monthly report: which AI features were used, how many times by which groups, and how many credits were consumed. This helps you quantify value (e.g., “300 emails generated this month”) and watch for anomalies.
Budgeting AI Spend:
CIOs should treat AI usage like a cloud utility – part of the IT spend that needs active management. Set an AI budget either in terms of credits (preferred) or dollars. For example, allocate a certain number of credits per quarter to each department. This doesn’t have to be a hard cap, but it provides a framework.
If Sales has a 10,000-credit budget and by mid-quarter, they’ve used 9,000, you either shift resources (maybe borrow some from another department’s allocation if they’re under) or you decide to purchase more, but it becomes a conscious decision.
Finance teams will appreciate this proactive approach, as opposed to getting a surprise bill. Some organizations choose to implement charge-back or show-back, for example, by internally “charging” the sales department’s cost center for the AI credits they consume, which drives accountability for usage. Regardless of the method, regularly reconcile actual usage with the budget and adjust forecasts as you gather more historical data.
Preventing Runaway Usage: Generative AI is so easy to use that users might not realise how often they’re invoking it. “Runaway” usage could occur if, for example, someone incorporates GPT into an automated script or if an enthusiastic user constantly hits the “Generate” button. To prevent this:
- Role-Based Access: Not every user in Salesforce needs the ability to call Einstein GPT from the start. You could roll it out by role or permission sets. For instance, maybe interns or new employees shouldn’t use it without oversight, so you give Einstein GPT permission to certain profiles first. This limits exposure.
- Volume Limits: Check if Salesforce allows setting org-level usage limits or cooldowns (e.g., limiting each user to X generations per hour). If such settings exist, configure them to sane values. If not, an administrative workaround is to create monitoring that alerts an admin if any single user crosses a threshold (like more than 50 AI outputs in a day), so you can follow up.
- Automations using AI: Be cautious about incorporating GPT into automated workflows. For example, you might be tempted to have a Flow that auto-generates a summary for every case at closure. While this is a cool idea, if you close thousands of cases, you’ll suddenly incur thousands of AI calls without human checkpoints. Consider keeping a human in the loop (perhaps by adding a button that an agent clicks to generate the summary, rather than doing it automatically for every case). At least until you’re very comfortable with the cost and output quality, avoid using fully automated, high-volume triggers for AI.
Quality and Ethical Governance:
Monitoring isn’t just about quantity; it’s also about quality and compliance. Set up processes to review the content AI is generating, especially in the early stages. Are the AI-generated emails and answers accurate?
Are they staying within company guidelines for tone and compliance? For example, an AI might inadvertently create a response to a customer that sounds authoritative but is slightly inaccurate. You need a feedback mechanism.
Salesforce’s Trust Layer provides a Feedback Store where users can thumbs-up or down an AI response and note if they edited it. Use that! Regularly review the feedback data. Suppose many users find the AI suggestions unhelpful and often require heavy editing. In that case, that’s either a training issue, or the feature might not be ready for prime time in that context – you might decide to dial back its use until it improves.
Moreover, ensure compliance governance:
If you’re in a regulated industry, have compliance officers review how AI is used. The audit trail from the Trust Layer logs all prompts and responses – decide who will have access to review those logs. It might be necessary to sample them to check their appropriateness.
For example, you wouldn’t want the AI to generate an email that accidentally includes confidential info or violates legal disclaimers. Establish guidelines, perhaps by forbidding the use of AI for certain sensitive communications (such as responding to a legal inquiry or crafting HR communications) unless reviewed by a person.
User Guidance and Training:
Governance also means guiding your end-users on best practices. Provide a short “dos and don’ts” for using Einstein GPT. For instance, do use it to draft content in your own words and then check it. Don’t use it to answer something you don’t understand (you must verify its accuracy).
Remind them that the AI can “hallucinate” – make things – so they must not consider its output as gospel. By instilling these practices, you mitigate the risks of misinformation or embarrassing errors going out.
Recommendations for CIOs:
- Set Up an AI Governance Team: Form a cross-functional team (IT, compliance, business user reps, maybe data security) to oversee Einstein GPT usage. This team should meet periodically to review usage reports, user feedback, and any incidents that have occurred. They can update policies as needed and also champion success stories. Having a governance committee in place formally signals to the entire organization that AI is being introduced responsibly.
- Leverage the Tools for Control: Use the controls that Salesforce provides – for example, turn on the Audit Trail and Feedback features from the start. If there are admin settings to restrict which data Einstein GPT can access or to mask certain fields from being used in prompts (for example, to ensure it never includes a customer’s personal phone number in an AI-generated message), configure those. Engage Salesforce support for help in configuring trust settings that meet your organization’s needs.
- Train on Responsible AI Use: Include a brief training or policy document for all users who will use Einstein GPT. It should cover confidentiality (e.g., not including data that shouldn’t be there), verification of outputs, and respectful use. For example, an employee might think it funny to have the AI draft a joke response to a customer, but that could backfire. Make it clear that standard company policies, such as professionalism and data protection, apply equally to AI-generated content. The AI is, in a way, an extension of your workforce, and its outputs should be treated as such.
- Plan for Scaling or Throttling: As usage grows, be ready to adjust. If one department wants to drastically expand use (say, marketing suddenly wants to generate social posts via Salesforce in bulk), loop in the governance process to evaluate the cost and risk. It might be fine, but it shouldn’t happen without IT awareness. Conversely, if budgets tighten, you may need to temporarily reduce AI usage, perhaps by limiting access or usage. Having pre-thought “if-then” plans for these scenarios will make your governance nimble.
ROI Evaluation
Measuring the return on investment (ROI) of Salesforce’s AI features is crucial for justifying ongoing spending and deciding whether to expand or scale back. CIOs should approach Einstein GPT ROI in both quantitative and qualitative ways:
Productivity Gains:
The most direct ROI from Einstein GPT is in productivity/time savings. Use the pilot metrics and extended usage data to quantify how much time the AI saves employees. For example, if Sales GPT saves a sales rep 5 hours per week on writing emails and research (a figure echoed by Salesforce’s surveys of users), that equates to 5 hours * $Rep_Hourly_Cost * number of reps using it, in value.
Suppose an agent normally handled 20 cases a day and now handles 22 because note-writing is faster. In that case, that’s a 10% throughput increase – maybe allowing you to handle more customer inquiries with the same staffing.
Over a year, these efficiency gains can be significant. Document these productivity metrics:
- Time per task: “Email draft generation cuts the average writing time from 15 minutes to 5 minutes, saving 10 minutes per email.” Multiply by the number of emails per week for an average rep – say 30 emails, that’s 300 minutes (5 hours) saved per week for one rep.
- Volume metrics: “Support agents closed 15% more cases per week with no drop in quality because AI helped with after-call summaries and knowledge article drafts.”
- Speed to the outcome: Perhaps sales cycles were shortened because follow-ups were faster, or proposal documents got out in 1 day instead of 3, thanks to AI first drafts. Faster cycles can mean quicker revenue recognition – tie that to financial impact if possible.
Quality Improvements and Impact on Outcomes:
Some ROI will come not just from doing the same work faster but from doing it better. For instance, if marketing emails are more personalized (thanks to AI suggestions drawing on rich CRM data), you might see higher campaign engagement – that’s an outcome improvement attributable to AI. Or, if service agents consistently give well-phrased, thorough answers (with AI helping to draft them), customer satisfaction (CSAT) scores might improve.
Track these business KPIs:
- Sales: conversion rates, pipeline generated, win rates – did any move up in pilot vs control groups?
- Service: CSAT or Net Promoter Score, first-contact resolution rates, average handle time – any positive shifts when AI is in use?
- Marketing: email open/click rates, landing page conversion (if AI is used for web content), etc. For example, you might find AI-generated subject lines A/B tested 5% better than human-written ones.
Even anecdotal wins can be powerful: for example, a salesperson might close a deal and mention that the prospect was impressed by the quick, tailored follow-up that was assisted by AI. Collect these stories because they bolster the quantitative data.
Cost of AI vs Value: Now, weigh these gains against the cost. The cost side includes:
- The licensing fees (e.g., $50 per user, plus any purchased overage credits).
- Indirect costs like the time spent by admins or the AI governance team (small, but consider it).
- Perhaps training time or initial implementation consulting, if any.
On the value side, translate the improvements into dollars where you can:
- Time saved can be translated to cost saved (hours * fully loaded cost of those employees) – though be careful, saving time doesn’t always equal saving money unless you repurpose that time for additional work. Alternatively, the time saved can be translated into a capacity for growth (e.g., the support team can handle 10% more volume without needing new hires, thereby avoiding the cost of hiring 10% more staff).
- Improved outcomes, such as higher sales and better retention, directly translate to revenue and profit. If you can isolate the portion attributed to the AI, attribute a portion of that uplift to it.
- Reduced opportunity cost: for instance, if AI reduces mundane work, employees can focus on higher-value activities (salespeople selling more, service reps upselling or doing proactive outreach). That can indirectly boost revenue.
Assessing Payback:
Calculate a simple ROI or payback period if possible. For example, if the AI costs $ 100,000 per year and you estimate $ 300,000 per year in value delivered (through a combination of cost avoidance and extra sales), that’s a 3x ROI or payback in 4 months. Such figures are great to present to the CFO and to use when considering expanding the rollout.
Intangible/Strategic Benefits: Don’t forget the softer benefits in your evaluation report:
- Employee Experience: Reps and agents may feel more empowered and less overwhelmed by tedious tasks. This can improve morale and reduce burnout and turnover, which have real cost implications (hiring and training new staff is expensive). You can use employee surveys to gauge if they feel their workload is more manageable or their work quality has improved thanks to AI help.
- Customer Experience: Faster responses and more personalized interactions likely boost customer loyalty. While it is hard to put a dollar on loyalty, you can cite these as strategic benefits that protect or enhance the company’s brand and revenue in the long run.
- Innovation and Competitive Edge: Adopting AI early can position your company as innovative in the eyes of customers or partners. For example, if your sales team is leveraging AI to provide insights that competitors are not, that could help you win deals. Mentioning that in ROI (even qualitatively) shows the business value of being an AI leader.
Finally, treat ROI evaluation as an ongoing process. Continuously measure these metrics because the value might increase as adoption grows (or, if at some point diminishing returns set in, you catch that, too). Also, as Salesforce releases new features, they could unlock new areas of ROI. Perhaps next year, Einstein GPT will introduce advanced forecasting or AI-driven recommendations that add another layer of benefit. You’d want to incorporate those into your ROI analysis when relevant.
Recommendations for CIOs:
- Use Control Groups: To truly measure the impact, consider maintaining a control group— a similar set of users not using Einstein GPT —for some time. Compare their performance to the AI-empowered group. This isolates the effect of AI from other factors. For example, one sales region uses GPT, and another doesn’t. If the GPT region’s performance jumps relative to historical trends more than the other, that’s strong evidence of AI’s value.
- Build an ROI Dashboard: Work with your analytics team to create a dashboard that tracks key performance indicators for AI users vs before. Include things like average task time, volumes, CSAT, etc., alongside the costs (license count, credits used). This live dashboard can be reviewed quarterly. It not only justifies the current investment but also highlights areas for optimization (maybe one metric isn’t improving – you might need to adjust how AI is used or conduct more training).
- Review ROI with Stakeholders: Regularly communicate the value being realized to executives and department heads. For instance, show the head of sales how much time has been freed up or how it correlates with the creation of more opportunities. This keeps support for the program strong and can help secure the budget for expansion. Conversely, if the ROI is lower than expected, have an honest discussion: is it due to low adoption (which is fixable), or is the AI simply not yet as effective in that area? Perhaps pausing or trying a different use case could be prudent – it’s better to pivot than to sink costs into something that’s not yielding returns.
- Consider Future Value: When calculating ROI, factor in that AI often has a compounding effect. The more data and feedback the system gets, the better it may perform (Salesforce’s feedback loops mean the suggestions should improve over time). Also, your teams will get more adept at using it. So, ROI after one quarter of use might be lower than what you’ll see after a year of cultural integration. Communicate this trajectory – initial ROI might be modest, but the trendline is upward, which is common with transformative technologies after an initial learning curve.
Negotiation Strategies with Salesforce
Adopting Salesforce’s AI Cloud solutions can significantly impact your Salesforce contract value, so CIOs must approach licensing negotiations shrewdly. Salesforce, like any vendor, will be keen to upsell these new AI capabilities, but that interest can be turned to your advantage in negotiations:
Bundle into Renewals:
Salesforce sales reps are often more flexible when you’re at a major renewal or expansion point. Plan your Einstein GPT adoption around those cycles if possible. If your Sales Cloud renewal is coming up, that’s a prime time to say, “We’re considering adding 200 Sales GPT licenses, but we’d like to see them bundled at a favourable rate as part of the renewal.”
Salesforce may offer a discount on the AI add-on or even throw in a certain amount of usage credits at no extra cost to secure the renewal and upsell. From your side, consolidating these discussions helps ensure that AI costs are part of the big picture – you can negotiate the overall contract value rather than piecemeal add-ons later, when you have less leverage.
Pilot Discounts and Trials:
Don’t pay full price for an unproven deployment. Salesforce is often willing to offer a pilot program discount, for instance, a 3-month trial of Einstein GPT for X users at no cost or a significant discount. At a minimum, request that the first few months of usage credits be provided at no charge while you gauge value. Salesforce knows customers need to see results to justify long-term investments, and they have a precedent of working with early adopters.
If your account executive hasn’t offered, proactively ask: “Can we do a paid pilot with an option to roll into a bigger contract? Perhaps we start with 50 licenses for 6 months at a 50% rate, and if we like it, we expand to 500 at a better price.” Structure it formally in the contract if possible (an opt-out or opt-in clause based on pilot success).
Negotiate Credit Overage Terms:
A tricky part is how extra credits are charged. Push for clarity and caps. For example, negotiate a fixed overage rate (e.g., “additional generative AI credits at $___ per 1,000 credits”) and ask for a volume discount if you buy a large pack.
Better yet, see if you can get some overages waived during an initial period. Salesforce might agree not to charge for overages up to a certain point while you’re refining usage. Also, consider negotiating an upgrade path – say, if you consistently go over your credits, that cost can be applied towards purchasing a bigger expansion pack or a higher-tier solution later, rather than just being a sunk cost.
Leverage Salesforce’s AI Push:
Salesforce is highly motivated to get customers on AI (it’s a strategic priority for them). This can create opportunities: some customers have found that including any level of AI spend in a deal can unlock better discounts on the entire deal. Salesforce account reps might have quotas or incentives tied to AI Cloud sales.
For instance, simply committing to a small amount of Einstein GPT usage might qualify your account for an extra discount on your core licenses. Use this to your benefit: even if you start small, let Salesforce know you’re investing in their AI vision – they may respond with goodwill incentives.
It’s been reported that even buying a handful of credits (at a nominal cost) could trigger better pricing brackets. While this “hack” may not be officially documented, savvy negotiators bring it up: “We are looking at Vendor X for some AI needs, but if Salesforce can make it attractive, we’d rather consolidate with Einstein GPT.” This signals that your AI budget is in play, and they should compete for it.
Multi-year and Volume Commitments:
If you are fairly confident in the long-term need for these AI features, you can negotiate a multi-year contract that locks in pricing. This can protect you against future list price increases. Salesforce raised prices by roughly 9% around 2023, and they could do so again. In exchange for a longer commitment or a larger user volume, offer stepped discounts.
For example, if in year 1 you license 100 users and might expand to 500 in year 2, try to get a tiered pricing structure (the per-user cost drops at certain thresholds). Make sure there’s flexibility to scale down as well, in case of an economic downturn or a strategy change. Sometimes, a clause to reduce seats by a certain percentage without penalty can be negotiated for large deals.
Manage Upsell Tactics: Expect that once you start using AI, Salesforce will encourage you to expand it – e.g., “Why not enable it for all salespeople?” or “Have you considered using Marketing GPT as well?” They will showcase success metrics to your executives.
To manage this:
- Come to the table with your data – know your ROI and usage so you can say, “We’ll expand when these criteria are met, not just because it’s available.”
- If Salesforce offers a bundled deal to expand, review it carefully. Sometimes, they’ll offer a slight discount per user if you double the number of users, but ensure it’s beneficial and within your budget, rather than feeling pressured to accept “just because it’s a deal.”
- Be open about budget constraints; if the rep knows you have a firm budget, they might focus on fitting the solution into it rather than pushing beyond it.
- Also, don’t be afraid to involve Salesforce’s technical team in upsell discussions – ask them to demonstrate the value of new use cases in your context. This shifts the conversation from pure selling to problem-solving, and you might catch if something is not yet mature enough to buy.
Competitive Alternatives as Leverage:
While Salesforce Einstein GPT is unique in its native integration, remember that generative AI capabilities can also be achieved through other means (for example, some companies integrate OpenAI’s API directly with Salesforce using custom code or utilize third-party AI assistants).
If Salesforce’s pricing is too high, you can subtly remind them that you have other options for incorporating AI into CRM workflows. You don’t need to threaten ripping out Salesforce – rather, indicate how you do AI in Salesforce is optional (native vs. external).
This can sometimes cause them to be more flexible on price, as they’d rather you use their solution (and pay them) than a workaround. However, balance this by noting that Salesforce also knows the native solution is more convenient and secure; use this tactic judiciously.
Finally, ensure that any negotiated terms, especially those related to credits, overages, and future discounts, are written into the contract or order form. Salesforce contracts can be complex, so work with procurement and legal to explicitly document what was promised.
Verbal assurances from a sales rep about “we’ll take care of you if you run over” must be codified to be enforceable.
Recommendations for CIOs:
- Do Your Homework: Before negotiation, gather internal data (from pilots or industry benchmarks) on expected usage and value. Also, find out what other enterprises are paying if you can (network with other Salesforce customers or user groups). This information arms you to counter-offer knowledgeably. For example, “We know of a similar org that got 30% off the GPT add-on for a similar size – we’d expect a comparable consideration.”
- Ask for Incentives: Don’t shy away from asking, “What incentives can you offer us to become an early adopter of AI Cloud?” Salesforce often offers promotional programs, such as extra support, training credits, or even funding for a pilot. Sometimes, Salesforce co-funds innovative customer projects. These can reduce your total cost.
- Negotiate Renewal Protections: One concern is that you start at a low price, and then Salesforce could raise the cost later once it’s embedded in your processes. Try to lock in a price increase cap for renewals (e.g., “no more than 5% increase year-over-year for next 3 years on this add-on”) or have a multi-year term. If they don’t cap, at least ensure you have the right to discontinue the add-on without penalty at renewal if it’s not delivering value, so you’re not handcuffed.
- Engage Executive Sponsors: If your spending with Salesforce is significant, involve your Salesforce executive sponsor or account manager’s boss in the conversation about AI. High-level alignment (e.g., Salesforce’s VP for your region, knowing you are piloting AI) can sometimes green-light special pricing or extra attention. It also helps if things go awry; having that relationship might get you more support in addressing issues or getting exceptions approved.
