Salesforce

To deliver seamless and personalized buying experiences to its customers, businesses integrate a variety of add-ons, customization tools, and other features into Salesforce CRM. One such add-on is Sales Cloud from Salesforce and now called Agentforce Sales. It offers a robust and flexible suite of tools to support sales, marketing, and customer support teams to work more efficiently and effectively. Whether you’re a small firm wanting to get started with Agentforce Sales or a large corporation looking to optimize your existing sales processes, this guide has you covered. In this blog, we’ll explore what Sales Cloud is, understand its capabilities, and discuss how to successfully implement Salesforce Sales Cloud. What is Salesforce Sales Cloud? Sales Cloud from Salesforce is a cloud-based solution designed purpose-built to empower sales teams and centralize the complete customer data. In Dreamforce 2025, Salesforce rebranded it to Agentforce Sales. According to Salesforce the change is due to how “In the era of humans and agents working alongside each other, Salesforce is not only rebranding Sales Cloud into Agentforce Sales, but also highlighting a clear direction for agentifying the end-to-end sales processes.” With Sales Cloud, sales professionals can track every customer and automate tedious tasks. The platform provides sales reps & teams with a common ground to track customer-related activity, communications, and buying history. Salesforce Sales Cloud has a variety of features for pipeline management, deal monitoring, auto-quote creation, sales forecasting, and customer behavior, all of which help sales professionals close more deals. Salesforce Sales Cloud Core Capabilities for Sales Teams Sales Cloud has an abundance of tools and features to fine-tune even the smallest aspects of the sales process, activities, and goals. Salesforce Sales Cloud includes a feature for every purpose that is specifically designed to fit your company’s models, style, needs, and operations. Here is some key business objectives aligned with Sales Cloud: Acquire More Deals The obvious key to maximizing sales is to acquire more deals. It is easier to close more business by continuously optimizing campaigns based on market response and interacting with channel partners. Lead Management With Sales Cloud, you can create your own lead machine to boost conversion rates and revenues. Track your leads from start to finish and optimize campaigns across all channels. Make better selections by investing in marketing dollars with comprehensive calculations. Sales Data To augment effective decision-making, get access to real-time sales data. With the most up-to-date and accurate data, you can easily plan territories and boost your sales and marketing productivity. Close More Deals Higher sales translate to higher revenue. To ensure that your sales reps can maximize deal closure, you will require these Sales Cloud components. Account and Contact Management Take a comprehensive look at your customers’ activity history, important contacts, customer conversations, and internal account discussions. Gain insights from popular social media platforms such as Facebook, Twitter, and LinkedIn using Salesforce Sales Cloud. Opportunity Management: View deals created by your sales reps. Sales Cloud provides access to all stages of the sales lifecycle, as well as products, competitors, quotes, and other services. Keep up with the people and information you need to complete every transaction. Users can collaborate using Chatter cooperation to close sales by soliciting feedback from team members. Close Deals Faster Visual Workflow helps you to quickly develop and automate business processes using drag-and-drop functionality. Create custom approval processes for sales, offers, expenses, and more. Whereas with feature File Sync and Share, you can share and discuss documents, publish the most relevant ones, and track content in real-time using the File Sync and Share feature. Find what you need instantly, share it securely, and get alerts whenever updates are made. Automation Sales automation streamlines sales operations by deploying automation tools and creating workflows. It streamlines the sales process and reduces manual tasks for sales professionals. One of the most popular tools is Sales Cloud Einstein. It’s a predictive and analytics solution that enables sales representatives to take data-driven actions and boost conversion rates. The tool also provides analysis based on prior data from robust data models and provides accurate sales forecasts to enable smarter decision-making. Faster Decisions When multiple tools are aligned properly, the decision-making process becomes seamless and thorough. Dashboards allow you to see real-time information about your business in a single view. Create detailed reports and access them from any device. You get real-time updates on team predictions, modify them, override visibility, support multiple currencies, and more such capabilities to stay on top of your business. How Sales Cloud Facilitates Business Growth? There are a lot of advantages of using Agentforce Sales to maximize revenues, scalability, and efficiency, let’s discuss few of them here: Reliable Reporting Every organization has a humongous amount of data that must be managed, processed, and sifted to generate actionable insights that can then be used to develop strategies. Data collection and management are straightforward, but analysis demands intelligence. Sales Cloud organizes data after it has been processed through multiple rounds of analysis, focusing on error elimination. Managers can plan the next best actions using insightful reports and dashboards. Boost Customer Engagement & Service Salesforce captures data that helps the sales team target marketing. When efforts are made in the proper direction, it results in increased customer engagement and service efficiency. When the Sales team is fully aware of their clients’ needs, they can deal with them proactively. Boost Productivity with AI Salesforce Sales Cloud’s AI capabilities use machine learning and Gen-AI technologies to provide deep insights from multiple sales channels. Analyzing sales efforts allows AI to evaluate the effectiveness of a strategy. It also enables the team to automate operations, handle leads, and support teams in closing deals in the shortest possible time. Efficient Interaction between Teams Sales Cloud brings all data together, and it also provides a single source of truth that can be used by the sales, marketing, and service units at the same time. This openness removes repetition of efforts, miscommunication, and makes all stakeholders on track what is the best thing to do. Resulting in reduced deal cycles, hand-offs, and consolidated customer experience that directly drives growth. How to Successfully Implement Sales Cloud Here’s the Salesforce Sales Cloud implementation guide to enable your business to better decision-making with the help of data and automation. Define Goals & Readiness Before you start with the implementation process, it’s essential to understand what’s your goals or expectations are out of Sales Cloud. In addition, setting goals and understanding implementation readiness helps you identify sales challenges, adoption goals, and set success metrics. This also makes it easier to initiate CRM customization, boosting sales process scalability, increasing efficiency, and reducing response times. Risk Planning & Response No implementation strategy is complete without understanding the potential risks and challenges. So, identify those roadblocks like data migration issues, user adoption concerns, or the lack of integration. The next step is to create plans to prevent these in the early stages with a detailed change management strategy that is communicated, has contingency and alignment among stakeholders. This will reduce disruption, boost confidence in the implementation, and facilitate smooth transition, leading to stable success in the long run in your deployment of Salesforce. Align Salesforce with Teams To ensure the successful implementation of Sales Cloud in your company, make its usage the focal point of the department. Every detail regarding the customer should pass through Sales Cloud, which will later be used to evaluate the rep’s performance. Salesforce centralization also improves accountability by ensuring the quality of dashboard setups. Make all meetings and prospective ideas a reliable source of information for the entire team. Automate All Sales Processes A Salesforce study revealed that sales reps spent 70% of their time on non-sales tasks and the remaining time to deal handling and data processing. Companies can enhance their efficiency by adopting automation in processes related to sales. By managing such mundane tasks, teams clearly understand what and how they need to strategize selling. With the help of automated workflows and approvals, it leads to faster and more efficient decision-making. Utilize Sandboxes Before you launch any new functionality in Sales Cloud, test it thoroughly, and sandboxes are the best place for this. Sandboxes protect your existing system by serving as an identical twin of the operational system, allowing for secure testing. Endorse the use of sandboxes to ensure that the implementation does not disrupt your existing infrastructure. Set-Up Data Readiness Data management is an eclectic combination of science and art and therefore can be quite a daunting task. Finding the right balance between maintaining and analyzing data that allows the sales staff to make informed decisions is challenging. But doing so ensures you can prepare accurate, structured, and reliable data before advanced intelligence and analytics are applied. Use Einstein for Sales Optimization Sales Cloud Einstein makes things easier by interpreting factual data with artificial intelligence. This processed data generates a network of leads, allowing managers and leaders to take specific actions. Using Sales Cloud Einstein’s intelligence, you can discover high priority leads and focus on their conversion. Salesforce Einstein AI can help you expand your business by providing the features listed above. Test Thoroughly Before Implementation Ensuring smooth Salesforce Sales Cloud roll-out is only possible through extensive testing of all workflows. Test project roadmap, test features in sandboxes, and develop elaborate UAT scripts. Also, verify data migration accuracy, test email integrations, and address issues promptly. This ensures stability, reliability, and conformity to business objectives, resulting in a smooth go-live process. Drive User Training and Adoption Successful Salesforce adoption depends on effective training and support. Begin with group sessions, reinforce learning through refreshers, and provide access to demos, videos, and Trailhead courses. Encourage administrator knowledge-sharing and organizing hands-on workshops to build confidence. With continuous education and practical guidance, teams embrace Salesforce fully, driving higher adoption, optimized usage, and long-term success. Optimize & Scale Post-Live After the implementation of the Agentforce Sales is completed and the sales team is using it, it’s not the end of the process. Post Go-live, you must ensure a consistent evaluation of how well it performs, adoption rate, and data quality. And once you gather the feedback and the insight, implement changes or updates wherever needed to ensure that the Sales Cloud supports your ever-evolving growth without a hassle. Reasons to Hire a Salesforce Implementation Partner Outsourcing Salesforce implementation to a certified partner can provide various benefits, including: Expertise and Experience Salesforce implementation companies typically use a team of skilled and trained specialists who have a thorough understanding of the platform and its possibilities. They deliver the greatest implementation solution to businesses by following the best business approach. Time and Cost Savings Hiring a Salesforce partner rather than training your internal team will save you time and money. Teams with an implementation partner collaborate more effectively on implementation in less time and at a lesser cost. Customized Solutions The partners offer customized solutions based on business needs and goals. They also thoroughly analyze existing processes to discover gaps and potential change and then create a solution appropriately. Reduced Risk Partners mitigate the risks involved with installing a new Salesforce solution, managing complicated projects, and detecting possible risks and concerns before they become problems. This can help ensure smoother installation and reduce business disruption. Ongoing Support The Salesforce implementation partner offers continuous support and maintenance services so that your Salesforce solution does not go obsolete, rather it evolves with your evolving business needs. In addition, they provide you with relevant training, resources, and assistance on how to use your Salesforce instance to achieve your business objectives. Closing Remark Salesforce Sales Cloud has revolutionized and transformed the sales process and made it more competitive and agile. It has also streamlined the Sales team and enabled them to provide personalized experience to the customers. Nonetheless, to achieve its potential, businesses should strategically and rightly apply it. If you’re also interested in how to use Agentforce Sales to achieve better customer experience, increased sales goals and revenue growth, speak to our certified Salesforce consultants for a free Sales Cloud demo.

Over 150,000 companies worldwide rely on the Salesforce platform, which unifies data from multiple sources, including users, customers, and integrations. However, the need for effective data management grows along with the amount of data. A crucial element in this process is archiving Salesforce data. Even if your Salesforce data grows over time, you can still manage data efficiently, save storage expenses, and preserve a flawless user experience. What is Salesforce Data Archiving? Some of the data in your Salesforce system eventually becomes less important as it accumulates over time. While not all data needs active access, a large portion of it nevertheless needs to be kept on file in order to comply with legal obligations and for possible future use. Businesses are required by industry-specific norms and data regulation laws to retain various types of data for specified amounts of time. Thus, long-term, safe data storage is required by law in addition to being good practice. Even while maintaining your data is one of your legal responsibilities, these same regulations frequently require that sensitive data be deleted or have access to it restricted after specific periods of time or under pre-defined circumstances. Laws like the California Privacy Rights Act (CPRA) and the General Data Protection Regulation (GDPR) are based on the principle of data minimization, which states that companies should not retain more data than is essential or for longer than is necessary. Difference between Data Archiving and Data Lifecycle? Since both data archiving and data lifecycle management are essential components of data governance, it's easy to mix up the terms. However, these two ideas serve different functions and follow different procedures. The process of managing data throughout its lifecycle, from creation and initial storage to the point at which it becomes outdated and is finally removed, is known as data lifecycle management. It consists of multiple crucial phases that center on the lawful use and preservation of information. Anonymizing production data is essential in this situation to maintain compliance. On the other hand, data archiving is a particular phase in the data lifecycle. It entails transferring dormant data from main storage to a system made especially for preservation over time and sporadic access. Here, keeping data available when needed is important, but it shouldn't clog the system or impair its functionality. To put it briefly, data archiving is the crucial decision on what to do with your data as its operational life draws to a close. Data lifecycle management, then, is a more comprehensive procedure while the data is being used. Gaining a full understanding of these ideas can help you make informed decisions about what data to archive and when, so your Salesforce system will function as efficiently as possible while still adhering to regulations. Why should you archive your Salesforce data? There may be an urgent need to delete data from Salesforce if you are already experiencing system delay. You can improve system performance, simplify your archiving, and maintain compliance with a strong data lifecycle strategy. Data privacy is of utmost importance in the modern world, particularly when it comes to personal data. Strict laws like the CPRA and GDPR necessitate this. But it's important to remember that other factors should also be considered, in addition to data archiving. To respect privacy requirements and maintain the functionality of your data, you should also take data anonymization and pseudonymization into consideration. If you manage these legal obligations well, archiving may only be necessary when system performance is an issue. While you are erasing data, initiating data archiving may seem like a risky idea, but it doesn't have to be. You won't find data deletion or even archiving to be as daunting once you fully grasp the data lifecycle. Your archiving project will be a lot easier to handle if you practice effective data lifecycle management. A backup plan serves as your security. Your backup can act as a safety net in case you ever make a mistake with your archiving criteria, guaranteeing that no important data is lost in the process. Salesforce ensures your safety and security. Large Data Volumes and more complex use cases can be more effectively managed with a third-party archiving solution. Benefits of Salesforce data archiving There are several reasons for organizations to consider Salesforce data archiving. An effective archiving solution can solve: System performance Legal and compliance needs Obsolete data Salesforce data archiving improves system performance Archiving Salesforce data can enhance system performance in these ways: Because there is less data to manage, activities proceed more quickly. In the end, archiving helps systems operate better by preventing restrictions from being reached, such as API call limits and data and file storage capacity constraints. Salesforce data archiving facilitates adherence to legal, regulatory, and data retention policies: Businesses operating in highly regulated industries are frequently required to comply with several regulatory standards, which might vary depending on the business model and regional presence. You cannot keep data in your Salesforce Org permanently since noncompliance might have detrimental financial effects. Sensitive information in your Salesforce data might need to be kept safe in case the necessity to exhibit the documents later on arises. In that scenario, archiving this data would be preferable over completely deleting it. Certain organizations might have regulations requiring the removal of information, while others might have lengthy data retention periods that include keeping a lot of data. Your data must comply with the company's data retention policy, depending on the terms of the policy. By removing data from production Orgs and restricting access to a smaller set of users, archiving Salesforce data can assist in adhering to these laws and standards. This guarantees that should it become necessary in the future, the data will still be searchable and unarchived. Your org can benefit from archiving obsolete Salesforce data It may not always be the best idea to delete outdated data in order to make storage space available because doing so may result in the loss of important business data. You can make sure you have the data in case you need it later by properly archiving Salesforce data rather than deleting it. How do you archive Salesforce data? Identify the data Establish effective ways to help you identify the data that has to be removed. Learn the best practices and go beyond the capabilities of Salesforce native queries by collaborating with a Salesforce consulting company. Retain your data Keep your data safe and unchangeable for extended periods of time by storing it in an encrypted manner. For an audit trail, make sure the data is safe and secure for many years to come. Remove your data Remove data from Salesforce at scale in a safe and organized manner. To maintain data balance, queue up data loads over the course of several days, weeks, or months. Design your User Experience Give your Salesforce users a familiar appearance and feel by creating custom page layouts. Create the connections and relationships that your CRM data needs to have. Best practices for Salesforce data archiving To achieve efficient Salesforce data archiving, thorough planning and analysis are required. Organizations should plan for the following when archiving: Storage and Limits Monitor how much storage your company has available and how much is being used. This can help you determine how much storage space you need to free up, allowing you to outline the scope of your archiving job. Usage Trends To effectively archive data in your Salesforce system, you must first understand the data utilization metrics. Make sure you have the proper tools for assessing data volume and tracking trends. Use APIs based on Salesforce Einstein Analytics to create numerous dashboards and track data usage and trends. The software can also help you identify misuse or strange events. Parent-Child Relationships and Data Integrity To retrieve data from the Salesforce data archive and bring it to production, make sure to keep the archived object's Parent-Child record. Otherwise, the data may be incomplete or inaccurate. For example, unarchiving an account without its related contacts would not be desirable. And, if you intend to delete data from the Salesforce data archive, check with your organization's legal team first, since there may be data integrity consequences, such as Parent-Child Relationships or Field Removal. Determine how often you want to archive Salesforce data. Establish the frequency of archiving and develop archiving processes that allow for automatic archiving. Encrypt your Salesforce data archives Your archived data should be protected both in transit and at rest to ensure that only authorized individuals have access to it. Use the best Salesforce archiving tool When selecting a Salesforce data backup and archiving tool, look for one that is certified by Salesforce and has a proven track record. It must be user-friendly and efficient. Archiving Salesforce data is critical for large-scale businesses. As Salesforce data grows, data archiving becomes increasingly important because it improves Salesforce system efficiency, automates compliance, and reduces storage without losing access to data. For efficient and successful Salesforce data archiving, collaborate with a team of certified Salesforce consultants from Girikon, a Gold Salesforce partner. Here’s what you can do with Girikon Data Archiving Services Organizations looking to archive their Salesforce data at scale need a comprehensive strategy that ensures safe Salesforce data archiving at scale. With over a decade of experience in working with Salesforce customers, Girikon’s professional Salesforce Consulting Services can support the most complex Salesforce data archiving use cases for businesses. Contact us today to learn more.

It is obvious that artificial intelligence (AI) will transform the way solutions are designed. It is time to acknowledge that there is a fundamental change in how one needs to approach architecture. In the past, solutions were developed based on an algorithmic understanding of the problem, guaranteeing consistency in output with the same input. For example, in a CRM system with an account segmentation process, the conventional approach involved defining fields on the account and applying business logic for segmentation, driving other automation in the system. However, in the era of artificial intelligence, models are created using a lot of data, leading to the creation of predictive models. Large language models (LLMs) change the way solutions are designed because they can handle more intricate personalized segmentation and consider a much larger range of data. In order to better comprehend this, let's examine how AI is affecting solution design and delivery by closely examining the following topics. Transforming the user experience Honestly, the transformative impact of artificial intelligence (AI), especially generative AI, is kind of behind its current surge in popularity. It feels like for the first time , people are able to talk with technology using natural language, so the whole digital experience becomes more intuitive, and also way more accessible. And you know, this is not just an upgrade, it’s more like a real paradigm shift. AI systems can now understand and then answer requests that line up with what the user actually meant, not just what they typed. So, because of that, companies are more often turning to salesforce ai services to automate workflows, boost customer engagement, and deliver more personalized, data-driven experiences, which is pretty much the point. Moving to a Natural Language Processing (NLP) experience Platforms are starting to focus more on NLP (Natural Language Processing) and less on if-then-else scenarios. The user is spared from having to search through numerous fields. Rather, the user receives an English response to their questions. This streamlines onboarding, increasing its speed and effectiveness without requiring agents to undergo in-depth training. Increased productivity AI empowers businesses to do more with fewer customizations translating to increased work efficiency. The challenges of AI While AI provides benefits, it also presents new challenges, such as: Having to account for a wider range of data in a probabilistic context. Performance guarantees are not identical, therefore factors like error management and observability must be re-evaluated. Without direct insight into how language models work, troubleshooting becomes more challenging. Problems like hallucinations, where the model will make things up – while there are solutions to address these problems, none are completely dependable. Prompts can introduce biases and additional security problems into a language model. In an era where data privacy and trust are paramount, it is critical to create approaches for error management and improving the predictability of AI output in order to secure data security and privacy. How is AI impacting engagement with professional services companies? Even while processes have evolved and agility has increased over the past couple of decades, the traditional approach to delivery has stayed mostly unchanged. This is how AI can change the engagement model with professional services firms: Fast-track every stage of the delivery process. The kinds of jobs that people can have and the kinds of skills they need will change dramatically as a result of NLP. If the volume of data generated by the sales team during the discovery phase can be summarized into a handover, it would save the project team and the customer a lot of time, accelerating all the stages of a typical delivery and making the process more efficient. Maximize human potential Artificial Intelligence provides the capacity to generate commodities for manual labor, particularly in professional services engagements. When carrying out an engagement, be it a Salesforce delivery, AI powered salesforce consulting, or anything else, a lot of manual tasks are frequently required to keep everything organized and in sync. With the help of AI, we can do away with that and make it a commodity, freeing up the human brain to focus on more difficult jobs and providing customers with greater commercial value. For intricate CPQ (Configure, Price, Quote) projects, for instance, the user doesn't have to worry about billable hours for manual tasks—instead, they can concentrate on creating appropriate pricing policies and working with customers. Can AI solve everything? AI is pervasive and has an impact on professional services and architecture. Can it resolve every issue? Or is it just a fantastical idea with dubious practical application? Let's examine this in more detail. AI as a co-pilot People have very high expectations of AI. Consequently, there is always a concern about losing jobs to AI. But the reality is that AI helps people do tasks more quickly and easily, freeing up their time to pursue other interests. Approach AI with an open and curious mindset The revolutionary journey of AI has only just begun, and given the hype and its ongoing progress, it's critical to recognize its potential. Instead of seeing AI as a closed subject, but rather as a new frontier, one should approach it with curiosity and a commitment to improvement. The energy impact of AI It is important to pay attention to how AI affects energy. The extensive usage of AI may result in a considerable carbon footprint. Globally, addressing this challenge—which includes data management, data security, and environmental aspects—is imperative, meaning that solutions must be found as quickly as possible. Language generation is no longer just a human ability Natural language generation capability is no longer restricted to humans. Up until recently, language was thought to be an ability unique to humans. Large language models can now mimic complex ideas and emotion-based communication that were previously thought to be specific to humans, even though they don't fully comprehend the material they generate. This is a fundamentally important change that calls into question the idea that language production is exclusively a human ability. AI is ultimately a tool that requires a human at the helm Even with its advances, artificial intelligence still needs clear guidance on our goals. No matter how complicated the task or its execution, human intelligence, and minds are essential for directing AI to get the intended results. Even though AI can expedite activities and increase productivity, in the end, it is still a tool that needs human guidance. How does AI impact innovation? Problem-solving capabilities The evolution of AI signifies a shift in problem-solving capabilities. AI can be utilized in the context of the current technology landscape, by identifying the low-hanging fruits, and determining what can easily be delivered to end-users. Simplifies intent-based testing Important side discussions are frequently overlooked in team communication, especially when testing is involved. Intent-based testing, which can transform the testing process by guaranteeing that user intent and requirements continuously guide testing efforts, may be made possible by AI's capacity to retain a continuous grasp of intent. AI as a solution to persistent issues AI provides a set of tools to solve enduring issues. Artificial Intelligence (AI) has the potential to revolutionize the way that chronic problems in numerous disciplines, like sales pipeline predictability and routing, are approached and improve work efficiency. How does AI impact DevOps? Depth vs. breadth in knowledge When it really gets to understanding and establishing value, it's about depth. In some sectors of the economy, like healthcare, there are generations of expertise where people are retiring after 40 or 50 years of experience. The difficulty lies in archiving that data, incorporating it into a domain-specific large language model (LLM), and utilizing centuries' worth of healthcare-related knowledge at our disposal—all the while being mindful of whether information from the previous century or earlier is still relevant today. Democratizing DevOps Within the DevOps process, the essential phases are plan, develop, build, test, release, and deploy. Testing is the main area of influence. Exploratory testing gives the end user the freedom to simply investigate and identify edge cases. AI has the ability to quickly democratize DevOps, enabling participation from those who have never been able to take part in software delivery. Key security and ethics concerns raised by AI? Large language models give rise to completely new categories of security risks, which the developer community is still learning about. As of this moment, it is unknown how serious these threats are. Regarding the degree of autonomy given to AI-driven processes and the ways in which users can provide feedback, a degree of caution is urged. Simple prompt injection attacks are very successful in tricking the huge language model into going against its instructions. They can even fool the defenses that are currently in place. The conflict between those looking to breach systems and those trying to secure them has long been a part of traditional security. But since we are still learning about and addressing the potential risks associated with generative AI, especially with regard to the newer varieties, we should proceed very cautiously when it comes to defining rights, establishing protocols for monitoring, and including humans at crucial points in the development and implementation of these systems. Want to learn about more ideas, opportunities, and strategies to maximize the value of Salesforce data + AI? 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In the past, anyone who was required to need a Salesforce professional was to find someone proficient with Apex and Visualforce. However, it is no longer the case today as companies are hiring professionals who can work in a more complicated Salesforce ecosystem characterized by incorporation of automation, Agentforce, Data Cloud, Lightning Web Components, and industry-specific clouds. This shift requires businesses to hire Salesforce developers who can manage integration of various business systems, build AI-driven automations, operate with Data Cloud, and develop Lightning Web Components across Experience Cloud, Service Cloud, Marketing Cloud, and Sales Cloud. The challenge is to find a Salesforce expert possessing all these skills and on top of that, having knowledge of the target industry. Therefore, we came up with this guide, where you can learn what to pay attention to when choosing a Salesforce specialist, what skills and qualifications are needed in 2026, and how to make the right decision according to your goals. What’s inside What Does a Salesforce Solution Developer Do in 2026? When Should You Hire Specialists in Salesforce Implementation? Development Team vs Administrator Team: Which One Do You Need? Specialized Salesforce Experts You May Need Skills You Need to Check Before You Hire How to Find Developers that Deliver Business Value Common Errors Committed During Hiring Conclusion What Does a Salesforce Solution Developer Do in 2026? A Salesforce engineer in 2026 is responsible not only for creating smart solutions but also for implementing AI-powered workflows and integration of enterprise applications. Let us see what tasks they perform. Create Scalable Lightning Applications Salesforce development teams create contemporary, efficient, and responsive interfaces by means of Lightning Web Components that offer greater functionality than previous Visualforce pages. In addition, they extend Salesforce capabilities with the aid of Experience Cloud by creating self-service portals that allow customers, employees, and business partners to receive information, collaborate, and make requests. Besides, with the use of Dynamic Forms and Dynamic Actions, they create intuitive page layouts based on business processes, data, and user roles. Develop AI-Powered CRM Experiences The capabilities of Agentforce, Prompt Builder, and Einstein AI allow the Salesforce CRM experts to develop such assistants who would qualify leads, summarize cases, answer clients’ questions, and recommend the right actions. AI combined with Salesforce Flows and custom logic allows businesses to automate processes, improve productivity, enhance decision-making, and create a higher level of individualized customer experience. Generally, this means that AI-enabled CRM systems give companies a chance to expand their businesses in an efficient manner. Connect Enterprise Systems With the help of GraphQL, REST APIs, and MuleSoft, a Salesforce developer combines separate applications used for customer service, operations, accounting, marketing, and supply chain management. Also, they integrate Salesforce with different platforms such as Oracle, NetSuite, Microsoft Dynamics, and SAP to have a smooth data exchange between different systems. Thus, eliminating duplicate information, providing employees with a single source of information about every customer interaction, and offering consistent experiences across all touchpoints. Optimize Customer Data with Salesforce Data Cloud Managing fragmented customer information with data collected from websites, marketing campaigns, and connected devices becomes increasingly challenging. That’s why organizations often hire Salesforce CDP developers who use identity resolution to merge customer records from multiple sources, creating a single and accurate customer profile. Professionals also design AI-driven personalization strategies and real-time segmentation of audiences for offering relevant and personalized recommendations according to current behavioral data. When Should You Hire Specialists in Salesforce Implementation? While delay in appointing Salesforce expertise may begin affecting system, customer experiences, productivity and revenue, recruiting the right CRM development specialists at the right stage can help accelerate digital transformation and prevent costly rework. Here are some signs of knowing when it’s time to hire Salesforce managed services. Your CRM Adoption Has Stalled When the team avoids using Salesforce, increases their reliance on manual work, and struggles to complete the routine tasks signify that CRM no longer supports the way your team works. The reason could be disconnected workflows, inefficiency page layouts, outdated customizations, etc. that may reduce the value of your Salesforce investment. You’re Planning AI Initiatives Companies interested in implementing Agentforce, predictive analytics, and Einstein AI solutions find out that the current Salesforce environment is not ready for intelligent automation. That is why they should hire Salesforce programmers who can develop a scalable, AI-ready, and integrated CRM platform. Your Business is Scaling Faster Than Your CRM An implementation that worked for a small team may struggle to support increasing customer interactions, larger teams, and growing operational demands. Common indicators include inefficient workflows, declining system performance, and frequent requests for new customizations. All these are strong signs that your Salesforce implementation has likely outgrown its original design. You’re Expanding into New Markets Regional compliance requirements, distributed teams, and localized business processes, these are some operational challenges that come with business expansion. Managing these complexities via standalone configurations becomes increasingly difficult. This is where organizations must seek additional customization and development to support long-term growth. You’re Integrating Multiple Business Systems While adopting marketing automation tools, customer support systems, ERP platforms, accounting software, and eCommerce platforms, companies usually struggle with inconsistent customer information, delayed reporting, and duplicate records. In order to avoid the negative impact on the effectiveness of the operations, companies should take the help of Salesforce development specialists. Salesforce Development Team vs Salesforce Administrator Team: Which One Do You Need? Salesforce Development Expert Salesforce Administrator Build custom business logic and applicationsDevelop reports and dashboards Create lightning experiences and advanced automationConfigure Salesforce using clicks instead of codes Build the solutions in Lightning Web Components, Visualforce, and ApexPerform daily CRM data management Optimize platform scalability, architecture, and performanceManage the users, profiles, permissions, and security settings. Set up APIs and connect Salesforce with marketing and ERPStructure flow, validation rules, and approval processes Scroll the table sideways on smaller screens. While recruiting the Salesforce experts helps you focus on building custom applications, automation, and integration of CRM with other systems, hiring administrators lets you configure and optimize Salesforce with its out-of-the-box features. This way, you must ensure to hire a Salesforce administrator and developer together to form a balanced team for ongoing platform management and scalable solutions. Skip the six-week hiring cycle. Girikon fields pre-vetted, certified Salesforce developers and admins — Agentforce, Data Cloud, LWC, integrations. Tell us the gap and we will match the profile, usually within days. Request developer profiles Specialized Salesforce Experts You May Need Here is a group of Salesforce specialists that you may need to deliver your project efficiently. Hire Force.com Developers Recruiting these developers helps businesses build custom applications beyond Salesforce’s standard capabilities. Using business logic, custom objects, and workflow automation, they streamline complex processes, thus enabling organizations to enhance efficiency without investing in entirely new software. Hire Salesforce Lightning Developers Having such specialists in place helps businesses build high-performance applications and reusable components that create digital experiences for Experience Cloud. They also perform exceptionally when issues like slow page performance, lower user adoption, and outdated interfaces are affecting employee productivity. Hire Marketing Cloud Implementation Company When companies want to create automated and cross-channel customer journeys using tools like Email Studio, Mobile Studio, and Journey Builder, it’s more effective to hire marketing cloud specialists. Starting from performance management of campaigns to personal communication via SMS and mobile, everything is handled by these developers. Skills You Need to Check Before You Hire a Salesforce Developer AI and Automation Skills Salesforce is continuously embedding AI across its platforms. This makes it important for developers to adopt and work with modern AI capabilities like Einstein AI, Agentforce, generative AI, and Prompt Builder. Integration Skills Today, modern enterprises integrate their Salesforce with a variety of applications in finance, sales, marketing, and operations in order to facilitate data exchange on a timely basis. This requires the need for such Salesforce experts who have experience in APIs, cloud platforms, and middleware. Business and Collaborating Skills Besides technical proficiency, Salesforce developers also possess certain business and collaboration skills such as understanding the business process, converting business needs into technical solutions, and communicating effectively with stakeholders. Salesforce Development Skills Apex, SOQL and SOSL, Git, Visualforce, Lightning Web Components, DevOps and CI/CD, etc., there are some core platform development technologies used to extend Salesforce beyond its standard functionality. Having familiarity with these modern deployment methodologies enables developers to build scalable applications. Security and Compliance Skills Stored customer and business information within Salesforce makes developers with key skills like OAuth authentication, permission sets, Salesforce shield, data authentication, GDPR, HIPPA, and other compliance frameworks an essential consideration. How to Find Salesforce Developers that Deliver Business Value Review Their Portfolio Developers who can explain why they built a solution, not just what they build often bring greater strategic value. So, rather than focusing on the number of Salesforce projects, you must examine the complexity and business value of those implementations by assessing their expertise in: Data Cloud Agentforce Enterprise Integrations Marketing Cloud Industry-specific Salesforce solutions Experience Cloud Lightning Web Components Assessing Trailhead Activity In comparison to a resume, Trailhead profiles give an idea of the process of learning and practical participation in the Salesforce ecosystem of a developer. Assessing the activities of a developer implies the identification of proof that he/she enhances his/her professional skills on a constant basis. Here is what you must consider: Superbadges Certifications Hands-on projects Recently earned badges Learning paths Verify Salesforce Certifications Validating a developer’s technical knowledge and commitment via Salesforce certifications is one of the easiest ways. While certifications alone do not guarantee project success, it indicates that developers understand Salesforce best practices and keep up with the platform’s evolving capabilities. Here are a few of certifications that can be relevant to your project: Platform Developer I Platform Developer II AI Specialist JavaScript Developer I Data Cloud Consultant Application Architect Assess Their Architectural Thinking In an interview or while reviewing portfolios, it is important for you to hire a developer with good architectural skills who can create solutions which are scalable, secure, reliable and meet business needs. Also, ensure to assess whether the developer understands: Salesforce architecture Platform scalability Integration strategy Solution design Performance optimization Searching for Relevant Experience in the Industry Due to the fact that Salesforce implementation varies from one branch to another, it is better to look for a developer experienced in the sphere you work in because he/she is more likely to be aware of all the peculiarities of regulation, processes in your company, and needs of your clients. For instance, a developer with experience in financial services is likely to be familiar with regulatory compliance, risk management, and financial services cloud. Similarly, those who worked in Manufacturing have experience in the field of supply chain automation and ERP integration. Common Errors Committed by Companies During Hiring of Salesforce Developer While focusing on short-term hiring decisions, many organizations make certain common mistakes that lead to delayed projects, expensive rework, and poor user adoption. Here a few of them that you can avoid: Choosing the lowest cost freelancer Hiring based only on certifications Not evaluating communication skills Ignoring AI expertise Overlooking DevOps knowledge Skipping code reviews Not defining testing standards Conclusion It requires a team that understands your business objectives and follows Salesforce best practices when it comes to hire Salesforce admins and developers. As a Salesforce consulting partner, Girikon brings certified professionals with experience across Experience Cloud, Service Cloud, Sales Cloud, and Financial Services Cloud products. From custom app development to API integrations, and lightning web components, the experts provide end-to-end Salesforce development services tailored to complex business requirements. So, are you looking for the right Salesforce expertise? Connect Girikon today to discuss your project requirements and discover how these certified professionals can accelerate your Salesforce implementation, scale the platform, and maximize your investment with confidence. Salesforce Development Services Hire the specialist, or hire the team that already has them. Girikon is a certified Salesforce consulting partner with developers, architects and admins across Sales, Service, Experience, Marketing and Financial Services Cloud. Scale up for a single build or retain an ongoing squad — without the recruitment cycle, the ramp-up or the bench cost. Certified developers, admins & architects Agentforce, Data Cloud & AI capability LWC, Apex & enterprise integrations Flexible full-time, part-time or project engagement Get matched with a specialist Explore development services Hiring this quarter? 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Enterprise technology has always moved faster than enterprise confidence. Systems became connected long before organizations fully understood the risks that came with that connectivity. Data moved across teams, tools, and systems without proper security and control measures. This leads to data privacy risks, poor or no governance frameworks, and compliance issues. Generative AI adoption brings this gap into sharper focus, and most enterprises struggle to fully embrace it. The hesitation is not resistance to AI but inability to move forward without guardrails. Salesforce Einstein Trust Layer helps in mitigating these challenges. Einstein Trust Layer is a secure architecture built within the Salesforce platform to ensure businesses can use GenAI solutions while keeping their data and privacy controls intact. So, how does Salesforce address the concerns of access, oversight, and accountability with the Einstein Trust Layer? How can businesses overpower the issues with security and compliance as they adopt AI at scale. In this blog, we will examine how Salesforce AI Cloud addresses these concerns and explains the role of the Einstein GPT Trust Layer. In addition, we’ll explore why trust has become the deciding factor in enterprise AI adoption. What is Salesforce AI Cloud Salesforce AI Cloud is designed to bring generative AI into the core of Salesforce applications without separating innovation from governance. Its purpose is straightforward: enable businesses to use large language models within CRM workflows while maintaining control over data, access, and outcomes. Rather than treating AI as an external add-on, AI Cloud embeds it across Sales, Service, Marketing, Commerce, and custom applications built on the Salesforce platform. The scope is intentionally broad, but the approach is conservative in the right ways. AI Cloud does not replace existing systems or bypass security layers. It works within them. Within Salesforce’s broader generative AI roadmap, AI Cloud acts as the execution layer. With the help of this, AI cloud can connect enterprise data, AI models, and real business workflows that are usable at scale. AI Models and Architecture Within AI Cloud AI Cloud includes purpose-built tools and functionality to deliver enterprise-grade AI and is Salesforce’s latest multidisciplinary endeavor to add AI capabilities to its product line. In many respects, it is a continuation of the company’s generative AI program, which was introduced in March 2023 and endeavors to integrate generative AI throughout the Salesforce technology stack. AI Cloud hosts and serves text-generating AI models from a variety of partners, including Amazon Web Services (AWS), Cohere, Anthropic, and OpenAI, on Salesforce’s cloud platform. Salesforce’s AI research group offers first-party models, which support services such as code creation and business process automation. Customers can also introduce a custom-trained model to the platform, storing data on their own infrastructure. Einstein GPT: Generative AI Inside CRM Einstein GPT is the next generation of Einstein, Salesforce’s AI engine. By merging proprietary Einstein AI models with ChatGPT or other leading LLMs, customers may use natural-language prompts on CRM data to trigger powerful, real-time, tailored, AI-generated content. Einstein GPT Use Cases by Function Here’s a look at how Einstein GPT helps teams to boost productivity. Einstein GPT for Sales: Automate routine sales tasks such as drafting emails, scheduling meetings, and preparing for follow-ups. Einstein GPT for Service: Automatically generate knowledge of articles from past case notes. Auto-generate tailored agent chat responses to boost customer satisfaction through personalized and faster service engagements. Einstein GPT for Marketing: Generate tailored and targeted content in real-time to engage customers and prospects via email, mobile, social media, and advertising. Einstein GPT for Slack: Get AI-powered customer insights such as smart sales summaries via Slack and reveal user behaviors such as knowledge article updates. Einstein GPT for Developers: Leverage Salesforce’s proprietary LLM to boost developer productivity by using an AI-powered chat assistant to generate code for languages such as Apex. What is the Salesforce Einstein Trust Layer Salesforce Einstein Trust Layer is a robust safeguard that protects an organization’s data as it flows through the AI system, ensuring that internal and external security protocols are followed. This comprehensive layer consists of advanced encryption, data privacy measures, and access control to protect sensitive information. Its significance becomes more essential, especially when a user interacts with generative AI inside Salesforce; the Trust Layer governs that interaction before it ever reaches a language model. In simple words, Einstein GPT Trust Layer exists for a simple reason: Enterprises cannot send raw customer data directly to external models and hope for the best. The Trust Layer enforces rules around masking sensitive fields, preventing data retention by model providers, and ensuring responses stay within approved boundaries. This is also where Salesforce’s approach differs sharply from using standalone large language models. With a public or loosely governed LLM, the responsibility for data handling falls almost entirely on the user. With the Salesforce AI Trust Layer, that responsibility is built into the platform itself. Why the Salesforce Trust Layer Matters for Enterprises For enterprises, as they move towards adopting AI, the focus is more on control and less on experimentation. The Salesforce Einstein Trust Layer enables organizations to fully embrace AI and be confident that their data is not only delivering better outcomes but is also always protected. It also offers following benefits: Treats AI adoption as a governance decision, not just a technical one Aligns AI usage with existing compliance and risk frameworks Standardizes prompts to reduce inconsistency and unintended outputs Maintains audit trails for visibility and accountability Enables controlled, centralized rollout across teams and functions Enterprises can use third-party LLMs, Salesforce-owned models, or custom models through the Einstein GPT Trust Layer, allowing flexibility without compromising governance Core Capabilities of the Einstein Trust Layer Data Masking Before providing AI prompts third-party LLMs, automatically mask sensitive data such as personally identifiable information and payment information and customize the masking settings as per your company’s requirements. The availability of the Data masking capabilities of EinsteinGPT varies by feature, language, and geography. Dynamic Grounding Generate AI prompts with business context securely from structured or unstructured data by taking advantage of multiple grounding methodologies and prompt templates that can be scaled across your organization. Secure Data Retrieval Allow secure data access and contextualize every generative AI prompt while retaining permissions and data access limits. Zero Data Retention and Data Control Salesforce does not retain prompts or outputs. Once content is generated, the model forgets both the input and the response. Eliminate toxic and harmful outputs Scan and evaluate each prompt and output for toxicity and empower employees to share only suitable content. Ensure that no output is shared unless a moderator or designated content approver accepts or rejects it and saves every step as metadata to leave an audit trail to promote compliance at scale. Enterprise Readiness and Future Outlook: Salesforce AI Cloud The outlook on Generative AI seems promising as it is predicted that it could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5% points over a 10-year period. These are remarkable numbers and therefore AI Cloud will propel businesses to new heights, with efficiency and productivity being the key differentiators. Key Salesforce AI Cloud Trends to Look Out for in 2026 Especially when with AI Cloud, Salesforce has created a user-friendly solution that generates AI prompts that rationalize data and ensure that the content provided is in complete alignment with an organization’s unique context. Intelligent CRM: CRM will be evolving into an autonomous, predictive partner for enterprises across the industry. Agentic AI: AI agents will handle and manage enterprise-wide workflows and decisions. Data Strategy Overhaul: Businesses will be focusing on clean, governed data that drives responsible AI success. AI-First Operating Models: It’s already evident with how AI is integrated into different CRMs but expect AI to be embedded across all functions. Closing Remarks As generative AI becomes an integral part of modern enterprise systems, it’s clear that trust and governance can’t be treated as an afterthought. These two are also crucial to your business because you cannot rely on one-off safeguards, or assuming native security features will cover every scenario in complex enterprise environments. However, with the help of Salesforce Trust Layer, you can integrate and use AI responsibly and still fit within existing security and compliance frameworks. This gives us an idea that AI adoption will accelerate, and enterprises need strong measures to protect customer trust and reduce risk without slowing progress. Therefore, to fully explore the potential of AI Cloud, connect with a trusted and certified Salesforce implementation partner. Our Salesforce AI services help marketing, sales, service, commerce, engineering, and IT teams work in providing scalable generative AI solutions that meet both business objectives and regulatory expectations. To learn more about how we can tailor unique scalable solutions for you by leveraging the power of GenAI, connect with an expert for Generative AI consulting services today! ▶ Watch Video

While the secret to understanding customers lies in your data, making sense of that data is a totally different ball game. Evolution in technology and concerns around user privacy have mushroomed new challenges for marketers to know their audience and deliver data-driven experiences. An AI-powered customer data platform (CDP) addresses these challenges and more. CDPs can connect with a single storehouse of data – one that is proprietary, trusted, and acquired with consent. Salesforce’s own CDP, Marketing Data Cloud, takes things up a notch. It puts marketers in control of the entire customer journey, allowing them to connect, unify, and act on data across all marketing touchpoints and enhance the customer experience across teams and departments – from sales, marketing, service, commerce, and more. Marketing Data Cloud from Salesforce accomplishes four primary functions: It connects. Connect all your customer data across apps, channels, and devices with out-of-the-box connectors, at scale. It harmonizes. Aggregate all your data into a single customer profile, autonomously. Data across multiple channels and teams all integrate seamlessly using configurable rules. It engages. Empower all departments with unified customer profiles and update them in real-time via AI-powered analytics. It delivers an experience. Data activated from Marketing Data Cloud drives real-time, tailored, timely customer experiences. In this article, we talk about eight use cases of how Marketing Data Cloud applies these aspects to resolve common challenges faced by marketers, along with their colleagues in sales, service, and commerce. From enhancing engagement to winning customer loyalty, these data-driven methodologies ensure a robust CDP can make every interaction count.  The Engagement Booster Engage your customers at the right moment with real-time data. Benefits: Better engagement with improved efficiency KPIs: Email Click-Through Rates, Conversions, Revenue Data Involved: Customer engagement data, web data, sales data, web and app visits, browsing history. CONNECT. CDP connects data from all sources within and outside of Salesforce. HARMONIZE. The customer's unified profile is created in the CDP. It includes all their engagement activity from across multiple channels and departments. And automatically updates the data in real time with every interaction. And if a customer opts in, CDP can automatically send personalized texts with tailored offers at the right time. ENGAGE. Geolocation data from a customer’s phone activates an engagement action. And when they walk into a physical store, a tailored offer is sent to their phone via the Salesforce messaging app to nudge them to make a purchase. EXPERIENCE. A customer is out shopping for a new smartphone that they have been eyeing for a while. To their surprise, they get a discount on the exact same product that they wanted to buy, right when they get to the aisle. The Smart Advertiser Make every dollar spent on ads count. Benefits: Higher Efficiency KPI: Return on Ad Spend Data Involved: Customer loyalty status, purchase history, case history, email interactions, browsing history, and geo-location history. CONNECT. CDP connects all customer data within as well as outside Salesforce – loyalty, purchases, case history, engagement data, demographics, and affinity data. HARMONIZE. CDP pulls out the customer’s unified profile and creates AI-powered segments.  Segment-level data insight from ad partners is incorporated to refine customer segments further for eg, customers looking for specific products and services. ENGAGE. CDP activates these segments on popular ad platforms to hyper-personalize ads for customers, all this while protecting the customer’s privacy. At the same time, CDP also suppresses ads to customers with unresolved service cases, customers who already purchased the item or returned it, and those unlikely to engage. EXPERIENCE. Customers view ads of products or upgrades, precisely what they had in mind and within their preferred price band. The Shopper Styler Drive Increase revenue with hyper-personalized e-commerce. Benefits: Higher Conversions KPIs: E-commerce Revenue Data Involved: Purchase history, browsing history, activity behavior, loyalty status, case history, and email interactions. CONNECT. CDP pulls data from all touchpoints between the customer and the brand such as purchase history, buying preferences, loyalty data, service engagement, website, and app engagement, and more. HARMONIZE. Leveraging the customer’s unified profile, CDP derives intelligent Insights on new metrics such as “propensity score” to predict the customer’s likelihood to buy a particular product. These insights enable marketers to make faster, data-driven, decisions. CDP can drive tailored shopping experiences and promote those products. ENGAGE. Commerce Cloud leverages insights from Data Cloud to provide tailored shopping experiences to the customer on their brand’s online store or app. And with the help of the customer’s propensity score, data points such as reward points, recent purchases, and recommended products are automatically served up. CDP can automatically activate relevant and timely actions in the customer’s journey. Actions like clicks and cart abandonment can initiate a background process that anticipates the customer’s needs and encourages action.   EXPERIENCE. When a customer visits their favorite mobile accessories brand’s website or app, they get personalized product recommendations. And if they abandon the cart before checkout (for whatever reason), CDP can automatically fire a reminder email with a discount incentive to nudge them to complete the order. The Website Winner Improve conversion with personalized experiences. Benefits: Increased engagement, higher conversions KPIs: Bounce rate, browsing history, average time spent on a product, session duration. Data Involved: Purchase history, engagement data, loyalty status. CONNECT. CDP draws together customer data across marketing, commerce, sales, and service interactions. HARMONIZE. After unifying all the customer data into a single customer profile, CDP identifies a customer’s past purchase behavior, including their recent purchases. CDP then places the customer in the post-sale segment focused on helping them to derive immediate value from their latest purchase.  ENGAGE. Based on the customer’s recent purchase data, CDP fires a personalized text via the Salesforce messaging app, with a link to the brand’s website to prompt them to learn more about the product and its usage. And as soon as the customer lands on the website, the page is dynamically populated with relevant how-to articles, care instructions, and other relevant and personalized content. EXPERIENCE When the customer clicks on the link to the website, they land on a webpage populated with relevant content based on their recent activity. This includes product-related articles, videos, images, and additional offers. The Cross-Seller Intelligent predictions for your customers’ next purchase. Benefits: More upsell and cross-sell opportunities, higher conversions KPIs: Sales, Product popularity, Average cart size Data Involved: Purchase history, browsing history, engagement data, loyalty status. CONNECT. CDP connects sales, loyalty, and service data to generate unified customer profiles and offers intelligent insights to reveal opportunities for cross-selling and up-selling based on the data. It can also suggest customer lifetime value (CLV), propensity scores, engagement scores, and more. HARMONIZE. CDP-powered insights create a new metric called affinity score which predicts a customer’s affinity towards other products. CDP then leverages this data to define new customer segments based on the insights. ENGAGE. CDP then activates this customer segmentation data across multiple customer engagement platforms. Customers get personalized emails, texts, tailored web and app experiences, and personalized ads on their preferred channels. EXPERIENCE. As customers browse an online store or app, personalized product recommendations are automatically served up. Customers can view these items and complete the purchase. The Insight Viewer Analyze marketing performance. Benefits: Optimized performance, Deeper Insights, Improved average time for ROI. KPIs: Product Views, Sales, ROI. Data Involved: Purchase history, cross-channel activity, Engagement, and Campaign performance. CONNECT. CDP connects data from all touchpoints across marketing, sales, service, and commerce, to create unified customer profiles. Analytics tools such as Tableau and Marketing Cloud Intelligence leverage this data to augment audience discovery and measurement. HARMONIZE. Marketing Cloud Intelligence helps marketers optimize campaigns and customer journey performance. Tableau provides deep customer insights to help teams discover new customer segments and behaviors that drive adoption and increase their lifetime value.  ENGAGE. CDP drives the wheel of optimization. Marketing Cloud Intelligence uses data from CDP to refine campaigns. Tableau serves up intelligent audience insights, identifying high engagement areas. These insights then flow back to CDP to drive hyper-personalization in every moment. EXPERIENCE. As customers enjoy their purchases, brands stay connected with personalized offers on their preferred channels. As data is being gathered and analyzed on the go, brands can measure and optimize campaign performance, discover new segments, and act on high-value actions. The Service Solver Convert service cases into happy customers. Benefits: Customer Satisfaction KPIs: Service Cases Created, Duration of open cases, CSAT (Customer Satisfaction Score) Data Involved: Purchase history, Sales data, Service Data, Engagement data, Browsing activity. CONNECT. CDP pulls in comprehensive service data like service cases, customer service feedback, lifetime value, loyalty data, and more.   HARMONIZE. Service data in CDP augments the customer segmentation process. This helps marketers refine their engagement strategy based on customer service interactions. ENGAGE. In a scenario where a customer has an open service case, CDP gets notified and pauses all marketing activities tailored for that customer until the case is closed.  Additionally, because CDP is receiving all service data, the customer service team has access to the customer’s profile enabling them to be aware of their problem as soon as they reach out to a service rep, and then quickly resolve the issue. EXPERIENCE. Customers get their order related issues resolved in a matter of minutes. When a new case is logged, the service team quickly reaches out to the customer, being aware of their order and having access to their unified profile. Not only does the customer get the issue resolved quickly, but they automatically get a personalized email or text with a 10% discount voucher for their next purchase to make up for the mistake. The Loyalty Earner  Reward customers at every stage.            CONNECT. CDP connects data from a brand’s loyalty system into a customer’s unified profile, along with marketing, sales, and service data. HARMONIZE. Based on interactions with customers in a particular segment, CDP automatically places them into the relevant loyalty tier giving them access to tiered marketing offers and deals automatically. ENGAGE. CDP activates this segment across multiple engagement platforms and customers in this segment automatically start receiving personalized content. The content (which includes product recommendations and offers) is linked to their loyalty status and encourages them to aspire to be in the next loyalty tier for further exclusive benefits such as rewards, discounts, preorders, and more. EXPERIENCE. A customer’s latest purchase of mobile accessories automatically moves them to the next tier of loyalty status. This gives them access to exclusive discounts and offers. It’s time to build your own customer data strategy, and if you have one, you can always refine it. Our extensive experience in Salesforce consulting services can help. With a robust CDP, marketing teams can connect every interaction throughout the customer journey with a unified source of actionable, real-time data. They can truly understand their audience and deliver personalized engagement that drives revenue and builds lasting relationships. And that’s not where the value of CDP ends. In fact, it is just the beginning. Every department and team across sales, service, and commerce can also benefit from the power of a CDP. Powered by Customer 360, Marketing Data Cloud unifies all customer data across all channels and departments to create a single, unified customer profile that is updated in real-time with every interaction. With a unified view of your customer, Marketing Data Cloud empowers marketing, sales, service, and commerce teams to make every moment count. With a robust Customer Data Platform, your business can interact with your customers not as disparate departments, but as one brand with one voice. A brand that understands and engages with confidence, relevance, and trust. Whether it is prompt Salesforce support, hyper-personalized product recommendations or hyper-segmented targeted advertising, with Marketing Data Cloud you can make every customer interaction count and unlock the true power of real-time customer data. Want to learn more? Connect with our Marketing Data Cloud specialist today.

Business leaders, lawmakers, academicians, scientists, and many others are looking for ways to harness the power of generative AI, and reduce the risks of Generative AI. This can potentially transform the way they learn and work. In the corporate world, generative AI has the power to transform the way businesses interact with customers and drive growth. The latest research from Salesforce indicates that 2 out of 3 (67%) of IT leaders are looking to deploy generative AI in their business over the next 18 months, and 1 out of 3 are calling it their topmost priority. Organizations are exploring how this disruptive technology of generative AI could impact every aspect of their business, from sales, marketing, service, commerce, engineering, HR, and others. Business Adoption Trends and Risk Perceptions While there is no doubt about the promise of generative AI, business leaders want a trusted and secure way for their workforce to use this technology. Almost 4 out of 5 (~79%) of business leaders voiced concerns that this technology brings along the baggage of security risks and biased outcomes. At a larger level, businesses must recognize the importance of ethical, transparent, and responsible use of this technology. Why Managing Generative AI Risk Matters to Enterprises A company using generative AI services & technology to interact with customers is in an entirely different setting from individuals using it for private consumption. There is an imminent need for businesses to adhere to regulations relevant to their industry.  Irresponsible, inaccurate, or offensive outcomes of generative AI could open a pandora’s box of legal, financial, and ethical consequences. For instance, the harm caused when a generative AI tool gives incorrect steps for baking a strawberry cake is much lower than when it gives incorrect instructions to a field technician for repairing a piece of machinery. If your generative AI tool is not founded on ethical guidelines with adequate guardrails in place, generative AI can have unintended harmful consequences that could back come to haunt you. Companies need a clearly defined framework for using generative AI and to align it with their business goals including how it will help their existing employees in sales, marketing, service, commerce, and other departments that generative AI touches. Ethical and Responsible AI as a Business Imperative A while back, Salesforce published a set of trusted AI practices that covered transparency, accountability, and reliability, to help guide the development of ethical AI systems. These can be applied to any business looking to invest in AI. But having a rule book on best practices for AI development isn’t enough; companies must commit to operationalizing them during the development and adoption of AI. A mature and ethical AI initiative puts into practice its principles via responsible AI development and deployment by combining multiple disciplines associated with new product development such as product design, data management, engineering, and copyrights, to mitigate any potential risks and maximize the benefits of AI. There are existing models for how companies can initiate, nurture, and grow these practices, which provide roadmaps for how to create a holistic infrastructure for ethical, responsible, and trusted AI development. With the emergence and accessibility of mainstream generative AI, organizations have recognized that they need specific guidelines to address the potential risks of this technology. These guidelines don’t replace core values but act as a guiding light for how they can be put into practice as companies build tools and systems that leverage this new technology. Guidelines for the Development of Ethical Generative AI The following set of guidelines can help companies evaluate the risks associated with generative AI as these tools enter the mainstream. They cover five key areas. Accuracy and Reliability Businesses should be able to train their AI models on their own data to produce results that can be verified with the right balance of accuracy, relevance, and recall (the large language model’s ability to accurately identify positive cases from a given dataset). It’s important to recognize and communicate generative AI responses in cases of uncertainty so that people can validate them. The simplest way to do this is by mentioning the sources of data which the AI model is retrieving information from to create a response, elucidating why the AI gave those responses. By highlighting uncertainty and having adequate guardrails in place ensures certain tasks cannot be fully automated. Safety, Bias, and Toxicity Mitigation Businesses need to make every possible effort to reduce output bias and toxicity by prioritizing regular and consistent bias and explainability assessments. Companies need to protect and safeguard personally identifying information (PII) present in the training dataset to prevent any potential harm. Additionally, security assessments (such as reviewing guardrails) can help companies identify potential vulnerabilities that may be exploited by AI. Honesty, Transparency, and Data Provenance When aggregating training data for your AI models, data provenance must be prioritized to make sure there is clear consent to use that data. This can be done by using open-source and user-provided data, and when AI generates outputs autonomously, it’s imperative to be transparent that this is AI-generated content. For this declaration (or disclaimer), watermarks can be used in the content or by in-app messaging. Human Empowerment and Responsible Automation While AI can be deployed autonomously for certain basic processes which can be fully automated, in most cases AI should play the role of a supporting actor. Generative AI today is proving to be a powerful assistant. In industries, such as financial services or healthcare, where building trust is of utmost importance, it’s critical to have human involvement in decision-making. For example, AI can provide data-driven insights and humans can take action based on that to build trust and transparency. Furthermore, make sure that your AI model’s outputs are accessible to everyone (e.g., provide ALT text with images). And lastly, businesses must respect content contributors and data labelers. Sustainability and Environmental Impact of AI Models Language models are classified as “large” depending on the number of values or parameters they use. Some popular large language models (LLMs) have hundreds of billions of parameters and use a lot of machine time (translating to high consumption of energy and water) to train them. To put things in perspective, GPT3 consumed 1.3 gigawatt hours of energy, which is enough energy to power 120 U.S. homes for a year and 700k liters of clean water. When investigating AI models for your business, large does not necessarily mean better. As model development becomes a mainstream activity, businesses will endeavor to minimize the size of their models while maximizing their accuracy by training them on large volumes of high-quality data. In such a scenario, less energy will be consumed at data centers because of the lesser computation required, translating to a reduced carbon footprint. How to Safely Integrate Generative AI into Business Operations Integrating generative AI Most businesses will embed third-party generative AI tools into their operations instead of building one internally from the ground up. Here are some strategic tips for safely embedding generative AI in business apps to drive results: Using Zero-Party and First-Party Data Businesses should train their generative AI models on zero-party data (data that customers consent to), and first-party data, which they collect directly. Reliable data provenance is critical to ensure that your AI models are accurate, reliable, and trusted. When you depend on third-party data or data acquired from external sources, it becomes difficult to train AI models to provide accurate outputs. Let’s look at an example. Data brokers may be having legacy data or data combined incorrectly from accounts that don’t belong to the same individual or they could draw inaccurate inferences from that data. In the business context, this applies to customers when the AI models are being grounded in that data. Consequently, in Marketing Cloud, if all the customer’s data in the CRM came from data brokers, the personalization may be inaccurate. Keeping Training Data Fresh, Labeled, and Bias-Free Data is the backbone of AI. Language models that generate replies to customer service queries will likely provide inaccurate or outdated outputs if the training is grounded in data that is old, incomplete, or inaccurate. This can lead to something referred to as “hallucinations”, where an AI tool asserts that a misrepresentation is the truth. Likewise, if training data contains bias, the AI tool will only propagate that bias. Organizations must thoroughly review all their training data that will be used to train models and eliminate any bias, toxicity, and inaccuracy. This is the key to ensuring safety and accuracy. Ensuring Human-in-the-Loop Oversight Just because a process can be automated doesn’t mean that’s the best way to go about it. Generative AI isn’t yet capable of empathy, understanding context or emotion, or knowing when they’re wrong or hurtful. Human involvement is necessary to review outputs for accuracy, remove bias, to ensure that their AI is working as intended. At a broader level, generative AI should be seen as a means to supplement human capabilities, not replace them. Businesses have a crucial role to play in the responsible adoption of generative AI, and integrating these tools into their everyday operations in ways that enhance the experience of their employees and customers. And this goes all the way back to ensuring the responsible use of AI – maintaining accuracy, safety, transparency, sustainability, and mitigating bias, toxicity, and harmful outcomes. And the commitment to responsible and trusted AI should extend beyond business objectives and include social responsibilities and ethical AI practices. Testing, Validation, and Continuous Monitoring Generative AI tools need constant supervision. Businesses can begin by automating the review process (partially) by collecting AI metadata and defining standard mitigation methods for specific risks. Eventually, humans must be at the helm of affairs to validate generative AI output for accuracy, bias, toxicity, and hallucinations. Organizations can look at ethical AI training for engineers and managers to assess AI tools. Feedback Loops and Ethics Review Councils Listening to all stakeholders in AI – employees, advisors, customers, and impacted communities is vital to identify risks and refine your models. Organizations must create new communication channels for employees to report concerns. In fact, incentivizing issue reporting can be effective as well. Some companies have created ethics advisory councils comprising of employees and external experts to assess AI development. Having open channels of communication with the larger community is key to preventing unintended consequences. The Future of Trusted and Responsible Generative AI As generative AI becomes part of the mainstream, businesses have the responsibility to ensure that this emerging technology is being used ethically. By committing themselves to ethical practices and having adequate safeguards in place, they can ensure that the AI systems they deploy are accurate, safe, and reliable and that they help everyone connected flourish. As a Salesforce Consulting Partner, we are part of an ecosystem that is leading this transformation for businesses. Generative AI is evolving at breakneck speed, so the steps you take today need to evolve over time. But adopting and committing to a strong ethical framework can help you navigate this period of rapid change.