AI/ML

Salesforce – a leader in the cloud CRM arena, has always been at the forefront of technology with more and organizations embracing cloud-powered solutions. Over the years, Salesforce has attained a significant market share and growth owing to its wide array of tools. Besides expanding its suite of tools and applications, this innovative platform also releases new updates regularly to cater to evolving market needs. This has positioned it to maintain its dominance in the enterprise software development market while empowering businesses to streamline operations, augment customer experiences while driving growth. The year 2025 is expected to be transformational for the users of Salesforce with the integration of AI into Salesforce remarkably changing the way businesses optimize, implement and leverage the platform. Let’s understand how AI is bound to impact Salesforce implementation strategies while driving efficiency, growth and innovation. To avail Salesforce AI services, make sure to connect with a reliable service provider. All About Salesforce Implementation? Salesforce implementation includes setting up the platform to suit the unique needs of an organization. This includes attuning the platform, migrating data, integrating it with existing systems, and training users to make the most of its capabilities. The goal is to align Salesforce with the process workflows of an organization and helping them streamline their operations while boosting efficiency thereby strengthening customer relationships. Companies can implement Salesforce by engaging their internal team or consider collaborating with a reputed Salesforce consulting partner. They may also adopt a hybrid approach that brings together in-house expertise with external consulting support. What are the Challenges Involved in Traditionally Implementing Salesforce? Customization Complexity: While Salesforce offers extensive customization options, over-customization can introduce complexity and might create technical glitch. Traditional implementation practices often fail to strike a balance between customization and sustainability. This makes future upgrades more challenging. Data Migration: Moving data from existing systems and integrating Salesforce with other applications can be a complex and time-intensive process. Traditional implementation approaches often struggle to maintain data precision, consistency, and smooth integration, particularly when handling large datasets from diverse sources. Training and Adoption: The success of a Salesforce implementation relies on strong user adoption and their training. Traditional methods often struggle to engage users, address resistance to change, and deliver relevant training to help users make the most of the platform. Scalability and Performance: As businesses expand and evolve, traditional Salesforce implementation methods may face challenges in ensuring scalability and performance. Growing data volumes, higher numbers of users, and increasingly intricate business processes often mandates additional resource allocation. Budget Constraints: Conventional methods of implementation often demand significant investment w.r.t time and money, especially for large-scale deployments. Striking the right balance among budgets, timeframe and desired outcomes might be difficult. This finally leads to budget overruns and delays. Key Benefits of AI-driven Salesforce Implementation Services Data-driven Insights: Salesforce's future offers immense potential for businesses across industries, driven by the integration of powerful technologies like AI and machine learning. With AI-powered advanced analytics, the platform extracts intelligent insights from the vast datasets stored within its CRM. These insights empower businesses to make intelligent decisions and optimize the allocation of resources effectively. Forecasting and Next Steps: AI-powered Salesforce implementation services enable businesses to move beyond analyzing past data and make precise predictions about future customer behavior. By leveraging machine learning models trained on historical data, AI uncovers patterns and factors that influence customer actions. This predictive capability helps businesses anticipate customer preferences and conversion potential. Optimizing the Sales Funnel: Salesforce implementation companies can transform the sales funnel by automating lead nurturing, qualification and prioritization. Tools such Einstein Lead Scoring automatically assesses leads based on several factors such as level of engagement, demographics, and more. This empowers sales teams to focus on high-value prospects with greater conversion potential. Einstein Opportunity Insights further augments the process by analyzing deal data and offering actionable suggestions to advance opportunities through the funnel. These insights include offering the most relevant content for sharing, deciding the right timing for outreach, and identifying the most effective communication channels. By leveraging these insights, businesses can simplify their processes, reduce attrition, and ensure faster conversions. Streamlining Processes: AI-powered Salesforce implementation services authorize organizations to automate mundane and time-consuming tasks, allowing teams to focus on strategic activities. For example, Salesforce Einstein Bots manage customer queries, account details, order tracking, issue resolution and more. This improves response times and customer engagement besides enabling human agents to concentrate on high-value interactions. Additionally, AI can streamline tasks such as data entry, lead assignment, and more, ensuring crucial data is captured and actions are executed at the right time. Boost Productivity: AI-enabled Salesforce implementation services significantly enhance productivity across business operations. By automating everyday tasks, offering smart insights, and streamlining processes, AI empowers teams to operate more efficiently. For example, Einstein Activity Capture records calendar events, emails and customer interactions, saving precious time on manual data entry. Similarly, Einstein Opportunity Insights prioritizes tasks and provides guided selling recommendations, enabling sales reps to concentrate on high-impact activities. Additionally, AI-driven projection and pipeline management tools assist sales leaders in optimizing resource allocation, identifying tailbacks, and making data-driven decisions to accelerate revenue growth. How is AI Enhancing Salesforce Implementation Strategies? Smarter Data Management: The foundation of any CRM platform is data but managing large sets of data can be intimidating. AI integration in Salesforce simplifies this process by: Data Cleansing: AI tools can be used to figure out and correct duplicate and incomplete entries to maintain data integrity. Predictive Analytics: By analyzing legacy data, AI tools help in anticipating customer behavior and preferences. Real-Time Insights: AI offers intelligent insights that enable teams to make informed decisions quickly. By enhancing data accessibility and precision, AI maximizes the value of Salesforce investments. Personalized Experiences: In the coming year, personalized interactions will be the norm that too at every touchpoint. With AI taking center stage, businesses can fulfill these expectations by leverage analytics and machine learning to create tailored experiences. AI-powered Salesforce implementation strategies now include: AI-Driven Segmentation: By analyzing customer behaviors, AI tools develop segments for targeted marketing efforts. Content Recommendations: Based on individual preferences, AI suggests relevant products and services. Proactive Support: AI-enabled chatbots manage regular queries and channel complex issues to human agents. This sort of personalization not just augments customer engagement and satisfaction but also fosters long-term retention and allegiance. Automated Processes: Automation is a key aspect of Salesforce AI integration, which enables businesses to eliminate redundant tasks, optimize resource utilization, and increase productivity. Key applications include: Lead Scoring: By using predefined criteria, AI evaluates leads thereby helping sales teams to focus on high-priority opportunities. Sales Forecasting: By predicting revenue trends, AI-powered models support effective resource planning. Workflow Automation: AI reduces manual effort and errors by streamlining processes such as email campaigns, task assignments, and more. Improved Association and Decision-Making: AI tools in Salesforce enable teams to collaborate more effectively by delivering real-time and actionable insights. The Bottom-line: In the years to come, AI is all set to revolutionize the way Salesforce implementation will be performed by organizations. AI-driven implementation will not just enable data-driven decision making but will also pave the way for customized solutions and efficacy. Organizations should seek Salesforce support from a reliable service provider to avail AI enabled implementation.

AI is undergoing a significant transition, with the accuracy and dexterity of specially designed autonomous agents replacing the broad scope of massive, general-purpose AI. It's more than just evolution of technology. It's about conceiving how machines can complement the workforce. Purpose-built agents are specially designed digital agents that are focused on a single task and perform it almost perfectly, whether that task is assisting salespeople in nurturing leads, coming up with campaign concepts for marketing teams, or answering customer support queries. They have capabilities few GPT-based systems can match – the capacity to act and complete tasks. Autonomous agents will transform the workplace. You've likely been both amazed by AI's potential and frustrated by some of the practical constraints of GPT-based systems at work if you've utilized generative AI to help generate an email copy or conceive a campaign idea. Because they were trained on publicly available data and information, they are unable to produce outputs that accurately represent your everyday reality because they are unfamiliar with your company and its customers. For instance, they are unable to help you with analytics on the performance of past campaigns or insightful information on open leads. Innovative new data platforms that gather, integrate, and connect all of the data points are already helping future-thinking businesses start to fill that knowledge gap. However, a second prerequisite must be fulfilled for AI to be genuinely useful in an organizational context. AI agents should be able to perform actions on behalf of humans. Autonomous agents accomplish this by fusing large action models (LAMs) with the natural language processing capabilities of LLMs. Language models that can carry out operations in other programs and systems are known as LAMs. Because LAMs are trained on data that is handpicked for task execution, autonomous agents can initiate a variety of actions on their own. Large Language Models (LLMs) and Large Action Models (LAMs): The Pillars of Autonomous AI In what ways do LLMs and LAMs collaborate? Consider a scenario where you want an AI agent to send a tailored SMS or WhatsApp message with a discount code for subsequent purchases to the first 50 customers who buy a new product. An LLM would struggle to accomplish this on its own. Indeed, it could produce the material, but action is needed to segment 50 customers and deliver each one a tailored message by leveraging organizational CRM data. This is where the LAM comes in. The LAM would leverage its function-calling abilities to make requests to perform certain tasks, such as an API call to a third-party system or querying the CRM to retrieve customer and product data. In the field of healthcare, an agent could assist patients in finding the best physician by identifying their symptoms, preferences, and location. It streamlines what is frequently a tedious and long-drawn process by identifying a time that works for the patient and physician and scheduling the appointment. In retail, an agent can answer common questions like "Where's my shipment?". AI agents can also launch targeted marketing campaigns and offer customer service replies at the precise moment when the customer is most responsive. In financial services, agents can examine the client's spending patterns, analyze past investments, and financial objectives, and analyze market movements to recommend modifications in their existing portfolio enabling money managers to focus on what they do best – deliver exceptional service to their clients instead of spending time making sense out of their data. AI agents can significantly augment the abilities of an organization's workforce. Imagine a world with zero customer service wait times, apps that adapt to user activity, and AI sales coaches who are always assisting sales reps in closing deals. Autonomous agents collaborate with existing teams. A future in which workers collaborate with agents to provide better results for both customers and businesses is already here. These solutions increase productivity and free up the workforce to enable them to focus on strategy and creativity.  People can spend time solving complex problems and refining their strategies when AI handles the specifics, expanding the realm of what is feasible and igniting innovations across industries. This collaboration between AI and human creativity ushers in a new era of efficiency, productivity, and innovation. LAMs – Leading the Next Wave of Autonomous AI With the advent of AI agents that can act as skilfully as they can converse, generative AI has formally begun its second act. Through the use of external tools and access to knowledge beyond their training data, these autonomous AI agents are able to perform tasks that either augment their existing tasks or act on their behalf. We believe that AI assistants and AI agents will be the two main forms of autonomous AI. Both have two key characteristics in common. Agency is the capacity to take meaningful action, sometimes completely independently, to achieve a predetermined objective. The ability to learn and change over time, although in different ways, is the second. Artificial intelligence assistants will adjust in creative ways to better understand a specific user for whom they are meant to provide support. AI agents will learn shared processes, best practices, and much more to better complement a particular team. In other words, AI agents are designed to be shared at scale, whereas AI assistants are designed to provide a personal touch. Both hold the promise of opening up new opportunities for businesses. Creating a tangible impact With applications ranging from customer care to IT support to sales enablement, agents and assistants collectively represent a revolutionary way of working. Consider, for instance, a jam-packed schedule of sales meetings, including video calls to in-person trips around the world. Sales professionals in almost every business live in a hectic reality. And that reality is made even more complex by the need to carefully go through vast amounts of CRM data that is created across every interaction. Now imagine an AI assistant, that accompanies you to every call and meeting, keeps track of all relevant information automatically organizes it perfectly, and responds to questions about it anytime anywhere. Wouldn't scheduling be a lot simpler? Knowing that their only goal and task was to focus on building stronger customer relationships, how much more productive would a sales professional be? It's fascinating to imagine how all of this would operate. With a focus on privacy, of course, your AI assistant would be there at every meeting, following the discussion from one point to the next and gaining a greater grasp of your needs, behavior, and how you work. Your AI assistant will either assign higher-level subtasks to an AI agent or invoke an action for a specific subtask, such as querying a knowledge article, as it recognizes the need to complete specific tasks, such as retrieving organizational information or summarizing meeting notes. The challenges ahead There will be technological, sociological, and even ethical obstacles as we step into the future of autonomous AI. The issue of memory and perseverance is the most important of these. AI assistants can get to know us well, including our daily routines, peculiarities, and long-term goals, if we so want. Like our relationships with friends and coworkers, every new engagement should be built upon a foundation of prior experiences. However, it's not easy to accomplish this using the AI models available today. Initiatives to create autonomous AI systems with rich, reliable memory and attention to detail are hampered by variables like computation and storage costs, latency issues, and even algorithmic constraints. Humans are exceptionally skilled at distilling minutes or even hours of material into a few main points, whether in a meeting, lecture or even a discussion with someone. Similar skills will be required of AI assistants. Trusting the results of an AI is even more crucial than how well it remembers things. Despite its incredible potential, generative AI is still frequently constrained by issues like hallucinations and toxicity concerns. Autonomous AI's inclination for continuous learning will help alleviate this issue because hallucinations often result from knowledge gaps, but more work needs to be done along the way. The ethical issues will be just as complicated. Will the development of autonomous AI systems, for example, necessitate the creation of whole new standards and protocols? How should teams and AI agents communicate with one another? How should they establish confidence in a particular course of action, settle disagreements and ambiguities, and foster consensus? How can we assess their risk tolerance or how they handle competing objectives, such as time against money? Regardless of their values, how can we make sure that their choices are open and simple to examine if the results don't suit us? In short, where is the locus of accountability in an era of such advanced automation? One thing is certain: humans should always be at the helm of affairs. They should decide when and why digital AI agents are deployed. Autonomous AI can be a powerful addition to almost any team, but only if humans are fully aware of its presence and have complete authority. Furthermore, interactions with all types of AI should be clearly designated as such, with no attempt to muddy the distinction between humans and machines. Conclusion We are still at an early stage as far as enterprise AI is concerned. There's a lot to be done, both in terms of tech innovation and establishing guidelines to ensure AI’s AI has a positive and fair influence on everyone. However, with so many obvious advantages now becoming apparent, it's important to take a moment to understand how significant this present phase of AI is turning out to be. Want to know more about Salesforce AI services for your business? Talk to global Salesforce implementation partner today.

Agents are assistive and autonomous software systems. Based on user input or environmental conditions, they reason, plan, and take action to achieve given tasks or goals. They are like intelligent digital assistants, equipped with the aggregated knowledge and experience of human experts, and access to all relevant data. Agents are set to become ubiquitous across every area of our lives and to profoundly transform how businesses operate and interact with customers. For example, a service agent can act as your company's most knowledgeable technical support representative, available 24/7 to handle every request. A marketing agent, much like a self-driving car, can use "sensors" (real-time data) to detect changing business conditions and respond proactively (adjust pricing, launch a campaign, and so on). Agentforce, an AI initiative from Salesforce, was announced on August 28, 2024. Described as part of “the Third Wave of AI,” it moves beyond copilots to introduce intelligent agents designed for greater accuracy and reliability, aiming to enhance customer success. This launch marks a practical step toward integrating artificial intelligence into enterprise workflows. Created to support employees and simplify operations, Agentforce helps businesses manage customer interactions and internal processes more efficiently. By automating routine tasks and offering useful insights, Agentforce aims to boost productivity, improve customer service, and support business growth. Agentforce Agents use a multilayered approach to enforce guardrails: Einstein Trust Layer: The Einstein Trust Layer enables agents to use LLMs in a trusted way, without compromising company data. It uses a secure gateway, data masking, toxicity detection, audit trails, and more to control LLM interactions. Instructions: When defining an Agentforce Agent, you can use natural language to provide clear instructions, including what to do and what to avoid, effectively setting the guardrails for its behavior. Shared metadata: Salesforce metadata defines overarching rules that are enforced regardless of whether the data is accessed from traditional applications or agents. This includes permissions, sharing models, validation rules, and workflow automation to guarantee data security and adherence to business practices. Agent Analytics: This observability tool provides insights into agent and action performance, usability, and reliability, enabling you to identify areas for improvement. AI Test Center: A unified testing framework, the AI Test Center supports batch testing for agents, prompt templates, retrieval-augmented generation (RAG), and model use cases. With just a few clicks, companies can scale their workforce on demand using the robust capabilities of Agentforce’s AI agents.  These digital agents can analyze data, make informed decisions, and handle tasks such as responding to customer inquiries, qualifying sales leads, and optimizing marketing campaigns. Here’s what distinguishes Agentforce Agents: Trustworthy: With the Einstein Trust Layer, your data remains secure, utilizing the same metadata, permissions, and sharing models you are accustomed to in traditional Salesforce applications. Powerful: Agentforce Agents leverage industry-leading Salesforce apps to create transformative experiences across sales, service, commerce, marketing, and various other sectors. Data-Driven: By tapping into all relevant data through Data Cloud, Agentforce Agents deliver more accurate and meaningful outcomes. Customizable: Utilizing a suite of low-code tools—such as Agent Builder, Prompt Builder, Model Builder, and Flow Builder—you can easily build, customize, test, and manage these agents. Key features and benefits of Agentforce: 1. Autonomous AI Agents: Agentforce is comprised of self-contained AI agents that can perform tasks independently, without constant human intervention. These agents are trained on large datasets and leverage machine learning to learn and adapt over time. 2. Task Automation: Agentforce can automate a wide range of tasks across various departments, including customer service, sales, marketing, and commerce. This frees up employees to focus on more strategic and complex work. 3. Intelligent Insights: It provides valuable insights and recommendations based on data analysis. This enables businesses to make data-driven decisions and identify opportunities for improvement. 4. Natural Language Processing (NLP): It can understand and respond to natural language queries, making it easier for employees and customers to interact with the system. 5. Integration with Salesforce Ecosystem: Agentforce seamlessly integrates with other Salesforce products, such as Sales Cloud, Service Cloud, and Marketing Cloud. This allows for a unified and cohesive experience. 6. Scalability: Agentforce can scale to meet the growing needs of businesses, ensuring that it remains effective as the organization expands. 7. Customization: Agentforce can be customized to fit the specific requirements of different industries and use cases. This flexibility allows businesses to tailor the solution to their unique needs. 8. Security and Privacy: Agentforce is built with robust security measures to protect sensitive data. Salesforce also adheres to strict privacy regulations to ensure that customer information is handled responsibly. Pre-built Agentforce Agents Here are some pre-built Agentforce Agents for your business needs: Service Agent The Service Agent efficiently handles customer inquiries around the clock, using reliable data to provide accurate and personalized support. It can be quickly set up with templates or customized with minimal coding, ensuring a smooth implementation process. In cases where human intervention is required, the agent seamlessly escalates the issue while maintaining high standards of data security. Sales Development Representative (SDR) Agent The SDR Agent engages potential customers 24/7, answering product questions, managing tasks, and scheduling meetings for sales representatives. It offers accurate, data-driven responses and is versatile, interacting across various communication channels and languages, ensuring comprehensive customer engagement. Sales Coach Agent The Sales Coach Agent provides sales representatives with personalized role-playing scenarios to practice pitching, handling objections, and negotiating. It gives feedback on performance and suggests areas for improvement, helping to refine sales techniques. By analyzing deal outcomes, this agent can measure the effectiveness of training and provide insights for continued growth. Personal Shopper Agent The Personal Shopper Agent enhances the customer shopping experience by offering tailored product recommendations. It interacts with customers on your website or through messaging apps, assisting them in finding products and making purchases by suggesting relevant items, increasing customer satisfaction and conversion rates. Campaign Agent The Campaign Agent simplifies marketing efforts by generating campaign briefs, identifying target audiences, developing content, and creating customer journeys. It continuously monitors campaign performance and provides actionable insights to optimize results, ensuring your marketing strategy remains effective and data-driven. Which Agentforce will you build?  Agentforce is a flexible platform that allows you to create custom agents using existing Salesforce tools. This enables you to adapt agents to fit various business needs. Here are a few: Healthcare Agent: Interacts with patients, healthcare providers, and payers to answer questions, provide information, and take action. Banking Agent: Analyzes data, assists customers, and offers personalized service in retail, commercial, and investment banking. Retail Agent: Shares campaign information, reaches out to customers and resolves issues for fashion, grocery, and convenience stores. Operations Agent: Helps operations teams manage plans, resources, and progress. CX Agent: Analyzes customer feedback, suggests ways to improve customer satisfaction, and manages omnichannel experiences. Analytics Agent: Provides data insights, creates visualizations, and recommends data-driven actions. IT Agent: Monitors security threats, shares network information, and resolves customer and employee support issues. Finance Agent: Shares insights on financial reporting and risk assessments, detects fraud, and addresses compliance-related inquiries. Agentforce’s availability and price  Agentforce for Service and Sales will be generally available on October 25, 2024, with select components of the Atlas Reasoning Engine launching in February 2025. Pricing for Agentforce starts at $2 per conversation, with volume discounts available. At $2 per conversation, Salesforce anticipates a significant ROI for customers. Agentforce agents offer a more cost-effective solution by handling routine tasks, freeing up human agents to focus on more complex inquiries. Summary: How Agentforce Agents are transforming business and application development Agents are set to become ubiquitous in every area of our lives. They can reason, orchestrate tasks, and take action, delivering personalized experiences at scale. By combining the language and reasoning capabilities of LLMs with software building blocks, they are transforming how businesses operate and how software is built. Agentforce Agents are leading this transformation with key differentiating characteristics, including: Trusted. Agentforce protects your data using the Einstein Trust Layer and the same metadata, permissions, and sharing models as traditional Salesforce applications. Powerful. Agentforce Agents make use of industry-leading Salesforce applications to deliver transformative experiences across sales, service, commerce, marketing, and industries. Grounded in unified data. Agentforce Agents deliver more accurate and relevant outcomes by grounding AI in all the relevant data made available and unified by Data Cloud. Low-code tools. Agentforce Agents can be built, customized, tested, and managed using a set of low-code tools including Agent Builder, Prompt Builder, Model Builder, Flow Builder, and more. In conclusion, Agentforce is the powerful integration of Humans + AI + Data + Actions, transforming how businesses operate. By combining assistive and autonomous AI agents, employees are empowered to focus on high-value tasks, while AI handles routine work and escalates when necessary. Access to the right data through Data Cloud ensures that agents are intelligent, secure, and scalable, making them capable of delivering dynamic customer and employee experiences.  Finally, Agentforce agents aren’t just passive bots—they take meaningful action across systems, driving efficiency and completing tasks like drafting emails, creating close plans, and initiating customer nurture cadences. This blend of human expertise, AI capabilities, data access, and actionable insights ensures businesses can work smarter and faster.

Consider the following scenario: you've recently searched for running shoes and now you're seeing adverts for them everywhere. Or you just searched for a flight to a holiday destination. And now you see travel ads every time you open an app or a website. All this is powered by Artificial Intelligence. This blog provides valuable insights into the disruptive power of AI in cloud computing. Learn how this dynamic partnership introduces a new breed of solutions and transforms operations. AI and Cloud Computing AI and cloud computing have collaborated seamlessly in recent years due to their shared goal of automation. AI automates tedious tasks, allowing IT teams to focus on the development of cutting-edge solutions. AI however needs a strong foundation to thrive. Cloud computing, on the other hand, provides the ideal environment for AI apps to thrive. Why? Because the Cloud provides the muscle (computing power), data storage space, and flexibility that AI algorithms require. Democratizing AI Development: Codeless AI One of the biggest promises of AI is that literally anyone can create and deploy AI models. It needs no prior computer science expertise. AI service providers prioritize user-centric growth, with tools such as large language models (LLMs) like ChatGPT at the forefront. These user-friendly interfaces enable consumers and small enterprises to exploit the power of AI without relying on large technology organizations. It’s no surprise therefore that organizations are scrambling to embrace this technology, Generative AI: Adding Value to AI Training and operating Generative AI models require massive computational power, which the Cloud excels at due to its unparalleled scalability. According to research, more than 70% of cloud environments currently use managed AI services such as Azure OpenAI, SageMaker from Amazon, Vertex AI from Google, and Salesforce's Generative AI for CRM. This indicates that cloud-based AI services are witnessing an unprecedented growth spike. But there's a catch: despite impressive AI adoption, 32% of firms are still experimenting. This means that many organizations still need to scale their AI deployment. The key question is: Is your business geared up to leverage the power of AI? How Has AI Transformed Cloud Computing? Apart from automating processes, AI has evolved into a versatile tool for Cloud users, capable of data management, data security, data analysis, and informed decision-making. Its intrinsic ability to employ machine learning and derive interpretations of data-driven insights improves the efficiency of various operations, resulting in significant cost savings for the organization. Businesses that deploy AI on the cloud provide customers and users with intuitive and connected experiences. Machine Learning Models and Cloud Systems Machine learning models consist of massive datasets that train algorithms, which can be structured, unstructured, or raw, and require powerful CPUs and GPUs. Cloud computing also offers Machine Learning related apps including serverless computing, batch processing, and more. These services extend the potential of AI in the Cloud by offering scalable and robust infrastructure. The emergence of AI models has set the ball rolling  for cloud users to explore new opportunities and build the foundation for accelerated growth: Here's how AI has affected the Cloud Computing domain: Automation and Efficiency Cloud-based AI can help automate operations, reduce human errors, improve efficiency, adopt cost-cutting initiatives, and boost overall productivity. Accelerating AI applications in cloud environments enables customers and users to have more intuitive, integrated experiences. Data Insights and Analysis The coming together of Artificial Intelligence and cloud computing ushers a new era of organizing and harmonizing huge datasets, providing realistic blueprints for future cloud migrations. Processing such enormous amounts of data requires very powerful CPUs and GPUs. Only by combining private, public, and hybrid clouds with AI algorithms that control cloud computing can we untangle the complexities and reveal hidden insights. Intelligent Automation for Traffic Generation Today, organizations are forging ahead by using sophisticated automated bots and strategies that drive traffic. Choosing this path opens up more options for growth. Collaboration with a Salesforce consulting partner gives you access to a team of cloud professionals that can help you negotiate the complexity of cloud application development and harness the cloud's revolutionary power in your business. The Potential of AI in Cloud Computing Predicting the current market for AI in cloud computing represents a quantum leap. This duo is undeniably a potent combo that promotes robust security, adaptability, scalability, and cost-effectiveness for enterprises to thrive. Flexibility at Your Fingertips The need for cloud-based apps is increasing as more employees desire remote work environments. With the ever-increasing demand for flexibility and security, businesses are increasingly developing cloud apps that incorporate artificial intelligence capabilities. These applications provide durability, improved customer experience, and cost savings. Security, Reliability, and Scalability Security and reliability are two key components of a cloud service. Cloud-based AI improves your security by analyzing vast volumes of data in real-time to detect and mitigate security risks, assuring the protection of your data and systems. Cloud-based artificial intelligence brings a pay-as-you-go model, making cutting-edge AI accessible to enterprises of all sizes. This allows you to scale your AI systems efficiently and cost-effectively, maximizing your ROI. AI Apps Powering Up Cloud Computing Cloud computing provides businesses with flexibility and scalability, but artificial intelligence adds a new level of power. Here are some significant applications of AI in cloud computing, including an example of Salesforce Generative AI: Automating Workflows (AI as a Service – AIaaS) Popular Cloud platforms such as Google Cloud and Azure provide AI as a Service, which allows you to train your large language models for automation tasks. For example, a retail business can leverage AI as a Service to automate product recommendations for customers across their ecommerce and retail stores. AI-Powered Customer Service (AI Chatbots) Chatbots leverage natural language processing (NLP) capabilities to engage with users. They offer 24/7 customer support, answer commonly asked questions, and collect important customer data. These chatbots are hosted on cloud platforms, which provide the resources necessary for their smooth functioning AI-powered Business Intelligence Business Intelligence also received a boost with AI. It automatically captures market data, analyzes customer behavior, and discovers competition trends. Cloud computing delivers the processing capacity that AI models require to translate data into actionable insights. Salesforce Generative AI Salesforce also entered the Generative AI revolution, launching its own AI for CRM to give organizations an extra advantage in augmenting customer experiences. Salesforce Sales GPT is a cutting-edge innovation that uses artificial intelligence to send personalized emails, update product descriptions, and deliver lead qualification scores. AI in Cloud Computing: Key Benefits For Businesses The Automation Advantage Automation is the foundation that supports the interaction between cloud computing and AI. Modern AI technologies, powered by machine learning algorithms, automate many operations, enhancing IT workers' efficiency and allowing them to focus on more creative and strategic development. According to a Forbes report, 92% of organizations believe cloud computing plays an important role in enabling AI-powered automation in conventional business processes. Cloud technology is energizing the industry with its comprehensive algorithm distribution methods, paired with the experience of top cloud service providers delivering a wide range of AI-focused solutions and services. AI-powered chatbot assistants are one of the best examples of AI-powered automation, allowing businesses to fast-track customer interactions. Augmented Cloud Security With AI Cloud security has always been a cause of concern for businesses. But artificial intelligence is changing that perception. AI-powered security solutions serve as diligent watchdogs, analyzing huge volumes of data to detect suspicious activity before it turns into a full-fledged intrusion. The best part about AI is that it does more than just scan for threats, it also learns from them. Every intrusion attempt improves the system's intelligence and ability to deal with new cyber threats. This means that your sensitive data and apps are constantly and proactively protected. Edge and Computing AI Many IT professionals want to process data across systems at lightning speed the minute it is generated. This is due to edge computing and AI. Edge computing enables data processing closer to its source, reducing any lags in data transfer. AI algorithms ensure real-time data analysis without dependence on the cloud. Analyzing Customer Behavior For Smarter Decisions Making AI leverages machine learning models to analyze data in real time to give you a holistic view of your customers. With a better understanding of their needs and preferences, you can personalize engagement to maximize impact. By analyzing large datasets, AI can predict future trends and customer behavior, allowing you to make more informed decisions. Conclusion Artificial Intelligence and cloud computing are here and now. They are tools that all businesses regardless of size and industry can adopt to transform themselves.  Automating mundane tasks, personalizing customer experiences, and leveraging data to drive growth is the dream of any business. With Girikon as your Salesforce development partner, that vision is within reach. Our certified Salesforce consultants will guide you through the exciting potential of AI and cloud computing, assuring a smooth transition and maximum return on investment.

Businesses have a never-seen-before opportunity to learn more about their operations, markets, and customers by leveraging the humongous amounts of data aggregated from a variety of sources – apps, software, websites, and social media. The need to dive deeper into and derive insights from this data has never been greater. Legacy business intelligence and analytics products use structured, relational databases as their underlying technology. Relational databases lack the agility, speed, and deep insights required to turn data into value. Salesforce has transformed business intelligence technology by taking a novel approach to analytics, combining a non-relational approach to diverse data forms and types with advanced search capability, an engaging interface, and an intuitive mobile-friendly experience. Salesforce's Einstein Analytics Platform enables businesses to explore their data quickly without relying on data scientists, complex data warehouse schemas, or monolithic resource-intensive IT infrastructures. Legacy Business Intelligence (BI) tools restrict an organization's agility, and their application is limited to IT and analysts. Interestingly, while Business Intelligence tools have become more sophisticated over time, the core architectural approach to BI and analytics has largely remained unchanged. When an organization sets out to investigate an issue or question, the BI team responds by creating a relational database or data warehouse. Data warehouses comprise relational databases that add and store data in rows and columns, with each piece of information stored as a value in the table. Relationships across tables develop into schemas. Every fresh infusion of data expands the schema by adding new rows and dimensions. Once the structure is established, it is sacrosanct and cannot accommodate new data; adding new data necessitates the creation of a new schema from the ground up. The relational database paradigm remains effective for a wide range of applications, particularly transactional activities involving highly organized data. However, during the last decade, developments in technology, data volume and diversity, and dynamic markets have created a chasm between historical business intelligence and analytics capabilities based on classic relational database design and today's business requirements. The relational database model poses a number of issues in today's corporate landscape: User  Challenges The model limits agility. The waterfall nature of traditional Business Intelligence acts as a deterrent for discovering new ways of doing business, restricts team members' ability to challenge existing processes, and prevents teams with the most access to customers and the market from invoking their curiosity and asking their own questions for exploring innovative modeling techniques to improve the business. It is not representative of the way in which users explore information. Traditional Business Intelligence projects do not have the flexibility to refine the user query or add new data for context. Users ask a question and then wait weeks or even months for an answer; if they learn that the initial question was incorrect, the schema build-out must begin all over again. Another limitation of traditional BI is that it pre-aggregates the data which limits insights. It forces compromise. A typical BI setup balances expected queries and performance. Compromise leads to discontent. For instance, data is rolled up to a higher granularity to improve query efficiency, but this precludes users from answering second or third-order queries. They must then return to IT to figure out the solution or utilize an alternative tool to solve their questions. Business  Challenges The model slows down the business. Creating a BI schema can take weeks or even months depending on its size and complexity. On top of that, this does not include the time internal users must wait in line for BI or IT resources to become available. This delay indicates a poor time to value for BI investments; and imposes severe constraints on the business, which frequently relies on BI insights to move forward proactively which can hamper its ability to act quickly. It is resource-intensive. The current setup of designing BI solutions necessitates an army of professionals from architects and business analysts to data scientists and project managers to manage an organization's BI requirements. Because businesses rely heavily on BI, these teams are frequently well rewarded and in high demand. Pivot business intelligence on its head for agile, end-user discovery. In recent years, a number of new solutions have attempted to address the issues raised above. Many of them, however, have continued to rely, at least partially, on the same design and technological approaches that created the problems in the first place. One example of an emerging innovation is the usage of columnar or in-memory databases, which BI companies have implemented during the last decade. While they made progress, the relational model and its limitations remained a hindrance. Salesforce, on the other hand, has created and launched an analytics platform that challenges traditional business intelligence. The Einstein Analytics Platform rejects most of the preconceived concepts of data warehousing and database design, instead adopting a "Google-inspired" approach to business analytics. It includes a proprietary, non-relational data store, a search-based query engine, powerful compression methods, columnar in-memory computation, and a fast visualization engine. The  Einstein Analytics Platform combines the complexity of heterogeneous data, the fluidity of questions and problems users are trying to solve, and the end user’s need for exploring data with agility, all without any restrictions on time and information. Einstein Analytics was architected from the ground up to allow enterprises to quickly find value in data. The platform was built first for a native mobile app, allowing users to rapidly find answers and take action using their smartphones. Technology principles underlying the Einstein Analytics Platform. Agility Einstein Analytics does not differentiate between data types. It onboards data by embracing any data structure, kind, or source and making it available quickly, eliminating the need for a lengthy ETL procedure. Speed Heavy compression, optimization methods, multi-threading, and other techniques enable extremely fast and highly efficient queries on massive datasets. Search-based exploration It uses an inverted index to search data similar to Google search which provides query results in seconds. Actionability When a user gains insight or makes a key decision, they may immediately take the next best action straight from within Einstein Analytics. Columnar, in-memory aggregation In Einstein Analytics, quantitative data is stacked up in a columnar store in RAM in the Salesforce Cloud rather than the row structure of a relational database on disk. Interactivity Fast, intuitive visualization encourages user adoption and contextual understanding, offering genuine self-service analytics to all business users. Open, scalable cloud platform Einstein Analytics is an extensible platform with easy-to-use APIs and its scalable architecture compliments existing BI systems and allows businesses to have deep relationships with third-party tools and systems. It is also deeply integrated with Salesforce so you can see your Sales Cloud and Service Cloud data like never before, collaborate, and take action from within Salesforce. Mobile-first design Einstein Analytics is an open, scalable, and extendable platform. Einstein Analytics' architecture, which includes simple APIs, allows for extensive integration with third-party applications and complements existing BI systems. It is also deeply linked with Salesforce, allowing you to see your Sales Cloud and Service Cloud data like never before, collaborate, and take action directly from Salesforce. Security The Einstein Analytics Platform is built on Salesforce's tried-and-true, multilayered approach to data availability, privacy, and security, with the added benefit that data on the Salesforce platform does not need to leave Salesforce servers to be available for analytics. A unique approach to Business Intelligence that offers faster time to value. In order to provide an open, agile, self-service solution for enterprise business intelligence, Salesforce has brought together a number of unique approaches, including a non-relational inverted index data store, a quick and potent query engine, an intuitive and compelling visualization, mobile-first technology, and the trusted, scalable, high-performance power of the cloud. Given that numerous companies have made significant investments in business intelligence technology, Salesforce developed Einstein Analytics to enhance current offerings, facilitate seamless integration with external data tools, and allow businesses to easily tailor their analytics programs. The goal of enterprises using BI solutions to accelerate time to value is supported by this new BI analytics platform. Additionally, Einstein Analytics facilitates enterprise-wide adoption, supports a unified data governance strategy, and frees IT teams from labor-intensive and low-value data retrieval and preparation tasks so they can concentrate on more strategic endeavors. The open Einstein Analytics Platform positions Salesforce and its partners to continuously innovate and add layers of intelligence to help business users gain insights even faster, through automated analytics, as the world enters the third phase of computing — from today's systems of engagement to tomorrow's systems of intelligence. The basis for true business intelligence in the future is Einstein Analytics, which is quick, flexible, perceptive, and capable of not just capturing past customer and business behavior but also anticipating future trends. If you want to harness the true power of business intelligence for sales, marketing, and customer service, connect with a trusted Salesforce Consulting partner. Our certified Salesforce consultants can empower you with the tools and insights aligned with your business needs and help you get started. To find out more, schedule a free Salesforce Einstein Analytics demo today.

In June 2023, the world’s foremost Customer Relationship Management (CRM) product company announced the launch of AI Cloud, a path-breaking enterprise AI solution. This dependable, open, and business-ready platform is intended to boost organizational productivity by embedding generative AI experiences into all Salesforce apps. This significant achievement demonstrates Salesforce's continued commitment to trusted AI, as well as its ambition to enable businesses regardless of size and industry to digitally transform and provide a 360-degree view of their customers. 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. Generative AI Across Salesforce Products Salesforce-built models in AI Cloud enable new capabilities in Salesforce's marquee products – Salesforce Data Cloud, Mulesoft, Tableau, and Salesforce Flow. Einstein GPT in CRM Einstein GPT is the next generation of Einstein, Salesforce's AI engine, which now makes over 210 billion AI-powered predictions per day. By merging proprietary Einstein AI models with ChatGPT or other leading large language models, customers may use natural-language prompts on CRM data to trigger powerful, real-time, tailored, AI-generated content.  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 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. Einstein Trust layer What is the key differentiator of AI Cloud? Salesforce is promoting Einstein Trust Layer, a cutting-edge moderation solution that prevents text-generating algorithms from storing sensitive data such as consumer orders and contact information. The Einstein Trust Layer is a powerful set of features and safeguards that protect your data's privacy and security, increase the safety and accuracy of your AI output, and encourage responsible AI use throughout the Salesforce ecosystem. The Einstein Trust Layer, which includes capabilities such as dynamic grounding, zero data retention, and toxicity detection, is intended to let you harness the power of generative AI while maintaining your safety and security standards. A rising number of global corporations have prohibited or restricted the usage of generative AI, such as ChatGPT, citing privacy concerns. Einstein Trust Layer is tailor-made for such enterprises that have stringent compliance and governance constraints that prevent them from adopting generative AI tools. The first question that arises in everyone's mind is how much can we trust generative AI. The Einstein Trust Layer is purpose-built around trust and security and designed to enable these enterprises to approach these new technologies safely and securely. The Einstein Trust Layer acts as a bridge between an app or service and a text-generating model. It detects when a prompt may include sensitive information and automatically deletes it before it reaches the model. This layer can also screen for toxicity (eg. racism or other types of discrimination), whether in a prompt or the model's response. Users who link third-party models to AI Cloud such as Google's Vertex AI can use Einstein Trust Layer. Salesforce's partnership with OpenAI ensures cooperative content moderation by leveraging OpenAI's safety tools and the Einstein Trust Layer. Salesforce is providing a set of prompt templates and prompt template building tools to set AI Cloud apart from other managed AI service offerings available today. The Einstein Trust Layer’s optimized AI prompt templates leverage harmonized data to contextualize outputs generated by the models in alignment with the organization’s needs, improving the quality and relevance of the created content. The Einstein Trust Layer reduces the time and cost to adapt a generative AI model for a particular use case. For instance, a customer could design a template that instructs a model to draft emails in accordance with a particular style, or one that retrieves specific information from a Salesforce record. AI Cloud marks a fundamental shift in the automatic creation of email content – one that is grounded in CRM data. AI Cloud represents the powerful combination of data, customer relationship management, and AI. As prompts become smarter and better, AI Cloud is poised to become an invaluable tool for businesses delivering greater value to customers across the Salesforce technology stack. Trusted AI begins with secure prompts. A prompt is a series of instructions that guides a large language model (LLM) to produce relevant results. The more contextual the prompt, the better will be the outcome. The Einstein Trust Layer allows you to safely enter AI prompts with context about your business while its data masking and zero data retention capabilities ensure the data's privacy and security when delivered to a third-party LLM. Seamless privacy and data controls. Utilize the scale and cost-effectiveness of third-party LLMs while ensuring your data's privacy and security at every stage of the generating process. Data Masking Before providing AI prompts to 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. Your data is the real product. Salesforce allows customers complete control over the use of their data for AI. Whether you use Salesforce-hosted models or third-party models such as OpenAI, AI Cloud does not retain any context. Once the output is generated, the LLM forgets both the prompt as well as the output. 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 save every step as metadata to leave an audit trail to promote compliance at scale. Securely Unlock Enterprise-Grade Generative AI with AI Cloud Einstein, Data Cloud, Flow, Tableau, and Mulesoft all benefit from AI Cloud's capabilities. Salesforce AI Cloud empowers organizations to unlock the future of their AI journey with a solution that is trustworthy, open, and intelligent. Developing trust and embracing openness The Einstein GPT Trust Layer enables businesses to employ generative AI with confidence by facilitating the deployment of relevant models for a range of tasks. This trust layer allows enterprises to use a variety of large-language models (LLMs) while adhering to their trust and openness standards, which prioritize data privacy, security, and compliance. Leveraging capabilities of Third-party Large Language Models (LLMs) Salesforce's AI Cloud promotes open development by integrating third-party LLMs such as Amazon Web Services (AWS), Cohere, and others. These LLMs are hosted within Salesforce's secure infrastructure, so user prompts and responses stay within the Salesforce environment. Salesforce has also formed a trusted partnership with OpenAI, utilizing their Enterprise API and security capabilities, as well as the Einstein GPT Trust Layer, to secure data retention within Salesforce. Salesforce's own large language models such as CodeTF, CodeGen, and CodeT5+, assist companies in reducing talent gaps, lowering implementation costs, improving team efficiency, and detecting incidents that require immediate attention. Bring Your Own Model With AI Cloud, companies that have trained their unique models elsewhere to integrate seamlessly with their desired infrastructure. These custom models, whether built with Google's Vertex AI, Amazon's SageMaker, or any other platform, can connect directly to AI Cloud over the secure Einstein GPT Trust Layer. Organizations can maintain control and privacy over their information by storing it within their trusted perimeter. Enterprise Ready Solution Salesforce predicts that by the end of 2030, Generative AI will drive $15 trillion in global economic growth and increase GDP by over 25%. These are remarkable numbers and Salesforce believes that AI Cloud will propel businesses to new heights, with efficiency and productivity being the key differentiators. Prompt Template and Builders 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. As a young company (we are only a decade old!), we are driven by the immense potential of emerging technologies such as Generative AI to deliver value to our clients and help them bring their ideas to fruition. 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 seamlessly with generative AI.  To know more about how we can tailor unique scalable solutions for you by leveraging the power of generative AI to enhance the customer experience, connect with an  https://www.youtube.com/watch?v=qxyQDcap4Ic – video

Generative Artificial Intelligence (Generative AI) is opening up opportunities to develop a new breed of apps: smart, intelligent workhorses that can do the work of hundreds of individual apps – all from a simple natural language prompt. When you think of a copilot, the first thing that comes to mind is someone assisting a captain fly an airplane. But by the end of 2023, the word “copilot” was trending in a big way in the AI world. Take generative AI technology that we’ve come to know of recently via apps like ChatGPT and Bard and put that power right into your workflow, that is what an AI copilot is. At a fundamental level, an AI copilot is an AI-powered assistant that can help you execute simple tasks faster than ever. Imagine you’re about to book a business dinner with a customer in another city. Before AI copilots came along, you’d first go through the customer’s customer relationship management (CRM) data to check for any food preferences. Next, you’d open one of the table booking apps to look for a suitable restaurant to check for availability. Then, you’ll open one of the travel apps to book your travel itinerary, and, finally, you’ll open your email app to send a personalized confirmation to your customer with all the details. You’re looking at a minimum of four separate apps and at least a half hour of toil. Now imagine this. You open one app, your AI copilot app. Instead of navigating through 4 different apps which might take several minutes or even hours, you simply type in your AI copilot app, “Book dinner with Jonathan next Monday.” Your AI copilot will work in the background and execute all of the above steps. Once done, it will send you confirmations by email and/or text, all of this with minimal intervention from you. Beyond the evident savings in time and the obvious novelty of cutting-edge technology, it’s hard to fully convey in words the true value of this digital transformation using conventional methods. These AI copilots can do the work of dozens of apps concurrently – generate draft reports, author relevant and accurate customer service responses, compose sales emails, renew product subscriptions, pay our bills, and more. But first things first, how exactly do they get the job done? How does an AI copilot work? At the heart of AI copilots are building blocks referred to as copilot actions. A copilot action can refer to a single task or can include a collection of tasks required for a specific job. These may include: Updating a CRM record. Generating product descriptions from CRM data. Composing customer email replies. Handling a range of customer service use cases. Summarizing transcripts from chat sessions. Highlighting action items from meeting notes. These tasks can be triggered via automation or on-demand in any pre-defined sequence or can be autonomously executed by the AI assistant. A copilot’s ability to understand natural language requests, work out a logical plan of action, and execute the tasks is what makes it unique. An AI assistant can handle multiple instructions (we literally mean thousands) and learn from those actions. So, the more they act, the better they get. When multiple tasks are required to be accomplished, actions allow your AI assistant to perform a wide range of business tasks. For example, an AI copilot can help a service rep quickly resolve a case in which a customer was overbilled for a service. Or it can help a sales rep close a deal by recommending the next best actions. Want to understand in depth? Let’s get our AI copilot into action. Take the earlier example of setting up dinner with your customer, Jonathan. If you use Einstein Copilot in Salesforce, it would know Jonathan’s initial context, like his name and CRM interaction history, but it would need a little more information from you, like date, time, and location. It could then execute actions based on your earlier one-liner instruction and respond with any other questions relevant to the associated actions: It might ask you which Jonathan you want to set up the dinner meeting with (in case of multiple contacts with the name Jonathan) and what type of cuisine Jonathan prefers if those preferences are not already there in the CRM. What’s interesting about Einstein and other AI copilots is that they make you feel you are having a conversation with a fellow employee just like you would do over SMS or WhatsApp. But in reality, you’re just chatting with a highly sophisticated computer program. The native Salesforce SMS app serves as the conversational interface acting as a bridge between your CRM data and you and serves up information over a text conversation. The AI copilot determines what actions to execute and then generates dialogs in runtime, summarizes the output data, and paraphrases it in common human language. To you, it feels like you’re having a reasonably sophisticated chat conversation with your AI assistant. It lasts only a few seconds and then your travel itinerary is done, and your dinner is set up with minimal effort on your part. You just tell an AI copilot – “Do so and so task” and it diligently works in the background choreographing a complex workflow of processes and rummaging through data to deliver a result that would otherwise have taken a human far more time and much more actions. What are the different types of AI copilots? Although the technology of artificial intelligence has been around for a while, the concept of AI copilots is fairly new. Ever chatted with a customer service rep on an app or website only to realize it was actually a bot? That’s a type of copilot. It helps customers with basic service questions but often fails to get to the deeper details of your issue. And when you get frustrated with a back-and-forth conversation that’s going nowhere, you turn to an actual human for assistance. Chatbot technology got a shot in the arm with the launch of recent AI platforms such as ChatGPT, Bard, Google's Gemini, etc. These generative AI platforms can compose emails, write code, generate reports, and even analyze data. With AI copilots, the interaction becomes even more sophisticated, with your own AI copilot working in the background to help you improve everything you do. The AI chatbot for Salesforce called Einstein bot is one of the several new copilot entrants in the market along with similar solutions from Microsoft and GitHub. Here’s the key takeaway: When you are doing your research to identify an AI copilot for your business, establish one key decision parameter. Will it only use external sources for information like ChatGPT, or whether you will be able to securely connect it with all your organizational data – structured and unstructured? Why you should use an AI Copilot If you are reasonably well-read about the recent developments in the AI space, you would be familiar with popular large language models (LLMs) such as Google’s Gemini or OpenAI’s GPT-4. These LLMs power chatbots such as ChatGPT and are great for specific tasks. Their responses can be limited though since some of them have access to data only till 2022. And models like the ones used by ChatGPT only have access to public information about your business, they obviously don’t have access to your trusted CRM data.  Which means they can’t help you create relevant and accurate customer service replies or tell you about promising sales opportunities, nor can they act on your behalf to reply to an email or make a dinner reservation. But an AI copilot changes everything. Let’s go back to dinner with Jonathan. Your trip was successful. Now, you may wish to thank him with a bottle of his favorite wine. Because your AI assistant already has the necessary actions to look up Jonathan’s CRM record to find his favorite brand and to charge your card on record, all you need to do is type, “Send Jonathan a bottle of his favorite wine.” And this example is akin to the first chapter in a beginner's course on AI copilots. Imagine executing thousands of actions in virtually limitless combinations. With an AI copilot, retail marketers can create product descriptions in multiple languages in minutes, path lab clinicians can review lab results and help doctors make diagnoses, and finance professionals can analyze mountains of data in no time to propose multiple investment opportunities. The use cases are virtually endless. With an AI copilot, you can quickly transform your business to be more efficient and productive, regardless of the industry you work in. A conversational, generative AI-based digital assistant will do all those routine tasks that are limiting your bandwidth to scale by helping you to engage with your data like never before. Does it seem that development around AI is happening at a breakneck pace and the very idea of wanting to figure out what you should do around AI to help your business is giving you a headache? Well, you’re not alone. As a trusted Salesforce Implementation partner for over a decade, our experts can guide you on how to combine the power of CRM, Data, and AI to propel your business into the next phase of growth.