AI

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.

It’s an exciting time for knowledge workers. Many new work opportunities are opening up quickly in the AI-related workspace.  Artificial Intelligence and the game-changing technology of generative AI are helping to create a range of new career options, starting from prompt engineers, and use case designers, to AI trainers. Our team of experts has compiled a list of a dozen new and upcoming AI-related roles, along with tips on how to prepare for these roles. Everywhere. For everyone. Yes, that’s the scope of leveraging AI technologies in business. And that includes the job market as well. The holistic view According to a McKinsey report, generative AI has the potential to add over $4 trillion in value to the world’s economy pan-industry. This includes manufacturing, retail, financial services, telecom, construction, high tech, healthcare, and pharma. It will impact job functions such as sales, marketing, customer service, engineering, HR, and research and development. While AI holds limitless promise for transforming businesses in the way they work, there is also an underlying current of uncertainty and fear around AI taking jobs away. In this article, we quash that myth and talk about how this new disruptive technology will, in fact, create a variety of new career opportunities for the global workforce.  For instance, lucrative roles like prompt engineering, the art of creating effective prompts for GPT interfaces, and AI roles such as AI product manager are currently trending on popular job portals. Salesforce recently sponsored an IDC-authored white paper where they surveyed 500 organizations that are currently using AI-powered solutions. The whitepaper concluded that over the next 12 months, we will witness a sharp rise in demand for data architects, ethical AI specialists, AI product designers, and AI solution architects. The report also predicts nearly 12 million new jobs will be created within the Salesforce ecosystem alone over the next six years. Now that’s a number business leaders and HR departments cannot ignore. What you can do now AI needs people to be at the helm of affairs for it to work effectively and deliver on the promise it holds. And the global workforce, across all levels across all industries, has the golden opportunity, at this very moment, to sharpen their existing skills and acquire new ones to grow with the economy. The exciting thing about AI tools and solutions is that they are still in the early stages of deployment and are mostly democratized. So, if people have the will, they can learn on their own how to augment their current value. And the requisite resources are already there. Platforms such as Trailhead (from Salesforce), Coursera, Udemy, etc offer free and paid courses to certify you on AI-related skills. AI will eliminate redundancy and create new roles Let’s understand one thing very clearly. Yes, AI will probably eliminate repetitive tasks such as scheduling social media posts, going through resumes, examining data, answering common customer service questions, and composing and sending follow-up emails. But all this will free up a lot of time for workers to spend adequate time on strategic, creative, and productive tasks in their existing roles. With the adoption of AI, workers will now have time to do actual work. If you’re a sales professional or work in customer service, you can now allocate more time to what matters – interacting with customers to nurture those relationships. If you work in marketing, you can spend more time crafting marketing strategies or working on creative projects. And if you work in legal or healthcare, you can leverage AI technology to research and analyze agreements or help interpret CT scans and X-rays. While new AI jobs in engineering or data-related fields are obvious, new roles in healthcare, financial services, legal, construction, etc will evolve with the evolution of smart AI. AI will be like the sky, the background of everything else that happens over it. 12 new roles that may be created with the advancement in generative AI Curious about AI and how it can augment your current skill set and role? Here are 12 opportunities to look out for. Some of these are already in the initial stages of existence while others are what our experts believe, will crop up in the near term. Do you see yourself in one of these in the future? Prompt engineer Prompt engineers are masters at composing prompts for AI tools such as GPT tools or chatbots. Writing great prompts is key to unlocking the effectiveness of generative AI. Some AI ambassadors refer to it as AI whispering. After all, you are basically guiding the AI tool to provide you with a creative answer to your prompt or question. AI trainer AI trainers work in the background to ensure the learning algorithms driving AI do what they are supposed to. AI gets better as it gets more and more data to play with. AI trainers prepare these data sets to teach the learning algorithms how to think and respond to user inputs (prompts) in a more human-like language. AI trainers also refine the data and direct engineering teams to achieve more relevant and accurate outcomes. In a nutshell, AI trainers teach AI tools on how to think, communicate, and be useful. AI learning designer As AI technology evolves rapidly (and we have only seen the tip of the iceberg), businesses will need workers to optimize individual learning at scale. AI learning designers assist businesses in training their workers on AI tools and systems, including training them on how AI copilots can complement their work. Not only that, they will go one step further to refine the very ways in which people learn. Businesses that have better learning frameworks and strategies will be in a better position to adapt to emerging AI technology. AI instructor As businesses continue to invest in AI tools and systems, they will also need people to train their employees on how to use them. AI instructors help people further their careers by teaching them the necessary AI skills even if they are currently not involved in AI. An AI instructor’s responsibilities include developing a curriculum, creating teaching methodologies, conducting hands-on classes, and providing a more holistic AI education. Sentiment analyzer While AI can understand and interpret natural language, it is still not human and does not possess empathy. AI cannot recognize nuances of language, particularly when we have so many, and cannot interpret human emotion. This is why a sentiment analyzer’s role is important. They leverage a sentiment analysis program to establish if data extracted from a public source such as social media comments or feedback is positive, neutral, or negative by identifying its emotional tone. Stitcher A stitcher’s role is a generic one. They use AI to stitch together a variety of skills across multiple roles into a single role. For instance, they leverage AI to combine modular apps and tools into a single workflow that delivers unique value to customers. Interpersonal coach This role, as the name suggests, is based on a soft skills development function. Interpersonal coaches help the digital workforce and the ones working with AI, to grow their interpersonal skills such as social intelligence, empathy, mindful listening, and managing face-to-face interactions. It’s similar to a soft-skills trainer, except that it's more focused on helping people who work in the background or mostly with computers. Workflow optimizer This role is critical for companies as it deals with the soul of any business – data. They leverage data and system intelligence to have a 360-degree view of a business and identify areas where AI could help workers be more productive. A workflow optimizer uses AI to analyze how people and teams work and identify productivity gaps to boost overall efficiency. AI compliance manager AI is still at a nascent stage and the regulations and guidelines are fluid and ever-changing. As they continue to get more refined and standardized, an AI compliance manager’s job is to make sure his company’s AI processes abide by existing regulations, guidelines, and ethical standards. They ensure that their organization’s data management practices are aligned with privacy laws and mitigate AI’s potential legal impact on the company. AI security manager AI technology can become dangerous if it gets into the wrong hands. The function of an AI security manager is to ensure AI systems are used with honesty and integrity. They also ensure sufficient guard rails are in place to protect against any threats and vulnerabilities. Chief AI officer The newest entrant in the C-suite league, the CAIO’s primary function is to guide and manage a holistic AI strategy for the organization. This includes ensuring the development and deployment of responsible and trusted AI systems across the organization. Chief data and analytics officer This role entails overseeing everything related to data and analytics in an organization.  Depending on the size of the enterprise or the scale of AI being used by the company, this role is sometimes shared between two people, a chief data officer and a chief analytics officer. How to prepare for new AI careers With all of these AI opportunities opening up, it’s time to buckle up, commence training, and start having fun with some of the free AI tools. View these tools as someone who can help you to improve the way you work and how you do it. With so many online learning platforms available at our fingertips, we can quickly start educating ourselves on AI-related technologies and upgrade our current skill set. At Girikon, a Gold Salesforce Implementation Partner, we believe that if we embrace change and the opportunities that come with it, we open doors to new possibilities. The need of the hour is to be curious and bold. Connect with an expert today. Our team of certified Salesforce Consultants would be happy to guide you.

Salesforce Chatbot enables businesses to offer personalized and prompt service using AI-powered bots available natively in the CRM. Now you can supercharge customer case resolution with clicks not code by automating mundane, time-consuming tasks by linking AI with your CRM data. This empowers service teams to do more by leveraging AI-generated responses to customer queries. Before going into how AI chatbots can be pivotal in customer service, let’s educate ourselves on the basics. What is a chatbot? A chatbot (derived from “chat robot”) is a software program that can simulate human conversation (voice or text) and can solve a problem. Businesses typically use chatbots to augment customer service to complement traditional service channels such as phone and email. Just like software can be configured and customized in any way you want, chatbots can also be customized and used in ways that are aligned with your goals. We are already familiar with bots for customer service that are used with popular messaging platforms like SMS and WhatsApp. With AI chatbots, users can interact with a computer program to find answers quickly. Most notably, chatbots can enhance customer relationships by responding to queries faster at their convenience by being available round the clock. With 24/7 availability to serve up responses, chatbots free up time for service teams so that they can work on more complex issues that require a touch of empathy. How do chatbots work? Chatbot development has evolved leaps and bounds over the last decade or so, as developers have adopted sophisticated techniques and technological advancements in machine algorithms to enable chatbots to contextually understand user questions and offer more useful responses. While bots today still aren’t equipped to handle all user queries, they can easily respond to commonly asked questions or execute simple, repetitive tasks without any human intervention. One such example is when a chatbot parses customer input, identifies keywords or phrases, and then scans the organization's data to retrieve relevant articles based on those keywords or phrases. Chatbots typically follow a pre-defined decision tree, which is why they are often referred to as rule-based chatbots. Rule-based chatbots execute pre-defined actions based on user input. Rule-based chatbots are based on click actions, like a user responding with a binary input like a “yes” or “no,” or by recognizing specific keywords. You would have come across instances when you typed a question into a website’s pop-up box and got an answer that had no relevance to the question. That usually happens when although the chatbot recognized keywords in your input, it did not understand their context. This is where AI chatbots come in. What is an AI chatbot? The level of sophistication involved in chatbot technology cannot be overstated.  With inbuilt natural language processing (NLP) capability, chatbots can engage in human-like conversations with users effortlessly. Engineering teams are relying on NLP to build AI chatbots that can understand human speech and text better. With NLP, it is now possible to better recognize user intent and consequently provide better, more intelligent responses. With the latest disruptive tech of generative AI, chatbots can interpret context in written text, which allows it to work on its own. In simple terms, AI chatbots can understand language outside of pre-defined rules and offer responses by relying on existing data. This allows users to navigate the conversation and allows the bot to follow. By drawing on huge data sets and the processing power of the machine, AI- chatbots can leverage deep learning algorithms to significantly improve their quality of understanding questions and offer more accurate responses. When chatbots connect with technologies such as Large Language Models (LLMs) and NLP, they can train themselves to simulate human conversation better by: Maintaining the context of the interaction. Managing a personalized conversation. Refining responses based on the changing customer needs. AI chatbots get better with every interaction. They do this by connecting with deep learning algorithms and drawing on enormous amounts of conversational data stored in the CRM database. 3 Benefits of Using AI Chatbots in Salesforce: Businesses, irrespective of size and the domain they operate in, can derive the benefits of process automation, particularly a function that delivers direct value to their customers. With chatbots, you are available to your customers round the clock, giving them 24/7 access to your business. They are also able to get quick responses to common questions anytime, from any device. Reduce Human Intervention As a business leader, you would be aware that not every customer query needs you to dedicate human resources to respond to that query. Just like a knowledge base or a library of FAQs in Service Cloud can offer relevant and accurate information to customers whenever they need it, a chatbot can automate this process by understanding their queries and serving up the right answers. Chatbots can be very useful in increasing the deflection rate of customer support cases. Reduce Costs and Improve Productivity Leveraging chatbots to automate mundane, repetitive tasks and straightforward processes gives your internal teams more time to focus on more critical and creative tasks. This leads to a significant reduction in manpower especially in your customer service teams. The ROI of using a chatbot to free up agent time so that they can focus more on doing what’s most important- nurturing customer relationships, is a figure you cannot ignore. Your internal team performance will witness a significant improvement as well, since your service agents are focused on solving complex problems where human intervention is necessary, translating to higher-quality customer service. Time is a commodity that is available in limited quantity to every organization, and chatbots allow service teams to do more with less. If you wish to scale up your business without the associated costs of additional resources, you should look at AI-powered chatbots. Entrusting many of the repetitive, mundane tasks across departments to an AI chatbot and having the provision to escalate a case to a human agent as and when required will boost the morale of your teams, improve staff retention, and allow them to shine in their work. Customers Notice Innovation Customers often compare 2 or more brands that offer the same products or services that they are looking for. And if your business is completely human powered it means customers sometimes will have to wait for their turn for a human agent to be available to get their issue resolved. If your competitor is offering chatbot-powered customer service which allows customers to self-serve and find answers quickly, they will notice the difference in service availability which will compel them to choose the latter. Let’s look at an example. A visitor to your website asks the chatbot for pricing information and more details about a particular product or service. The chatbot can immediately dive into Salesforce data and serve up the information instantly to the website visitor.  Compare it with getting a message “Please wait a moment while we find an agent to talk to you.” Let’s look at another scenario. The website visitor wants to book a demo to see how your product actually works. All he needs to do is type – “I want to book a demo”. The chatbot can immediately open a calendar for him to select a convenient time and date and once the visitor has made a selection, the bot can immediately check rep availability by diving into the booking system which is also connected to Salesforce, and then confirm the appointment. All this without ever leaving the chat conversation. The use of chatbots in customer service has increased dramatically over the last 5 years and with the advancement in AI technology, it is going in only one direction. Why Should You Consider an AI Chatbot for Salesforce? Looking to invest in chatbot technology? Heard and read a lot about them and their benefits in the context of business but don’t know where to start? There are several ways of approaching this, with so many options available in the market. If you are starting out, the best way to do this is within your single source of truth – Salesforce. And the reason is very simple. A Salesforce native chatbot can leverage customer data, product and service data, and knowledge base, to engage customers and serve up relevant and accurate answers. A Salesforce native chatbot can also trigger automations at appropriate events within Salesforce making it very productive and tightly aligned with your business goals. Salesforce does come with AI-powered bots called Einstein Bots. Einstein Bots are powerful, and available out-of-the-box in Salesforce. They require a Service Cloud license along with a chat or messenger license with each license offering 25 bot conversations per user per month. Einstein Bots also come with an inbuilt Salesforce Messaging App allowing businesses to engage in text conversations with customers via SMS and WhatsApp. AI Chatbot from Salesforce is a powerful tool to re-imagine customer experiences, automate processes, and improve productivity. With round-the-clock availability and immediate responses, AI Chatbots from Salesforce transform the way businesses connect with their customers. To learn more about AI Chatbots for Salesforce, connect with an expert today.

Business leaders, lawmakers, academicians, scientists, and many others are looking for ways to harness the power of generative AI, which can potentially transform the way we 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. 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. A company using generative AI 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. In 2019, 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 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 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 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 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. Empowerment 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 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. 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: Use zero or 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. Keep data fresh and labeled 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. Ensure human intervention 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. Test thoroughly 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. Get feedback 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. 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.

Businesses today are generating mountains of data and forward-looking business leaders recognize that there are critical insights hidden inside their data. With AI, businesses can unlock these insights to identify trends, opportunities, and challenges. Building a strong enterprise-wide data culture along with robust and trusted AI holds the key to unlocking these hidden insights. While business leaders recognize the value of data for decision-making, a recent global survey conducted by Salesforce amongst 10,000 of them reveals some interesting facts. 67% of them are not using data for making critical decisions like product or service pricing Less than 33% use data to drive strategies for new markets 79% don’t leverage data for diversity and inclusion While the above numbers revealed something unexpected, here is what the survey summarized. Companies that make data-driven decisions are more likely to beat sales targets than those that don't Companies that combine AI with their data showed an average increase of 30% in revenue Companies that embrace this approach are able to reassign human and financial capital quickly and can create personalized customer experiences much faster What can you do now? Here are some suggestions for creating a strong data culture. We will take these items up later in this article. Put together the right team Provide them with the right tools and training Test your theories on a pilot scale and iterate Prioritize the human aspect of your data culture Identify areas where AI can derive more value from your data Data-driven V/s data-informed In a data-driven company, most of the organization’s employees can access and analyze data, draw inferences about what it means, create a dashboard, visualize data, and use all of these to determine the next steps. Employees in a data-driven organization don't depend on data analysts to do this. Being data-informed enables organizations to make decisions based on a mix of data, research, experience, and insights. Data-informed organizations may or may not have the skills that data-driven organizations have. Why is it critical to build a strong data culture Business leaders have to deal with countless challenges before embarking on building a data culture. Avoid over-analysis by starting with a single use case that validates the value of your new data culture approach. McKinsey research has shown that data-driven organizations achieve their goals faster and their data culture initiatives contribute at least 20% to earnings. Here’s why this works: Data analysis identifies actionable trends Data analysis identifies patterns that unlock value and enable organizations to utilize opportunities faster. Adding AI to the mix can accelerate the process by doing a deeper dive into data analysis at scale and serving up recommendations. Combining data and AI drives growth, promotes innovation, fosters collaboration, and creates uniqueness. AI and machine learning increase success by 30% Organizations that still rely on legacy knowledge and instinct to guide decision-making are missing out on opportunities. With AI and machine learning, organizations can make quick and accurate decisions. According to Salesforce research, adding AI to organizational data and business functions eliminates the guesswork from the decision-making process and increases success by an average of 30% across important metrics like operational efficiency, employee productivity, and topline growth. Strategic work keeps employees engaged When decision-making is guided by data analysis, employees spend less time on mundane tasks that add little or no value and can focus on strategic or creative tasks. This keeps them engaged and improves their productivity. Salesforce research shows that 84% of organizations that have adopted a data culture observed higher employee retention. Empower the right team The best way to create a team of data champions is by showing not telling. Illustrate with real numbers how data-driven decision-making increases revenue and customer satisfaction and streamlines operations. Instead of choosing any random use case to illustrate your point, capture their attention by selecting a project that scores a financial win and one that you can scale for greater impact. Here’s how you can start: Step 1: Put together the right team Create a working group of employees from across the organization with diverse backgrounds and functions. These team members should have a collaborative mindset, unique skills and abilities, and individual organizational perspectives. Ensure that you include employees across the corporate strata such as senior executives, managers, engineers, consultants, and machine learning scientists. Step 2: Provide them with the right tools and training Salesforce research stats on data literacy don’t paint a pretty picture.  Only 35% of the surveyed workforce has received training on data visualization tools and 29% on statistical tools. 27% percent workers say they can interpret data outputs relevant to their job function, and only 26% say they can use that data to make decisions. With proper access to training on technology-driven data analytics, organizations can empower their entire workforce to unlock the power of data to drive decision-making. Step 3: Test your theories on a small scale and iterate Start small, analyze results, refine your theories and iterate. Eventually, a winner will reveal itself when your employees can measure the impact of your project on their bottom line. Step 4: Prioritize the human aspect of your data culture Encourage involvement of all team members in the entire process from setup, testing, fine tuning, to data analytics and its application for decision making. This will ensure that you avoid bias and guesswork which can have a negative long-term impact. Take data at face value to avoid bias by proxy. Let’s consider ZIP codes as an example. At face value, they are just a location indicator. But sometimes ZIP codes can be a proxy for an area’s racial makeup and financial services companies consider ZIP codes in loan applications. Decisions based on this data point must be free of bias. Step 5: Identify areas where AI can derive more value from your data You can start your AI journey at many places, in any department, for any function, or extend it further if you’ve already started. Start small, demonstrate results, and bring everyone on board. Establish guidelines and standards for consistency, security, accountability, and ethics from day one. Ensure completeness and accuracy of your data to make the best use of AI. Incorporating an AI-driven data culture can be a daunting task.  It takes time and effort to bring people on board, retain their interest, and demonstrate results. For most business leaders, this transformation may be a whole new experience. This is where working with a Salesforce Consulting Partner could prove to be very useful. At Girikon, our certified consultants can guide you on this transformational journey of embracing AI with a strong data culture. Contact us today.  Take the next step to build your AI-powered data culture.

In the rapidly evolving business environment, it is essential for companies to utilize state-of-the-art technology to stay competitive. Nowadays, forward-thinking businesses are incorporating artificial intelligence (AI) into their operations, particularly through the adoption of customer relationship management (CRM) software, to automate and enhance their CRM processes. Salesforce, a leading CRM platform, has consistently been a pioneer in innovation, especially in the realm of artificial intelligence (AI). Notably, Salesforce AI has transformed the way organizations handle their customer service processes. The integration of Salesforce and AI is more than just an augmentation. It has indeed opened new avenues in Customer Relationship Management (CRM). Rather, it offers a smarter, efficient, and a highly custom-made customer interaction. To know more about Salesforce AI integration, businesses should consider partnering with a reliable Salesforce consulting partner. Salesforce and Generative AI: A Dynamic Relationship As a cloud-based platform, Salesforce is highly customizable and configurable and can be leveraged by organizations to meet their unique business needs by tailoring their services. By leveraging tools like Salesforce Flow, users can automate intricate business processes, create agile service experiences, while streamlining data management. The next phase of transformation will involve incorporating the capabilities of generative AI into a versatile platform using Einstein GPT. This integration holds the potential to transform the way businesses function and engage with their customers How to Leverage AI to Improve Customer Service? Listed below are ways how AI can help businesses provide better service to their customers:  Improved Customization: Utilizing AI will empower businesses to deliver personalized experiences by harnessing customer data and their preferences. This will pave way for tailored recommendations, quick support, and a deeper comprehension of customer requirements. Unified Omnichannel Support: AI-driven chatbots can integrate easily with several communication channels such social media, web chat and more. This guarantees uniform interactions across several platforms, offering customers a unified experience. Intelligent Automation: AI can be leveraged to automate repetitive and mundane tasks thereby saving a lot of time that can be used up by human agents to focus on more complex and strategic activities. This will boost productivity, quicken response times, and optimize cost for businesses. Sustained Learning and Development: AI systems will keep gathering insights from customer interactions, feedback, and real-time data, which in turn will foster continuous improvement. This continuing improvement will yield more precise responses, intelligent recommendations, and enhanced overall performance. What are the benefits of AI in customer service? AI in customer service offers several benefits that can improve the overall customer experience and streamline business operations. Some of the crucial advantages include: Increased Productivity: Leading IT players believe that AI can be adopted by organizations to serve their customers in a better way. Research conducted reveal that access to AI assistants and tools can increase productivity for support agents significantly.  Increased Efficiency: Carrying out tasks manually can be burdensome for service agents. This includes tasks such as navigating between different systems to access customer history, searching for relevant informative articles, sending field staffs to service locations, and manually inputting responses. These manual processes are usually prone to errors as they are executed by humans. The integration of AI in customer service can provide intelligent suggestions to service workers drawn from knowledge bases, and customer data. A more Personalized Interaction: When a customer interacts with a chatbot, artificial intelligence (AI) has the capability to retrieve vital details, such as the name of customer, location, account type, and language preferred. If the inquiry demands the involvement of a field service technician, AI can promptly convey all relevant information to the technician, allowing them to deliver tailored service as soon as they arrive on-site. Less Exhaustion and Enhanced Morale: AI empowers agents to do away with monotonous, time-intensive tasks, enabling them to focus on tasks that demand creative thinking, problem-solving, and intricate critical thinking. These activities significantly impact the overall customer experience. Consequently, it shouldn’t come as surprise that majority of IT leaders anticipate that generative AI will alleviate workload of teams, while reducing burnout. Scalability: AI systems can simultaneously handle a huge rush of customer queries making it simpler for businesses to scale their customer service operations without consistently increasing staffing levels. A Practical Service Experience: AI has the capability to draw information from contracts of customers, warranties, buying history, and marketing data. This ensures the identification of optimal actions for agents to pursue with customers, even post the conclusion of the service engagement. The future of AI in Customer Service: The future AI seems to be quite promising in the customer service industry. In the years to come, artificial intelligence is poised to gain prominence in workplaces given the ongoing advancements in technologies such as machine learning and natural language processing (NLP). Besides handling routine tasks, these AI programs will offer significant insights into consumer behaviors and habits through big data analysis. Organizations can utilize this valuable data to optimize their return on investment in marketing strategies and branding initiatives. As technology evolves, AI is set to play a key role in uplifting customer experiences and boosting operational efficiency. Final Words: The fusion of AI and Salesforce is reshaping the CRM terrain, presenting matchless possibilities for organizations to elevate both their customer relationships, as well as their operational efficiencies. This integration when leveraged by businesses enables them to position themselves at the frontline of technological advancement, ensuring they stay competitive and in agreement to the ever-changing needs of their customers. In doing so, organizations can provide value to customers and stakeholders while future-proofing their operations in this quickly evolving digital era. Organizations should consider availing Salesforce implementation services if they wish to make the most of the integration of Salesforce and AI. 

Generative Artificial Intelligence (Generative AI) is the latest next generation technology. Generative AI tools have made it very easy for employees and professionals to compose and refine emails, fine tune presentations and reports, write code, put together social media campaigns, and fast track customer service interactions. But not everyone is able to maximize its full potential. More often than not it comes down to the prompt, the statements or questions you feed into a Generative AI tool. The better your prompts, the better will be the Generative AI response. The key takeaway If you want to get the most out of Generative AI and the generative pre-trained transformer (GPT) models that generate conversational language, you might want to get your hands dirty in prompt engineering. This gives the Generative AI model clearer details for what you want instead of being ambiguous. Generative AI is getting smarter as you read this, but it cannot read your mind. It can only give you responses based on its understanding of the prompt you give it. So be specific. GPT works better when the prompt is longer. The prompt, which is the question you are asking the tool, needs to be precise and contextual for it to generate the right response. And that’s the key to unlocking the full potential of Generative AI. What you need to know When writing a prompt, approach the tool like you’re having a normal day to day conversation with a colleague. Use clear language and descriptions.  The devil is in the detail. The Generative AI tools will work better for you if your prompts are precise and detailed. You can have an interactive conversation with your Generative AI tool and dive deeper into what you’re looking for. These following tips would be helpful when writing prompts for Generative AI: Write clearly and concisely so your Generative AI tool understands your specific request. Write linguistically correct, complete sentences with descriptive words, that clearly describes what you’re looking for. For precise responses, ask specific questions, and avoid questions that offer a yes/no response. Add context to your prompt. Explain what is it that you wish to achieve and define your target audience. Engage in a back-and-forth conversation. Follow up the initial response with further questions to go deeper and get even more specific and relevant responses.  What is prompt engineering? Prompt engineering is the art of asking clear, descriptive questions or providing detailed information to Generative AI tools, such as a GPT tool or chatbot, to fetch the best results. With the meteoric rise in adoption of Generative AI tools for personal as well as business use, effective prompt engineering skills can help you improve the efficacy of these generative AI tools. The more specific and descriptive your prompt, the better the AI generated results. And you can get creative like you would ask an expert of the subject of your enquiry. For instance, you can even ask the Generative AI tool to reply as someone well known, like Isaac Newton, to get a response from that individual’s point of view. Generative AI uncovers information from piles of data available on the internet. However, narrowing down your query by providing specific questions or instructions in your prompt and adding context will deliver better results. So get creative. Whether you are an expert prompt engineer or a novice in generative AI, it would be prudent to follow these 6 tips mentioned below to get the most from this disruptive technology. Be specific: For example, instead of writing, “Create a social media campaign,” which is a very generic instruction, you can write, “Create a social media campaign for an ecommerce website that sells sports apparel for tennis fans of Roger Federer and Rafael Nadal.” Engage conversationally: Generative AI may not understand localized dialect or colloquial language. Imagine you are speaking to a co-worker, not a computer. Use open-ended questions: Avoid question with binary responses like a yes/no response.  These prompts limit the Generative AI’s ability to surface detailed, contextual information. Set a persona: Get creative. Ask the Generative AI tool to give answers from the point of view of a public figure (past or present) like Isaac Newton or Christine Amanpour depending on the subject you want to ask about. In fact, you can even define a specific role for specific answers like an operations manager or lawyer.   Define your audience and channel: Specify in your prompt whether you are writing for millennials or GenX. Specify where the audience is going to read it – such as on a social media platform, a blog post, or on website. Ask follow-up questions: The beauty of Generative AI is that you can engage in a back-and-forth conversation with it while maintaining context. It’s akin to speaking with a human. Except that it’s not. If you are not happy with the initial response, you can ask follow-up questions to get more specific responses. This technique is sometimes referred to as “prompt chaining,” where you split your prompts sequentially to get more specific and tailored answers and use answers from one prompt to draw out the next. Use prompt engineering effectively for Generative AI products to work with you and not against you Generative AI tools are new and evolving as you read this. They are not perfect and they’re definitely not human. They are designed to make you feel like you’re having a conversation with a human on the other side, but in reality, it’s a back-and-forth with a computer that has access to heaps of data. Keep these points in mind during your Generative AI prompt writing and subsequent usage of the responses: Generative AI is not always factual. Sometimes it makes up answers, so ensure that you verify what you get. Avoid any copyright infringements. Ensure that what your Generative AI tool gives you isn’t plagiarized from somewhere. Generative AI tools do not understand nuance, local dialects and subtlety. They are not from your neighbourhood. Ensure that your prompts are as specific and clear as possible. Generative AI is not always completely accurate. It’s a fundamental reality of this technology and as a user you need to ensure that you verify any factual data or information before publishing it. Generative AI is already creating a revolution in CRM applications. Girikon is a Certified Salesforce Implementation Partner with a global delivery model. To know more about how Generative AI can work for you, connect with an expert today.