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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.

Global Digital Skills Index from Salesforce research conducted in 2022 indicates a growing digital skills crisis. The in-depth research about digital skills is based on a survey with 23,000+ respondents (existing and prospective workers) across 19 countries. It includes areas such as their impact on the future of work, their job readiness concerns, and the importance of continuous up-skilling.

COVID has changed the way we live. Today, we spend most of our time online, looking for products and services. For marketing professionals, this translates into a great opportunity to stay connected with their audience in today’s pandemic context. But there is a catch. Customers are overwhelmed with digital information, and they are interacting with brands across multiple channels with so many options available. Which means not every interaction is attracting engagement. Today, engaging with customers is a whole new ball game. It requires faster and precise decision making about what needs to be communicated, how it needs to be communicated, when and where. The need of the hour is to deliver relevant messaging that conveys a deep understanding of the customer context. AI-driven solutions, with their ability to make intelligent recommendations, can help marketing teams make the right decisions, in quick time. They can also help marketing teams to launch contextual campaigns with personalised communication with their audience, achieving better engagement levels and enhanced customer experiences. So what exactly is AI Marketing? AI marketing is based on technology that uses machine learning algorithms to make automated decisions based on data aggregation and its analysis, along with analysing market trends and data that may impact marketing initiatives. Typically, AI technology is used in digital marketing activities where speed is critical. AI-powered marketing solutions use data to understand the best fit ways to communicate with your audience, and deliver personalised messaging at the right time, on the right channel without intervention from marketers, thereby ensuring a high level of efficiency. Digital marketers across the world today use AI to augment the efficacy of their efforts or to perform more complex tasks that would otherwise consume huge amount of time or resources. Here’s a look at how AI-powered solutions are transforming the marketing game: 1.  Smarter segmentation for enhanced audience discovery Understanding the customer is the foundation of today’s digital marketing strategy. AI-powered solutions can intelligently segment your audience. By scouring hundreds of data points across multiple marketing campaigns, AI-powered solutions can help marketers discover new sub-segments. Let’s look at an example. An outdoor hiking gear brand has an audience segment of “trekkers”. AI reveals to marketers that there are two categories in this segment – leisure trekkers and tech-savvy trekkers. With these insights, they customize their product recommendations based on the segment they are targeting. For example, leisure trekkers are shown apparel and accessories, and tech-savvy trekkers are shown the latest navigation devices or solar chargers. Identifying the right segment helps marketing teams deliver hyper-personalised marketing messages. Marketing solutions powered by AI technology can reveal unique customer traits within a segment. This can then be used by marketers to customize the marketing content and drive more engaging interactions. 2.  Accurate predictions to improve lead conversion Before the digital revolution, marketers relied on their experience and “gut-feel” to arrive at marketing decisions. AI-powered marketing solutions eliminate the guesswork by surfacing accurate, data-driven predictions and recommendations. These predictions allow marketing teams to personalise every customer journey and improving lead conversion through the marketing funnel. Let’s look at Einstein Engagement Scoring. This AI-powered feature of Marketing Cloud applies machine learning algorithms on customer data to arrive at a score for a company's email subscribers. The score tell you how likely each subscriber is to engage with your email campaigns, and eventually, to convert. The feature can also tell you the likelihood of each subscriber opening an email, click the links within the email body, or to un-subscribe. Marketers can use these AI powered predictions to build more tailored customer journeys. For instance, customers with a low likelihood of opening emails can be targeted through social media and mobile messaging. 3.  Personalised messaging across channels to drive engagement AI algorithms can use data such as browsing history, age, and recent interaction history with a brand to serve up a campaign landing page that is most likely to resonate with the subscriber. The same applies in advertising. The algorithm can use that data and serve up the right content for an ad in real time based on the user’s profile. This allows marketing teams run tailored ad campaigns that are relevant and better targeted at users, thus boosting ROI. With the evolution of technology, today’s AI-powered marketing solutions can be further targeted to help marketing teams deliver just the right amount of content. For example, Marketing Cloud Einstein has a feature called Engagement Frequency. It tells you the just the right number of emails to send out to customers and prospects for brand recall without being perceived as spam. Likewise, it also tells you which subscribers are being left out or being contacted too often. Based on this intelligence, marketing teams can customize their messaging strategy for improved customer engagement. In fact, AI-powered solutions can also tell you if a social media strategy would be a better bet than an email marketing campaign. Customers value the experience as much as the product and service, and brands will need to deliver personalised messaging across channels to stay ahead of the curve. AI can take your marketing team's understanding of your customers to the next level. Conclusion Marketing teams across industries are rapidly adopting intelligent technology solutions to improve overall operational efficiency and the customer experience and drive growth. This need for customer intelligence has ushered in a new era of Artificial Intelligence (AI) marketing solutions. With these AI-powered marketing solutions, marketing teams can get a deeper and nuanced understanding of their audience. The AI-powered insights can help marketing teams to drive conversions at scale. Regardless of the size of your marketing team, AI-powered marketing technology can help improve productivity, boost ROI, improve organizational efficiency, all while processing heaps of data your team may not have the bandwidth to deal with. If you are new to AI, even your first small step into AI-marketing like using a machine learning program to draft an email subject line and a greeting for your upcoming marketing campaign, can keep your brand ahead of the curve. It’s a small but significant step towards an AI-powered future. As a Gold Salesforce Consulting Partner, Girikon is in a great position to help you leverage the powerful technology of the World’s No1 CRM platform. To know more about how your marketing teams can use Einstein for Marketing Cloud to deliver personalised, contextual marketing campaigns, contact us today.

Imagine a scenario where a customer calls customer support only to navigate through multiple options and then being put on hold for several minutes before getting through to an agent. Only to be put on hold again as they look for answers to your problem. We’ve all had to deal with this at some point. And this is only half the story. Imagine being asked the same question over a hundred times a day by different customers. And not knowing answers to most of those questions. That’s what customer service teams have to deal with on a daily basis. In a world of fickle customer loyalty, how do businesses deliver excellent customer service? Disruptive technology like Artificial Intelligence (AI) may have the answer. Let us look at 10 ways in which AI can enhance the customer experience. 1. Chatbots Customer service reps today have to deal with a large number of calls on any given day. And on top of that there’s performance pressure to reduce the average resolution time. Enter Chatbots. Not only can chatbots provide quick answers in real-time, they can also reduce the case load on human agents by resolving common customer queries quickly. 2. Cost reduction Chatbots can help businesses trim customer service costs significantly by accelerating response times, freeing up agents to work on more complex cases, and resolving a very high % of routine customer queries automatically. A great example of this is call automation, which combines machine learning and voice recognition to augment existing IVR systems while delivering a significant cost reduction as compared to human agent assisted set ups. 3. Round-the-clock support Customers want service delivered at the time and on the channel of their choice. Businesses must be available at all times to customers to support them. Automated customer service makes that possible. It allows enterprises to deliver 24/7 customer service and resolve cases as soon as they come to light. This means customers don’t have to wait for long periods for a response. Prompt case resolution improves customer satisfaction and builds trust, loyalty and brand reputation. 4. Improved human interactions with customers AI can play a key role in supporting human interactions with customers. Two of the most common ways in which AI is supporting customer service is through AI-driven messaging and email tagging. AI-driven messaging allows service reps to handle a big chunk of cases with chatbot assistants. With AI-driven email tagging, service reps don’t have to read every customer email. AI-powered tools can scan and tag emails, and direct them to the right inbox. This frees up time for service reps so they can work on more complex tasks that necessitate human intervention. 5. Personalized experiences According to Salesforce, 72% of customers want to be able to solve service issues by themselves. AI technology can play a significant role in enabling customers to find what they are looking for more efficiently. AI analyzes customer data and key metrics, and makes intelligent recommendations on products or services to customers.  AI is always working in the background, analyzing every incoming piece of data, and suggests best fit content to customers. AI enables service reps to have a better understanding of customers, so they can send relevant content to them at the right time on their preferred channel. As a result, customers are able to find what they are looking for without having to call customer service. 6. Gathering data AI-powered technology simplifies data aggregation and serves a unified customer snapshot. Earlier, AI relied on existing customer data that was fed manually. Today however, things are far more advanced. Today’s AI-powered solutions proactively request data automatically. They can easily analyze patterns in behaviour, understand customer sentiment and quickly respond to their needs. 7. Predictive insights It is critical for businesses to deliver engaging and personalised experiences to customers.  AI powered personalization makes it easy for businesses to serve up tailored products or services to customers. Many businesses around the world that have integrated AI technology into their systems to deliver relevant information to customer, have seen significant improvement in their customer satisfaction scores. This improves brand reputation and builds loyalty. 8. Deeper insights from customer data In the early days, data mining was tedious and time-consuming. Today with AI-powered tools and solutions, huge amounts of data can be captured and analysed faster than ever, to get deeper customer insight, opening up new market segments and opportunities for brands. With AI, businesses can capture every customer action, uncover their interests, and apply these insights to drive targeted campaigns. AI can help businesses get faster results, get deeper insight, and eliminate human error and bias. And freed up human resources can be utilized for more complex tasks. 9. Assisting customers to drive decision making In today’s COVID 19 context, customers spend a lot more time online. They engage with brands across devices, and personalization across every touchpoint becomes all the more critical to assist customers in making the right decision. AI-powered assistants respond to customer queries in real time, and with a deeper insight on customers, are able to serve up intelligent recommendations to accelerate decision making. This frees up agent time and they can focus on more pressing tasks. In case of service requests when the conversation between a chatbot and a customer becomes complex, the interactions is automatically handed over to a human agent with a snapshot of the entire interaction history. AI powered solutions can sense behaviour patterns based on which they can make smart predictions. 10. Simplified task management One huge advantage of customer service chatbots is that you need only one chatbot to handle literally thousands of concurrent customers. Imagine the amount of agent time that can be freed up to resolve routine issues like serving up expected delivery date of a product they ordered or when is their insurance renewal due. This has transformed the relationship between brands and customers. How many times have you hung up on the customer support line because you lost your patience wating for an answer? And how many times have your support calls been left unresolved? You are not alone. Brands around the world are proactively investigating the use of AI into their business to interact directly with customers. While human agents can get overwhelmed performing tasks when they have to deal with mountains of data, AI can deliver answers without breaking a sweat. AI can easily sift through piles of data, analyzing, searching and serving up relevant information to customers in real time. AI can analyze unstructured data at lightning speed, something a human cannot do. AI analyses data and identifies patterns, which can be easily overlooked by a human. AI has in-built Natural Language Processing (NLP) capability. It can read a support ticket and instantly direct it to the right team. Customers are growing increasingly digital today.  It is becoming imperative for businesses to integrate AI into their existing systems to acquire new customers and retain them, at scale. AI has the potential to take the customer experience to whole new level. By making the customer journey more engaging, it can help you stay a step ahead of your competition. And it also eases the lives of service reps. In many ways they are the flag bearers of your brand. Automated responses, personalization, case routing, data analysis and intelligent recommendations, predictive insights, case prioritization are some of the things AI can do without breaking a sweat.   As a Gold Salesforce Consulting Partner, Girikon has been helping organizations around the world leverage the world’s most powerful CRM platform to drive productivity and growth. We recognise that improved efficiency and quality of your customer support will lead to happier customers. To know more about how AI can help your customer service teams improve your CSAT scores, contact us today.

CRM is reshaping customer service today and Salesforce Consultants are helping customers around the world remodel their customer service operations with the world’s leading Customer 360 platform. With rising customer demands and fickle brand loyalty, it is time to stop escalating customer issues and resolve them using a collaborative approach. With the help of the right Salesforce Partner, you can build an intelligent service swarming model to make your service teams become more efficient by bringing expertise to customers faster. Imagine a situation when a key customer reaches out to you with a complex issue. it’s the moment of truth. Does your agent escalate the problem or collaborate on it? If the process you follow is always to escalate then visualize this: a team of experts comes together quickly to help your service agent to resolve the problem. This is service swarming. Service swarming eliminates guesswork from customer service.  It allows service agents to share resources and expertise to resolve complicated customer problems faster. Let’s dive deeper into what service swarming is and how it can benefit your agents and therefore your customers. What is service swarming? Service swarming, often referred to as Intelligent Swarming, is a collaborative approach to customer service. A team of experts from across your organization collaborate with your service agents to resolve complex cases or larger incidents faster. These experts can be from any department such as sales, commerce, operations, legal, finance, or any other department, depending on the issue. This enables teams to leverage their expertise and collaborate on complex issues as and when they come to light. These experts share their knowledge and resources with service agents during the service swarming process. Once they arrive at a solution, the team documents the process and creates a knowledge article so other agents can reference it in the future when similar issues emerge. In today’s digitally connected world, businesses must be prepared to respond in real quick time to large incidents such as security attacks and service outages. The moment an incident like this occurs, the clock starts ticking. There is a barrage of customer calls. Service agents scramble to juggle between diagnosing the problem and dealing with the overwhelming number of calls. An SLA breach looms large which would lead to a PR nightmare. It’s critical for customer-facing teams to be able to quickly and seamlessly collaborate across departments to identify and resolve the problem. Swarming is particularly useful when there is a larger and complex issue facing a single customer like a security breach.  Swarming can also be scaled to address major incidents that affect multiple customers, like a Denial of Service (DoS). In either case, a collaborative approach that brings together multiple teams, departments, and in certain cases even external partners, is vital to finding a resolution. For instance, if a customer contacts a brand about goods showing up as delivered but not received, the agent can bring in the logistics partner to help.  The benefits of service swarming in customer support In a traditional customer service model, agents resolve most cases on their own. They search the knowledge base and seek the help of colleagues for issue resolution. But as more time passes, the customer starts to lose patience. The agent escalates the case to an agent at the next hierarchal level or connects with a supervisor, or in some cases transfers the case to an entirely department, which frustrates the customer even more. A swarming service model turns this entire process on its head. Agents collaborate with a team of experts and are able to arrive at a resolution faster. Not only that, in the process they also become more knowledgeable and efficient, which leads to cost savings for your business. Service Swarming leads to: Personalized customer engagement: According to Salesforce, 82% of customers expect resolution to their problem by interacting with just one person. Service swarming significantly reduces the complexity of larger problems because now the agent is their single point of contact for the customer throughout the case. This fosters a one-to-one relationship that builds trust and loyalty. Accelerated skills development: In any organization, knowledge spreads across many layers and sources. When a complex case is passed off by agent because of lack of knowledge, they lose out on an opportunity to gain valuable experience. However, when they collaborate with experts in a swarm, they learn something with every case resolution. The learning that comes over time with a swarm approach would otherwise take years to build. Scaled automation: According to Salesforce, 63% of agents say it’s extremely challenging to balance promptness and high-quality service. But isn’t that exactly what customers expect from you? With automation, agents can save time and lower operational costs by eliminating repetitive tasks, thereby boosting team efficiency at scale. Service teams more time to focus key activities like building strong, trusted customer relationships. Teams working together: Service Cloud has a unique feature called Expert Finder. The name says it all. Customer service agents no longer have to work in isolation. Service agents can quickly identify and access a support network of experts and resolve the issue. In fact, agents can be incentivized based on their participation and performance. When a case is resolved, supervisors can recognize those involved and award points which encourages greater participation. Evolved success metrics: Performance metrics such as average resolution time and first-contact resolutions are always valuable. In service swarming scenarios however, those metrics don’t always apply. Other key metrics such as lower customer wait times, escalation rates, and case handover take priority. Using these indicators, customer service managers can track agent productivity, expert utilization, customer satisfaction, and retention. Swarming is a new approach to customer service and gives you a fresh perspective of your service teams. There is a paradigm shift in the way your agents and experts work together to resolve customer issues. Now both have a customer centric approach. Collaboration becomes central to customer service; no one is working in isolation. A swarming support model requires a unified platform At Salesforce, the customer is at the centre of everything they do. With a unified platform, you can bring together automation and AI to drive productivity and efficiency. With automation and AI, building on a collaborative approach to problem solving, teams can do more with less, allowing you to focus on the most important thing – making customer delight the goal of every experience. A delightful experience leads to greater trust and lasting value. If you want to implement service swarming in your business to scale your service operations and make it more efficient, you need to invest in the right technology.  Empower your service reps a unified platform that is built for team success, allows for a high degree of automation, delivers insights with AI and helps you to deliver personalized customer experiences every time. With a unified platform, your teams can work together from anywhere and deliver the value that your brand stands for. Salesforce Service Cloud is the world’s leading customer service platform and can help your teams resolve issues and incidents seamlessly. With Slack, you can bring in cross functional swarm experts and easily navigate seamlessly across text, voice and video to deliver case resolution in quick time, thereby building on customer trust and loyalty. And while all this is happening, your service teams are being empowered with fresh knowledge that makes them future ready. Girikon is a Certified Salesforce Development Partner delivering value to customers across the globe. To know more about how we can help you deliver best in class SLAs in customer service with service swarming, contact us today.

PyTorch is an open source ML library used for developing and training deep learning models. The primary contributor to PyTorch is Facebook’s AI research group. PyTorch can be used with Python and C++. As one would expect, the Python interface is more sophisticated. PyTorch, backed by Facebook and supported by Amazon, Microsoft and Salesforce, is quite popular amongst developers and researchers. Unlike other more popular neural network based deep learning frameworks such as TensorFlow, which use static computation graphs, PyTorch uses dynamic computation, which allows for greater flexibility when one wants to build complex architectures. PyTorch works very well with Python, and uses its core concepts like classes, structures and loops, and is therefore more intuitive to understand. When compared with TensorFlow, which has its own programming style, PyTorch is simpler to work with. Why do we need PyTorch? The PyTorch framework can be viewed as the future of deep learning. There are many deep learning frameworks accessible to developers today, with the more preferred frameworks being TensorFlow and PyTorch. PyTorch however offers more flexibility and computing power. For machine learning and AI developers, PyTorch is easier to learn and work with. Here are some advantages of PyTorch: 1. Easy to Learn PyTorch has the same structure as traditional programming which makes it more accessible to developers and enthusiasts. It has been documented very well and the developer community is continuously improving the documentation and support. Thereby making it easy to learn for programmers and non-programmers alike. 2. Developer Productivity It works seamlessly with python, and with many powerful APIs can be easily deployed on Windows or Linux. Most of the tasks in PyTorch can be automated. Which means with just some basic programming skills, developers can easily boost their productivity. 3. Easy to Debug PyTorch can use debugging tools of python. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE from Python, for debugging. 4. Data Parallelism PyTorch can assign computational tasks amongst multiple CPUs or GPUs. This is made possible with its data parallelism feature, which wraps around any module and allows parallel processing. 5. Useful Libraries PyTorch is supported by a large community of developers and researchers who have built tools and libraries to extend the accessibility of PyTorch. This developer community contributes actively in developing computer vision, reinforcement learning, Natural Language Processing (NLP) for research and production. GPyTorch, BoTorch, and Allen NLP are some of the libraries used by PyTorch. This provides access to a powerful set of APIs that further extends the PyTorch framework. Benefits of using PyTorch 1. Python-friendly. PyTorch was created keeping Python in mind (that’s why the prefix), as against other deep learning frameworks that were ported over to Python. PyTorch provides a hybrid front end enabling programmers to easily move most of the code from research to prototyping to execution for production. 2. Optimized for GPUs. PyTorch is optimized for GPUs to accelerate training cycles. PyTorch is supported by the largest cloud service providers: AWS currently supports the latest version of PyTorch. AWS includes its Deep Learning AMI (Amazon Machine Image) and is optimized for GPU.  Microsoft also has plans to support PyTorch in Azure – their cloud service platform. PyTorch has a built-in feature of data parallelism, that allows developers to leverage multiple GPUs on leading cloud platforms. 3. Plethora of tools and libraries. PyTorch comes with a rich ecosystem of tools and libraries for extending its availability and potential. For instance, Torchvision, PyTorch’s built-in set of tools allows developers to work on large and complex image datasets. The PyTorch community of researchers across academia and industry, programmer and ML developers have created a rich ecosystem that provides tools, models, and libraries to extend PyTorch. The objective of this community is to support programmers, engineers and data scientists to further the application of deep learning with PyTorch. 5 ways in which AI apps can use PyTorch With PyTorch, engineering teams can create deep learning predictive algorithms from data sets. For instance, developers can leverage historical housing data to predict future housing prices or use a manufacturing unit’s past production data to predict success rates of new parts. Other common uses of PyTorch include: Image classification: PyTorch can be used to build complex neural network architectures called Convolutional Neural Networks (CNNs). These multilayer CNNs are fed thousands of images of a specific object, say a tree, and much like how our brains works, once the CNN is fed a data set of tree images, it can identify a new image of a tree it has never seen before. This application can be particularly useful in healthcare to detect illnesses or spot patterns, much faster than what the human eye can do. Recently a CNN was used in a study to detect skin cancer. Handwriting recognition: Human handwriting has its inconsistencies as one moves across people and regions.  Handwriting recognition involves interpreting the inconsistencies in human handwriting across people and languages. Forecast time sequences: Another type of neural network is Recurrent Neural Networks (RNNs). They are designed for sequence modelling and are particularly useful for training an algorithm on past data. It can make predictions based on historical data, allowing it to make decisions based on the past. For instance, an airline operator can forecast the number of passengers it will have 3 months from now, based on the data from previous months.  Text generation: RNNs and PyTorch are also used for text generation. In text generation an AI model can be trained on a specific text to create its own output on its learning (for eg interpretation of poetry). Style transfer: One of the most exciting and popular applications of PyTorch is a style transfer. It uses a set of deep learning algorithms to manipulate images and use the visual style of that image on another image to create a new set of images, combining the data of one with the style of another. For example, you can use your vacation album images, apply a style transfer app and make it look like a painting by a famous artist. And as you would expect, it can do the reverse as well. Convert paintings to look like contemporary photos. AI is going to reshape many enterprise functions and how their respective teams work. And one of those areas is CRM. Salesforce, the world’s leading CRM platform is leading the way in embedding trusted AI into all their product offerings. As a Gold Salesforce Partner, Girikon is the preferred choice for many Salesforce customers across the globe. To know more about how AI can work for your business, contact us today.

What is generative CRM? Generative CRM combines the power of generative AI with CRM data to boost productivity and efficiency of teams. It has the power to execute limitless functions such as responding to queries, generating conversational text, suggesting next steps, drafting emails and more. The beauty of Generative AI is that the more people use it, the smarter and faster it will become. In the coming months and years, Generative CRM will effortlessly perform tedious everyday tasks, freeing up time of your teams so they can focus on more important tasks. With the ability to comb the internet for relevant data in a matter of seconds, it can help draft more meaningful responses thereby significantly boosting the efficiency of teams. How generative CRM can boost productivity, efficiency, and customer relationships People spend hours executing ordinary day to day tasks. They sift through data and information, wrack their brains to come up with new social media ad campaigns, iterate multiple times to create a perfect email pitch for a prospective customer, and engage in a fire fight to resolve issues of dissatisfied customers. What if they had a tool to streamline all of that, irrespective of the industry or department they work in?   Generative AI is on the brink of redefining CRM across companies in the coming years. Let us dive deeper to understand how this new age tech, when combined with your CRM, can help teams become more productive and deliver stunning customer experiences. The employee view If you are a new sales rep, and you have just been assigned a new account, it would take you many hours, perhaps even days to get an overview of the company, catch up on the latest company activities, discover the right contacts, and prepare an introductory email. With Generative AI, all this can be done in a matter of seconds by your CRM. So you can refine that email and connect with the right person sooner than ever. This is the potential of generative CRM. When the power of generative AI combines with your CRM data, it unlocks a never seen before power of your CRM. The view across teams Generative AI is poised to reshape how teams work across departments in the years to come. It will empower enterprises to quickly and effortlessly generate AI-driven content across multiple departments -sales, customer service, marketing, commerce, and IT. Service teams would have the power to create automated, smarter, more personalized chatbots that can engage with customers just the way a human rep would, but much faster.  They would have the ability to anticipate, comprehend and respond to customer requests faster than ever. For marketers, generative CRM can help in quickly creating accurate, compelling product descriptions that are optimized for web search. Here are some key benefits that generative CRM would deliver going forward. Reduce time to value AI has already been around for a while with Salesforce Einstein delivering over 200 billion predictions every day. Today, AI products like ChatGPT and Dall-E are empowering millions of people across industries to work more effectively. Generative AI is a deep tech that will filter out the noise that we encounter on the web. If you can ask the right questions contextually, generative CRM will be smart enough to know what to look for and how to present it to you. Free up humans for high-value work If you are a sales rep, imagine trying to acquire a potentially big new customer. You will have to spend hours sifting through data to strengthen your sales pitch, and by the time you do so, it may end up being archaic. You then comb your network and the prospect website and social media handles to find that perfect person to connect with, only to find that they moved on to another company recently. These repetitive, cyclical and routine tasks to acquire a new customer often waste precious time.  Generative AI can speed up these routine activities to make you far more productive. It will allow you to spend more time to do the real thing, which is building relationships with prospects and customers. AI that you can trust Security and privacy will be a critical aspect of generative CRM. Governed by guidelines that specifically address security and privacy concerns, generative AI will build on long standing principles for trusted AI. While publicly available generative AI tools depend only on publicly available data and information, generative CRM will be grounded on private and secure customer data, while also drawing on publicly available data and information such as social media and corporate websites. The ability to fuse public and private data is what makes generative AI driven CRM a trusted, and impactful experience for customers. Generative AI at Salesforce AI is already an integral part of the Salesforce Customer 360 platform, and its potential is limitless. Salesforce Einstein AI technology delivers over 200 billion predictions on a daily basis across multiple Salesforce’s business apps. This includes: Sales, which utilises AI powered insights, to establish the best next steps so reps can close deals faster. Service, which utilises AI to have bot-based natural conversations and provide the best fit answers, freeing up reps to work on more complex and important tasks. Marketing, which uses AI to better understand customer behaviour and personalize marketing campaigns to boost their efficacy.   Commerce, which utilises AI to deliver personalized buying experiences and smarter ecommerce. With generative AI, businesses can connect with their audiences in completely new, more engaging ways across every interaction. Guidelines for Trusted Generative AI Like they do with all their technology innovations, Salesforce is rooting ethical guidelines across all their products to assist businesses innovate rapidly and responsibly.  With the tremendous potential and challenges emerging in generative AI, Salesforce is building further on their Trusted AI Principles with a new set of guidelines to push for responsible development and deployment of generative AI. Here are 5 such guidelines. Accuracy: Use models to deliver verifiable results allowing customers to train models on their own data. Communicate when authenticity of the AI’s response cannot be established with certainty and enable users to ratify these responses. This can be achieved by citing sources, explaining why the AI gave those responses, underscoring areas to double-check such as stats, dates, and creating checks and balances that prevent certain tasks from being fully automated (like code review before deployment) Safety: Effort should be made to mitigate any bias or harmful output by conducting robustness assessments. The privacy of any personal private information should also be protected by creating guardrails. Honesty: When aggregating data to train and evaluate AI models, the source of data should be respected by ensuring their consent for use. Transparency in communication should be maintained by clearly stating that autonomously generated AI content has been delivered. Empowerment: While in some cases, a fully automated AI driven process may be the best option especially for non-critical, publicly available data, there are cases where AI should augment a human role, especially where human judgment is necessary. One needs to establish the right balance to turbo charge human capabilities and make generative AI solutions accessible to all. Sustainability: In our endeavour to establish more and more accuracy in our models, we should develop most appropriate-sized models wherever possible to reduce our carbon footprint. Summary If you are a Salesforce Consultant, this is an exciting time for you. Generative AI has the power to take CRM to the next level. By following the above guidelines, you can deliver never before seen value to your customers with the power of AI. Girikon is a Certified Salesforce Development Partner delivering value to customers across the globe. To know more about how Generative CRM can work for you, contact us today.