May 2023

Artificial intelligence (AI) is growing in stature in the marketing realm.  Marketers today rank AI as their #1 priority for investment, according to a recent State of Marketing Report published by Salesforce. The adoption of AI is staggering with about 84% of marketers reporting that they use AI in their customer acquisition and retention engines, that’s almost 300% growth in 2 years. What exactly are marketers doing with AI? The usage of AI is crossing all barriers, from improved segmentation and personalisation, deeper insight, forecasting and process automation. With the growth in advertising technology, riding on big-data driven AI, advertising companies reimagined the process of delivering digital ads. According to eMarketer,  online ad sales rose from $60 billion in 2019 to an estimated $97 billion in 2022. What do sales teams feel about the impact of AI on what they do? They would tell you-cautiously optimistic. According to latest research from Salesforce, 86% of sales reps view AI as having a positive impact on their future roles. However, 68% of the same reps had concerns as well. And as you would imagine, mostly concerning the very relevance of their existing jobs. 31% percent said technology might eventually negatively impact the art of selling, as AI driven optimisation of sales interactions replaces human-to-human relationships. Five reasons to be happy about AI in advertising While it is only natural to fear the unknown, there is reason to believe that AI will make the existing jobs of sales reps better. AI augments the sales process and leaves people to do what they do best which is to be human. AI has the potential to level the playing field for both advertisers and publishers. It empowers sales teams, who don’t have the budgets that large corporations or media companies may have, with the tools they would like. AI-driven ad technology can help sales teams boost both the effectiveness of their messaging and their efficiency. 1. Improved data unity and synchronization With the impending censure of third-party cookies in Apple and Chrome’s privacy policies, marketing professionals are increasingly seeking first-party data to power their marketing initiatives. Merkle’s 2021 Customer Engagement Report stated that first-party data was a strategic priority for a staggering 88% of the marketers. For publishers, first-party data is vital to building advertiser centric audiences; and for advertisers, first-party data is increasingly vital for targeted advertising. And that’s where the challenge is. First-party data is not only complex, more often than not, it is fragmented and ill organised. According to Salesforce research, 64% of customers start their purchase process on one device and finish on another. With the proliferation of smartphones and devices, marketers have to deal with an average of 12 primary sources of customer data, an increase of 20% from 2020. And as one would expect, most of the data residing in these sources has inconsistent identity information, expired or outdated information, and unconventional taxonomy. AI can be a great asset to a common Customer Data Platform to significantly improve identity matching. Algorithms can execute “fuzzy logic” on IDs and resolve or isolate discrepancies. AI can also ensure data consistency by mapping data from siloed systems to a common data model. 2. Better audience segmentation and discovery Customers today prefer digital first, and they want relevant experiences. They want the messaging to be useful and timely in their digital lives. Delivering a personalised experience mandates the need for organised data as well as smart algorithms to discover customer segments and reveal their needs that may be impossible to do manually in real-time. AI stands out at intelligent segmentation, discovering groups of customers and prospects with common attributes at a scale that is impossible for a human analyst to achieve. AI algos can sift through billions of records of customer data to identify meaningful patterns and segment audiences intelligently for more effective and targeted marketing programs. 3. Natural language shakes hands with technology Natural-language processing (NLP) and image recognition are 2 areas which are extremely promising when it comes to AI. Chatbots have taken customer service to the next level with conversational customer service. Voice assistants such as Siri and Alexa are already changing the user experience whether we want to book a ticket or are in the mood to have Chinese. As voice recognition technology approaches 95%+ accuracy, voice navigation will become an intrinsic part of customer engagement. Voice navigation is already built into call centre systems and used in analytics as well to some extent. However, AI based marketing technology is at the cusp of something never seen before. Imagine an ad sales rep never having to use a keypad again to make the right selection. With less and less human clicks efficiency will improve significantly, and so will the efficacy of the whole process. And this will lead to ad reps spending less time in searching and updating and have more time to do what they do best – selling. 4. Back-end processes efficiency We’ve already talked about how AI can help unify and harmonize data. It can also help sales teams become more efficient by prioritising their efforts, and allowing them to focus their time on key tasks while automating the mundane ones. Lead Scoring was one of the first areas to benefit from AI technology. While there was resistance from sales teams initially in adopting the technology, and rightly so, with the uncertainty of the what the unknown will do to their current jobs. Questions such as – How can a software qualify a good lead better than me, were commonplace. But with technology integration into existing systems, many sales reps now use algorithms regularly to augment their priority their prospects. The same is true for determining next best actions based on interaction history and scheduling meetings. AI can help ad sales teams match available inventory with “most likely to close” sales opportunities thereby significantly trimming the time spent on low-probability leads. 5. Improved measurement and optimisation In the post pandemic period, digital ad spend has seen a meteoric rise. Brands now spend more than half of their advertising budgets on digital platforms. AI can help aggregate and analyse all that data seamlessly to help advertisers establish the impact of campaigns viz a viz desired outcomes, such as sales. Simply put, AI can help ad sales teams separate the noise and identify what works. To measure the efficacy of multi-channel campaigns, one needs to harvest information from dozens of sources. And that’s not all. They also need to apply complex models to determine which aspects of the campaign, channels, devices, and tactics made an impact. AI-driven tools can help ad sales teams with automated recommendations to optimize campaigns based on the historical performance of ads. AI excels at automating tedious tasks, and is also fantastic at sifting through huge amounts of data at unimaginable speed. Something humans can’t do. By allowing AI technology to do what it does best, we make the entire process of ad sales less artificial and more intelligent. Girikon is a Gold Salesforce Consultant delivering value to customers across the globe for over a decade. Contact one of our experts to know how AI can help unlock the true value of your advertising sales.

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.

TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algoriths. TensorFlow bundles together a host of machine learning models and algorithms and with the use of common programming frameworks, makes them useful. TensorFlow uses Python and JavaScript to build user friendly APIs for connecting with apps, and uses core C++ to execute the app functionalities. While it is still early days for machine learning technology, it continues to evolve rapidly, introducing us to a new world of advanced algorithms and deep learning. Deep learning uses algorithms commonly referred to as Neural Networks. As the name suggests, they draw inspiration from our biological nervous systems, led by the brain, to process information. Deep learning algorithms enable computing devices to identify every single bit of data, establish what it means and learn patterns. TensorFlow is a tool to develop deep learning models. It is an open-source AI library that uses data flow graphs to build learning models. With TensorFlow, programmers can build large-scale, multi-layered neural networks. TensorFlow is primarily used to perceive, understand, classify data and create predictive models. Main Use Cases of TensorFlow While TensorFlow can be used for many applications, here are 5 commonly used applications in the world of artificial intelligence. Voice/Sound Recognition One of the most popular use cases of TensorFlow is audio signal based applications. When fed appropriate data, neural networks can perceive and understand audio signals. These can be: Voice recognition — primarily used in Internet of Things (IoT) applications, Automotive applications (Voice command based actions), Security (Authentication) Voice search — Commonly used in Telecom and by mobile phone manufacturers Sentiment Analysis — used in CRM applications Flaw Detection (noise analysis) —Automotive and Aviation applications The world is familiar with the common use case of voice-search and voice-activated assistants. This use case has been widely popularised by smartphone manufacturers and Mobile OS developers such as Apple’s Siri, Google Assistant and Microsoft Cortana. Understanding and analyzing language is another widely used use case for Voice Recognition. Speech-to-text applications are used to extract and understand sound bites in larger audio files, and convert it into text. CRM is another area were voice/sound based applications can be implemented to deliver a better and smarter customer service experience. Imagine a scenario where TensorFlow algorithms fill in for customer service reps, and guide customers to the right set of information much faster than an agent. Text Based Applications This is another commonly used application of TensorFlow. Text based applications for instance sentiment analysis can be used in CRM apps and Social Media for improving the customer or prospect experience, Threat Detection, used in Social Media and Government applications and Fraud Detection, used by Insurance, Finance companies are some common examples. Language Detection is another popular use case of TensorFlow for text based applications. We are quite familiar with Google Translate. More than 130 languages can be translated into each other using this service. An AI powered version of a translate engine can be used in common real world situations like translating heath diagnosis technical terminology or legal jargon in contracts into plain language. Text Summarization Google also came up with sequence-to-sequence learning, a technique for shorter text summarization. This can then be used to build headlines for news and articles. Another use case for TensorFlow popularised by Google is SmartReply. It automatically generates e-mail responses based on text recognition and contextual understanding. Image Recognition This use case is primarily used by Social Media and Smartphone Manufacturers. Facial Recognition, Image Search, Motion Detection, Computer Vision and Image Clustering are nowadays being deployed in Warehousing, Healthcare, Automotive, and Aviation Industries. Google Lens is another example where Image Recognition is being used to understand the content and context to help identify people and objects within images. TensorFlow object recognition algorithms have the ability to identify and classify random objects within larger images. This has found use in engineering modelling applications such as creating 3D spaces from 2D images. Facebook’s Deep face is another example of photo tagging using image recognition technology. Deep learning technology can identify an object in an image never seen before by analyzing thousands of images with similar objects. Healthcare Industry is also at the cusp of using Image Recognition for faster diagnosis.  TensorFlow algorithms can process information and recognize patterns much faster than the human eye to spot illnesses and detect health problems faster than ever. Time Series TensorFlow Time Series algorithms is another method used today to establish patterns and forecasting of time series data. Meaningful statistics can be derived by these algos along with recommended actions. TensorFlow Time Series algorithms allow forecasting of generic time periods apart from generating alternative versions of the predicted time series. A popular use case for Time Series algorithms is Recommendations. Time Series Recommendations has seen widespread usage amongst leading organizations such as Netflix, Google, Amazon, where they analyze and compare activity of millions of users to determine what a customer might wish to view or purchase. And with every interaction, while recording the activity of every action, these recommendations get even smarter. For instance, they throw up content what your family members or friends like or offer you a gift they might like. Finance, Insurance, Government, Security and Threat detection, Predictive Analysis, Resource Planning and forecasting are some of the other use case scenarios of TensorFlow Time Series algorithms. Video Detection TensorFlow deep learning algorithms can also be used on video data. This is used in Motion Detection in Automotive and Aviation, Role based Gaming, Security and Threat Detection. Today, universities are doing deep research on Video Classification at a large scale to perceive, analyze, understand, classify video data. NASA is using TensorFlow algorithms to build a system for orbit classification and object clustering of asteroids. Consequently, they will be able to classify and predict near earth objects. TensorFlow is an open-source framework, allowing developers the freedom to work on innovative and disruptive use cases, which will contribute further to Machine Learning technology. Amongst many things, TensorFlow’s popularity is primarily due to the computational graph concept, automatic differentiation, and the adaptability of its python API structure. This makes TensorFlow more accessible to developers to solve real problems. Here are some advantages of TensorFlow. 1. Scalable The TensorFlow library is well defined and structured. This means it works just as efficiently on a mobile device as on a powerful computer. 2. Open Source The TensorFlow library is available free of cost. Anyone, anywhere can work on it and use it to solve problems. 3. Graphs Tensorflow has a very powerful, inbuilt data visualization capability. This makes it easier for developers to work on neural networks. 4. Debugging Tensorboard, which is a part of TensorFlow, allows easy debugging of code blocks. This reduces the need for combing through the whole code. 5. Parallelism TensorFlow uses Central Processing Unit (CPU) and Graphics Processing Unit (GPU) for its functioning. Developers can use the architecture freely based on the problem they are trying to solve. A system uses GPU by default, which is why  TensorFlow is sometimes referred to as a hardware acceleration library because it reduces memory usage. 6. Compatible TensorFlow is compatible with popular programming languages like Python, C++, JavaScript, etc. This allows developers the freedom to work in an environment they are most comfortable with. 7. Architectural Support The TensorFlow architecture uses Tensor Processing Unit (TPU). This makes computation faster than what one would get when using CPU and GPU. TPU models can be easily deployed on the cloud and work faster than CPU and GPU. 8. Library management With the Google backing, TensorFlow is updated regularly with enhanced capability and flexibility with every release. AI is already an intrinsic part of Salesforce, the world’s leading CRM platform. 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.

In March 2023, Salesforce launched Einstein GPT, the World’s First Generative AI for CRM. Einstein GPT uses the power of generative AI to deliver personalized content across every Salesforce product, thereby making teams more productive and delivering a better customer experience. Einstein GPT is open and scalable, and supports public and private AI models built specifically for CRM. Einstein GPT is trained on trusted, real-time data and seamlessly integrates the with the OpenAI framework to deliver out-of-the-box generative AI capabilities to Salesforce users. The new ChatGPT app for Slack integrates seamlessly with OpenAI’s deep AI technology to power instant conversations, and provide research and writing assistance. Whether it be sales, service, marketing or commerce, Einstein GPT for Salesforce will transform every customer experience at never seen before scale. In a sense, it opens a new door to the AI future for all Salesforce customers. Salesforce has fused its proprietary AI models with generative AI technology in Einstein GPT that unifies and synchronises all of a company’s customer data. Using Einstein GPT, customers can now easily connect that data to OpenAI’s advanced AI models, or use their own model and natural-language prompts directly within their Salesforce CRM to seamlessly generate content that self-adapts continuously to changing customer needs in real time. For instance, Einstein GPT can create personalized email drafts for sales reps, generate automated and tailored responses for service reps to respond to customer queries more quickly, generate appropriate content for marketers to augment campaign efficacy, and developers can get access to auto-generated code allowing them to build and deploy apps much faster. Going Deeper: Einstein GPT in CRM Salesforce Einstein is already delivering over than 200 billion AI-driven predictions everyday.  Einstein GPT is the next generation of Einstein, and by combining proprietary Salesforce AI models with OpenAI, customers can use natural-language prompts on CRM data to trigger powerful, time-saving automations, and create personalized, AI-generated content. Sales: Auto-generate routine tasks like drafting emails, preparing meeting schedules, and prepping for follow ups. Service: Auto generate articles from case note archives. Auto-generate conversational and personalised chat to smartly engage with customers. Fast track service interactions and enhance the customer experience. Marketing: Dynamically generate personalized and engaging content faster than ever to interact with potential and existing customers across channels. Slack : Get AI-driven customer insights in Slack for eg. smart summaries of sales opportunities and self updating knowledge articles. Developers: Developers can auto generate code by utilising Salesforce’s proprietary language model to ask contextual questions for languages like Apex through an AI chat assistant. Einstein GPT is in-built in Salesforce. Which means you can use your private data to tailor everything it generates suited to your unique business. And since Einstein GPT is available across the entire Salesforce platform, it can improve every single customer experience. Salesforce understands that generative AI encompasses more than just ChatGPT. Einstein GPT  has been designed to allow seamless integrations with other language models. This allows developers to bring their preferred model using normalized APIs and an open network of AI partners. Einstein GPT is designed to empower businesses with path breaking AI capabilities, using your own data and models to drive customer experiences. Embedding AI into the Salesforce platform has delivered huge operational efficiencies for partners and customers.  Generative AI technology has the potential to transform the way companies engage with their customers, deliver powerful experiences, and drive customer retention. Generative AI technology will drive the next generation of customer experience. Einstein GPT for Salesforce Developers As technology innovation progresses, so does the way developers write and analyze code. Generative Artificial Intelligence is perhaps the most exciting development in recent years for code generation and analysis.  This technology has the power to make development faster, more efficient and accurate. Let’s look at how Salesforce AI Research is powering Einstein GPT for developers across the globe and how it will change the way apps are deployed on Salesforce. Generative AI for code (Apex) Code generation involves using machine learning algorithms to analyse large amounts of existing code, and then generate new code based on that analysis. This is particularly useful for tedious tasks, such as drafting emails. One obvious and huge benefit of code generation is that it saves a lot of time for developers. Instead of writing every line of code from ground up, developers can use AI-powered tools to generate most of the required code automatically. Not only does this accelerate the entire development process, it also reduces the chances of human error. Code generation has many benefits, including: Code standardization: Automating generation of repetitive code blocks that guarantees consistency and standardization of code. Accelerated prototyping: Generative AI based code generation accelerates the prototyping process by quickly creating run of the mill code. Codebase becomes more scalable because of standardization. Simplified code: Generative AI automates the creation of repetitive code blocks thereby simplifying it. Code becomes easier to maintain and scale. Salesforce Consultants and Developers can now derive the benefits of Einstein GPT within the IDE experience. With inbuilt natural language processing capability, developers can have auto generated code created for them within the Code Builder as per their specific needs. The machine learning algos that drive the generative AI experience in Einstein GPT are based on Salesforce’s proprietary models and enhanced with best-in-class coding guidelines. Static and dynamic Apex analysis Code analysis is another field where AI is making significant progress. As software development become more and more complex, it becomes increasingly challenging for developers to precisely analyse and understand the code. Salesforce is piloting a new capability this year for Apex analysis. With this feature, developers can quickly and precisely analyse large amounts of Apex code, identify potential defects, runtime and other inefficiencies. This will save Salesforce Partners and developers a substantial amount of time and effort. They would no longer have to manually sift through each line of code to find potential problems. One of the key benefits of AI is that it can identify potential problems easily that developers may miss at runtime. AI-driven code analysis and code generation work synchronously. Using AI powered static and dynamic analysis, patterns in your code base will be fed back into the code generation process in run time, and vice versa. Conclusion AI-driven code generation and analysis is changing the entire development paradigm. And this is just the beginning. Going forward, Salesforce has plans to assist with automated test generation, intelligent code clarification, and more. 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. With Einstein GPT you can get multi-dimensional insights of your CRM data and 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 Einstein GPT can work for you, contact us today.