AI/ML

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

Business leaders, lawmakers, academicians, scientists, and many others are looking for ways to harness the power of generative AI, which can potentially transform the way we learn and work. In the corporate world, generative AI has the power to transform the way businesses interact with customers and drive growth. The latest research from Salesforce indicates that 2 out of 3 (67%) of IT leaders are looking to deploy generative AI in their business over the next 18 months, and 1 out of 3 are calling it their topmost priority. Organizations are exploring how this disruptive technology of generative AI could impact every aspect of their business, from sales, marketing, service, commerce, engineering, HR, and others. While there is no doubt about the promise of generative AI, business leaders want a trusted and secure way for their workforce to use this technology. Almost 4 out of 5 (79%) of business leaders voiced concerns that this technology brings along the baggage of security risks and biased outcomes. At a larger level, businesses must recognize the importance of ethical, transparent, and responsible use of this technology. A company using generative AI technology to interact with customers is in an entirely different setting from individuals using it for private consumption. There is an imminent need for businesses to adhere to regulations relevant to their industry.  Irresponsible, inaccurate, or offensive outcomes of generative AI could open a pandora’s box of legal, financial, and ethical consequences. For instance, the harm caused when a generative AI tool gives incorrect steps for baking a strawberry cake is much lower than when it gives incorrect instructions to a field technician for repairing a piece of machinery. If your generative AI tool is not founded on ethical guidelines with adequate guardrails in place, generative AI can have unintended harmful consequences that could back come to haunt you. Companies need a clearly defined framework for using generative AI and to align it with their business goals including how it will help their existing employees in sales, marketing, service, commerce, and other departments that generative AI touches. In 2019, Salesforce published a set of trusted AI practices that covered transparency, accountability, and reliability, to help guide the development of ethical AI systems. These can be applied to any business looking to invest in AI. But having a rule book on best practices for AI isn’t enough; companies must commit to operationalizing them during the development and adoption of AI. A mature and ethical AI initiative puts into practice its principles via responsible AI development and deployment by combining multiple disciplines associated with new product development such as product design, data management, engineering, and copyrights, to mitigate any potential risks and maximize the benefits of AI. There are existing models for how companies can initiate, nurture, and grow these practices, which provide roadmaps for how to create a holistic infrastructure for ethical, responsible, and trusted AI development. With the emergence and accessibility of mainstream generative AI, organizations have recognized that they need specific guidelines to address the potential risks of this technology. These guidelines don’t replace core values but act as a guiding light for how they can be put into practice as companies build tools and systems that leverage this new technology. Guidelines for the development of ethical generative AI The following set of guidelines can help companies evaluate the risks associated with generative AI as these tools enter the mainstream. They cover five key areas. Accuracy Businesses should be able to train their AI models on their own data to produce results that can be verified with the right balance of accuracy, relevance, and recall (the large language model’s ability to accurately identify positive cases from a given dataset). It’s important to recognize and communicate generative AI responses in cases of uncertainty so that people can validate them. The simplest way to do this is by mentioning the sources of data which the AI model is retrieving information from to create a response, elucidating why the AI gave those responses. By highlighting uncertainty and having adequate guardrails in place ensures certain tasks cannot be fully automated. Safety Businesses need to make every possible effort to reduce output bias and toxicity by prioritizing regular and consistent bias and explainability assessments. Companies need to protect and safeguard personally identifying information (PII) present in the training dataset to prevent any potential harm. Additionally, security assessments (such as reviewing guardrails) can help companies identify potential vulnerabilities that may be exploited by AI. Honesty When aggregating training data for your AI models, data provenance must be prioritized to make sure there is clear consent to use that data. This can be done by using open-source and user-provided data, and when AI generates outputs autonomously, it’s imperative to be transparent that this is AI-generated content. For this declaration (or disclaimer), watermarks can be used in the content or by in-app messaging. Empowerment While AI can be deployed autonomously for certain basic processes which can be fully automated, in most cases AI should play the role of a supporting actor. Generative AI today is proving to be a powerful assistant. In industries, such as financial services or healthcare, where building trust is of utmost importance, it’s critical to have human involvement in decision-making. For example, AI can provide data-driven insights and humans can take action based on that to build trust and transparency. Furthermore, make sure that your AI model’s outputs are accessible to everyone (e.g., provide ALT text with images). And lastly, businesses must respect content contributors and data labelers. Sustainability Language models are classified as “large” depending on the number of values or parameters they use. Some popular large language models (LLMs) have hundreds of billions of parameters and use a lot of machine time (translating to high consumption of energy and water) to train them. To put things in perspective, GPT3 consumed 1.3 gigawatt hours of energy, which is enough energy to power 120 U.S. homes for a year and 700k liters of clean water. When investigating AI models for your business, large does not necessarily mean better. As model development becomes a mainstream activity, businesses will endeavor to minimize the size of their models while maximizing their accuracy by training them on large volumes of high-quality data. In such a scenario, less energy will be consumed at data centers because of the lesser computation required, translating to a reduced carbon footprint. Integrating generative AI Most businesses will embed third-party generative AI tools into their operations instead of building one internally from the ground up. Here are some strategic tips for safely embedding generative AI in business apps to drive results: Use zero or first-party data Businesses should train their generative AI models on zero-party data (data that customers consent to), and first-party data, which they collect directly. Reliable data provenance is critical to ensure that your AI models are accurate, reliable, and trusted. When you depend on third-party data or data acquired from external sources, it becomes difficult to train AI models to provide accurate outputs. Let’s look at an example. Data brokers may be having legacy data or data combined incorrectly from accounts that don’t belong to the same individual or they could draw inaccurate inferences from that data. In the business context, this applies to customers when the AI models are being grounded in that data. Consequently, in Marketing Cloud, if all the customer’s data in the CRM came from data brokers, the personalization may be inaccurate. Keep data fresh and labeled Data is the backbone of AI. Language models that generate replies to customer service queries will likely provide inaccurate or outdated outputs if the training is grounded in data that is old, incomplete, or inaccurate. This can lead to something referred to as “hallucinations”, where an AI tool asserts that a misrepresentation is the truth. Likewise, if training data contains bias, the AI tool will only propagate that bias. Organizations must thoroughly review all their training data that will be used to train models and eliminate any bias, toxicity, and inaccuracy. This is the key to ensuring safety and accuracy. Ensure human intervention Just because a process can be automated doesn’t mean that’s the best way to go about it. Generative AI isn’t yet capable of empathy, understanding context or emotion, or knowing when they’re wrong or hurtful. Human involvement is necessary to review outputs for accuracy, remove bias, to ensure that their AI is working as intended. At a broader level, generative AI should be seen as a means to supplement human capabilities, not replace them. Businesses have a crucial role to play in the responsible adoption of generative AI, and integrating these tools into their everyday operations in ways that enhance the experience of their employees and customers. And this goes all the way back to ensuring the responsible use of AI – maintaining accuracy, safety, transparency, sustainability, and mitigating bias, toxicity, and harmful outcomes. And the commitment to responsible and trusted AI should extend beyond business objectives and include social responsibilities and ethical AI practices. Test thoroughly Generative AI tools need constant supervision. Businesses can begin by automating the review process (partially) by collecting AI metadata and defining standard mitigation methods for specific risks. Eventually, humans must be at the helm of affairs to validate generative AI output for accuracy, bias, toxicity, and hallucinations. Organizations can look at ethical AI training for engineers and managers to assess AI tools. Get feedback Listening to all stakeholders in AI – employees, advisors, customers, and impacted communities is vital to identify risks and refine your models. Organizations must create new communication channels for employees to report concerns. In fact, incentivizing issue reporting can be effective as well. Some companies have created ethics advisory councils comprising of employees and external experts to assess AI development. Having open channels of communication with the larger community is key to preventing unintended consequences. As generative AI becomes part of the mainstream, businesses have the responsibility to ensure that this emerging technology is being used ethically. By committing themselves to ethical practices and having adequate safeguards in place, they can ensure that the AI systems they deploy are accurate, safe, and reliable and that they help everyone connected flourish. As a Salesforce Consulting Partner, we are part of an ecosystem that is leading this transformation for businesses. Generative AI is evolving at breakneck speed, so the steps you take today need to evolve over time. But adopting and committing to a strong ethical framework can help you navigate this period of rapid change.

AI has reached an inflection point, the experimentation phase is over. In 2026, AI Trends moves from “interesting pilot projects” to a core operating system for enterprise growth, efficiency, and competitiveness. The conversations inside boardrooms are changing from “What can AI do?” to “How do we redesign the business rules with AI at the center?” Major industry research, along with online articles from technology leaders such as Microsoft, Google, OpenAI, Deloitte, Gartner, and Salesforce, shows a decisive shift: AI is becoming more contextual, more autonomous, more predictive and more deeply embedded in everyday business workflows. For C-suite leaders, understanding these trends is no longer optional. It shapes budget decisions, transformation roadmaps, talent strategies, customer experience initiatives, and risk management frameworks. This guide explores 10 practical 2026 AI trends that will affect every organization,—what they mean, why they matter, and how leaders can act on them today. Why 2026 Is a Defining Year for Enterprise AI Between 2023 and 2025, most companies adopted AI in pockets, marketing content, chat-bots, case summarization, sales forecasting, and internal productivity tools. But as Microsoft highlighted in its 2026 outlook, the next wave of AI is not about isolated use cases. It’s about work transformation, data connectivity, and responsible autonomy. Three forces make 2026 a pivotal year: AI shifts from responding to acting: Agentic AI can execute multi-step tasks and collaborate across workflows. Enterprise data foundations mature: Unified customer and operational profiles unlock more accurate, trusted AI outputs. Governance frameworks mature: Boards demand accountability, regulation accelerates, and leaders need defensible AI programs. In short, 2026 is when AI becomes the backbone of operations, not a side project. Top 2026 AI Trends Every Business Leader Should Watch 1 — AI Becomes a Collaborative Partner in Work According to insights shared by the leadership team at Microsoft, AI is evolving from a tool that responds to prompts into an active partner that collaborates with humans in real time. These new models don’t just generate text or images, they analyze context, monitor progress, and anticipate next steps. In practical terms, this means AI will: guide employees through multi-step business processes offer suggestions during complex decisions surface risks before humans notice them draft, refine, and validate work outputs Instead of replacing roles, AI enhances human judgment. Managers will increasingly evaluate performance based on decision quality and outcomes, not manual task completion. Leadership implication: Redesign roles and KPIs around augmented work, train teams to collaborate with AI, not just use it for emails or research. 2 — Rise of Intelligent Agentic AI Inside the Enterprise Global businesses are focusing on 2026 vision, and it highlights a major movement toward AI agents. Everyone want systems that can plan, act, and execute work across business functions. These are not simple chat-bots, they are action-taking entities capable of automating entire workflows. Examples inside enterprises include: automatically triaging and resolving support tickets updating CRM and ERP systems based on rules, customer chat or emails and context managing procurement workflows handling onboarding or compliance tasks end-to-end For example: Salesforce-native automation tools such as GirikSMS can read customer chats or inbound messages and update CRM records automatically, ensuring agents and teams always work with accurate, up-to-date information. The power of agentic AI is not task automation, it’s autonomous orchestration. But this introduces risk. Without proper guardrails, agents might trigger actions that are irreversible or costly. Leadership implication: CIOs and COOs must build governance frameworks before deploying agents. Policies, audit trails, testing environments, and role-based access control become crucial. 3 — Predictive Intelligence Becomes Standard Across Operations Predictive AI will no longer be limited to data science teams. It becomes embedded into planning, forecasting, and resource allocation across business units. Examples include: dynamic demand forecasting real-time operational risk scoring scenario-based pricing optimization automated forecasting that adjusts with market signals Unlike dashboards or BI tools, predictive AI provides forward-looking guidance, helping leaders make decisions with confidence under uncertainty. Leadership implication: Move from descriptive analytics (“what happened”) to predictive guidance (“what will happen and why”). Mandate predictive tools in quarterly planning cycles. 4 — Data Unification Becomes the Foundation for Accurate AI AI’s effectiveness depends entirely on data quality, completeness, and connectivity. In 2026, the competitive differentiator is not the AI model, it’s the enterprise data foundation underneath it. Leaders are prioritizing: unified customer profiles common data models standardized taxonomies clean data pipelines with lineage policy-based data access Organizations skipping data unification often experience poor predictions, hallucinations, compliance risk, and limited ROI. Leadership implication: Treat data consolidation as a board-level initiative. AI maturity depends on it. 5 — Multimodal and Contextual AI Transform Business Processes 2026’s biggest breakthrough is the rise of multimodal AI—systems that can understand and combine text, audio, images, video, documents, and structured data. Microsoft emphasized that multimodal understanding enables AI to reason in ways closer to human analysis. Practical use cases include: analyzing defective product images + service tickets reading contracts + financial data to flag risk interpreting call transcripts alongside CRM context auto-generating reports that tie charts to narrative insight Context-aware AI reduces irrelevant outputs and increases accuracy because it understands what the user is trying to achieve, not just the text of the request. Leadership implication: Reevaluate workflows where employees switch between tools or data types. These are prime candidates for multimodal AI automation. 6 — Low-Code and No-Code AI Expands Ownership to Business Teams AI development is no longer limited to data scientists or engineers. With low-code and no-code AI platforms, business teams can build prototypes, automate processes, and test models without depending on long IT cycles. This democratizes innovation but also raises governance concerns. Examples of emerging low-code AI use cases include: service leaders building automated case classification flows HR teams creating onboarding assistants sales teams generating account insights and next-best-actions marketing teams automating personalization without engineering support This shift accelerates value delivery but creates a dual responsibility: empower teams while protecting the business. Leadership implication: Enable business users with low-code tools but enforce centralized guardrails—model review, access controls, data policies, and monitoring. 7 — Predictive and Proactive Customer Experience (Anticipatory CX) Customer expectations continue rising, and reactive service is no longer enough. In 2026, AI-driven organizations will move to anticipatory CX—predicting needs and intervening before problems materialize. Examples include: flagging accounts at churn risk weeks before traditional indicators identifying customers ready for renewal upsell detecting product usage anomalies early providing agents with proactive recommendations before the customer asks Leading platforms already show this shift; predictive insights now sit alongside customer records, giving service teams actionable intelligence with AI instead of dashboards. Leadership implication: Redesign CX strategies around prediction, not just personalization. Invest in data models and journey mapping that support proactive engagement. 8 — Continuous Learning, Embedded Onboarding, and Knowledge Capture AI is redefining workplace learning. Traditional training courses, long documents, LMS modules are too slow for today’s pace. AI enables in-the-flow-of-work learning, where employees receive contextual guidance as they perform tasks. AI can now: generate playbooks and checklists tailored to the task summarize tribal knowledge and convert it into searchable libraries provide coaching based on real work patterns automatically update documentation as processes evolve The long-term impact is substantial: faster ramp time, consistent execution, and less dependency on expert individuals. Leadership implication: Shift L&D strategy toward embedded learning. Treat AI as a capability that institutionalizes expertise across the organization. 9 — Smarter and More Efficient AI Infrastructure Reduces Cost and Latency 2026 is not just about model innovation. It’s about infrastructure innovation. Microsoft and other cloud providers are pushing toward distributed compute, efficient inference, hybrid deployments, and energy-friendly architectures. For enterprises, this translates into: lower operational costs for AI at scale reduced latency, improving user experience more predictable budgeting through AI cost governance models domain-specific models optimized for speed and efficiency This matters because AI costs can quickly balloon without transparency. In 2026, C-suites will demand clear chargeback models and visibility into consumption patterns. Leadership implication: Treat AI infrastructure as a strategic asset. Optimize models, monitor cost drivers, and establish cross-functional policies for AI spend. 10 — Governance, Safety, and Responsible AI Become Mandatory As AI becomes more autonomous and integrated into core operations, risk exposure increases—privacy, copyright, bias, security, misinformation, and compliance issues. Regulatory frameworks are accelerating worldwide, and boards will expect documented governance structures. Responsible AI in 2026 includes: model inventories and risk classifications explainability guidelines access and permission controls bias detection and continuous monitoring audit trails for actions taken by AI agents AI safety is no longer an afterthought—it is part of operational resilience. Leadership implication: Establish an enterprise-wide AI governance council. Treat AI standards like cybersecurity standards—non-negotiable and regularly audited. What These Trends Mean for C-Suite Leaders The shift to operational AI redefines executive responsibilities. AI is no longer a technology decision; it is an organizational design decision. Leaders must focus on four areas: 1. Business redesign: AI changes workflows, team structures, KPIs, and accountability. 2. Operating model: Governance must scale across tools, departments, and data streams. 3. Talent strategy: Teams need AI literacy, training, and augmented roles—not replacement. 4. Risk posture: Every AI initiative now has ethical, security, regulatory, and quality implications. Organizations that treat AI as an add-on will fall behind. Leaders who treat it as a system-level redesign will create sustainable competitive advantage. A 2026 AI-Readiness Framework for Executives Below is a simple framework to help leaders assess readiness for enterprise-scale AI adoption: Data Readiness: Do we have unified, governed, high-quality data accessible to AI systems? Process Readiness: Are our workflows documented, standardized, and measurable? People Readiness: Are employees trained to collaborate with AI and understand its outputs? Technology Readiness: Do we have scalable, cost-efficient infrastructure and integrations? Governance Readiness: Do we have risk controls, auditing mechanisms, and safety policies? Weakness in any one dimension will limit AI ROI. How to Prepare: A Practical Roadmap for 2026 Below is a simple roadmap to help organizations transition from experimentation to operational AI maturity. Quarter 1 — Stabilize Data Foundations: Consolidate data models, unify customer profiles, establish lineage, and clean key datasets. Quarter 2 — Deploy Controlled Agentic Workflows: Choose 1–2 low-risk workflows (support triage, onboarding, compliance checks) and deploy AI agents with human oversight. Quarter 3 — Democratize AI with Guardrails: Empower business teams with no-code AI while enforcing policy-based constraints, monitoring, and approvals. Quarter 4 — Operationalize Governance and Metrics: Implement monitoring dashboards, cost management processes, bias detection, and model documentation. Quick Wins Leaders Can Activate Now Automate repetitive documentation tasks: Use AI summarization to reduce manual note-taking, triage, and reporting. Create a model inventory: Centralize all AI initiatives across departments with owners, risks, and evaluation metrics. Use AI in quarterly planning: Add predictive models to budgeting, forecasting, and capacity planning cycles. What Not to Do in 2026! Do not scale AI without governance: This leads to regulatory risk and operational failures. Do not deploy AI on fragmented data: Inconsistent inputs = inconsistent performance. Do not focus only on cost-cutting: AI’s value lies in innovation, speed, and competitive agility. Do not expect AI to replace strategy: Leaders must still define goals and measure outcomes. Do not over-automate customer interactions: Human judgment is critical in escalations and complex scenarios. Conclusion 2026 is not just another year in the AI hype cycle, it is a structural turning point. AI will transform enterprise operations, decision-making, customer experience, training, and governance. C-suite teams that prepare now, by investing in data, redesigning workflows, enabling employee augmentation, and establishing governance, will build a durable competitive advantage. Those that delay will find themselves outpaced by faster, more adaptive competitors. The next era of enterprise AI belongs to leaders who can balance innovation with responsibility, speed with governance, and automation with human judgment. The companies that get this right will shape the next decade of business performance. To dive deeper into how data-driven companies use AI to outperform their competitors, explore our detailed analysis.

As an IT manager, you would have handled several roll-outs and migrations, streamlined legacy systems, and upgraded cybersecurity. And now AI is staring you in the face. How ready are you to build AI apps that your business needs? Do you have in-house skills to build and deploy AI apps? Whether you are building a customer service app or a marketing app, you can adopt a systematic approach to going about it. Here are 5 key steps to building effective AI apps for your organization. 1. Define exactly what you want from your app before starting to build one Businesses across industries have started to embrace the disruptive technology of AI for their everyday operations. Your competitors are likely deploying AI chatbots to provide 24/7 automated, intelligent, customer service. But before you start investing time and resources in building AI apps, you need to answer some key questions. What is the problem you’re trying to solve? Talk with your business’s leaders. Do you want to boost sales? Improve customer satisfaction score?  As a starting step, clearly define use cases. Next, define the desired end state for each use case. This will help you estimate how much effort is required, who to involve, and whether you have adequate resources. What are your competitors doing? Understand what your competitors are doing with their AI tools and for whom. And how can you innovate further on those ideas? And of course, you need to answer one important question – can you build AI apps in-house? Do you have the necessary skills and experience in your team to do this? Based on the use cases you have identified, will you require generative or predictive AI If you don’t have the skills internally like Machine Learning and Natural Language Processing, look for partners and ISVs for solutions and do a thorough comparison of their offers and capabilities. 2. Define the perimeter for ethics and security As an IT manager, security, privacy, and accuracy are not alien to you. But AI amplifies the challenges and raises many risks such as bias and toxicity. AI bias: Negative bias can be caused by algorithm error based on human prejudices or false assumptions. The consequence is an AI tool that works in unintended ways. Generative AI can propagate outputs based on errors and further amplify the problem. Toxicity: Abusive language and hurtful comments can appear in AI-generated outputs. Researchers have found that assuming certain personas can amplify the toxicity of the response. Before you start building your AI app, define trust and ethics parameters. Trusted AI should be empowering, and inclusive apart from being responsible and transparent. 3. Good data is the foundation of effective AI apps If you are building generative AI apps, your machine learning models will train on the data that is fed to them. AI machine learning models train on all kinds of data. And that data needs to be clean and free of redundancy. The more data your LLMs can be trained on, the better will be the output of your AI. 4. Choose the right technology for your AI app The technology you select for building your AI depends to a certain extent on your use case. If your app summarizes text, processes language, or a knowledge base, you will need an LLM. Over time, as the LLM learns more about your business and its data, it can make logical interpretations and draw conclusions. Building your own learning model can be expensive. You will need to hire data scientists and engineers with expertise in ML and NLP. While it is a lengthy cycle, if you do decide to take this route, once your team is ready you can take the help of libraries and toolkits and integrate them into your development. Generative AI platforms and libraries ML and DL platforms: Amazon SageMaker and Google Vertex AI have built-in libraries and tools to train your AI model and support multiple programming languages. NLP toolkits: If you are building chatbots or virtual assistants, SpaCy is a great NLP toolkit for Python enthusiasts. OpenAI allows you to customize their GPTs for your apps. Deep learning libraries: If you want to build apps for speech or image recognition, you can look at a deep learning library to find a framework for building, training, and deploying your apps. Open-source libraries such as PyTorch and MXNet can be used in combination depending on your use case. Computer vision libraries: If you want your app to analyze images or video, you can use open-source libraries such as OpenCV and TensorFlow. PyTorch is another option that can be helpful. Building AI apps with CRM data If you want to build customer-interfacing apps, you will need to leverage your customer data. And without all your data in one place, that’s hard to do. You need an enterprise-grade CRM like Salesforce to make your AI app work best for you. You can connect AI models to Salesforce Data Cloud without running into a wall. With the Model Builder (erstwhile called Einstein Studio), you can bring your own model into Salesforce. 5. Build AI apps and start deploying In a recent developers’ survey conducted by Salesforce, it was found that 70% of developers use or intend to use AI for development. The biggest benefit developers see is reduced development cycles. Try AI for code generation Whether you use AI or not for code generation, you can reduce development time with the Einstein 1 platform for Salesforce. Einstein for Developers understands natural language prompts to write code in seconds. The more precise your prompt, the better will be the quality of the code generated. Once the code is generated you can accept, revise, or reject it. Einstein for Developers uses a customized Large Language Model based on the open-source CodeGen AI model from Salesforce. Use an IDE to accelerate development A web-based integrated development environment (IDE) allows your teams to work from anywhere, anytime. You can modify and debug code and maintain source control in one place.  Code Builder, the new IDE from Salesforce is preloaded with frameworks, has built-in integration with Git, and is free for admins and developers. Salesforce also allows you to integrate other IDEs with it. Follow App Lifecycle Management and DevOps practices Building and launching great AI apps need solid processes across stages of app development, along with collaborative tools for developers, data scientists, testers, and project managers. Salesforce has inbuilt AI tools like Einstein for Developers and Prompt Builder to come to your aid. DevOps Center, available on the Einstein 1 platform, can help you to maintain version control, track changes and push your build for UAT and production. If you prefer working with your own tools for IDE, project management, and DevOps, you can bring them into the Salesforce environment. Connect with an AI expert today. With over a decade of experience as a Salesforce Consulting Partner, our experts are always available to guide you through the process and answer any questions you might have regarding the potential of AI in your business.

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

According to Salesforce research, close to 90% of customers say that a business's overall experience is as important as its products or services. In today’s competitive landscape where companies are juggling between staffing shortages and overwhelmed resources, they need to be able to do more with less.  Customer expectations are at an all-time high, and given the plethora of options available to them, anything less than an exceptional experience will lead to customer churn. Automation and self-service technologies have given many businesses across industries a significant improvement in productivity, cost savings, and customer satisfaction. In 2021, Salesforce reported that customers using its Cloud products and self-service tools such as AI chatbots witnessed a 30% increase in customer satisfaction along with a 27% improvement in agent productivity. To meet this ever-growing demand, Salesforce launched Virtual Assistant – an Einstein-powered chatbot solution built specifically for financial services businesses to automate routine customer requests faster across popular digital channels like SMS or messaging platforms. This enables agents to focus on complex cases while chatbots can promptly resolve routine service requests, such as updating credit card information, renewing subscriptions, making payments, modifying subscription plans, and more. Virtual Assistant offers multilingual support, allowing businesses to use a single chatbot across multiple geographic regions regardless of their native language. And in the future, Einstein-powered Virtual Assistants will automatically create support articles based on customer conversations. Salesforce Chatbots for financial services come with pre-built bot templates, leading to faster setup and deployment. They can streamline support and assist agents with routine questions such as “How do I calculate my tax?” or “How do I upgrade my insurance plan?” With Virtual Assistant, financial services companies can re-direct thousands of customer calls to the Salesforce Chatbot leading to significant cost savings. Salesforce Chatbots can handle thousands of concurrent conversations for queries such as loan application status, product information, insurance premium renewal, claim filing, technical support, and more, freeing up dozens of front-line agents. Salesforce Chatbots improve the customer experience by enabling seamless self-service for simple tasks, thereby significantly reducing wait times to speak with an agent. The rewards of embracing self-service technology can be substantial, and businesses need to leverage technology to scale quickly and deliver efficient customer service. Features of Salesforce ChatBots Salesforce Chatbots powered by Einstein are equipped with advanced features to solve customer issues by replying to their questions and understanding their behavior to evolve continuously. Here are some stand-out features of Salesforce Chatbots that you should be aware of before you hire a Salesforce Development Partner for its implementation. Natural Language Processing (NLP) Salesforce Chatbots use NLP to understand customer intent and provide relevant answers. This makes bot interactions more natural for customers. Multi-Channel Support Salesforce Chatbots can be deployed on multiple channels such as mobile apps, websites, online stores, social media pages, and on popular messaging platforms like WhatsApp and SMS. This allows customers the convenience and flexibility to interact with businesses on their preferred channels whenever they want. Personalization Salesforce Chatbots can personalize responses based on customer data, their preferences, past purchases, and browsing behavior, making every interaction more relevant and meaningful. Contextual Conversations Salesforce Chatbots can understand and maintain context across multiple conversations regardless of the channel, thereby providing more accurate and relevant responses to customer queries. Integration with Salesforce Salesforce Chatbots are built on the world’s leading CRM platform, allowing for seamless integration with other Salesforce cloud products. With Salesforce Chatbots, managing customer data has never been easier. Analytics and Insights With Salesforce Chatbots, businesses can get valuable insights into customer behavior. By analyzing customer interactions, Salesforce Chatbots can help businesses identify areas for improvement to enhance the customer experience. Continuous Learning Salesforce Chatbots leverage machine learning algorithms to learn continuously from every interaction and better their responses over time, improving the accuracy and relevance of responses as they gain more experience. Here are some generic features of Salesforce Chatbots. Re-direct bot conversations to human agents for complex customer queries. Understand the intent of customer queries. Rapid response times. Understand customer input and recognize errors. Available 24/7. Lead generation – collect customer data and qualify leads for sales teams. Scale customer service with personalized automation and connected customer data According to Salesforce, new features in Financial Services Cloud such as proactive service and call deflection will enable financial services firms to reduce operating expenses while delivering exceptional customer service experiences: In 2021, Salesforce customers reported a 27% increase in case resolution with self-service automation and AI-powered Chatbots (Virtual Assistant). Salesforce Chatbots automate routine request resolution across popular messaging channels such as SMS and WhatsApp, so agents can spend more time on cases that necessitate human intervention. With Customer Service Coordination, agents can collaborate in Slack to fast-track case resolution. With automated workflows and custom Chatbots, Customer Service Coordination gathers customer data and generates alerts in a central Slack channel allowing teams to respond to critical incidents faster like fraud incidents, executing time-sensitive trades, and claims processing. With the Customer Data Platform, financial services marketing teams can unify customer data from multiple sources with a point-and-click interface. This enables marketing teams to engage with customers across multiple channels such as web, email, mobile, and social media in a far more personalized way.  With Salesforce Chatbots, enhanced Insights, and Data Actions, one-on-one advisor interactions and transactions can be triggered in real time. A unified console with actionable insights and workflows for faster service New features in Salesforce Financial Services Cloud include AI-powered dashboards and Chatbots to deliver key insights: With Intelligent Agent Desktop, agents can get access to deeper customer insights from right within the console page. With Customer Identity Verification, agents can reduce the risk of fraud, and with Customer Record Alerts, Chatbots can serve up issues that customers may not be aware of when they initiate a conversation. With Analytics for Financial Services, financial services businesses can interpret customer data with insights for faster and better decision-making, eventually delivering more revenue, and strengthening customer engagement. Girikon has been a Salesforce Consulting Partner for over a decade providing unique and scalable solutions for the financial services industry. We have the necessary expertise in-house to deliver tailored experiences to every customer through self-service, automation, and AI to improve efficiency across your business. Contact us today to learn more about how Salesforce Chatbots can transform your financial services business.

In today’s increasingly connected world, data is the point on which the entire business world pivots. We are generating unimaginable amounts of data every day. And locked within these humongous stores of data are the insights that businesses can use to better understand themselves and more importantly their customers. To remain competitive, businesses need to do more than just collect data. They need to be able to capture and analyse that data and convert it into actionable insights in real time to succeed. Enter Salesforce Einstein Analytics Here are 40 reasons why Einstein Analytics is the no. 1 choice when it comes to data analytics for your business. Hit the Ground Running. Work with a someone you can trust: Enjoy peace of mind knowing that you are working with the world’s no 1 CRM platform. Cust Costs: Reduce operating costs by using a pay as you use cloud-based analytics platform. Say goodbye to expensive installation or maintenance costs, and onsite hardware. Get going quickly: Leverage powerful analytics within minutes, thanks to out of the box solutions. Cut out the fluff: Pay only for the features you use. Salesforce Einstein Analytics comes with flexible usage models, so you always have the tools you need, at a price that suits your budget. Customise your solution: Salesforce Einstein Analytics is fully customisable and can be easily tailored for your business. With Einstein Analytics, you can set up the solution that works best for you. Built-in support: Salesforce Einstein Analytics comes with comprehensive guides, tutorials, videos, and multiple support options across channels. Integrate your data: No need to depend on your IT teams to upgrade your software for data analysis. Einstein Analytics seamlessly integrates analytics tools with every application and system, giving you a coherent, integrated, easy-to-use solution that gives you faster results. Connect Across Departments. Integrate seamlessly with the entire Salesforce platform: Salesforce Einstein Analytics integrates perfectly with all Salesforce products such as Sales Cloud, Service Cloud, Marketing Cloud etc giving every user easy access to unified customer data. Collaborate: Collaborate across sales, service, marketing, and other teams with cloud-based data analytics that can be accessed from anywhere across any device. Unify your goals: Give your teams a unified vision and objectives they can strive for, with data that is insightful, reliable, and actionable. Generate stunning visuals: Use built-in tools to convert data into stunning insightful reports and dashboards for presentations. Get conversational: Leverage social media technology to enhance team communication, with Chatter for Einstein Analytics. Put it in context: Get consistent views across departments with embedded reports and dashboards. Be available always: Work on your data over any device, from anywhere on the planet. Analyse Your Business. Monitor team performance: Leverage real-time reports to view team performance and identify trouble areas early and optimize. Access KPIs: Discover key performance indicators across your organisation to ensure you do not deviate from the path of success. Track call-center efficiency: See customer support trends across channels right on your dashboard and make informed decisions to enhance the customer service experience. Empower teams to self analyse: Give your teams the power to measure their own performance and set new performance benchmarks. Find the Key to Sales Success. See the big picture: Explore all data in a unified dashboard. Get a 360 degree snapshot of the health of your business. Eliminate borders: Get a unified view of your business across geographies, products, customer segments and time periods, for a true picture of how your business is performing. Predict the future: View historical trends to intelligently forecast which strategies are most likely to work and which leads are the most promising. Reduce customer churn: Get detailed insights into each and every customer, deliver personalised customer experiences and ensure customer loyalty and retention. Prioritise leads: Analyse your leads to assess the likelihood of their conversion and focus on the most promising ones. Evaluate your lead sources: Discover which sources are the most productive, so you can focus your efforts where it pays off the most. Enhance the customer experience: Resolve issues and monitor customer satisfaction directly from within Salesforce, and optimise. Market Smarter. Dive deeper: Go deep into your marketing data and get a detailed analysis of funnels, campaigns, conversion rates and more across channels. Present the right message: Create messaging to attract your target audience, and get valuable insight into that audience. Be your own data analyst: Marketing data analysis is too precious to hand over to someone else. With easy-to-use tools and visually compelling reports, become your own marketing analyst. Take instant action: Act in real time with up-to-the-minute marketing data from unified dashboards. Specialize in B2B marketing: Leverage the power of unique and effective B2B marketing tools in Salesforce to stay ahead of the competition. Understand the brand experience: Analyze data to see what your customers see, and optimize the customer experience. Optimise Service. Set your priorities: Prioritise open cases with service manager, and give your teams a clear view of customers that need their attention. Evaluate your accounts: Identify accounts with the highest number of cases and highest opportunity. Connect with your agents: Get a complete view of agents and their cases, and assign notifications based on configurable conditions. Review your service backlog: Compare data and identify service trends over time to assess how service levels compare across years. Revolutionise Analytics for Your Organization. Integrate with third-party apps: Leverage advanced integration options for any third-party application and extend your analytics beyond Salesforce. Optimise your pipelines: Leverage data-driven strategies to manage your pipelines. Automate analysis: Salesforce Einstein AI is designed to automatically analyse millions of data combinations for informed actions. Data security: Share data across devices securely using the cloud platform security services trusted by over 150,000 businesses worldwide. Push the limits: Extend your analytics abilities with custom-made apps or find the right ready-made app for your specific analytics needs on the Salesforce AppExchange. Everyday, we are producing mind-bogglingly huge amounts of data. Businesses need to use that data as a foundation for data analytics, to understand themselves and their customer better, to drive enhanced customer experiences. Girikon is a Salesforce Consulting Company and has helped businesses across the globe achieve success on the Customer 360 platform. To know more about how you can turn your data into intelligent actionable insights with Salesforce Einstein Analytics, contact us today.