March 2024

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.

Most business leaders across the world recognize the value of Salesforce. They are aware of how the world's leading cloud-based CRM platform can help them cultivate rewarding customer relationships, address gaps in customer service, and enable them to adopt a more holistic approach to managing day-to-day operations. What many of them are not aware of, however, is the role of a Salesforce consultant to help them navigate the challenges involved in transitioning to Salesforce. And that's not the full picture. Salesforce consultants can also help to optimize and automate business processes to enable a seamless transition. What is a Salesforce Consultant? Salesforce consultants help customers achieve their long-term business goals by ensuring the smooth implementation of Salesforce tailored to their unique business needs.  They also assess the dynamic market landscape and adapt the customer's operations to be future-ready. Salesforce consultants also manage customer relationships, and project plans, do market research, understand user needs and sentiment, gather requirements, research organizational data, and train employees to get the most out of Salesforce. The best Salesforce consultants have deep knowledge of Salesforce technology, rich experience in similar prior implementations, and awareness of the best business practices. Challenges that Salesforce Consultants face and how they can solve them As a business leader, if you really want to unlock the true power of Salesforce, we recommend that you choose the best Salesforce Implementation Partner. Apart from implementing the CRM, they can also help you solve various key business challenges. Here’s a look at some of them. 1)   Handling sensitive information One of the key challenges organizations must deal with while transitioning to Salesforce is ensuring their customer data is safe. Any data breach can result in drastic consequences for the business. A Salesforce consultant will follow industry best practices to ensure that there are adequate safeguards in place to secure the data exchange between your existing systems and Salesforce. Additionally, they also ensure that once your data is in Salesforce, it can be accessed easily by users. This allows them to get a comprehensive view of their customers including contact details and interaction history in the least number of clicks. This helps them to make data-driven decisions leading to greater customer satisfaction. 2)   Providing personalized assistance All businesses are unique. This is true even for businesses offering similar products or services with a similar customer base. Each business has its unique vision, goals, and strategies that set them apart from each other. Consequently, they may have completely different reasons to implement Salesforce and completely different expectations from the implementation based on their unique business objectives.  A vastly experienced Salesforce is well aware of the nuances of customer goals and expectations and works closely with customer and implementation teams to ensure the CRM implementation is fully tailored to the customer’s unique needs. 3)   Offering valuable insights Salesforce has been purpose-built to provide a 360-degree view of your customers. It includes everything from your contact information, purchase history, service interactions, interests and preferences, and social media interactions. As a business owner, you want to have deeper insights into customer behavior along with intelligent recommendations in a single place. A good Salesforce consultant makes that possible. They can help aggregate siloed data and give you a unified, single-window view to help you better understand customer preferences and behavior patterns, regardless of which channel they interact with you on.  This enables you to provide seamless interaction experiences to your customers across sales, marketing, and customer service across multiple channels. 4)   Providing instant support When customers interact with a business's customer support, they want instant resolution to their problem. And businesses are no different. When you invest in a platform like Salesforce, which includes licensing and implementation costs, you want your users to be up and running quickly so that they can close deals faster and resolve customers' concerns quickly. While a library of documents, manuals, and videos may be helpful for your users, they may not be able to help in resolving a critical issue that may be disrupting operations. What businesses need is the support of an expert who has been there and done that.  That expert is your Salesforce consultant. A Salesforce consultant understands the nuances of the platform and knows where to make tweaks to resolve common issues. 5)   Configuring user permissions Would you allow complete access to your financial data to all your employees? Would you allow access to customer information to your HR department? Sounds absurd, right? Assigning the right access privileges to users is a critical part of any Salesforce implementation. Creating the right user-profiles and customizing permissions in Salesforce requires a certain level of expertise that your existing IT team may not be equipped to handle.  This is where the roles of Salesforce consultants become important.  They can ensure that your users have access to only that functionality of Salesforce that allows them to do their job effectively.  Not less and certainly not more. A Salesforce consultant can help you navigate the complex domain of user management to get the most out of your users. 6)   Discovering business-specific solutions The goals and needs of small and medium-sized businesses (SMBs) are different from their larger counterparts. Consequently, their CRM platform needs are also different. How do you go about designing a solution that is tailored to your unique requirements? Purchasing a Salesforce license is just the starting step. Salesforce has virtually limitless capabilities and you may not have the requisite expertise in-house to choose the best-fit features of the platform to achieve your vision. A good Salesforce consultant leverages their prior experience and deep knowledge of the domain to establish clear goals and business requirements. They then use this information to recommend solutions that are aligned with your goals and needs. 7)   Boosting user adoption Poor user adoption is one of the primary reasons why many Salesforce implementations fail to achieve their true potential. In fact, industry research indicates that the problem is so acute that in the absence of adequate guidance and training, teams are likely to stop using the platform altogether. And this should be a matter of concern for you. When users don't use the platform, each of them relies on personal, non-standardized methods to manage customers and their data. Without a unified view of customers based on gold-standard industry practices, your executives are unable to make critical strategic decisions. Which is why you need a Salesforce consultant. With adequate guidance and training programs tailored to each user group based on their job functions, they can ensure that your employees feel empowered when they look at customer data, giving their productivity a shot in the arm. 8)   Other challenges Quick setup Setting up and managing Salesforce can be complex unless done by an expert. A Salesforce consultant knows the nitty-gritty of configuration, where to tweak the platform and configure the right security settings to ensure a robust and secure CRM that works for your business. Enhance customer experience Organizations that have achieved success with Salesforce will be able to tell you the value a Salesforce consultant brings to the table to improve productivity and provide enhanced customer experiences. With deep insights on customer behavior, and automation of customer service, they can help you close service cases faster translating to greater customer satisfaction. Easier and faster Salesforce adoption A good Salesforce consultant can help you go live quickly with minimal disruption in your day-to-day operations. They draw upon their extensive experience and deep business domain knowledge to come up with a comprehensive roadmap that includes implementation, training, and strategy to drive user adoption. Risk reduction A certified Salesforce consultant has the right experience and technical know-how to help organizations manage uncertainties associated with Salesforce implementation such as data migration and security, process automation, and migration to ensure a seamless holistic transition. Salesforce Consulting Services from Girikon, a Gold Salesforce Partner, can help you unlock the true power of Salesforce. With customized solutions aligned with your unique business needs, we have helped many businesses improve productivity, boost efficiency, and increase revenues. Get in touch with an expert to learn more.

Salesforce, the world’s leading CRM platform empowers businesses with tools and services to manage customer data, automate processes, streamline operations, and drive customer satisfaction. Salesforce consultants aren’t just experts at handling the software; they recognize that Salesforce is a great tool to improve business productivity and align themselves with customer goals, wants, and needs to unlock the true power of the platform.  In short, they empower teams to do what was earlier not thought to be possible. As a Salesforce Consulting Partner for over a decade, our experts have compiled a list of tips to guide the next crop of Salesforce consultants on their path to success. These tips are a result of over a 100-man years of Salesforce experience, countless hours spent with customer teams, and millions of lines of coding. Let’s dive right in. 1. Understand the ‘Why’ While there are certain skills that can be acquired such as data management, data analytics, and process automation, critical thinking is one area that can only be awakened from within. Critical thinking entails having a larger-than-life view of the business while having an in-depth view of the everyday activities of the organization. Many Salesforce consultants have a great technical background and are adept at turning requirements into recommendations and ideas. But what is truly required to be understood is the "Why". Why do they need it? What are their business goals and what is stopping them from achieving them? What are their people like? What are their pain points? What do they want in terms of work satisfaction? Finding answers to these fundamental questions will go a long way to advance your career in Salesforce consulting. You need to evolve from being a manager or business analyst to being a problem solver, and for that, you need to identify and understand the problem and all its constituents and dependencies to the last detail. 2. Adapt your communication strategy Salesforce consultants have to work closely with engineering teams, customer user groups, and senior leadership which may sometimes be spread across the globe. For you to be successful as a global Salesforce consultant, you need to adapt your communication depending on who you are interacting with. Understanding the audience, their vision, and the way they see the business and its horizon (both short-term and long-term) plays a vital role in the success of the projects you are working on. As an example, if you are interacting with engineering teams, you may want to interact in a way that they understand via a common language. As a representative of your organization (like a front office), you need to be aware of your unique communication style and how your interactions are received by others. As a thumb rule, communication strategy varies from who you are interacting with to the size of the group you are addressing. 3. Know Your Audience Whether you are presenting to a CXO at a Fortune 500 company or interacting with individual project members, knowing your audience is one of the key skills all Salesforce consultants need to have. When you are involved in multiple projects and have to interact with different audiences, whether it is for project updates or course corrections, an instinct is to use the same template for interaction. This approach rarely works as each individual user group has its own perspective and level of understanding. Recognize the value each stakeholder is seeking and develop a mindset to tailor your presentations to align with the audience. 4. Develop a self-help attitude One of the job responsibilities of a successful Salesforce consultant is to hire new consultants. And you can only attract talent as good as yourself. When recruiting new consultants, veterans of the game often look for leadership traits such as taking self-motivated initiatives. What homework did they do before they sought help from seniors? Demonstrating a self-help attitude would go a long way in cultivating strong leadership and problem-solving skills. 5. Always be willing to learn   Salesforce consultants, especially ones who are still young in their consulting journey, need to constantly upgrade themselves on products, processes, and frameworks, but more often than not they get no guidance or direction. Well, if you want to grow, you are on your own. It’s your mindset to self-direct your learning and find solutions to challenges that will take you on the path to growth. Having a keen learner’s mindset goes a long way in building a keenness to take on new challenges and learn to grow. 6. Pick an Area of Expertise While it’s great to be a jack of multiple trades (skills), it’s important to be very good at something. Whether it’s a certification in Sales Cloud or your early experience as a Salesforce administrator, it's important that you pick one area in which you are an expert. Expertise in a certain area builds client trust and establishes credibility. And once you pick an area you want to build further on, make sure you are up to date with the latest product innovations in that area and establish how you can help businesses leverage your expertise in these new innovations. 7. Know When to Say No Customers want the moon. Literally. If it was left to them, they would want to implement their entire roadmap for the next 10 years as of yesterday. Many times, consultants agree to customer requests while working on a project even when they know it is not in their best long-term interest, or it just cannot be done at this stage. While customer satisfaction is extremely important in your line of work, you also need aware of what is in the best interest of the customer and the project. Make your point politely yet firmly, with an irrefutable basis. You were hired because you are an expert in your field, and this is time to say it emphatically. While it's always tempting to say yes to every customer request, learn to say no when you have a reasonable basis for it. Draw on your past experience working with multiple customers on a variety of Salesforce projects. Offer alternative suggestions and help the customer see the larger picture. Your job as a Salesforce consultant is to be a partner with your customer, working towards a common goal and that job includes knowing when to say no. Conclusion Hiring a Salesforce consultant is an investment you make to achieve the heights that you envision for your business. Ready to unlock the true power of Salesforce? Team up with a Salesforce Implementation Partner who can guide you on the right path.