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Customization and Configuration in Salesforce

Salesforce Customization vs. Configuration: Choosing the Right Approach for Your Business Salesforce has become a top choice for businesses aiming to strengthen customer relationships and achieve their goals. Its flexibility to adapt to diverse needs through customization and configuration makes it stand out. While both approaches aim to tailor your Salesforce Org to meet specific business requirements, they differ in methodology and use cases. This insight will help you understand their differences and provide insights into when and how to choose between them. Let’s get the insight! What is Salesforce Customization? Salesforce customization involves enhancing your Salesforce Org by introducing tailored features, functionalities, and applications through coding. It goes beyond the out-of-the-box capabilities, enabling you to extend your platform to meet unique and complex business requirements. This approach requires expertise from a Salesforce developer who leverages tools such as Apex, Lightning Components, and the Salesforce Code Builder to create custom solutions. Examples of Customization: What is Salesforce Configuration? Salesforce configuration refers to adapting Salesforce’s native features to meet business needs without modifying the underlying code. By using tools such as drag-and-drop builders, configuration allows users—even those without technical expertise—to optimize the platform’s functionality. Examples of Configuration: Key Differences Between Customization and Configuration Basis Customization Configuration Level of Personalization High personalization, tailored to unique needs Limited to Salesforce’s native capabilities Implementation Requires coding expertise and detailed development Simpler, relies on drag-and-drop tools Time to Deploy Longer development cycles Faster implementation and deployment Maintenance Can require ongoing updates and compatibility adjustments during Salesforce upgrades Easier to maintain, as it aligns with standard platform updates Cost Higher costs due to skilled developer involvement Cost-effective; can be handled by in-house admins Risk Higher risks due to potential code conflicts or errors Lower risks, but over-configuration can lead to complexity Best Practices for Customization and Configuration Choosing the Right Approach The decision to opt for customization or configuration depends on factors like business requirements, budget, timeline, and project complexity. Sometimes, a hybrid approach that combines customization and configuration is the best solution, providing flexibility while optimizing costs and implementation speed. Why Partner with Salesforce Experts? Partnering with experienced Salesforce consultants at Tectonic ensures your Org is tailored to meet your specific business needs. They analyze your workflows, processes, and challenges to recommend the most effective approach—whether it’s customization, configuration, or a blend of both. At Tectonic, our team of 200+ Salesforce experts specializes in delivering tailored solutions that maximize ROI. From development to ongoing maintenance, we ensure your Salesforce Org aligns with your long-term goals. Ready to transform your Salesforce platform? Let’s discuss how we can help. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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November 8, 2024in Data, Salesforce, Salesforce Development Services, Salesforce Implementation Services, Salesforce Optimization
Clean Energy Transition Campaign
04Nov

Clean Energy Transition Campaign

EnergyFlex Launches Clean Energy Transition Campaign in Partnership with Carat SA EnergyFlex, an Australian veteran-owned energy analytics and renewables education company, has joined forces with Carat SA to launch its first-ever brand campaign. This initiative is designed to accelerate Australia’s clean energy transition by equipping individuals and businesses with the tools, knowledge, and confidence to become “Renewables Ready.” Empowering Australians with Free Energy Tools Founded in 2021, EnergyFlex aims to put every Australian on the path to free and clean energy. The company’s free app, launched in May, helps users: The app, available on both iOS and Android, is part of EnergyFlex’s mission to make renewable energy adoption accessible and impactful for all Australians. Voices from the Partnership Garry Harding, CEO and Co-Founder of EnergyFlex, emphasized the campaign’s focus on financial, community, and environmental benefits: “We want to make it as easy as possible for Australians to understand the positive impact of the renewable energy transition. Partnering with Carat SA enables us to raise awareness and bring these tools and education to homes and businesses across the country.” Adele Gibb, Managing Director of Carat SA, highlighted the synergy between the campaign and Carat’s values: “Working with a forward-thinking brand like EnergyFlex aligns perfectly with dentsu’s B2B2S philosophy—creating solutions that are good for business, people, and society.” About Carat SA As a leading global media agency, Carat SA operates across 190+ offices in 135+ countries, bringing expertise and innovation to drive impactful campaigns. With this collaboration, EnergyFlex and Carat SA are poised to inspire a nationwide shift toward renewable energy adoption, helping Australia lead ,.the way in sustainability. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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November 4, 2024in Salesforce
ChatGPT and Politics?
02Nov

ChatGPT and Politics?

ChatGPT has also appeared in influence operations, with groups using it to generate political content for social media. OpenAI observed an Iranian-led operation, Storm-2035, using ChatGPT to publish politically charged content about U.S. elections and global conflicts. Yet, OpenAI noted that these AI-driven influence efforts often lack audience engagement.

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November 2, 2024in AI Tools, Generative AI
Winter 25 Release Notes
01Nov

Winter 25 Release Experience Cloud

Salesforce Winter ’25 Release: 6 Key Enhancements to Experience Cloud The Salesforce Winter ’25 Release brings a fresh suite of updates to Experience Cloud, focusing on design customization, SEO optimization, and streamlined navigation for enhanced user engagement. We’ve summarized six major updates and additional enhancements that make this release a significant step forward for Experience Cloud sites. 1. Enhanced Design Options for LWR Sites Winter ’25 empowers site designers with more granular control over the look and feel of LWR (Lightning Web Runtime) sites. New customization options in the Experience Builder Theme panel now allow for specific styling of individual components like columns and buttons, offering a new level of precision in visual design. Additional features include a Scoped Header and Footer layout that allows fixed positioning for headers and footers, enhancing user experience with persistent navigation elements. Site admins can define unique color palettes for buttons across various states (default, hover, focus), and apply color schemes to individual columns, which can now be set in the Theme panel. Further text customizations for headings have also been added, allowing a personalized touch for every element on LWR sites. 2. SEO-Friendly URLs for Accounts and Contacts (Generally Available) To drive organic traffic, the Winter ’25 Release introduces SEO-friendly URL slugs for Account and Contact pages, replacing traditional record IDs with easily readable URLs. This enhancement allows search engines to better index content, making it easier for users to find your pages. Site managers can configure SEO-friendly URLs directly in the Administration panel and import slugs in bulk for faster setup. 3. Data Providers for LWR Sites (Beta) Experience Cloud now includes an option to configure data providers on LWR site pages, enabling seamless integration with data from various sources, including Apex and Record providers. Admins can specify data sources within Experience Builder, allowing for real-time data updates across components and pages, providing a more dynamic and responsive experience for users. 4. Revamped Navigation and New Components The Navigation Menu component has been revamped, allowing admins to design a more intuitive navigation experience for both desktop and mobile users. The beta Site Header component further enhances branding with logo placement and customizable headers, while the Grid component now ensures consistent cell height, improving the visual balance of page layouts. Tailored navigation menus for desktop and mobile screens can be customized for color, spacing, text styles, and more to provide an optimized experience across devices. 5. Expanded Data Cloud Integration for Event Tracking Winter ’25 expands Data Cloud integration to capture checkout, order, and cart events on enhanced LWR sites. Ecommerce-focused organizations can now record user interactions—like checkout initiation and address input—automatically, giving businesses richer insights into customer behavior. Data captured through these events can be viewed within Data Cloud, allowing admins to understand user engagement and optimize site design accordingly. 6. Salesforce File Linking for LWR Sites (Beta) The new File Upload Lightning Web Component enables file uploads directly from an LWR site to Salesforce, an option previously available only on Aura sites. This update streamlines the file transfer process, allowing guest users to upload files securely, which are then accessible within Salesforce. Additional Experience Cloud Enhancements In addition to the primary updates, Winter ’25 introduces several valuable, albeit smaller, features: Availability of Features Some Winter ’25 features will be accessible immediately after release, while others require setup by admins. Consider notifying users about these updates to ensure a smooth transition and to leverage the full potential of new functionalities. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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November 1, 2024in Data, Marketing Automation, Salesforce Development Services, Salesforce Experience Cloud
Sales Incentives That Work
31Oct

Sales Incentives That Work

Sales Incentives That Actually Work Spooktacular Sales incentives to kick off your Halloween. Sales Incentives That Work are an essential tool to encourage and reward desired behaviors within a sales team. They serve as a motivational driver, helping teams achieve their goals while reinforcing the company’s objectives. Incentivizing a team goes beyond merely showing up for work—it involves sparking enthusiasm and a genuine drive to exceed expectations. With the right incentive program, companies can energize their teams, ensuring they come to work motivated and ready to achieve results. Below is a guide on different types of sales incentives, their impact on performance, and best practices for creating an effective incentive program. What Are Sales Incentives? Sales incentives are rewards offered to sales representatives in addition to their base pay for exceptional performance, typically when they meet or exceed sales targets. While monetary rewards such as bonuses are common, sales incentives can take many forms, all aimed at motivating the sales force toward specific goals. Types of Sales Incentives There are five main categories of sales incentives, each tailored to motivate different roles and sales activities: How Sales Incentives Motivate a Team Sales incentives work by reinforcing the behaviors a company wants its team to prioritize. For highly competitive reps, the prospect of a reward adds an extra layer of motivation. For others who may not be as driven by competition, incentives provide tangible goals to strive toward. Moreover, well-structured incentive programs can enhance job satisfaction, decrease turnover, and ensure top performers stay motivated. 10 Effective Sales Incentives While monetary rewards are often the most straightforward incentive, incorporating a mix of creative rewards can enhance a company’s incentive program. Some ideas include: Best Practices for Implementing Sales Incentives A well-designed sales incentive program ensures both clarity and motivation. Some best practices for implementation include: Measuring the Impact of Sales Incentives To gauge the success of a sales incentive program, companies should track key performance indicators (KPIs) like overall sales, new and returning customer numbers, program costs versus ROI, employee turnover, and employee satisfaction. Monitoring these metrics ensures that the program aligns with both sales goals and employee satisfaction. Tectonic’s Final Thoughts Sales incentives are an effective way to drive performance and boost morale. By designing a simple, clear, and motivating incentive program, companies can align their sales goals with individual motivation, ensuring both business success and employee satisfaction. If you need assistance utilizing sales incentives and your Salesforce platform, contact Tectonic today. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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October 31, 2024in Data, Salesforce, Salesforce Sales Cloud
Intelligent Adoption Framework
30Oct

Intelligent Adoption Framework

Intelligent Adoption Framework Marks a New Era for AI IntegrationAfter a surge of initial excitement, AI has now entered a phase of more thoughtful and strategic adoption, focusing on sustainable progress and measurable results. Following years of hype in which artificial intelligence was hailed as a revolutionary force poised to instantly transform industries, AI is now facing a more tempered reality. As it settles into Gartner’s “Trough of Disillusionment,” organizations are grappling with the reality of high costs and challenges scaling experimental projects. However, this phase of learning is typical for any emerging technology, and the journey to unlock AI’s full potential is far from over. Steve Daly, Senior Vice President of Solutions at New Era Technology, explains: “AI has been around for 70 years, but the recent hype inflated expectations. At $30 per user per month for tools like Microsoft 365 Copilot, they’re appealing for proof-of-concept projects. But once those initial tests are over, many companies struggle to find a clear ROI when scaling.” Cost is not the only barrier to broader AI adoption. Concerns over data security and sharing sensitive information are top priorities for many organizations. Daly adds, “New Era’s robust data and security practice has shifted to offer Copilot Studio, allowing companies to build GenAI solutions with tighter security controls. With Copilot Studio, you can limit access to specific files or libraries, ensuring greater control over sensitive data.” Moving Beyond OverpromisesBuilding confidence in AI requires addressing several factors. First, organizations must tackle security and data control issues, alongside developing a clear business model to justify AI investments. Equally important is maintaining momentum—patience and persistence are key to seeing projects through to success, or determining when to pivot. Daly observes, “We’re seeing many projects lose steam. Around half of AI initiatives stall due to poor security practices and suboptimal data management. Projects must demonstrate progress, and that’s difficult in the innovation phase when you don’t always know what you don’t know.” Introducing Intelligent AdoptionThis is where Copilot Studio and New Era’s Intelligent Adoption Framework come into play. The framework is designed to help organizations chart their AI development journey and ensure investments yield tangible results. Copilot Studio supports IT teams by focusing on the tasks that truly drive value, helping them stay on track toward their goals. The Intelligent Adoption Framework is built around three core pillars: technical redesign, organizational readiness, and user readiness. New Era’s framework leverages its expertise to guide businesses through the steps necessary to define their AI strategy, align their corporate vision, and identify the most valuable use cases for AI adoption. Daly concludes, “It’s not just about purchasing licenses—it’s about creating a roadmap for successful adoption. We’re developing packaged solutions, such as ‘train the trainer’ programs from day one, followed by proof-of-concept demonstrations using Copilot Studio. Our goal is to help customers answer key questions, like when to build a GenAI chatbot, while navigating the complexities of AI adoption and managing the pressures CIOs face from stakeholders.” In this new era of AI, success will be determined not by rushed deployment, but by strategic, intelligent adoption that ensures sustained value over time. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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October 30, 2024in Artificial Intelligence, Data, Technology
AI Strategy for Your Business
27Oct

AI Strategy for Your Business

How to Create a Winning AI Strategy for Your Business To maximize the value of AI, organizations must align their AI projects with strategic business objectives. Here’s a 10-step guide to crafting an effective AI strategy, including sample templates to support your planning. While AI adoption is on the rise, many companies still struggle to unlock its full potential. According to the 2024 IDC report Scaling AI Initiatives Responsibly, even organizations with advanced AI practices, termed “AI Masters,” face a 13% failure rate, while those still emerging in AI see a 20% failure rate. Challenges such as poor data quality and cultural resistance often contribute to these failures. To avoid these pitfalls, companies need to adopt a more deliberate and strategic approach to AI implementation. As Nick Kramer from SSA & Company states, “It’s not just about implementing the right technology; a lot of work needs to be done beforehand to succeed with AI.” What is an AI Strategy and Why is it Important? An AI strategy unifies all necessary components—such as data, technology, and talent—required to achieve business goals through AI. This includes: A well-designed AI strategy sets clear directions on how AI should be leveraged to achieve optimal outcomes within the organization. 10 Steps to Craft a Successful AI Strategy Resources for AI Strategy Templates If you’re ready to start building your AI strategy, here are several resources offering templates and guidance: By following these steps and utilizing the right resources, businesses can ensure they capture AI in ways that align with their strategic goals and maximize their competitive edge. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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October 27, 2024in Data, Generative AI, Technology
AI FOMO
26Oct

AI FOMO

Enterprise interest in artificial intelligence has surged in the past two years, with boardroom discussions centered on how to capitalize on AI advancements before competitors do. Generative AI has been a particular focus for executives since the launch of ChatGPT in November 2022, followed by other major product releases like Amazon’s Bedrock, Google’s Gemini, Meta’s Llama, and a host of SaaS tools incorporating the technology. However, the initial rush driven by fear of missing out (FOMO) is beginning to fade. Business and tech leaders are now shifting their attention from experimentation to more practical concerns: How can AI generate revenue? This question will grow in importance as pilot AI projects move into production, raising expectations for financial returns. Using AI to Increase Revenue AI’s potential to drive revenue will be a critical factor in determining how quickly organizations adopt the technology and how willing they are to invest further. Here are 10 ways businesses can harness AI to boost revenue: 1. Boost Sales AI-powered virtual assistants and chatbots can help increase sales. For example, Ikea’s generative AI tool assists customers in designing their living spaces while shopping for furniture. Similarly, jewelry insurance company BriteCo launched a GenAI chatbot that reduced chat abandonment rates, leading to more successful customer interactions and potentially higher sales. A TechTarget survey revealed that AI-powered customer-facing tools like chatbots are among the top investments for IT leaders. 2. Reduce Customer Churn AI helps businesses retain clients, reducing revenue loss and improving customer lifetime value. By analyzing historical data, AI can profile customer attributes and identify accounts at risk of leaving. AI can then assist in personalizing customer experiences, decreasing churn and fostering loyalty. 3. Enhance Recommendation Engines AI algorithms can analyze customer data to offer personalized product recommendations. This drives cross-selling and upselling opportunities, boosting revenue. For instance, Meta’s AI-powered recommendation engine has increased user engagement across its platforms, attracting more advertisers. 4. Accelerate Marketing Strategies While marketing doesn’t directly generate revenue, it fuels the sales pipeline. Generative AI can quickly produce personalized content, such as newsletters and ads, tailored to customer interests. Gartner predicts that by 2025, 30% of outbound marketing messages will be AI-generated, up from less than 2% in 2022. 5. Detect Fraud AI is instrumental in detecting fraudulent activities, helping businesses preserve revenue. Financial firms like Capital One use machine learning to detect anomalies and prevent credit card fraud, while e-commerce companies leverage AI to flag fraudulent orders. 6. Reinvent Business Processes AI can transform entire business processes, unlocking new revenue streams. For example, Accenture’s 2024 report highlighted an insurance company that expects a 10% revenue boost after retooling its underwriting workflow with AI. In healthcare, AI could streamline revenue cycle management, speeding up reimbursement processes. 7. Develop New Products and Services AI accelerates product development, particularly in industries like pharmaceuticals, where it assists in drug discovery. AI tools also speed up the delivery of digital products, as seen with companies like Ally Financial and ServiceNow, which have reduced software development times by 20% or more. 8. Provide Predictive Maintenance AI-driven predictive maintenance helps prevent costly equipment downtime in industries like manufacturing and fleet management. By identifying equipment on the brink of failure, AI allows companies to schedule repairs and avoid revenue loss from operational disruptions. 9. Improve Forecasting AI’s predictive capabilities enhance planning and forecasting. By analyzing historical and real-time data, AI can predict product demand and customer behavior, enabling businesses to optimize inventory levels and ensure product availability for ready-to-buy customers. 10. Optimize Pricing AI can dynamically adjust prices based on factors like demand shifts and competitor pricing. Reinforcement learning algorithms allow businesses to optimize pricing in real time, ensuring they maximize revenue even as market conditions change. Keeping ROI in Focus While AI offers numerous ways to generate new revenue streams, it also introduces costs in development, infrastructure, and operations—some of which may not be immediately apparent. For instance, research from McKinsey & Company shows that GenAI models account for only 15% of a project’s total cost, with additional expenses related to change management and data preparation often overlooked. To make the most of AI, organizations should prioritize use cases with a clear return on investment (ROI) and postpone those that don’t justify the expense. A focus on ROI ensures that AI deployments align with business goals and contribute to sustainable revenue growth. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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October 26, 2024in Generative AI
Generative AI Energy Consumption Rises
18Oct

Generative AI Energy Consumption Rises

Generative AI Energy Consumption Rises, but Impact on ROI Unclear The energy costs associated with generative AI (GenAI) are often overlooked in enterprise financial planning. However, industry experts suggest that IT leaders should account for the power consumption that comes with adopting this technology. When building a business case for generative AI, some costs are evident, like large language model (LLM) fees and SaaS subscriptions. Other costs, such as preparing data, upgrading cloud infrastructure, and managing organizational changes, are less visible but significant. Generative AI Energy Consumption Rises One often overlooked cost is the energy consumption of generative AI. Training LLMs and responding to user requests—whether answering questions or generating images—demands considerable computing power. These tasks generate heat and necessitate sophisticated cooling systems in data centers, which, in turn, consume additional energy. Despite this, most enterprises have not focused on the energy requirements of GenAI. However, the issue is gaining more attention at a broader level. The International Energy Agency (IEA), for instance, has forecasted that electricity consumption from data centers, AI, and cryptocurrency could double by 2026. By that time, data centers’ electricity use could exceed 1,000 terawatt-hours, equivalent to Japan’s total electricity consumption. Goldman Sachs also flagged the growing energy demand, attributing it partly to AI. The firm projects that global data center electricity use could more than double by 2030, fueled by AI and other factors. ROI Implications of Energy Costs The extent to which rising energy consumption will affect GenAI’s return on investment (ROI) remains unclear. For now, the perceived benefits of GenAI seem to outweigh concerns about energy costs. Most businesses have not been directly impacted, as these costs tend to affect hyperscalers more. For instance, Google reported a 13% increase in greenhouse gas emissions in 2023, largely due to AI-related energy demands in its data centers. Scott Likens, PwC’s global chief AI engineering officer, noted that while energy consumption isn’t a barrier to adoption, it should still be factored into long-term strategies. “You don’t take it for granted. There’s a cost somewhere for the enterprise,” he said. Energy Costs: Hidden but Present Although energy expenses may not appear on an enterprise’s invoice, they are still present. Generative AI’s energy consumption is tied to both model training and inference—each time a user makes a query, the system expends energy to generate a response. While the energy used for individual queries is minor, the cumulative effect across millions of users can add up. How these costs are passed to customers is somewhat opaque. Licensing fees for enterprise versions of GenAI products likely include energy costs, spread across the user base. According to PwC’s Likens, the costs associated with training models are shared among many users, reducing the burden on individual enterprises. On the inference side, GenAI vendors charge for tokens, which correspond to computational power. Although increased token usage signals higher energy consumption, the financial impact on enterprises has so far been minimal, especially as token costs have decreased. This may be similar to buying an EV to save on gas but spending hundreds and losing hours at charging stations. Energy as an Indirect Concern While energy costs haven’t been top-of-mind for GenAI adopters, they could indirectly address the issue by focusing on other deployment challenges, such as reducing latency and improving cost efficiency. Newer models, such as OpenAI’s GPT-4o mini, are more economical and have helped organizations scale GenAI without prohibitive costs. Organizations may also use smaller, fine-tuned models to decrease latency and energy consumption. By adopting multimodel approaches, enterprises can choose models based on the complexity of a task, optimizing for both speed and energy efficiency. The Data Center Dilemma As enterprises consider GenAI’s energy demands, data centers face the challenge head-on, investing in more sophisticated cooling systems to handle the heat generated by AI workloads. According to the Dell’Oro Group, the data center physical infrastructure market grew in the second quarter of 2024, signaling the start of the “AI growth cycle” for infrastructure sales, particularly thermal management systems. Liquid cooling, more efficient than air cooling, is gaining traction as a way to manage the heat from high-performance computing. This method is expected to see rapid growth in the coming years as demand for AI workloads continues to increase. Nuclear Power and AI Energy Demands To meet AI’s growing energy demands, some hyperscalers are exploring nuclear energy for their data centers. AWS, Google, and Microsoft are among the companies exploring this option, with AWS acquiring a nuclear-powered data center campus earlier this year. Nuclear power could help these tech giants keep pace with AI’s energy requirements while also meeting sustainability goals. I don’t know. It seems like if you akin AI accessibility to more nuclear power plants you would lose a lot of fans. As GenAI continues to evolve, both energy costs and efficiency are likely to play a greater role in decision-making. PwC has already begun including carbon impact as part of its GenAI value framework, which assesses the full scope of generative AI deployments. “The cost of carbon is in there, so we shouldn’t ignore it,” Likens said. Generative AI Energy Consumption Rises Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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October 18, 2024in Data, Enterprise, Generative AI, Google, Technology
Gap Between Data and Decisions
14Oct

Gap Between Data and Decisions

Bridging the Gap Between Data and Decisions One of the biggest challenges in enterprise technology is the fragmentation of data across various systems, commonly referred to as data silos. Salesforce research shows that 81% of IT leaders believe these silos prevent their organizations from fully utilizing data, while 94% of business leaders feel they should be extracting more value from their data. Despite the wealth of available information, only 30% of employees report using data to make decisions. Tableau Einstein addresses this challenge by enabling self-service analytics through a unified metadata framework. This framework ensures consistent data definitions and structures across an organization, allowing data architects and analysts to create semantic models. These models simplify complex datasets, making them more accessible to business users. Additionally, the platform integrates AI-powered agents into the data authoring process, automatically suggesting relationships between data objects and providing business context to improve the reliability of insights. Integrating AI for Proactive Insights Southard Jones, Chief Product Officer of Tableau, highlights the need for analytics tools to deliver proactive and autonomous insights within users’ workflows. “Employees often miss critical insights because they lack the time or expertise to analyze complex data sets,” Jones said. “With predictive and generative AI, Tableau Einstein can explore your data and surface insights you might not have considered.” Features like Pulse and Tableau Agent provide insights within familiar platforms like Salesforce, Slack, and third-party applications such as Workday. For example, a manager using Workday might receive real-time notifications about team performance metrics or potential indicators of employee attrition, allowing for timely intervention. Enhancing Trust Through Transparency Building trust in data analytics is key to adoption. Tableau Einstein introduces Tableau Semantics, which acts as a bridge between raw data and business terminology. “With Tableau Semantics, we provide clear explanations and data lineage, showing users exactly how metrics are created,” Jones explained. This transparency demystifies complex calculations, making it easier for users to understand metrics like campaign ROI. By aligning technical data with business understanding, the platform seeks to make analytics more accessible and trustworthy. Potential Challenges While AI-driven analytics holds great promise, it raises questions about data privacy, implementation complexity, and the learning curve for employees. Although AI-powered insights can revolutionize how organizations use data, it’s essential to address data governance concerns and ensure employees are trained to interpret AI-generated insights accurately. The competitive landscape for AI analytics tools is growing, with alternatives like Microsoft’s Power BI offering similar capabilities. Organizations will need to compare Tableau Einstein with other options in terms of features, integration potential, and overall cost. Facilitating Collaboration and Customization Tableau Einstein’s open, API-driven architecture enables developers to create customized analytical agents tailored to specific enterprise needs. “By opening our APIs, we allow developers to interact with the platform programmatically,” Jones noted. This flexibility supports the creation of custom analytics solutions that can seamlessly integrate with other business applications. The platform also introduces marketplace capabilities for sharing and discovering data models and analytics assets, fostering collaboration and innovation while reducing redundant efforts across organizations. Real-World Applications and Early Feedback Early adopters are already exploring how Tableau Einstein enhances decision-making processes. Leslie Bercher, Senior Director of Performance Marketing & Analytics at Arvest Bank, noted, “Embedding these insights into our daily applications empowers teams to make informed decisions quickly. It’s not just about data; it’s about having actionable insights when and where we need them.” Despite the potential, adopting advanced analytics tools may require investment in training and change management to ensure successful implementation. Without the right support, organizations may struggle to unlock the full potential of these technologies. Looking Ahead: The Future of AI-Powered Analytics Tableau Einstein is available through Tableau+, with future integrations planned across Salesforce products, including Sales, Service, Marketing, and Commerce Cloud. Salesforce is offering expert guidance, migration support, and a reimagined partner network to help businesses adopt the platform efficiently. As companies continue their digital transformation journeys, AI-powered analytics tools like Tableau Einstein will be pivotal in making data more accessible and actionable. Success will depend on how well organizations address challenges related to data privacy, employee training, and system integration. “The autonomous revolution in analytics is just beginning,” Jones observed. “The key will be how companies use these tools to gain insights and drive strategic actions that lead to measurable business outcomes.” Salesforce’s launch of Tableau Einstein marks a significant step in democratizing data analytics within organizations. By embedding AI-driven insights into daily workflows and increasing transparency in data metrics, the platform has the potential to transform operations. However, realizing this potential will require thoughtful implementation, strong employee engagement, and ongoing support. As AI and analytics evolve, companies that harness these technologies effectively will gain a competitive advantage in a data-driven world. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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October 14, 2024in Generative AI
collaboration between humans and AI
10Oct

Collaboration Between Humans and AI

The Future of AI: What to Expect in the Next 5 Years In the next five years, AI will accelerate human life, reshape behaviors, and transform industries—these changes are inevitable. Collaboration Between Humans and AI. For much of the early 20th century, AI existed mainly in science fiction, where androids, sentient machines, and futuristic societies intrigued fans of the genre. From films like Metropolis to books like I, Robot, AI was the subject of speculative imagination. AI in fiction often over-dramatized reality and caused us to suspend belief in what was and was not possible. But by the mid-20th century, scientists began working to bring AI into reality. A Brief History of AI’s Impact on Society The 1956 Dartmouth Summer Research Project on Artificial Intelligence marked a key turning point, where John McCarthy coined the term “artificial intelligence” and helped establish a community of AI researchers. Although the initial excitement about AI often outpaced its actual capabilities, significant breakthroughs began emerging by the late 20th century. One such moment was IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997, signaling that machines could perform complex cognitive tasks. The rise of big data and Moore’s Law, which fueled the exponential growth of computational power, enabled AI to process vast amounts of information and tackle tasks previously handled only by humans. By 2022, generative AI models like ChatGPT proved that machine learning could yield highly sophisticated and captivating technologies. AI’s influence is now everywhere. No longer is it only discussed in IT circles. AI is being featured in nearly all new products hitting the market. It is part of if not the creation tool of most commercials. Voice assistants like Alexa, recommendation systems used by Netflix, and autonomous vehicles represent just a glimpse of AI’s current role in society. Yet, over the next five years, AI’s development is poised to introduce far more profound societal changes. How AI Will Shape the Future Industries Most Affected by AI Long-term Risks of Collaboration Between Humans and AI AI’s potential to pose existential risks has long been a topic of concern. However, the more realistic danger lies in human societies voluntarily ceding control to AI systems. Algorithmic trading in finance, for example, demonstrates how human decisions are already being replaced by AI’s ability to operate at unimaginable speeds. Still, fear of AI should not overshadow the opportunities it presents. If organizations shy away from AI out of anxiety, they risk missing out on innovations and efficiency gains. The future of AI depends on a balanced approach that embraces its potential while mitigating its risks. In the coming years, the collaboration between humans and AI will drive profound changes across industries, legal frameworks, and societal norms, creating both challenges and opportunities for the future. Tectonic can help you map your AI journey for the best Collaboration Between Humans and AI. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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October 10, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Laws, Salesforce
AI Agents
10Oct

Gen AI and Software Development

The Future of Software Development with Generative AI Imagine developing software products at unprecedented speed and cost efficiency, allowing your company to test more ideas with real—and even virtual—customers. This capability could accelerate the time to market for targeted products while minimizing risk and resource waste. Generative AI (GenAI) is making this vision a reality. But how exactly will AI-powered product development work? We propose a four-stage framework that leverages GenAI to streamline today’s labor-intensive processes. The Challenge with Traditional Software Development As Marty Cagan of Silicon Valley Product Group has pointed out, most companies still rely on a lengthy, complex software development cycle. Typically, it follows this pattern: This approach is expensive and fraught with risk. Predicting a product’s ROI before release is notoriously inaccurate. Additionally, testing product designs with real users is both time-consuming and costly. If a final product fails to attract customers, the company loses valuable time, money, and human effort—something we’ve seen in cases like Quibi and Clubhouse. To mitigate these risks, some firms have embraced iterative development, involving end users early in the process and continuously refining their solutions. While this method improves outcomes, GenAI offers the potential to revolutionize the entire approach. How GenAI Transforms Software Development GenAI moves beyond traditional A/B testing and incremental improvements. Consider the perspective of Nikita Bier, Product Growth Partner at Lightspeed Venture Partners, who recently stated: “No, I just ship the app—and if it’s not ranked in the Apple Store, I change it until it is.” This mindset—enabled by GenAI—suggests a more agile, data-driven approach to product development, where software is rapidly iterated based on real-world feedback. We propose a simplified four-step framework that highlights GenAI’s role in transforming each stage: 1. User Research Today: Companies analyze user problems, market needs, and contextual factors to determine why a product should be built. With GenAI: AI can simulate realistic consumer behavior, reducing the need for expensive user research. For example, a recent study used OpenAI’s GPT-3.5 to predict laptop purchasing decisions based on simulated income levels. The AI accurately adjusted its price sensitivity based on whether it “earned” $50,000 or $120,000 annually—mimicking real consumer behavior. 2. Design Today: Product teams develop solutions, mapping interactions between users and the product. With GenAI: AI can translate ideas into designs for different types of creators. Visual thinkers can sketch concepts, which AI converts into formal design assets. Those who work better with words can use AI tools like Galileo and Genius to generate wireframes from natural language descriptions—seamlessly integrating with design platforms like Figma. 3. Build Today: Developers determine how the product’s components fit together, writing code to bring it to life. With GenAI: AI can generate functional software code with minimal human input. For instance, aerospace engineer Brandon Starr used a single sentence—“Create a bunny-themed Flappy Bird as an iOS app”—to instruct Replit Agent, which then built the app autonomously. 4. Learn Today: Companies analyze product performance and user feedback to refine future iterations. With GenAI: AI will integrate with top-tier product analytics tools, synthesizing data to automate improvements, rebuilds, and relaunches. As Wharton professor Ethan Mollick has demonstrated, GPT’s advanced data analysis capabilities can already perform this type of iterative optimization. The Future of AI-Powered Development What about traditional product development steps like market research, segmentation, and feature prioritization? Some will be absorbed into these four stages, while others—like extensive market analysis—will become less critical as development accelerates. An even more transformative shift is on the horizon: natural language interfaces that guide product developers through the entire process. Imagine describing a vague product idea, and AI not only builds it but also evaluates its business viability. This shift could redefine how companies structure development teams—or even empower individuals to create software on demand, much like smartphones democratized video production. As GenAI pioneers the next frontier, software development is poised to become one of its most revolutionary applications. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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October 10, 2024in AI Tools, Data, Generative AI
Marketing Cloud and Commerce Cloud Innovations
09Oct

Marketing Cloud and Commerce Cloud Innovations

What Our Dreamforce Marketing Cloud and Commerce Cloud Innovations Mean for You This year’s Dreamforce was nothing short of amazing. It was exciting to reconnect with fellow Trailblazers, exchange brilliant ideas, and showcase the innovations we’ve been crafting at Salesforce. A recurring theme throughout the event was how businesses can leverage data and AI to forge deeper customer-driven relationships by bringing internal teams closer together. These innovations are designed to transform not only how companies engage with customers but also how their teams work together. Marketing Cloud and Commerce Cloud Innovations. Seamless integration between Marketing, Commerce, Sales, and Service teams is crucial for creating unified customer experiences. Often, customers feel as though they are interacting with separate departments rather than one cohesive company—this is largely due to disconnected technology and processes. But thanks to Salesforce’s advancements in unified data, AI, and automation, those days are numbered. Now, departments can collaborate more effectively, delivering hyper-personalized, frictionless experiences across the entire customer lifecycle. Let’s explore the latest Marketing Cloud and Commerce Cloud innovations announced at Dreamforce 2024 and how they can benefit your business. What You’ll Learn Salesforce Marketing Cloud Innovations These four innovations in Marketing Cloud are built on the Salesforce Platform and powered by Data Cloud, offering marketers a seamless view of customer data across the business. This foundation makes it easier to deliver unified customer experiences, improve handoffs between teams, and measure success more effectively. 1. Agentforce Embedded in Marketing Workflows Agentforce for Marketing combines generative and predictive AI to create an end-to-end campaign experience that marketers can launch and optimize with ease. Here’s how it helps: Example: A marketer looking to prevent customer churn can launch a re-engagement campaign. Agentforce will identify the right audience, craft personalized messages, and optimize delivery based on customer behavior. 2. Empowering Small and Medium Businesses The new Marketing Cloud Advanced Edition brings enhanced AI and automation capabilities to SMBs, enabling them to scale personalization and improve productivity: 3. Automating Data Preparation and Analytics with Einstein Marketing Intelligence (EMI) EMI uses AI and Data Cloud to automate the ingestion, transformation, and analysis of marketing data: 4. Einstein Personalization for 1:1 Experiences Einstein Personalization uses AI to recommend products, content, or services based on individual customer preferences: Example: A service agent could offer a discount on a product a customer was recently viewing, creating a seamless, personalized experience. Salesforce Commerce Cloud Innovations As businesses scale and handle increasing amounts of data, managing complex commerce systems can be a challenge. The new Commerce Cloud updates simplify these complexities by extending unified commerce capabilities across the organization. 1. Simplifying Cross-Functional Commerce Tasks By unifying data from across the business, Commerce Cloud enables better cross-functional collaboration: 2. AI-Powered Commerce Agents with Agentforce Commerce Cloud introduces three AI-powered agents to streamline business processes: 3. Streamlining Checkout for a Faster, Easier Experience With new express payment options like Link by Stripe and Amazon Pay, Commerce Cloud Checkout speeds up transactions and improves conversion rates by 14%. Plus, Buy with Prime integration allows shoppers to use their Amazon Prime accounts for a faster checkout experience, complete with trusted delivery and hassle-free returns. The Future of Unified Commerce Salesforce Commerce Cloud offers a unified platform that brings together sales, service, and marketing, providing a 360-degree view of the entire customer journey. This unified commerce approach enables businesses to deliver seamless B2B and B2C experiences, all powered by a single platform. By integrating enterprise-wide data, trusted AI, and automated workflows, Salesforce helps businesses scale personalized, intelligent experiences across every touchpoint. Every interaction becomes an opportunity for growth, setting the standard for success in today’s customer-driven world. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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October 9, 2024in Data, Enterprise, Salesforce, Salesforce Commerce Cloud, Salesforce Marketing Cloud, Technology
Ethical AI Implementation
06Oct

Ethical AI Implementation

AI technologies are rapidly evolving, becoming a practical solution to support essential business operations. However, creating true business value from AI requires a well-balanced approach that considers people, processes, and technology. Ethical AI Implementation. AI encompasses various forms, including machine learning, deep learning, predictive analytics, natural language processing, computer vision, and automation. To leverage AI’s competitive advantages, companies need a strong foundation and a realistic strategy aligned with their business goals. “Artificial intelligence is multifaceted,” said John Carey, managing director at AArete, a business management consultancy. “There’s often hype and, at times, exaggeration about how ‘intelligent’ AI truly is.” Business Advantages of AI Adoption Recent advancements in generative AI, such as ChatGPT and Dall-E, have showcased AI’s significant impact on businesses. According to a McKinsey Global Survey, global AI adoption surged from around 50% over the past six years to 72% in 2024. Some key benefits of adopting AI include: Prerequisites for AI Implementation Successfully implementing AI can be complex. A detailed understanding of the following prerequisites is crucial for achieving positive results: 13 Steps for Successful AI Implementation Common AI Implementation Mistakes Organizations often stumble by: Key Challenges in Ethical AI Implementation Human-related challenges often present the biggest hurdles. To overcome them, organizations must foster data literacy and build trust among stakeholders. Additionally, challenges around data management, model governance, system integration, and intellectual property need to be addressed. Ensuring Ethical AI Implementation To ensure responsible AI use, companies should: Ethical AI implementation requires a continuous commitment to transparency, fairness, and inclusivity across all levels of the organization. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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October 6, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Salesforce, Technology
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