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Potential of GenAI in Healthcare
25Nov

Potential of GenAI in Healthcare

Clinicians spend about 28 hours per week on administrative tasks, mainly clinical documentation and communication. Medical and claims staff reported even higher administrative loads, with 34 and 36 hours spent weekly on tasks like documentation, communication, and prior authorization. Many respondents linked these demands directly to burnout, with 77% of claims staff, 81% of medical staff, and 82% of clinicians citing administrative burdens as significant contributors. Additionally, 78% of payer executives and 85% of provider executives noted that administrative work is a key driver of staffing shortages.

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November 25, 2024in Generative AI, Salesforce Data Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services0 Comments
Tools to Liberate Salesforce Data
25Nov

Salesforce Chose a Human-First Approach to Promote AI

Why Salesforce Chose a Human-First Approach to Promote AI Salesforce won Gold in the Use of GenAI category at The Drum Awards for Advertising by creatively addressing AI-related concerns while demonstrating the power of responsible AI adoption. Here’s a look at the award-winning campaign. Salesforce Chose a Human-First Approach to Promote AI. The Challenge The rapid adoption of AI last year triggered widespread anxiety. Many professionals felt their jobs were at risk, and concerns grew over AI’s trustworthiness, ethical implications, and potential to replace human talent. Businesses needed to address this apprehension while showcasing the transformative potential of AI in a responsible manner. The Strategy Amid the rising uncertainty, Salesforce saw an opportunity to lead the conversation by aligning the campaign with one of its core values: innovation. Rather than positioning AI as an independent solution, Salesforce sought to show that its true power lies in the hands of creative humans who apply it thoughtfully. The campaign aimed to demonstrate that AI isn’t inherently good or bad—it’s a tool, and its impact depends on how it’s used. Salesforce’s creative and production teams integrated generative AI as an assistant, ensuring that AI enhanced human creativity rather than replacing it. This approach positioned Salesforce as a leader in responsible AI adoption, both within the creative industry and across broader business applications. The Campaign Execution Salesforce embraced a “walk the walk” approach to responsible AI by using generative AI tools to assist, not replace, its human creatives. The result was a campaign that resonated deeply with Salesforce’s target audience of business decision-makers, sparking conversations around trust and innovation. The Results The Ask More of AI campaign achieved exceptional outcomes across various metrics: Salesforce Chose a Human-First Approach to Promote AI By adopting a balanced approach—leveraging AI to enhance human creativity without replacing it—Salesforce successfully addressed AI-related fears while positioning itself as a trusted innovator. The campaign not only elevated Salesforce’s brand but also set a benchmark for responsible AI use in marketing. Through “Ask More of AI,” Salesforce demonstrated that trust and purpose are the cornerstones of unlocking AI’s potential. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 25, 2024in AI Tools, Generative AI0 Comments
human centered ai
23Nov

Human-Centered AI

Be the change you want to see in the artificial intelligence world. Or scramble to catch up. Hope Is Not Lost for Human-Centered AIHow designers can lead the charge in creating AI that truly benefits humanity. The rapid proliferation of Artificial Intelligence (AI) brings with it a range of ethical and societal concerns. From inherent biases in datasets to fears of widespread job displacement, these challenges often feel like inevitable trade-offs as AI becomes deeply embedded in our lives. However, hope remains. Human-centered AI—designed to be fair, transparent, and genuinely beneficial—is not only possible but achievable when crafted with intentionality. For UX professionals, this is an opportunity to drive the creation of AI systems that empower rather than overshadow human capabilities. A Quick Note on AI Literacy To make meaningful contributions to AI product development, designers need a foundational understanding of how AI works. While a PhD in machine learning isn’t necessary, being an informed practitioner is essential. Think of learning about AI like learning to invest. At first, it seems daunting—what even is an ETF? But with time, the jargon and processes become familiar. Similarly, while you don’t need to be a machine-learning expert to work with AI, understanding its basics is critical. AI refers broadly to a computer’s ability to mimic human thought, while machine learning (ML)—a subset of AI—enables systems to learn from data. Unlike traditional programming, where explicit instructions are coded line by line, ML models identify patterns within training datasets. These models then function as “black boxes,” generating outputs based on user inputs—though the inner workings are often opaque. Understanding these fundamentals empowers designers to bridge the gap between AI’s technical potential and its real-world application. Design-Led AI Ideally, designers are involved from the very beginning of AI product development—during the discovery phase. Here, we evaluate whether AI is the right solution for a given problem, ensuring user needs drive decisions rather than the allure of flashy tech. Key questions to ground AI solutions in user needs include: Basic AI literacy allows designers to make informed judgments and collaborate effectively with engineers. Engaging early ensures that AI solutions are designed to adapt to users—not the other way around. But what happens when design isn’t brought in until after AI decisions have been made? Design-Guarded AI Even when AI is a foregone conclusion, designers can still shape outcomes by focusing on the two areas where users interact directly with AI: inputs and outputs. Input Design Whether inputs involve transaction data, images, or text prompts, the method of collection must be intuitive and user-friendly. Established design principles, such as affordances, help ensure clarity and simplicity. For example: Frequent user testing ensures input methods align with real workflows and pain points. The result? Streamlined, user-centric experiences that reduce friction and save time. Output Design Designing outputs requires a focus on transparency and mitigating automation bias—the tendency to over-rely on AI. Users must understand that AI is fallible. For instance: AI should act as a collaborator, not an authority. Outputs must empower users to make informed choices while supporting their next steps within a seamless workflow. Ethics Must Take Center Stage No discussion of human-centered AI is complete without addressing ethics. Designers must champion transparency, inclusivity, and fairness throughout the product lifecycle. Questions around bias, privacy, and unintended consequences must be raised early and revisited often. While ethical considerations may sometimes conflict with short-term business goals, prioritizing them is essential for building AI that serves humanity in the long term. These conversations won’t always be easy—but they are necessary. As designers, we have the tools and responsibility to ensure AI remains a force for good. By advocating for human-centered design principles, we can help shape an AI-powered future that enhances human potential rather than undermining it. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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November 23, 2024in Artificial Intelligence, Data, Generative AI0 Comments
AI Inference vs. Training
21Nov

AI Inference vs. Training

AI Inference vs. Training: Key Differences and Tradeoffs AI training and inference are the foundational phases of machine learning, each with distinct objectives and resource demands. Optimizing the balance between the two is crucial for managing costs, scaling models, and ensuring peak performance. Here’s a closer look at their roles, differences, and the tradeoffs involved. Understanding Training and Inference Key Differences Between Training and Inference 1. Compute Costs 2. Resource and Latency Considerations Strategic Tradeoffs Between Training and Inference Key Considerations for Balancing Training and Inference As AI technology evolves, hardware advancements may narrow the gap in resource requirements between training and inference. Nonetheless, the key to effective machine learning systems lies in strategically balancing the demands of both processes to meet specific goals and constraints. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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November 21, 2024in AI Tools, Data, Generative AI, Technology0 Comments
AI Productivity Paradox
20Nov

AI Productivity Paradox

The AI Productivity Paradox: Why Aren’t More Workers Using AI Tooks Like ChatGPT?The Real Barrier Isn’t Technical Skills — It’s Time to Think Despite the transformative potential of tools like ChatGPT, most knowledge workers aren’t utilizing them effectively. Those who do tend to use them for basic tasks like summarization. Less than 5% of ChatGPT’s user base subscribes to the paid Plus version, indicating that a small fraction of potential professional users are tapping into AI for more complex, high-value tasks. Having spent over a decade building AI products at companies such as Google Brain and Shopify Ads, the evolution of AI has been clearly evident. With the advent of ChatGPT, AI has transitioned from being an enhancement for tools like photo organizers to becoming a significant productivity booster for all knowledge workers. Most executives are aware that today’s buzz around AI is more than just hype. They’re eager to make their companies AI-forward, recognizing that it’s now more powerful and user-friendly than ever. Yet, despite this potential and enthusiasm, widespread adoption remains slow. The real issue lies in how organizations approach work itself. Systemic problems are hindering the integration of these tools into the daily workflow. Ultimately, the question executives need to ask isn’t, “How can we use AI to work faster? Or can this feature be built with AI?” but rather, “How can we use AI to create more value? What are the questions we should be asking but aren’t?” Real-world ImpactRecently, large language models (LLMs)—the technology behind tools like ChatGPT—were used to tackle a complex data structuring and analysis task. This task would typically require a cross-functional team of data analysts and content designers, taking a month or more to complete. Here’s what was accomplished in just one day using Google AI Studio: However, the process wasn’t just about pressing a button and letting AI do all the work. It required focused effort, detailed instructions, and multiple iterations. Hours were spent crafting precise prompts, providing feedback, and redirecting the AI when it went off course. In this case, the task was compressed from a month-long process to a single day. While it was mentally exhausting, the result wasn’t just a faster process—it was a fundamentally better and different outcome. The LLMs uncovered nuanced patterns and edge cases within the data that traditional analysis would have missed. The Counterintuitive TruthHere lies the key to understanding the AI productivity paradox: The success in using AI was possible because leadership allowed for a full day dedicated to rethinking data processes with AI as a thought partner. This provided the space for deep, strategic thinking, exploring connections and possibilities that would typically take weeks. However, this quality-focused work is often sacrificed under the pressure to meet deadlines. Ironically, most people don’t have time to figure out how they could save time. This lack of dedicated time for exploration is a luxury many product managers (PMs) can’t afford. Under constant pressure to deliver immediate results, many PMs don’t have even an hour for strategic thinking. For many, the only way to carve out time for this work is by pretending to be sick. This continuous pressure also hinders AI adoption. Developing thorough testing plans or proactively addressing AI-related issues is viewed as a luxury, not a necessity. This creates a counterproductive dynamic: Why use AI to spot issues in documentation if fixing them would delay launch? Why conduct further user research when the direction has already been set from above? Charting a New Course — Investing in PeopleProviding employees time to “figure out AI” isn’t enough; most need training to fully understand how to leverage ChatGPT beyond simple tasks like summarization. Yet the training required is often far less than what people expect. While the market is flooded with AI training programs, many aren’t suitable for most employees. These programs are often time-consuming, overly technical, and not tailored to specific job functions. The best results come from working closely with individuals for brief periods—10 to 15 minutes—to audit their current workflows and identify areas where LLMs could be used to streamline processes. Understanding the technical details behind token prediction isn’t necessary to create effective prompts. It’s also a myth that AI adoption is only for those with technical backgrounds under 40. In fact, attention to detail and a passion for quality work are far better indicators of success. By setting aside biases, companies may discover hidden AI enthusiasts within their ranks. For example, a lawyer in his sixties, after just five minutes of explanation, grasped the potential of LLMs. By tailoring examples to his domain, the technology helped him draft a law review article he had been putting off for months. It’s likely that many companies already have AI enthusiasts—individuals who’ve taken the initiative to explore LLMs in their work. These “LLM whisperers” could come from any department: engineering, marketing, data science, product management, or customer service. By identifying these internal innovators, organizations can leverage their expertise. Once these experts are found, they can conduct “AI audits” of current workflows, identify areas for improvement, and provide starter prompts for specific use cases. These internal experts often better understand the company’s systems and goals, making them more capable of spotting relevant opportunities. Ensuring Time for ExplorationBeyond providing training, it’s crucial that employees have the time to explore and experiment with AI tools. Companies can’t simply tell their employees to innovate with AI while demanding that another month’s worth of features be delivered by Friday at 5 p.m. Ensuring teams have a few hours a month for exploration is essential for fostering true AI adoption. Once the initial hurdle of adoption is overcome, employees will be able to identify the most promising areas for AI investment. From there, organizations will be better positioned to assess the need for more specialized training. ConclusionThe AI productivity paradox is not about the complexity of the technology but rather how organizations approach work and innovation. Harnessing AI’s potential is simpler than “AI influencers” often suggest, requiring only

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November 20, 2024in AI Tools, Data, Einstein 1 Platform, Generative AI, Google, Technology0 Comments
How Does Salesforce Use AI
19Nov

How Does Salesforce Use AI

With all the buzz in the news about AI, it may feel like AI is everywhere. In fact, as of 2023, over 80% of global companies report adopting AI to enhance their business operations. This means if your company isn’t yet leveraging AI to strengthen customer relationships, you risk falling behind. The good news is that Salesforce CRM already comes with a suite of AI tools ready for use. In this insight, we’ll explore how combining quality data, AI, and Salesforce can help you build more meaningful, lasting relationships with your customers. How Does Salesforce Use AI? Salesforce offers various built-in functionalities to create customizable, predictive, and generative AI experiences tailored to your business needs. One standout tool is Agentforce, which enables the creation of autonomous AI agents. If you have numerous routine tasks but limited staff, Agentforce could be the solution. For instance, if you lack an in-house customer support agent, Agentforce can build an AI service agent to handle incoming cases, responding intuitively in real-time. Not enough sales reps? No problem—create an AI sales agent to manage records, interact with leads, answer questions, and schedule meetings. Another significant AI feature is generative AI in Salesforce. According to KPMG, 77% of executives believe generative AI will have a more profound societal impact in the next three to five years than any other emerging technology. So, how can it improve your business? Salesforce’s in-house LLM, xGen, helps you generate human-like text and create original visual content from existing data or user input. This capability can enhance user experiences by automating the generation of dynamic and personalized imagery for applications. Generative AI also transforms how users interact with and consume data. Complex datasets can now be converted into easily understandable formats—visualizations, charts, or graphs—generated from natural language prompts. These insights make data accessible, enabling users to share knowledge and drive informed decisions. How Can You Use AI to Improve Customer Relationships? AI is reshaping business models, workflows, and customer engagement. By harnessing quality data, AI, and Salesforce, you can enhance how you connect with customers. Here are key ways to leverage this combination for a smarter customer strategy: Challenges You May Encounter on Your AI Journey Adopting AI in Salesforce, especially Einstein AI, offers many benefits, but it also comes with challenges. Here are some factors to consider for a successful rollout: Importance of Data Quality When Using AI Analytics Data quality is essential for AI accuracy and reliability. Poor data can skew predictions and erode user trust. Key factors that contribute to high data quality include: AI can also enhance data quality by automating data validation and cleansing. Machine learning algorithms can detect and address anomalies, duplicate records, and incomplete datasets, improving the reliability of your data over time. The Future of CRM: AI-Driven Customer Engagement and Business Growth Integrating AI into Salesforce is revolutionizing CRM by enabling businesses to engage with customers more intelligently. From automating routine tasks to enhancing decision-making and delivering personalized communication, AI-driven innovations are empowering businesses to build stronger relationships with customers. As AI continues to evolve, those who embrace it will gain a competitive edge and drive long-term growth. The future of CRM is here—and it’s smarter, faster, and more customer-focused than ever. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 19, 2024in AI Tools, Data, Generative AI, Salesforce, Technology0 Comments
Training Your Own AI Model
19Nov

Training Your Own AI Model May Not Win Customers – But Leveraging Data Will

While AI plays a crucial role in delivering personalized customer experiences, the real game-changer lies in data connectivity. It’s not about having the flashiest AI model but about how data is managed across every interaction. Imagine a global retail brand with a network of physical stores, an e-commerce site, and active social media. An in-store shopper “likes” a product on Instagram, triggering an autonomous agent to send a personalized discount code. This leads them to a tailored shopping page based on their preferences, with items in their size, style, and budget. After the purchase, the agent follows up to ensure satisfaction and offers discounts on complementary items, creating a VIP-like experience. While AI can automate these touchpoints, the real enabler is a robust data platform that brings each data point into a cohesive, connected system. Without this, AI’s effectiveness is limited; Gartner predicts that 30% of generative AI projects will be abandoned by 2025 due to poor data quality. Why Data – Not a Custom AI Model – Drives Results Many companies think they need a custom-trained language model (LLM) to achieve superior customer insights. However, training an LLM is costly, requires specialized expertise, and lacks built-in permission controls. And because LLMs require constant updates to stay relevant, it’s a significant commitment with diminishing returns. Instead, a more practical approach is to integrate a pre-trained LLM with a data platform that aggregates data from diverse systems in real time. By providing AI with current, connected data, companies can enhance accuracy, identify trends, and respond to customer needs quickly. The Power of Integrated Data Platforms Consider the shopper who “liked” a social media post and then interacted with an autonomous agent. Alone, these interactions may not signify much, but together they reveal a clear buying intent. Scaling this approach allows brands to spot valuable opportunities faster. Autonomous agents can help sales teams prioritize prospects, resolve service cases, and drive sales – all in real time. This isn’t limited to retail. In healthcare, agents can streamline patient benefits verification, summarize insurance details, and assist with pharmacy coverage. From finance to manufacturing, data-enabled AI agents have endless applications. Metadata: The Secret to Seamless AI For AI to deliver tailored, high-value interactions, it needs organized data – and metadata is key. By categorizing and tagging data, metadata enables AI to find the right information, whether drafting a customer message or identifying top sales prospects. A unified data platform that harmonizes data and metadata yields sharper predictions and more personalized outputs, outperforming siloed data systems. Data-Driven Precision in Customer Experience An effective data platform is the backbone of an agile, customer-centric business, making AI far more than guesswork. By using data to understand customer needs in real time, businesses can deliver targeted, timely experiences that build loyalty and growth. A comprehensive data strategy is essential to unlocking AI’s potential. This data strategy guide provides best practices for aligning data with business goals, unearthing insights, and enhancing customer engagement. A unified data platform connects your organization around the customer – transforming a simple “like” into a sale, and a sale into loyalty. Without it, your AI is just guessing. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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November 19, 2024in Data, Generative AI0 Comments
Generative AI Energy Consumption Rises
19Nov

AI for the Ho-Ho-Holidays

The Holiday Rush and AI’s Growing Role in Retail The holiday season is approaching quickly, with fewer days between Thanksgiving and Christmas this year than at any time since 2019. This condensed timeline makes Salesforce’s latest State of the Connected Customer report—this year titled State of the AI Connected Customer—particularly timely. The report, based on insights from over 15,000 consumers worldwide, focuses on the growing role of artificial intelligence (AI), specifically AI agents, in transforming customer experiences. With Salesforce’s recent launch of Agentforce, AI agents have taken center stage. According to Michael Affronti, SVP and General Manager of Commerce Cloud at Salesforce, the retail sector is already exploring this technology: “Retailers that we talk to are starting to implement AI agents. Unlike chatbots, AI agents can analyze customer data to make proactive recommendations and even take action. For consumers, AI agents create smoother checkout experiences, streamline returns, and deliver personalized shopping that feels like working with an incredible in-store associate. For retailers, AI agents drive higher margins and customer retention by delivering exceptional service. As we like to say, ‘There’s an agent for that.’” Rebuilding Trust with AI One of the most compelling use cases for AI agents, according to Affronti, lies in addressing declining consumer trust. Salesforce’s research highlights alarming trends: AI agents present an opportunity to rebuild trust by delivering reliable and transparent experiences. While consumer expectations for personalized service remain high, Salesforce data suggests that 30% of consumers would work with AI agents if it meant faster service. However, skepticism persists—curiosity is the top emotion associated with AI, followed closely by suspicion and anxiety. Transparency is crucial, as 40% of consumers are more likely to trust AI agents when their logic is explained, and there’s an option to escalate to a human. “Most people just want to know it’s AI, and then they’ll be comfortable,” Affronti notes. “Clarity about what the agent is doing, combined with the ability to talk to a real person, builds trust.” Three Opportunities for Retailers Affronti outlines three key strategies for retailers to embrace AI agents effectively this holiday season: Experimentation and Preparing for the Future For retailers not yet leveraging AI, Affronti advises starting small but experimenting now. For example, large brands like Saks are already piloting AI agents such as “Sophie,” which handles tasks like order management and learns new capabilities based on customer feedback. However, smaller businesses can also benefit from AI tools, such as generative AI for writing product descriptions or automating promotions, regardless of scale. “One of the great things about AI today is how democratized it has become,” Affronti explains. “Small businesses using Salesforce’s Commerce Cloud can leverage AI for tasks like creating product descriptions or automating translations, even if their catalog is limited.” Looking Ahead While this holiday season may not see a widespread rollout of AI-driven retail solutions, early adopters are already showcasing what’s possible. Retailers that embrace experimentation and lay the groundwork for AI-powered experiences today will likely see significant results by the 2025 holiday season. The key takeaway: now is the time to build the foundation for the future of AI in retail. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 19, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Technology0 Comments
AI platform for automated task management
18Nov

AI platform for automated task management

Salesforce Doubles Down on AI Innovation with Agentforce Salesforce, renowned for its CRM software used by over 150,000 businesses, including Amazon and Walmart, continues to push the boundaries of innovation. Beyond its flagship CRM, Salesforce also owns Slack, the popular workplace communication app. Now, the company is taking its AI capabilities to the next level with Agentforce—a platform that empowers businesses to build and deploy AI-powered digital agents for automating tasks such as creating sales reports and summarizing Slack conversations. What Problem Does Agentforce Solve? Salesforce has been leveraging AI for years, starting with the launch of Einstein in 2016. Einstein’s initial capabilities were limited to basic, scriptable tasks. However, the rise of generative AI created an opportunity to tackle more complex challenges, enabling tools to make smarter decisions and interpret natural language. This evolution led to a series of innovations—Einstein GPT, Einstein Copilot, and now Agentforce—a flexible platform offering prebuilt and customizable agents designed to meet diverse business needs. “Our customers wanted more. Some wanted to tweak the agents we offer, while others wanted to create their own,” said Tyler Carlson, Salesforce’s VP of Business Development. The Technology Behind Agentforce Agentforce is powered by Salesforce’s Atlas Reasoning Engine, developed in-house to drive smarter decision-making. The platform integrates with AI models from leading providers like OpenAI, Anthropic, Amazon, and Google, offering businesses a variety of tools to choose from. Slack, which Salesforce acquired in 2021, plays a pivotal role as a testing ground for these AI agents. Currently in beta, Agentforce’s Slack integration allows businesses to implement automations directly where employees work, enhancing usability. “Slack makes these tools easy to use and accessible,” Carlson noted. How Agentforce Stands Out Customizing AI for Business Needs With tools like Agentbuilder, businesses can create AI agents tailored to specific tasks. For instance, an agent could prioritize and sort incoming emails, respond to HR inquiries, or handle customer support using internal data. One standout example is Salesforce’s partnership with Workday to develop an AI-powered service agent for employee questions. Driving Results and Adoption Salesforce has already seen promising results from early trials, with Agentforce resolving 90% of customer inquiries autonomously. The company aims to expand adoption and functionality, allowing these agents to handle even larger workloads. “We’re building a bigger ecosystem of partners and skills,” Carlson emphasized. “By next year, we want Agentforce to be a must-have for businesses.” With Agentforce, Salesforce continues to cement its role as a leader in AI innovation, helping businesses work smarter, faster, and more effectively. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 18, 2024in Agentforce Platform, Data, Generative AI, Google, Salesforce, Technology0 Comments
NetSuite Salesforce Collaboration
17Nov

NetSuite Salesforce Collaboration

NetSuite Bets on Strategic Growth and Embraces Collaboration with Salesforce Growing on All Fronts At SuiteWorld 2024, the theme, “All Systems Grow,” reflected a pivotal moment for NetSuite. While the event lacked groundbreaking announcements, it showcased a fulfillment of past promises and a notable strategic shift toward openness and collaboration. Oracle and NetSuite are now welcoming competitors as partners, signaling a move toward interoperability that could redefine their market positioning. With over 40,000 customers, NetSuite continues its strong growth in the ERP space, particularly among SMBs. The company’s Q3 sales surged 20% year-over-year, underlining its momentum in the mid-market. Beyond traditional ERP capabilities, NetSuite’s expanded suite of solutions positions it as more than just an ERP provider. Delivering on AI Innovations While there were no splashy acquisitions, NetSuite made significant strides by rolling out 170 new modules and features, many leveraging AI. These enhancements blend predictive AI and generative AI to increase accuracy and user productivity. These updates aim to elevate both the platform’s quality and the efficiency of its users. Redwood Design: A Transformative User Experience NetSuite is adopting Oracle’s Redwood design language, promising a more intuitive and user-friendly interface. While Redwood is not new, its phased rollout within NetSuite is a significant step forward. Notable Additions: SuiteProcurement and Salesforce Integration SuiteProcurement: NetSuite’s new procurement automation solution integrates directly with Amazon Business and Staples Business Advantage, automating ordering, invoicing, approvals, and deliveries. Plans are underway to expand vendor support, offering broader applicability in the future. Salesforce Partnership: NetSuite’s most significant announcement was its strategic partnership with Salesforce, enabling real-time data exchange between the platforms. Evan Goldberg, NetSuite’s founder and EVP, explained the rationale:“It’s up to the customer to decide what software they want to use.” The partnership reflects NetSuite’s commitment to addressing customer needs, with more SaaS integrations expected in the future. Expanding Field Service Management (FSM) NetSuite’s Field Service Management (FSM) capabilities, acquired last year, are now better integrated into its platform. While development progress has been slower than anticipated, significant enhancements are expected in the coming year, leveraging Oracle technology to extend FSM’s functionality across industries. And Field Service Management is available in Salesforce, as well. Positioned for Continued SMB Growth NetSuite’s investments are yielding results, as demonstrated by its rapid growth and deeper integration of Oracle technology. The NetSuite Analytics Data Warehouse and Enterprise Performance Management are driving adoption among existing users, showcasing the platform’s scalability. NetSuite’s ability to quickly integrate Oracle updates into its infrastructure gives it a competitive edge, ensuring customers benefit from the latest innovations without delays. With its robust feature set, AI-powered tools, and strategic partnerships like the one with Salesforce, NetSuite has strengthened its position as a go-to ERP platform for SMBs. Its consistent 20% year-over-year growth indicates a bright future, making it an increasingly attractive option for mid-market businesses. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 17, 2024in Data, Enterprise, Generative AI, Salesforce, Technology0 Comments
AI Agent Rivalry
16Nov

AI Agent Rivalry

Microsoft and Salesforce’s AI Agent Rivalry Heats Up The battle for dominance in the AI agent space has escalated, with Salesforce CEO Marc Benioff intensifying his criticism of Microsoft’s AI solutions. Following remarks at Dreamforce 2024, Benioff took to X (formerly Twitter) to call out Microsoft for what he called “rebranding Copilot as ‘agents’ in panic mode.” The AI Agent rivalry winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. Benioff didn’t hold back, labeling Microsoft’s Copilot as “a flop”, citing issues like data leaks, inaccuracies, and requiring customers to build their own large language models (LLMs). In contrast, he touted Salesforce’s Agentforce as a solution that autonomously drives sales, service, marketing, analytics, and commerce without the complications he attributes to Microsoft’s offerings. Microsoft’s Copilot: A New UI for AI Microsoft recently unveiled new autonomous agent capabilities for Copilot Studio and Dynamics 365, positioning these agents as tools to enhance productivity across teams and functions. CEO Satya Nadella described Copilot as “the UI for AI” and emphasized its flexibility, allowing businesses to create, manage, and integrate agents seamlessly. Despite the fanfare, Benioff dismissed Copilot’s updates, likening it to “Clippy 2.0” and claiming it fails to deliver accuracy or transformational impact. Salesforce Expands Agentforce with Strategic Partnerships At Dreamforce 2024, Salesforce unveiled its Agentforce Partner Network, a global ecosystem featuring collaborators like AWS, Google Cloud, IBM, and Workday. The move aims to bolster the capabilities of Agentforce, Salesforce’s AI-driven platform that delivers tailored, autonomous business solutions. Agentforce allows businesses to deploy customizable agents without complex coding. With features like the Agent Builder, users can craft workflows and instructions in natural language, making the platform accessible to both technical and non-technical teams. Flexibility and Customization: Salesforce vs. Microsoft Both Salesforce and Microsoft emphasize AI’s transformative potential, but their approaches differ: Generative AI vs. Predictive AI Salesforce has doubled down on generative AI, with Einstein GPT producing personalized content using CRM data while also providing predictive analytics to forecast customer behavior and sales outcomes. Microsoft, on the other hand, combines generative and predictive AI across its ecosystem. Copilot not only generates content but also performs autonomous decision-making in Dynamics 365 and Azure, positioning itself as a comprehensive enterprise solution. The Rise of Multi-Agent AI Systems The competition between Microsoft and Salesforce reflects a broader trend in AI-driven automation. Companies like OpenAI are experimenting with frameworks like Swarm, which simplifies the creation of interconnected AI agents for tasks such as lead generation and marketing campaign development. Similarly, startups like DevRev are introducing conversational AI builders to design custom agents, offering enterprises up to 95% task accuracy without the need for coding. What Lies Ahead in the AI Agent Landscape? As Salesforce and Microsoft push the boundaries of AI integration, businesses are evaluating these tools for their flexibility, customization, and impact on operations. While Salesforce leads in CRM-focused AI, Microsoft’s integrated approach appeals to enterprises seeking cross-functional AI solutions. In the end, the winner may be determined not by flashy features but by delivering tangible, transformative outcomes for businesses navigating the complexities of AI adoption. AI Agent Rivalry. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 16, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Google, Salesforce0 Comments
Salesforce Agentforce Integration
14Nov

Agentforce at Work

Agentforce Salesforce Agentforce in Action: A Practical Example of Using Agents in Salesforce Autonomous Agents on the Agentforce Platform Agentforce represents a transformative shift in Salesforce’s strategy, poised to redefine how users engage with their CRM. By introducing both assistive AI—enhanced by generative AI for capabilities like summaries and sales emails—and autonomous AI, which empowers agents to automate actions without human oversight, Agentforce helps users operate more efficiently in Salesforce. Despite the excitement around Agentforce, most blogs and marketing materials focus on AI hype rather than practical applications. This insight focuses on illustrating how these tools work and the tangible value they can provide for your organization’s custom processes. Curious about setting up Agentforce agents using both out-of-the-box actions and custom actions? Let’s dive in. What is Agentforce? Agentforce is Salesforce’s conversational AI tool for CRM. In simple terms, it lets users “talk” to Salesforce. Powered by generative AI and the Atlas Reasoning Engine, Agentforce processes user input to perform tasks like summarizing data from objects, updating fields, and generating content such as emails or knowledge articles. This innovative tool is only at the beginning of its journey, likely setting the stage for a future where CRM interactions may evolve beyond traditional form-based interfaces to more intuitive chatbot-style engagement. Scenario: Managing Sales Pipeline Consider a salesperson with the daily objectives of tracking deals, managing pipeline opportunities, and identifying potential risks. Traditionally, this would require manually navigating numerous Salesforce objects, risking data inconsistencies and user errors. Agentforce’s assistive actions can streamline much of this, automating processes to identify key deals, summarize progress, and track deal risks across the pipeline. Let’s take a closer look at configuring a custom action for a pipeline summary. All powered by Salesforce Agentforce. Step-by-Step Guide to Configuring a Pipeline Summary Action Agentforce Use Cases: Getting Started Agentforce offers powerful tools for implementing AI-based functions within Salesforce, but to realize productivity gains, consider the following: Agentforce’s standard actions are a great starting point, providing immediate productivity impacts that can be enhanced as you customize actions to meet specific needs. For tailored guidance on integrating Agentforce, explore Tectonic’s Salesforce Agentforce Consulting Services. Tectonic’s expertise can support your organization in optimizing user experience, boosting productivity, and training users to responsibly leverage Agentforce’s capabilities across industries and channels. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 14, 2024in Agentforce Platform, Data, Generative AI, Mulesoft, Salesforce0 Comments
copilots and agentic ai
13Nov

Copilots and Agentic AI

Agentic AI vs. Copilots: Defining the Future of Generative AI Artificial Intelligence has rapidly evolved, progressing from simple automation to generative models, to copilots. But now, a new player—Agentic AI—has emerged, promising to redefine the AI landscape. Is Agentic AI the next logical step, or will it coexist alongside copilots, each serving distinct roles? Copilots and Agentic AI. Generative AI: Creativity with a Human Touch Since the launch of ChatGPT, generative AI has dominated tech priorities, offering businesses the ability to generate content—text, images, videos, and more—from pre-defined data. However, while revolutionary, generative AI still relies heavily on human input to guide its output, making it a powerful collaborator rather than an autonomous actor. Enter Agentic AI: Autonomy Redefined Agentic AI represents a leap forward, offering systems that possess autonomy and the ability to act independently to achieve pre-defined goals. Unlike generative AI copilots that respond to human prompts, Agentic AI makes decisions, plans actions, and learns from experience. Think of it as Siri or Alexa—enhanced with autonomy and learning capabilities. Gartner recently spotlighted Agentic AI as its top technology trend for 2025, predicting that by 2028, at least 15% of day-to-day work decisions will be made autonomously, up from virtually none today. Agentforce and the Third Wave of AI Salesforce’s “Agentforce,” unveiled at Dreamforce, is a prime example of Agentic AI’s potential. These autonomous agents are designed to augment employees by handling tasks across sales, service, marketing, and commerce. Salesforce CEO Mark Benioff described it as the “Third Wave of AI,” going beyond copilots to deliver intelligent agents deeply embedded into customer workflows. Salesforce aims to empower one billion AI agents by 2025, integrating Agentforce into every aspect of customer success. Benioff took a swipe at competitors’ bolt-on generative AI solutions, emphasizing that Agentforce is deeply embedded for maximum value. The Role of Copilots: Collaboration First While Agentic AI gains traction, copilots like Microsoft’s Copilot Studio and SAP’s Joule remain critical for businesses focused on intelligent augmentation. Copilots act as productivity boosters, working alongside humans to optimize processes, enhance creativity, and provide decision-making support. SAP’s Joule, for example, integrates seamlessly into existing systems to optimize operations while leaving strategic decision-making in human hands. This collaborative model aligns well with businesses prioritizing agility and human oversight. Agentic AI: Opportunities and Challenges Agentic AI’s autonomy offers significant potential for streamlining complex processes, reducing human intervention, and driving productivity. However, it also comes with risks. Eleanor Watson, AI ethics engineer at Singularity University, warns that Agentic AI systems require careful alignment of values and goals to avoid unintended consequences like dangerous shortcuts or boundary violations. In contrast, copilots retain human agency, making them particularly suited for creative and knowledge-based roles where human oversight remains essential. Copilots and Agentic AI The choice between Agentic AI and copilots hinges on an organization’s priorities and risk tolerance. For simpler, task-specific applications, copilots excel by providing assistance without removing human input. Agentic AI, on the other hand, shines in complex, multi-task scenarios where autonomy is key. Dom Couldwell, head of field engineering EMEA at DataStax, emphasizes the importance of understanding when to deploy each model. “Use a copilot for specific, focused tasks. Use Agentic AI for complex, goal-oriented processes involving multiple tasks. And leverage Retrieval Augmented Generation (RAG) in both to provide context to LLMs.” The Road Ahead: Coexistence or Dominance? As AI evolves, Agentic AI and copilots may coexist, serving complementary roles. Businesses seeking full automation and scalability may gravitate toward Agentic AI, while those prioritizing augmented intelligence and human collaboration will continue to rely on copilots. Ultimately, the future of AI will be defined not by one model overtaking the other, but by how well each aligns with the specific needs, goals, and challenges of the organizations adopting them. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 13, 2024in Artificial Intelligence, Data, Generative AI, Salesforce, Technology0 Comments
2025: The Rise of AI Agents and Industry-Focused Innovation
13Nov

2025: The Rise of AI Agents and Industry-Focused Innovation

Over the past few years, CX vendors have rapidly integrated generative AI (GenAI) across the customer experience landscape. This wave of innovation has brought advancements like auto-summarization, customer response recommendations, and intent analysis, especially within Contact Center as a Service (CCaaS) solutions. However, as these capabilities become standard, differentiation now hinges on more advanced AI solutions, orchestration of cross-platform workflows, and collection of industry-specific datasets. AI Agents and Industry-Focused Innovation. Agentic AI, where bots autonomously handle tasks without human intervention, is emerging as a critical differentiator. This shift is reshaping sector-specific processes. Take network providers, for instance; they can leverage agentic AI to detect service outages, create affected customer segments, and proactively send alerts. Salesforce exemplifies this trend with its Agentforce platform, which debuted at Dreamforce 2024, introducing 100 pre-configured, autonomous bots designed for specific industries. By 2025, such bots will likely proliferate, expanding across ecosystems like Workday to facilitate cross-functional automation. Toward a More Autonomous Enterprise As autonomous AI agents advance, they are poised to manage complex, multi-step workflows collaboratively. This move will help organizations move closer to an autonomous enterprise model, where human oversight drives the deployment, testing, and optimization of AI agents. In this model, collaboration platforms such as Microsoft Teams, Slack, and Zoom will serve as operational hubs for managing and refining AI-driven processes. While this full vision may take longer to achieve, 2025 promises substantial advancements in sector-specific efficiencies through AI agents. Not all industries, however, are equally poised to benefit; while healthcare, financial services, and retail lead in AI-enabled CX solutions, other sectors such as hospitality, travel, and education still lag. The Need for Sector-Specific Use Case Libraries CX vendors could empower businesses by providing industry-specific AI use case libraries, building confidence in AI-agent-driven experiences. For example, bots in the finance sector could streamline billing, invoice processing, and ledger management, while spotting and correcting errors. Other industries would benefit from AI innovations tailored to their unique challenges, but such solutions will require co-innovation across CX platforms. 2025 Strategic Technology Trends Gartner’s top technology trends for 2025 provide a framework for CIOs aiming to future-proof their organizations. These trends fall into three themes: AI imperatives, new computing frontiers, and human-machine synergy. These trends will push organizations to adopt cloud, AI, and sustainability-focused architectures, despite challenges. As AI capabilities evolve, so will the risks, emphasizing the need for robust security and ethical frameworks. Salesforce charges up its game with its Agentforce platform, which debuted at Dreamforce 2024, introducing 100 pre-configured, autonomous bots designed for specific industries. By 2025, such bots will likely proliferate, expanding across ecosystems like Workday to facilitate cross-functional automation. Preparing for 2025: Upskilling for the Future As organizations embrace these transformative trends, they must also address a persistent skill gap. Pluralsight’s recent survey reveals that 20% of organizations have deployed AI, while 55% are planning to. However, without strategic business alignment, technology adoption won’t necessarily translate to customer value. For organizations, a focus on responsible innovation and proactive skills development in AI, cloud security, and sustainability will be vital. By preparing for these 2025 trends, businesses can navigate the complexities of the tech landscape and position themselves for long-term success. AI Agents and Industry-Focused Innovation As you prepare for 2025. Tectonic can help you align your goals with your road map. Contact us today! Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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November 13, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Technology0 Comments

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