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What Should Enterprises Build with Agentic AI?
25Oct

What Should Enterprises Build with Agentic AI?

The rise of agentic AI has dominated recent discussions in enterprise technology, sparking debates over its transformative potential and practical applications. Just weeks ago, few had heard of the term. Now, every tech vendor is racing to stake their claim in this emerging space, positioning agentic AI as the successor to AI co-pilots. While co-pilots assist users with tasks, agentic AI represents the next step: delegating tasks to intelligent agents capable of independent execution, akin to assigning work to a junior colleague. But beyond the buzz, the pressing questions remain: Cutting Through the Hype Recent launches provide a snapshot of how enterprises are beginning to deploy agentic AI. Salesforce’s Agentforce, Asana’s AI Studio, and Atlassian’s Rovo AI Assistant all emphasize the ability of these agents to streamline workflows by interpreting unstructured data and automating complex tasks. These tools promise flexibility over previous rigid, rule-based systems. For example, instead of painstakingly scripting every step, users can instruct an agent to “follow documented policies, analyze data, and propose actions,” reserving human approval for final execution. However, the performance of these agents hinges on data quality and system robustness. Salesforce’s Marc Benioff, for instance, critiques Microsoft’s Copilot for lacking a robust data model, emphasizing Salesforce’s own structured approach as a competitive edge. Similarly, Asana and Atlassian highlight the structured work graphs underpinning their platforms as critical for accurate and reliable outputs. Key Challenges Despite the promise, there are significant challenges to deploying agentic AI effectively: Early Wins and Future Potential Early adopters are seeing value in high-volume, repetitive scenarios such as customer service. For example: However, these successes represent low-hanging fruit. The true promise lies in rethinking how enterprises work. As one panelist at Atlassian’s event noted: “We shouldn’t just use this AI to enhance existing processes. We should ask whether these are the processes we want for the future.” The Path Forward The transformative potential of agentic AI will depend on broader process standardization. Just as standardized shipping containers revolutionized logistics, and virtual containers transformed IT operations, similar breakthroughs in process design could unlock exponential gains for AI-driven workflows. For now, enterprises should: Conclusion Agentic AI holds immense potential, but its real power lies in enabling enterprises to question and redesign how work gets done. While it may still be in its early days, businesses that align their AI investments with strategic goals—and not just immediate fixes—will be best positioned to thrive in this new era of intelligent automation. 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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October 25, 2024in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Technology
Salesforce Says AI Should Be a Partner
24Oct

Salesforce Says AI Should Be a Partner

Salesforce Says AI Should Be a Partner, Not Just a Tool As AI continues to evolve rapidly, Salesforce’s chief ethical and humane use officer, Paula Goldman, urged businesses to rethink how they integrate AI in the workplace. According to Goldman, we are at a pivotal moment where AI should be seen as a partner rather than merely a tool. Goldman emphasized the concept of agentic AI, which refers to AI systems that can act independently to achieve goals or make decisions on behalf of the company. However, with this autonomy comes the need for proper safeguards to prevent issues like bias and misinformation, especially considering AI’s tendency to generate “hallucinations” or inaccurate outputs. One powerful example Goldman provided was during a company board meeting where AI identified bias in real-time. The AI flagged a pattern that participants either didn’t notice or were hesitant to address, leading to richer discussions and better decision-making. She also cited a healthcare scenario where a nurse used AI during patient intake. The AI collected information through questions and answers, freeing up the nurse to focus on the patient’s body language and emotional cues, enhancing the human element of care. Goldman concluded by saying that the future of AI depends on how businesses choose to leverage it. “To make AI work for our businesses, we have to make sure it works for the people our businesses serve and the people our businesses employ,” she said. In short, AI should act as a collaborative partner, enhancing human judgment and decision-making while staying within ethical boundaries. 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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October 24, 2024in Agentforce Platform
AI Agents and Digital Transformation
23Oct

Ready for AI Agents

Brands that can effectively integrate agentic AI into their operations stand to gain a significant competitive edge. But as with any innovation, success will depend on balancing the promise of automation with the complexities of trust, privacy, and user experience.

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October 23, 2024in Data, Enterprise, Generative AI, Google, Salesforce

GENAI Shows No Racial or Sexual Bias

Researchers from Mass General Brigham recently published findings in PAIN indicating that large language models (LLMs) do not exhibit race- or sex-based biases when recommending opioid treatments. The team highlighted that, while biases are prevalent in many areas of healthcare, they are particularly concerning in pain management. Studies have shown that Black patients’ pain is often underestimated and undertreated by clinicians, while white patients are more likely to be prescribed opioids than other racial and ethnic groups. These disparities raise concerns that AI tools, including LLMs, could perpetuate or exacerbate such biases in healthcare. To investigate how AI tools might either mitigate or reinforce biases, the researchers explored how LLM recommendations varied based on patients’ race, ethnicity, and sex. Using 40 real-world patient cases from the MIMIC-IV Note data set—each involving complaints of headache, abdominal, back, or musculoskeletal pain—the cases were stripped of references to sex and race. Random race categories (American Indian or Alaska Native, Asian, Black, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, and white) and sex (male or female) were then assigned to each case. This process was repeated until all combinations of race and sex were generated, resulting in 480 unique cases. These cases were analyzed using GPT-4 and Gemini, both of which assigned subjective pain ratings and made treatment recommendations. The analysis found that neither model made opioid treatment recommendations that differed by race or sex. However, the tools did show some differences—GPT-4 tended to rate pain as “severe” more frequently than Gemini, which was more likely to recommend opioids. While further validation is necessary, the researchers believe the results indicate that LLMs could help address biases in healthcare. “These results are reassuring in that patient race, ethnicity, and sex do not affect recommendations, indicating that these LLMs have the potential to help address existing bias in healthcare,” said co-first authors Cameron Young and Ellie Einchen, students at Harvard Medical School, in a press release. However, the study has limitations. It categorized sex as a binary variable, omitting a broader gender spectrum, and it did not fully represent mixed-race individuals, leaving certain marginalized groups underrepresented. The team suggested future research should incorporate these factors and explore how race influences LLM recommendations in other medical specialties. Marc Succi, MD, strategic innovation leader at Mass General Brigham and corresponding author of the study, emphasized the need for caution in integrating AI into healthcare. “There are many elements to consider, such as the risks of over-prescribing or under-prescribing medications and whether patients will accept AI-influenced treatment plans,” Succi said. “Our study adds key data showing how AI has the potential to reduce bias and improve health equity.” Succi also noted the broader implications of AI in clinical decision support, suggesting that AI tools will serve as complementary aids to healthcare professionals. “In the short term, AI algorithms can act as a second set of eyes, running in parallel with medical professionals,” he said. “However, the final decision will always remain with the doctor.” These findings offer important insights into the role AI could play in reducing bias and enhancing equity in pain management and healthcare overall. 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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October 23, 2024in AI Tools, Data
AI That Forgets
23Oct

AI That Forgets

Salesforce has introduced a generative AI system designed to prioritize data privacy through a unique “forgetting” feature. This innovation allows the AI to process information through large language models (LLMs) without retaining the data, helping companies manage sensitive information more securely. AI That Forgets. As part of the latest wave in generative AI, Salesforce’s solution takes the form of digital “agents”—intelligent systems capable of understanding and responding to customer inquiries autonomously. CEO Marc Benioff has hailed this development as a significant breakthrough for the company, emphasizing its potential to transform customer interactions. AI That Forgets. At a recent event, Patrick Stokes, Salesforce’s EVP of Products and Industries, highlighted how this system supports organizations by reducing the costs and risks associated with building their own AI models. According to Stokes, many companies lack the resources to develop in-house AI sustainably, and Salesforce’s privacy-first approach provides an appealing alternative. Rather than focusing solely on creating the most powerful LLM, Salesforce has built AI agents that connect data and actions securely, addressing privacy concerns that have hindered AI adoption. AI That Forgets Salesforce’s approach integrates privacy-focused safeguards, which Stokes describes as a “trust layer” within the AI system. This feature verifies that data retrieved during an AI query aligns with the user’s access permissions, protecting sensitive information. Stokes notes that unlike traditional AI models that retain data, Salesforce’s LLM processes only the information required for each interaction and then “forgets” it afterward. This zero-retention approach creates a more secure environment, where companies retain governance over data usage and minimize risks associated with long-term data storage. Zahra Bahrololoumi, CEO of Salesforce UK and Ireland, also emphasized that Salesforce’s AI solutions offer users the confidence to adopt generative AI without compromising security. With over 1,000 AI agents already implemented, companies are benefiting from reduced burnout and increased productivity while maintaining data trust and integrity. 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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October 23, 2024in Data, Generative AI
What is Explainable AI
22Oct

What is Explainable AI

Building a trusted AI system starts with ensuring transparency in how decisions are made. Explainable AI is vital not only for addressing trust issues within organizations but also for navigating regulatory challenges. According to research from Forrester, many business leaders express concerns over AI, particularly generative AI, which surged in popularity following the 2022 release of ChatGPT by OpenAI. “AI faces a trust issue,” explained Forrester analyst Brandon Purcell, underscoring the need for explainability to foster accountability. He highlighted that explainability helps stakeholders understand how AI systems generate their outputs. “Explainability builds trust,” Purcell stated at the Forrester Technology and Innovation Summit in Austin, Texas. “When employees trust AI systems, they’re more inclined to use them.” Implementing explainable AI does more than encourage usage within an organization—it also helps mitigate regulatory risks, according to Purcell. Explainability is crucial for compliance, especially under regulations like the EU AI Act. Forrester analyst Alla Valente emphasized the importance of integrating accountability, trust, and security into AI efforts. “Don’t wait for regulators to set standards—ensure you’re already meeting them,” she advised at the summit. Purcell noted that explainable AI varies depending on whether the AI model is predictive, generative, or agentic. Building an Explainable AI System AI explainability encompasses several key elements, including reproducibility, observability, transparency, interpretability, and traceability. For predictive models, transparency and interpretability are paramount. Transparency involves using “glass-box modeling,” where users can see how the model analyzed the data and arrived at its predictions. This approach is likely to be a regulatory requirement, especially for high-risk applications. Interpretability is another important technique, useful for lower-risk cases such as fraud detection or explaining loan decisions. Techniques like partial dependence plots show how specific inputs affect predictive model outcomes. “With predictive AI, explainability focuses on the model itself,” Purcell noted. “It’s one area where you can open the hood and examine how it works.” In contrast, generative AI models are often more opaque, making explainability harder. Businesses can address this by documenting the entire system, a process known as traceability. For those using models from vendors like Google or OpenAI, tools like transparency indexes and model cards—which detail the model’s use case, limitations, and performance—are valuable resources. Lastly, for agentic AI systems, which autonomously pursue goals, reproducibility is key. Businesses must ensure that the model’s outputs can be consistently replicated with similar inputs before deployment. These systems, like self-driving cars, will require extensive testing in controlled environments before being trusted in the real world. “Agentic systems will need to rack up millions of virtual miles before we let them loose,” Purcell concluded. 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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October 22, 2024in Data, Generative AI, Google, Technology
How Skechers Solved Its Ecommerce Challenges
21Oct

How Skechers Solved Its Ecommerce Challenges

Skechers Boosts Direct-to-Consumer Sales with Ecommerce Platform Upgrades Skechers, now a global brand in 2024, credits its recent ecommerce platform upgrades for saving time and increasing direct-to-consumer sales. However, it wasn’t always equipped with the right technology to support its massive growth. During Salesforce’s Dreamforce conference in San Francisco, Eric Cheng, Skechers USA Inc.’s director of ecommerce architecture, shared insights into how key technology decisions helped the brand expand and enhance its website and content capabilities. “Today, we’re present in over 180 countries worldwide,” Cheng said, speaking on stage at the Moscone Center. Skechers’ journey began in 1992, and its expansion has taken the brand across borders, reaching millions of customers worldwide. “We connect hundreds of millions of customers through our retail stores and ecommerce platform to deliver a unique experience,” Cheng noted, emphasizing the need to meet the diverse demands of each market. Skechers ranks No. 273 in the Top 1000, Digital Commerce 360’s ranking of the largest North American e-retailers by online sales, where it is categorized as an Apparel & Accessories retailer. Digital Commerce 360 projects that Skechers will reach 0.65 million in online sales by 2024. Ecommerce Platform Challenges Cheng acknowledged that Skechers’ digital transformation wasn’t immediate: “The journey did not just happen overnight; it took time and effort.” Skechers faced challenges in three key areas: content management, scalability, and customer experience. The legacy system was inadequate, lacking robust tools for efficient content delivery, previewing scheduled content, and handling localization. As Cheng described, launching a marketing page often required the content team to be on standby at midnight—an unsustainable approach for 17 countries. How Skechers Solved Its Ecommerce Challenges To overcome these hurdles, Skechers partnered with Astound Digital. Together, they implemented Salesforce Service Cloud and Manhattan Active Omni for order management. Kyle Montgomery, senior vice president of commerce at Astound Digital, joined Cheng on stage and highlighted the goal: “Their vision was to unify, supply, and scale.” This transformation enabled Skechers to bring 17 countries in Europe, Japan, and North America onto a single platform. Jennifer Lane, Salesforce’s director of success guides, also emphasized the flexibility achieved using Salesforce’s Page Designer and localization solutions from Salesforce’s AppExchange. Integrations with Thomson Reuters for tax, CyberSource for payments, and Salesforce Marketing Cloud for personalization further enhanced Skechers’ capabilities. The Results Cheng highlighted three key improvements after the ecommerce overhaul. First, content creation and localization tools improved operational efficiency by over 500%. The time to launch in new markets was dramatically reduced from five months to just a few weeks. Additionally, Skechers saw a notable sales boost, with a 24.5% increase in its direct-to-consumer segment during Q1 2023. Skechers’ success demonstrates the significant impact of a well-executed ecommerce platform upgrade, allowing the brand to scale globally while improving customer experience and operational efficiency. Contact Tectonic to learn what Salesforce can do for you. 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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October 21, 2024in Google, Salesforce, Salesforce Marketing Cloud, Salesforce Service Cloud, Technology
AI in Networking
21Oct

AI in Networking

AI Tools in Networking: Tailoring Capabilities to Unique Needs AI tools are becoming increasingly common across various industries, offering a wide range of functionalities. However, network engineers may not require every capability these tools provide. Each network has distinct requirements that align with specific business objectives, necessitating that network engineers and developers select AI toolsets tailored to their networks’ needs. While network teams often desire similar AI capabilities, they also encounter common challenges in integrating these tools into their systems. The Rise of AI in Networking Though AI is not a new concept—having existed for decades in the form of automated and expert systems—it is gaining unprecedented attention. According to Jim Frey, principal analyst for networking at TechTarget’s Enterprise Strategy Group, many organizations have not fully grasped AI’s potential in production environments over the past three years. “AI has been around for a long time, but the interesting thing is, only a minority—not even half—have really said they’re using it effectively in production for the last three years,” Frey noted. Generative AI (GenAI) has significantly contributed to this renewed interest in AI. Shamus McGillicuddy, vice president of research at Enterprise Management Associates, categorizes AI tools into two main types: GenAI and AIOps (AI for IT operations). “Generative AI, like ChatGPT, has recently surged in popularity, becoming a focal point of discussion among IT professionals,” McGillicuddy explained. “AIOps, on the other hand, encompasses machine learning, anomaly detection, and analytics.” The increasing complexity of networks is another factor driving the adoption of AI in networking. Frey highlighted that the demands of modern network environments are beyond human capability to manage manually, making AI engines a vital solution. Essential AI Tool Capabilities for Networks While individual network needs vary, many network engineers seek similar functionalities when integrating AI. Commonly desired capabilities include: According to McGillicuddy’s research, network optimization and automated troubleshooting are among the most popular use cases for AI. However, many professionals prefer to retain manual oversight in the fixing process. “Automated troubleshooting can identify and analyze issues, but typically, people want to approve the proposed fixes,” McGillicuddy stated. Many of these capabilities are critical for enhancing security and mitigating threats. Frey emphasized that networking professionals increasingly view AI as a tool to improve organizational security. DeCarlo echoed this sentiment, noting that network managers share similar objectives with security professionals regarding proactive problem recognition. Frey also mentioned alternative use cases for AI, such as documentation and change recommendations, which, while less popular, can offer significant value to network teams. Ultimately, the relevance of any AI capability hinges on its fit within the network environment and team needs. “I don’t think you can prioritize one capability over another,” DeCarlo remarked. “It depends on the tools being used and their effectiveness.” Generative AI: A New Frontier Despite its recent emergence, GenAI has quickly become an asset in the networking field. McGillicuddy noted that in the past year and a half, network professionals have adopted GenAI tools, with ChatGPT being one of the most recognized examples. “One user reported that leveraging ChatGPT could reduce a task that typically takes four hours down to just 10 minutes,” McGillicuddy said. However, he cautioned that users must understand the limitations of GenAI, as mistakes can occur. “There’s a risk of errors or ‘hallucinations’ with these tools, and having blind faith in their outputs can lead to significant network issues,” he warned. In addition to ChatGPT, vendors are developing GenAI interfaces for their products, including virtual assistants. According to McGillicuddy’s findings, common use cases for vendor GenAI products include: DeCarlo added that GenAI tools offer valuable training capabilities due to their rapid processing speeds and in-depth analysis, which can expedite knowledge acquisition within the network. Frey highlighted that GenAI’s rise is attributed to its ability to outperform older systems lacking sophistication. Nevertheless, the complexity of GenAI infrastructures has led to a demand for AIOps tools to manage these systems effectively. “We won’t be able to manage GenAI infrastructures without the support of AI tools, as human capabilities cannot keep pace with rapid changes,” Frey asserted. Challenges in Implementing AI Tools While AI tools present significant benefits for networks, network engineers and managers must navigate several challenges before integration. Data Privacy, Collection, and Quality Data usage remains a critical concern for organizations considering AIOps and GenAI tools. Frey noted that the diverse nature of network data—combining operational information with personally identifiable information—heightens data privacy concerns. For GenAI, McGillicuddy pointed out the importance of validating AI outputs and ensuring high-quality data is utilized for training. “If you feed poor data to a generative AI tool, it will struggle to accurately understand your network,” he explained. Complexity of AI Tools Frey and McGillicuddy agreed that the complexity of both AI and network systems could hinder effective deployment. Frey mentioned that AI systems, especially GenAI, require careful tuning and strong recommendations to minimize inaccuracies. McGillicuddy added that intricate network infrastructures, particularly those involving multiple vendors, could limit the effectiveness of AIOps components, which are often specialized for specific systems. User Uptake and Skills Gaps User adoption of AI tools poses a significant challenge. Proper training is essential to realize the full benefits of AI in networking. Some network professionals may be resistant to using AI, while others may lack the knowledge to integrate these tools effectively. McGillicuddy noted that AIOps tools are often less intuitive than GenAI, necessitating a certain level of expertise for users to extract value. “Understanding how tools function and identifying potential gaps can be challenging,” DeCarlo added. The learning curve can be steep, particularly for teams accustomed to longstanding tools. Integration Issues Integration challenges can further complicate user adoption. McGillicuddy highlighted two dimensions of this issue: tools and processes. On the tools side, concerns arise about harmonizing GenAI with existing systems. “On the process side, it’s crucial to ensure that teams utilize these tools effectively,” he said. DeCarlo cautioned that organizations might need to create in-house supplemental tools to bridge integration gaps, complicating the synchronization of vendor AI

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October 21, 2024in AI Tools, Data, Enterprise, Generative AI
Google on Google AI
21Oct

Google on Google AI

As a leading cloud provider, Google Cloud is also a major player in the generative AI market. Google on Google AI provides insights into this new tool. In the past two years, Google has been in a competitive battle with AWS, Microsoft, and OpenAI to gain dominance in the generative AI space. Recently, Google introduced several generative Artificial Intelligence products, including its flagship large language model, Gemini, and the Vertex AI Model Garden. Last week, it also unveiled Audio Overview, a tool that transforms documents into audio discussions. Despite these advancements, Google has faced criticism for lagging in some areas, such as issues with its initial image generation tool, like X’s Grok. However, the company remains committed to driving progress in generative AI. Google’s strategy focuses not only on delivering its proprietary models but also offering a broad selection of third-party models through its Model Garden. Google’s Thoughts on Google AI Warren Barkley, head of product for Google Cloud’s Vertex AI, GenAI, and machine learning, emphasized this approach in a recent episode of the Targeting AI podcast. He noted that a key part of Google’s ongoing effort is ensuring users can easily transition to more advanced models. “A lot of what we did in the early days, and we continue to do now, is make it easy for people to move to the next generation,” Barkley said. “The models we built 18 months ago are a shadow of what we have today. So, providing pathways for people to upgrade and stay on the cutting edge is critical.” Google is also focused on helping users select the right AI models for specific applications. With over 100 closed and open models available in the Model Garden, evaluating them can be challenging for customers. To address this, Google introduced evaluation tools that allow users to test prompts and compare model responses. In addition, Google is exploring advancements in Artificial Intelligence reasoning, which it views as crucial to driving the future of generative AI. 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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October 21, 2024in Generative AI, Google
Customer Engagement with AI
18Oct

Customer Engagement with AI

Funlab Explores AI to Boost Customer Engagement in Leisure Venues In a push to enhance customer experiences across its “leisure-tainment” venues, Funlab has begun experimenting with artificial intelligence. Speaking at a Salesforce Agentforce event in Sydney, Funlab’s Head of Customer Relationships and Retention, Tracy Tanti, shared that the company is “excited to be able to start experimenting” with AI. Agentforce, a Salesforce platform designed to create autonomous agents for supporting employees and customers, serves as a key part of Funlab’s AI exploration efforts. According to Tanti, Funlab has a range of AI-focused projects on its roadmap, with the goal of blending digital experiences into real-life interactions and supporting both venue and corporate teams with AI-driven tools. Reflecting the company’s dedication to careful planning, Tanti described how Salesforce connected Funlab with another customer, Norths Collective, to discuss its own AI implementation journey. Robert Lopez, Chief Marketing and Innovation Officer at Norths Collective, has seen success with enhanced personalization and analytics, which have contributed to increased membership and engagement. Tanti noted that Norths Collective’s transformation work would provide valuable insights for Funlab as it optimizes its data in preparation for AI adoption. Currently, Funlab is in a post-digital transformation phase, refining its processes to deliver more connected and personalized guest experiences throughout the customer lifecycle. With ongoing expansion into the U.S. market—including recent openings of Holey Moley venues—Funlab is also focusing on building robust support infrastructure and engaging local audiences through Salesforce. Tanti highlighted the company’s vision for the U.S. to become a significant portion of total revenues and emphasized how Salesforce will help Funlab nurture a strong customer database in this new market. Additionally, Funlab is leveraging Salesforce to grow its event and function sales, which are projected to reach 39% of total online revenue by year’s end, up from 23% earlier this year. This expansion underscores Funlab’s commitment to using AI and data-driven insights to fuel growth and deepen customer engagement across all its markets and venues. 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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October 18, 2024in Artificial Intelligence, Data, Salesforce
Latest on AI, CRM, and Data Innovations
18Oct

Latest on AI, CRM, and Data Innovations

What’s Happening at Salesforce? The Latest on AI, CRM, and Data Innovations OneMagnify and CX Today have collaborated to explore the latest advancements in AI, CRM, and data at Salesforce. The Salesforce suite is evolving rapidly, driven by the emergence of generative AI, large language models, and increasingly diverse customer demands. Discover how Salesforce is adapting to this dynamic landscape, what the future holds for the industry giant, and how business leaders can maximize the potential of the Salesforce platform. Adam MacDonald, a Salesforce Solution Engineer at OneMagnify, emphasizes, “Organizations often struggle with Salesforce implementation when they fail to align internally and address data silos as the first step in their digital transformation. Defining the solution with the end goal in mind, while allowing for quick, focused wins, is a solid strategy for securing the long-term organizational buy-in essential for successful implementation.” 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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October 18, 2024in Data, Generative AI, Salesforce
NYT Issues Cease-and-Desist Letter to Perplexity AI
16Oct

NYT Issues Cease-and-Desist Letter to Perplexity AI

NYT Issues Cease-and-Desist Letter to Perplexity AI Over Alleged Unauthorized Content Use The New York Times (NYT) has issued a cease-and-desist letter to Perplexity AI, accusing the AI-powered search startup of using its content without permission. This move marks the second time the NYT has confronted a company for allegedly misappropriating its material. According to reports, the Times claims Perplexity is accessing and utilizing its content to generate summaries and other outputs, actions it argues infringe on copyright laws. The startup now has two weeks to respond to the accusations. A Growing Pattern of Tensions Perplexity AI is not the only publisher-facing scrutiny. In June, Forbes threatened legal action against the company, alleging “willful infringement” by using its text and images. In response, Perplexity launched the Perplexity Publishers’ Program, a revenue-sharing initiative that collaborates with publishers like Time, Fortune, and The Texas Tribune. Meanwhile, the NYT remains entangled in a separate lawsuit with OpenAI and its partner Microsoft over alleged misuse of its content. A Strategic Legal Approach The NYT’s decision to issue a cease-and-desist letter instead of pursuing an immediate lawsuit signals a calculated move. “Cease-and-desist approaches are less confrontational, less expensive, and faster,” said Sarah Kreps, a professor at Cornell University. This method also opens the door for negotiation, a pragmatic step given the uncharted legal terrain surrounding generative AI and copyright law. Michael Bennett, a responsible AI expert from Northeastern University, echoed this view, suggesting that the cease-and-desist approach positions the Times to protect its intellectual property while maintaining leverage in ongoing legal battles. If the NYT wins its case against OpenAI, Bennett added, it could compel companies like Perplexity to enter financial agreements for content use. However, if the case doesn’t favor the NYT, the publisher risks losing leverage. The letter also serves as a warning to other AI vendors, signaling the NYT’s determination to safeguard its intellectual property. Perplexity’s Defense: Facts vs. Expression Perplexity AI has countered the NYT’s claims by asserting that its methods adhere to copyright laws. “We aren’t scraping data for building foundation models but rather indexing web pages and surfacing factual content as citations,” the company stated. It emphasized that facts themselves cannot be copyrighted, drawing parallels to how search engines like Google operate. Kreps noted that Perplexity’s approach aligns closely with other AI platforms, which typically index pages to provide factual answers while citing sources. “If Perplexity is culpable, then the entire AI industry could be held accountable,” she said, contrasting Perplexity’s citation-based model with platforms like ChatGPT, which often lack transparency about data sources. The Crux of the Copyright Argument The NYT’s cease-and-desist letter centers on the distinction between facts and the creative expression of facts. While raw facts are not protected under copyright, the NYT claims that its specific interpretation and presentation of those facts are. Vincent Allen, an intellectual property attorney, explained that if Perplexity is scraping data and summarizing articles, it may involve making unauthorized copies of copyrighted content, strengthening the NYT’s claims. “This is a big deal for content providers,” Allen said, “as they want to ensure they’re compensated for their work.” Implications for the AI Industry The outcome of this dispute could set a precedent for how AI platforms handle content generated by publishers. If Perplexity’s practices are deemed infringing, it could reshape the operational models of similar AI vendors. At the heart of the debate is the balance between fostering innovation in AI and protecting intellectual property, a challenge that will likely shape the future of generative AI and its relationship with content creators. 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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October 16, 2024in Data, Generative AI, Google, Laws
Third Wave of AI at Salesforce
16Oct

Third Wave of AI at Salesforce

The Third Wave of AI at Salesforce: How Agentforce is Transforming the Landscape At Dreamforce 2024, Salesforce unveiled several exciting innovations, with Agentforce taking center stage. This post explores the key changes and enhancements designed to improve efficiency and elevate customer interactions. Introducing Agentforce Agentforce is a customizable AI agent builder that empowers organizations to create and manage autonomous agents for various business tasks. But what exactly is an agent? An agent is akin to a chatbot but goes beyond traditional capabilities. While typical chatbots are restricted to scripted responses and predefined questions, Agentforce agents leverage large language models (LLMs) and generative AI to comprehend customer inquiries contextually. This enables them to make independent decisions, whether processing requests or resolving issues using real-time data from your company’s customer relationship management (CRM) system. The Role of Atlas At the heart of Agentforce’s functionality lies the Atlas reasoning engine, which acts as the operational brain. Unlike standard assistive tools, Atlas is an agentic system with the autonomy to act on behalf of the user. Atlas formulates a plan based on necessary actions and can adjust that plan based on evaluations or new information. When it’s time to engage, Atlas knows which business processes to activate and connects with customers or employees via their preferred channels. This sophisticated approach allows Agentforce to significantly enhance operational efficiency. By automating routine inquiries, it frees up your team to focus on more complex tasks, delivering a smoother experience for both staff and customers. Speed to Value One of Agentforce’s standout features is its emphasis on rapid implementation. Many AI projects can be resource-intensive and take months or even years to launch. However, Agentforce enables quick deployment by leveraging existing Salesforce infrastructure, allowing organizations to implement solutions rapidly and with greater control. Salesforce also offers pre-built Agentforce agents tailored to specific business needs—such as Service Agent, Sales Development Representative Agent, Sales Coach, Personal Shopper Agent, and Campaign Agent—all customizable with the Agent Builder. Agentforce for Service and Sales will be generally available starting October 25, 2024, with certain elements of the Atlas Reasoning Engine rolling out in February 2025. Pricing begins at $2 per conversation, with volume discounts available. Transforming Customer Insights with Data Cloud and Marketing Cloud Dreamforce also highlighted enhancements to Data Cloud, Salesforce’s backbone for all cloud products. The platform now supports processing unstructured data, which constitutes up to 90% of company data often overlooked by traditional reporting systems. With new capabilities for analyzing various unstructured formats—like video, audio, sales demos, customer service calls, and voicemails—businesses can derive valuable insights and make informed decisions across Customer 360. Furthermore, Data Cloud One enables organizations to connect siloed Salesforce instances effortlessly, promoting seamless data sharing through a no-code, point-and-click setup. The newly announced Marketing Cloud Advanced edition serves as the “big sister” to Marketing Cloud Growth, equipping larger marketing teams with enhanced features like Path Experiment, which tests different content strategies across channels, and Einstein Engagement Scoring for deeper insights into customer behavior. Together, these enhancements empower companies to engage customers more meaningfully and measurably across all touchpoints. Empowering the Workforce Through Education Salesforce is committed to making AI accessible for all. They recently announced free instructor-led courses and AI certifications available through 2025, aimed at equipping the Salesforce community with essential AI and data management skills. To support this initiative, Salesforce is establishing AI centers in major cities, starting with London, to provide hands-on training and resources, fostering AI expertise. They also launched a global Agentforce World Tour to promote understanding and adoption of the new capabilities introduced at Dreamforce, featuring repackaged sessions from the conference and opportunities for specialists to answer questions. The Bottom Line What does this mean for businesses? With the rollout of Agentforce, along with enhancements to Data Cloud and Marketing Cloud, organizations can operate more efficiently and connect with customers in more meaningful ways. Coupled with a focus on education through free courses and global outreach, getting on board has never been easier. If you’d like to discuss how we can help your business maximize its potential with Salesforce through data and AI, connect with us and schedule a meeting with our team. Legacy systems can create significant gaps between operations and employee needs, slowing lead processes and resulting in siloed, out-of-sync data that hampers business efficiency. Responding to inquiries within five minutes offers a 75% chance of converting leads into customers, emphasizing the need for rapid, effective marketing responses. Salesforce aims to help customers strengthen relationships, enhance productivity, and boost margins through its premier AI CRM for sales, service, marketing, and commerce, while also achieving these goals internally. Recognizing the complexity of its decade-old processes, including lead assignment across three systems and 2 million lines of custom code, Salesforce took on the role of “customer zero,” leveraging Data Cloud to create a unified view of customers known as the “Customer 360 Truth Profile.” This consolidation of disparate data laid the groundwork for enterprise-wide AI and automation, improving marketing automation and reducing lead time by 98%. As Michael Andrew, SVP of Marketing Decision Science at Salesforce, noted, this initiative enabled the company to provide high-quality leads to its sales team with enriched data and AI scoring while accelerating time to market and enhancing data quality. Embracing Customer Zero “Almost exactly a year ago, we set out with a beginner’s mind to transform our lead automation process with a solution that would send the best leads to the right sales teams within minutes of capturing their data and support us for the next decade,” said Andrew. The initial success metric was “speed to lead,” aiming to reduce the handoff time from 20 minutes to less than one minute. The focus was also on integrating customer and lead data to develop a more comprehensive 360-degree profile for each prospect, enhancing lead assignment and sales rep productivity. Another objective was to boost business agility by cutting the average time to implement assignment changes from four weeks to mere days. Accelerating Success with

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October 16, 2024in Data, Enterprise, Generative AI, Google, Google Analytics, Marketing Automation, Salesforce
Zendesk Launches AI Agent Builder
15Oct

The State of AI

The State of AI: How We Got Here (and What’s Next) Artificial intelligence (AI) has evolved from the realm of science fiction into a transformative force reshaping industries and lives around the world. But how did AI develop into the technology we know today, and where is it headed next? At Dreamforce, two of Salesforce’s leading minds in AI—Chief Scientist Silvio Savarese and Chief Futurist Peter Schwartz—offered insights into AI’s past, present, and future. How We Got Here: The Evolution of AI AI’s roots trace back decades, and its journey has been defined by cycles of innovation and setbacks. Peter Schwartz, Salesforce’s Chief Futurist, shared a firsthand perspective on these developments. Having been involved in AI since the 1970s, Schwartz witnessed the first “AI winter,” a period of reduced funding and interest due to the immense challenges of understanding and replicating the human brain. In the 1990s and early 2000s, AI shifted from attempting to mimic human cognition to adopting data-driven models. This new direction opened up possibilities beyond the constraints of brain-inspired approaches. By the 2010s, neural networks re-emerged, revolutionizing AI by enabling systems to process raw data without extensive pre-processing. Savarese, who began his AI research during one of these challenging periods, emphasized the breakthroughs in neural networks and their successor, transformers. These advancements culminated in large language models (LLMs), which can now process massive datasets, generate natural language, and perform tasks ranging from creating content to developing action plans. Today, AI has progressed to a new frontier: large action models. These systems go beyond generating text, enabling AI to take actions, adapt through feedback, and refine performance autonomously. Where We Are Now: The Present State of AI The pace of AI innovation is staggering. Just a year ago, discussions centered on copilots—AI systems designed to assist humans. Now, the conversation has shifted to autonomous AI agents capable of performing complex tasks with minimal human oversight. Peter Schwartz highlighted the current uncertainties surrounding AI, particularly in regulated industries like banking and healthcare. Leaders are grappling with questions about deployment speed, regulatory hurdles, and the broader societal implications of AI. While many startups in the AI space will fail, some will emerge as the giants of the next generation. Salesforce’s own advancements, such as the Atlas Reasoning Engine, underscore the rapid progress. These technologies are shaping products like Agentforce, an AI-powered suite designed to revolutionize customer interactions and operational efficiency. What’s Next: The Future of AI According to Savarese, the future lies in autonomous AI systems, which include two categories: The Road Ahead As AI continues to evolve, it’s clear that its potential is boundless. However, the path forward will require careful navigation of ethical, regulatory, and practical challenges. The key to success lies in innovation, collaboration, and a commitment to creating systems that enhance human capabilities. For Salesforce, the journey has only just begun. With groundbreaking technologies and visionary leadership, the company is not just predicting the future of AI—it’s creating it. The State of AI. 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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October 15, 2024in Artificial Intelligence, Data, Salesforce, Technology

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