Spark Archives - gettectonic.com

10May

Why AI Won’t Kill SaaS

Why AI Won’t Kill SaaS (Yet) – The Missing Link in Enterprise Intelligence The rise of generative AI has sparked speculation about the demise of SaaS—but the reality is more nuanced. While AI holds transformative potential, today’s enterprise software vendors still play a critical role: providing the structured context that AI lacks. The AI Accuracy Problem: Plausible vs. Precise Generative AI excels at producing human-like responses, but its probabilistic nature makes it unreliable for mission-critical enterprise tasks. Whether validating financial transactions, ensuring compliance, or executing safety protocols, businesses need deterministic accuracy—not just statistically likely answers. Why SaaS Isn’t Going Anywhere (For Now) AI won’t replace SaaS because raw data is meaningless without context. Just as Google’s PageRank used web links to rank authoritative sources, enterprise software provides the structured frameworks that make AI useful in business. How SaaS Vendors Are Bridging the Gap Leading enterprise platforms are codifying decades of institutional knowledge into AI-ready systems: As Rahul Auradkar (EVP, Salesforce) explains: “The intelligence of LLMs is just one piece. The real value comes from feeding them the right data, driving automations, and contextualizing insights—something SaaS platforms already do.” The Future: AI Absorbs SaaS Knowledge (Then Renders It Obsolete?) Today, AI relies on SaaS for grounding and governance. But what happens when LLMs develop their own “world models” of business? The Bottom Line AI won’t kill SaaS—it will consume it. Enterprise software’s structured knowledge is the training data for tomorrow’s autonomous agents. Once AI internalizes these systems, the need for traditional SaaS may fade. But for now, SaaS isn’t just surviving—it’s enabling AI’s evolution. The Takeaway: The real disruption isn’t AI replacing SaaS—it’s AI learning from it until it no longer needs to. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 10, 2025in Data, Enterprise, Generative AI, Google, Salesforce0 Comments

What is the Difference Between a Data Lake and a Data Warehouse

Data Lake vs Data Warehouse: Modern Data Architecture Explained Understanding the Core Differences Data Lake: The Flexible Data Reservoir Data Warehouse: The Structured Analytics Engine Comparative Analysis Feature Data Lake Data Warehouse Data Format Raw (structured/unstructured) Processed & modeled Schema Approach Applied when reading (flexible) Defined before loading (rigid) Primary Users Data engineers/scientists Business analysts Storage Cost $0.023/GB (cloud object storage) $25/TB/month (cloud DW) Query Speed Slower (minutes-hours) Faster (seconds-minutes) Best Use Cases ML training, data exploration Financial reporting, KPIs The Modern Data Stack: Lakehouse Architecture Why Organizations Need Both Delta Lake: The Game Changer python Copy Download # Example Delta Lake transaction from delta import DeltaTable DeltaTable.forPath(spark, “/data/events”) .update(“status = ‘processed'”, condition = “date > ‘2023-01-01′”) Key Benefits: Implementation Guide When to Choose Which Solution Scenario Recommended Approach Storing IoT sensor data Data Lake + Delta Financial reporting Cloud Data Warehouse Customer 360 analytics Lakehouse (both) AI/ML development Data Lake Top Cloud Platforms Future Trends “The lakehouse paradigm reduces analytics TCO by 40% while delivering warehouse-grade performance”*- Databricks 2023 Benchmark Report* 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 8, 2025in Data, Enterprise, Snowflake0 Comments

05May

Is Using DeepSeek a Security Risk?

Despite its efficiency and customizability, DeepSeek—a Chinese AI chatbot—raises serious concerns about data privacy, censorship, and security vulnerabilities for business users. DeepSeek’s Rapid Rise and Security Concerns Two years after ChatGPT’s launch, China introduced a major rival: DeepSeek. Within days of its release in January 2025, DeepSeek became the most downloaded freeware app on Apple’s iOS App Store and Google Play in the U.S. However, its fast adoption has sparked significant privacy and security concerns, especially for businesses that may input sensitive data. What Is DeepSeek? Developed by a Chinese AI startup of the same name, DeepSeek is an open-source generative AI chatbot that competes with ChatGPT, Google’s Gemini, and Anthropic’s Claude. Unlike its rivals, DeepSeek is entirely free to use via a web application, requiring only an email or phone number for registration. DeepSeek stands out for its efficiency—achieving results comparable to ChatGPT with significantly fewer GPUs. While it excels in technical and mathematical domains, ChatGPT offers broader, context-aware responses across a wider range of topics. DeepSeek Web App vs. Local Hosting Users can access DeepSeek through its web and mobile apps or by hosting models locally via GitHub. When using DeepSeek’s web app, interactions occur on servers controlled by DeepSeek, subjecting users to its data-sharing policies. In contrast, local hosting allows organizations to maintain control over their data but shifts security responsibilities to the user. Key Security Risks of DeepSeek 1. Data Privacy & Government Oversight DeepSeek operates under China’s strict data laws, which require companies to cooperate with government authorities. Its privacy policy explicitly states that user data—including account details, input history, device metadata, and location data—is stored on servers in China, making it accessible to the Chinese government. This centralization raises concerns for businesses, as sensitive corporate data entered into DeepSeek may be subject to state oversight without notification. Some Western governments, such as Italy, have already banned DeepSeek over privacy concerns, while U.S. lawmakers are considering similar restrictions. 2. Open-Source Risks: Disinformation & Security Exploits DeepSeek’s open-source model offers flexibility but also creates risks: Unlike OpenAI and Google, which enforce strict content safety measures, DeepSeek’s open-source nature makes it more vulnerable to manipulation. 3. Centralized Data Storage Risks Most Western AI providers use decentralized, region-specific data centers to comply with privacy laws like GDPR. DeepSeek, however, stores all user data in China, raising concerns for international businesses handling sensitive information. 4. AI Hallucinations & Misinformation DeepSeek has been found to be more prone to AI hallucinations—where the model generates false or misleading information. This can lead to: 5. Encryption & Security Weaknesses Security audits of DeepSeek’s iOS app revealed several vulnerabilities: How DeepSeek Compares to ChatGPT & Gemini Security Factor DeepSeek ChatGPT Gemini Data Storage China-only Global, region-specific Global, region-specific Government Oversight Subject to China’s data laws U.S./EU privacy laws apply U.S./EU privacy laws apply Guardrail Protection Easier to bypass Strong, regularly updated Strong, regularly updated Transparency Limited public disclosures Security white papers, third-party audits Security white papers, third-party audits Should Businesses Use DeepSeek? Businesses handling sensitive information should exercise extreme caution when considering DeepSeek. Its centralized data storage in China, potential government oversight, and open-source vulnerabilities make it a higher-risk alternative compared to Western AI tools like ChatGPT and Gemini. Organizations concerned about data security should either avoid using DeepSeek’s web application or only interact with the model through local hosting to maintain control over their data. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 5, 2025in AI Tools, Data, Generative AI, Google, Laws0 Comments

25Apr

Embracing “Intelligent Austerity”

Embracing “Intelligent Austerity”: How Scotland Can Lead the Way in Public Sector Innovation As the UK Government enforces a 15% reduction in operating costs across departments, the pressure to streamline workflows through generative AI has never been greater. While these targets have sparked concern in Westminster, Scotland’s legacy of innovation—from tidal energy to healthcare—positions it to redefine what austerity can achieve. Rather than resorting to blunt cuts that undermine services and hurt the most vulnerable constituents, Scotland has a unique opportunity to pioneer intelligent austerity: delivering significant cost savings and productivity gains without sacrificing the quality of essential public services. But how? A Smarter Approach to Public Services At Salesforce, we’re not just driving agentic transformation—we’re challenging governments to rethink efficiency. Our technology is already embedded across the UK public sector and beyond. With Agentforce, our goal isn’t to replace human workers but to empower them by eliminating repetitive, low-value tasks. When I speak with civil servants, I ask a simple question: “What parts of your day drain your productivity?” The answer is almost always the same: tedious administrative work that stifles innovation. The key to unlocking societal progress—whether in fighting child poverty, boosting the economy, or tackling climate change—lies in making small, daily efficiency gains. By automating routine tasks, we free up staff to focus on what they do best: high-impact, human-centric work. Agentforce serves as a practical blueprint for intelligent austerity, delivering lasting efficiencies while preserving—and even enhancing—the human touch in public services. Intelligent Austerity: Efficiency Without Sacrifice Traditional austerity often means deep, painful cuts that erode services and fuel public frustration. Intelligent austerity, by contrast, targets inefficiencies—like costly call centres and outdated administrative processes—while reinvesting savings where they matter most. Instead of lengthy, expensive IT overhauls that tie departments to consultants, we advocate for off-the-shelf AI solutions that deliver value in weeks, not years. These integrate seamlessly with existing systems, improving transparency, agility, and scalability from day one. The result? Departments can exceed cost-saving targets—even surpassing the 15% goal—without the downsides of traditional austerity. Agents in Action: Real-World Success Stories These examples prove that AI-driven transformation can counter fiscal pressures while improving service delivery—a win-win for both budgets and citizens. Scotland’s AI Opportunity Imagine every government department equipped with a 24/7 AI expert—an intelligent assistant capable of answering policy questions, processing documents, or even serving as a strategic advisor. Early AI adoption is like the first SatNav systems: helpful but imperfect. The real breakthrough comes when AI evolves into a collision avoidance system—actively preventing problems and enhancing decision-making. Our AI Agents Handbook outlines how Scotland can harness this potential. By adopting AI strategically, public services can achieve cost savings that are reinvested in key priorities—eradicating child poverty, growing the economy, and addressing the climate crisis. The Future: Smarter, More Agile Public Services AI isn’t about replacing humans—it’s about empowering them. With each small efficiency gain, departments become more agile, better equipped to deliver sustainable, high-quality services. Scotland has the chance to lead this shift, turning fiscal challenges into opportunities for innovation. Interested in learning more? Let’s discuss how AI Agents can transform your organization. Get in touch for a personalized consultation. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 25, 2025in AI Tools, Generative AI, Generative AI Tools, Salesforce, Technology0 Comments

05Apr

The 5G Letdown

The 5G Letdown: How Hype Outpaced Reality When 5G first arrived, it wasn’t just sold as an upgrade—it was pitched as the backbone of a futuristic society. Telecom giants promised self-driving car networks, remote robotic surgeries, and hyper-connected smart cities. Five years later, most of those visions remain science fiction. So what happened? The Grand Promises vs. The Reality 1. Remote Surgery? Not So Fast Marketing campaigns showed doctors performing precision operations from miles away using 5G’s “ultra-low latency.” But in reality:✔ Wired connections are still more reliable for critical medical procedures.✔ Regulatory and ethical hurdles (like patient consent and sterile environments) were glossed over.✔ Most hospitals never needed 5G for this in the first place. 2. Autonomous Cars Didn’t Need 5G The vision: A seamless 5G-powered traffic grid where cars communicate to prevent accidents. The truth?✔ Self-driving systems rely on onboard sensors and AI, not constant wireless signals.✔ Network dropouts would be deadly—so engineers designed cars to function independently.✔ 5G’s spotty coverage makes it an unreliable backbone for safety-critical systems. 3. Smart Cities? More Like Slow Rollouts While some cities have deployed IoT sensors (like smart streetlights), most “smart city” projects:✔ Use existing 4G or Wi-Fi instead of 5G.✔ Face budget and bureaucracy issues—not tech limitations.✔ Don’t actually require the speed 5G theoretically offers. Why 5G Fell Short 1. Millimeter Wave Limitations 5G’s fastest frequencies (mmWave) can’t penetrate walls and require antennas every few hundred meters. Carriers skipped the expensive infrastructure, relying instead on:✔ “Non-standalone 5G”—a rebranded 4G/5G hybrid that delivers barely noticeable speed boosts.✔ Misleading coverage maps showing 5G in areas where it barely functions. 2. Consumers Didn’t Notice (or Care) Most people’s daily use—streaming, browsing, social media—works fine on 4G. The average user sees little benefit from 5G, especially when:✔ Real-world speeds often match LTE.✔ Battery drain is worse on 5G phones.✔ Rural areas still lack coverage, despite ads claiming nationwide availability. 3. The Real Winners Were Equipment Makers Carriers spent $100B+ on spectrum licenses and infrastructure, but struggled to monetize 5G. Meanwhile:✔ Ericsson, Nokia, and Qualcomm made billions selling hardware.✔ Lobbyists pushed 5G as a “national priority”—even though the benefits were exaggerated. The Conspiracies & Health Panics The rapid deployment of 5G towers sparked baseless fears over radiation, despite studies showing:✔ 5G emissions are well below safety limits.✔ FM radio waves are stronger than 5G signals.✔ Scam products (like “5G-blocking” stickers) exploited public confusion. Was 5G a Scam? Not entirely—but it was the most overhyped tech of the decade. The truth?✔ Some industries (like factories) benefit from private 5G networks.✔ 6G is already being hyped—will we fall for it again?✔ The lesson? Demand proof, not promises. Final Verdict: 5G delivered incremental upgrades, not a revolution. And with 6G looming, we should ask: Will the next “game-changer” actually change anything? 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 5, 2025in Salesforce

03Apr

Databricks Launches Lakeflow Connect to Simplify Enterprise Data Ingestion

San Francisco, [April 2, 2025] – Databricks has taken a major step toward streamlining enterprise data integration with the general availability of Lakeflow Connect, its new low-code/no-code connector system. The initial release features preconfigured integrations with Salesforce and Workday, with plans to expand support to additional SaaS platforms, databases, and file sources in the coming months. Simplifying the Data Ingestion Challenge Data ingestion—the process of moving data from source systems into analytics environments—has long been a complex, resource-intensive task for enterprises. Traditional approaches require stitching together multiple tools (such as Apache Kafka or CDC solutions) and maintaining custom pipelines, often leading to scalability issues and high operational overhead. Lakeflow Connect aims to eliminate these pain points by providing: “Customers need this data, but before Lakeflow Connect, they were forced to rely on third-party tools that often failed at scale—or build custom solutions,” said Michael Armbrust, Distinguished Software Engineer at Databricks. “Now, ingestion is point-and-click within Databricks.” Why Salesforce and Workday First? The choice of initial connectors reflects the growing demand for real-time, structured data to power AI and generative AI applications. According to Kevin Petrie, Analyst at BARC U.S., more than 90% of AI leaders are experimenting with structured data, and nearly two-thirds use real-time feeds for model training. “Salesforce and Workday provide exactly the type of data needed for real-time ML and GenAI,” Petrie noted. “Databricks is smart to simplify access in this way.” Competitive Differentiation While other vendors offer connector solutions (e.g., Qlik’s Connector Factory), Lakeflow Connect stands out through: “Serverless compute is quietly important,” said Donald Farmer, Principal at TreeHive Strategy. “It’s not just about scalability—rapid startup times are critical for reducing pipeline latency.” The Road Ahead Databricks has already outlined plans to expand Lakeflow Connect with connectors for: Though the company hasn’t committed to a timeline, Armbrust hinted at upcoming announcements at the Data + AI Summit in June. Broader Vision: Democratizing Data Engineering Beyond ingestion, Databricks is focused on unifying the data engineering lifecycle. “Historically, you needed deep Spark or Scala expertise to build production-grade pipelines,” Armbrust said. “Now, we’re enabling SQL users—or even UI-only users—to achieve the same results.” Looking further ahead, Petrie suggested Databricks could enhance cross-team collaboration for agentic AI development, integrating Lakeflow with Mosaic AI and MLflow to bridge data, model, and application lifecycles. The Bottom LineLakeflow Connect marks a strategic move by Databricks to reduce friction in data pipelines—addressing a key bottleneck for enterprises scaling AI initiatives. As the connector ecosystem grows, it could further solidify Databricks’ position as an end-to-end platform for data and AI. For more details, visit Databricks.com. Key Takeaways:✅ Now Available: Salesforce & Workday connectors✅ Serverless, governed, and scalable ingestion✅ Future integrations with Google Analytics, ServiceNow, and more✅ June previews expected at Data + AI Summit 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 3, 2025in Agentic AI, Data, Enterprise, Generative AI, Google, Google Analytics, Salesforce

29Mar

Redefining AI-Driven Customer Service

Salesforce’s Agentforce: Redefining AI-Driven Customer Service Salesforce has made major strides in AI-powered customer service with Agentforce, its agentic AI platform. The CRM leader now resolves 85% of customer queries without human intervention—an achievement driven by three key factors: Speaking at the Agentforce World Tour, Salesforce Co-Founder & CTO Parker Harris emphasized the platform’s role in handling vast volumes of customer interactions. The remaining 15% of queries are escalated to human agents for higher-value interactions, ensuring complex issues receive the necessary expertise. “We’re all shocked by the power of these LLMs. AI has truly hit a tipping point over the past two years,” Harris said. Currently, Agentforce manages 30,000 weekly conversations for Salesforce, proving its growing impact. Yet, the journey to adoption wasn’t without its challenges. From Caution to Acceleration: Agentforce’s Evolution Initially, Salesforce approached the Agentforce rollout with caution, concerned about AI hallucinations and accuracy. However, the company ultimately embraced a learn-by-doing approach. “So, we went for it!” Harris recalled. “We put it out there and improved it every hour. Every interaction helped us refine it.” This iterative process led to significant advancements, with Agentforce now seamlessly handling a high volume of inquiries. Expanding Beyond Customer Support Agentforce’s impact extends beyond customer service—it’s also revolutionizing sales operations at Salesforce. The platform acts as a virtual sales coach for 25,000 sales representatives, offering real-time guidance without the social pressures of a human supervisor. “Salespeople aren’t embarrassed to ask an AI coach questions, which makes them more effective,” Harris noted. This AI-driven coaching has enhanced sales efficiency and confidence, allowing teams to perform at a higher level. Real-World Impact and Competitive Edge Salesforce isn’t just promoting Agentforce—it’s using it to prove its value. Harris shared success stories, including reMarkable, which automated 35% of its customer service inquiries, reducing workload by 7,350 queries per month. Salesforce CEO Marc Benioff highlighted this competitive edge during the launch of Agentforce 2.0, pointing out that while many companies talk about AI adoption, few truly implement it at scale. “When you visit their websites, you still find a lot of forms and FAQs—but not a lot of AI agents,” Benioff said. He specifically called out Microsoft, stating: “If you look for Co-Pilot on their website, or how they’re automating support, it’s the same as it was two years ago.” Microsoft pushed back on Benioff’s critique, sparking a war of words between the tech giants. What’s Next for Salesforce? Beyond AI-driven service and sales, Salesforce is making bold moves in IT Service Management (ITSM), positioning itself against competitors like ServiceNow. During a recent Motley Fool podcast, Benioff hinted at Salesforce’s ITSM ambitions, stating: “We’re building new apps, like ITSM.” At the TrailheadDX event, Salesforce teased this new product, signaling its expansion into enterprise IT management—a move that could shake up the ITSM landscape. With AI agents redefining work across industries, Salesforce’s aggressive push into automation and ITSM underscores its vision for the future of enterprise 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 29, 2025in Agentforce Platform, Agentic AI, Data, Enterprise, Salesforce

10Feb

AI and Related Tools Boost Holiday Sales

AI Drives Holiday Sales in 2024: A Record-Breaking Shopping Season with Rising Returns Artificial intelligence (AI) played a transformative role in shaping the 2024 holiday shopping season, with Salesforce reporting that AI-powered tools influenced $229 billion, or 19%, of global online sales. Based on data from 1.5 billion global shoppers and 1.6 trillion page views, AI tools such as product recommendations, targeted promotions, and customer service significantly boosted sales, marking a 6% year-over-year increase in engagement. Generative AI features, including conversational agents, saw a 25% surge in usage during the holiday period compared to earlier months, further highlighting their role in shaping consumer behavior. Mobile commerce amplified AI’s influence, with nearly 70% of global online sales being placed via smartphones. On Christmas Day alone, mobile orders accounted for 79% of transactions, showcasing the shift toward mobile-first shopping. “Retailers who have embraced AI and conversational agents are already reaping the benefits, but these tools will become even more critical in the new year as retailers aim to minimize revenue losses from returns and reengage with shoppers,” said Caila Schwartz, Salesforce’s Director of Consumer Insights. Record-Breaking Sales and Rising Returns Online sales hit .2 trillion globally and 2 billion in the U.S. during the holiday season, but returns surged to $122 billion globally—a 28% increase compared to 2023. Salesforce attributed this spike to evolving shopping habits like bracketing (buying multiple sizes to ensure fit) and try-on hauls (bulk purchasing for social media content), which have become increasingly common. The surge in returns presents a challenge to retailers, potentially eroding profit margins. To address this, many are turning to AI-powered solutions for streamlining returns processes. According to Salesforce, 75% of U.S. shoppers expressed interest in using AI agents for returns, with one-third showing strong enthusiasm for such tools. The Role of AI in Enhancing the Holiday Shopping Experience AI-powered chatbots saw a 42% year-over-year increase in usage during the holiday season, supporting customers with purchases, returns, and product inquiries. These conversational agents, combined with AI-driven loyalty programs and targeted promotions, were instrumental in engaging customers and increasing conversion rates. AI’s influence extended to social commerce, with platforms like TikTok Shop and Instagram driving 20% of global holiday sales. Personalized recommendations and advertisements, powered by AI algorithms, significantly boosted social media referral traffic, which grew by 8% year-over-year. Mobile Commerce and AI Synergy Mobile devices were the dominant force in holiday shopping, generating 2 billion in global online sales and 5 billion in the U.S. Orders placed via smartphones peaked on Christmas Day, with mobile accounting for 79% of all transactions. This mobile-first trend highlights the growing importance of integrating AI into mobile commerce to enhance the shopping experience. AI Integration Expands Across Retail Operations In the UK, retailers are increasingly leveraging AI to optimize operations and improve personalization. A study by IMRG and Scurri revealed that 57% of UK online retailers used generative AI for content creation in 2024, while 31% implemented AI-informed product search tools. By 2025, 75% of UK retailers plan to adopt AI for marketing efforts, and 42% aim to use AI-powered product information management systems to streamline processes. Tesco, for example, uses AI to analyze Clubcard data, enabling tailored product recommendations, healthier purchasing choices, and waste reduction. Meanwhile, Must Have Ideas, a homeware retailer, has launched an AI-driven TV shopping channel powered by proprietary software, Spark, which automates programming schedules based on real-time stock levels and market trends. Looking Ahead The 2024 holiday season underscored the transformative potential of AI in retail. While AI-powered tools drove record sales and engagement, the rise in returns presents a challenge that retailers must address to protect their bottom line. As AI continues to evolve, its role in shaping consumer behavior, streamlining operations, and enhancing customer experiences will become even more integral in the retail landscape. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 10, 2025in AI Tools, Artificial Intelligence, Data, Generative AI, Salesforce

06Feb

Decision Domain Management

Roger’s first week in the office felt like a wilder than 8 second ride on a raging rodeo bull. Armed with top-notch academic achievements, he hoped to breeze through operational routines and impress his new managers. What he didn’t expect was to land in a whirlwind of half-documented processes, half-baked ideas, and near-constant firefighting. While the organization had detailed SOPs for simple, routine tasks—approving invoices, updating customer records, and shipping standard orders—Roger quickly realized that behind the structured facade, there was a deeper level of uncertainty. Every day, he heard colleagues discuss “strategic pivots” or “risky product bets.” There were whispers about AI-based initiatives that promised to automate entire workflows. Yet, when the conversation shifted to major decisions—like selecting the right AI use cases—leaders often seemed to rely more on intuition than any structured methodology. One afternoon, Roger was invited to a cross-functional meeting about the company’s AI roadmap. Expecting an opportunity to showcase his knowledge, he instead found himself in a room filled with brilliant minds pulling in different directions. Some argued that AI should focus on automating repetitive tasks aligned with existing SOPs. Others insisted that AI’s real value lay in predictive modeling—helping forecast new market opportunities. The debate went in circles, with no consensus on where or how to allocate AI resources. After an hour of heated discussion, the group dispersed, each manager still convinced of the merit of their own perspective but no closer to a resolution. That evening, as Roger stood near the coffee machine, he muttered to himself, “We have SOPs for simple tasks, but nothing for big decisions. How do we even begin selecting which AI models or agents to develop first?” His frustration led him to a conversation with a coworker who had been with the company for years. “We’re missing something fundamental here,” Roger said. “We’re rushing to onboard AI agents that can mimic our SOPs—like some large language model trained to follow rote instructions—but that’s not where the real value lies. We don’t even have a framework for weighing one AI initiative against another. Everything feels like guesswork.” His coworker shrugged. “That’s just how it’s always been. The big decisions happen behind closed doors, mostly based on experience and intuition. If you’re waiting for a blueprint, you might be waiting a long time.” That was Roger’s ;ight bulb moment. Despite all his academic training, he realized the organization lacked a structured approach to high-level decision-making. Sure, they had polished SOPs for operational tasks, but when it came to determining which AI initiatives to prioritize, there were no formal criteria, classifications, or scoring mechanisms in place. Frustrated but determined, Roger decided he needed answers. Two days later, he approached a coworker known for their deep understanding of business strategy and technology. After a quick greeting, he outlined his concerns—the disorganized AI roadmap meeting, the disconnect between SOP-driven automation and strategic AI modeling, and his growing suspicion that even senior leaders were making decisions without a clear framework. His coworker listened, then gestured for him to take a seat. “Take a breath,” they said. “You’re not the first to notice this gap. Let me explain what’s really missing.” Why SOPs Aren’t Enough The coworker acknowledged that the organization was strong in SOPs. “We’re great at detailing exactly how to handle repetitive, rules-based tasks—like verifying invoices or updating inventory. In those areas, we can plug in AI agents pretty easily. They follow a well-defined script and execute tasks efficiently. But that’s just the tip of the iceberg.” They leaned forward and continued, “Where we struggle, as you’ve discovered, is in decision-making at deeper levels—strategic decisions like which new product lines to pursue, or tactical decisions like selecting the right vendor partnerships. There’s no documented methodology for these. It’s all in people’s heads.” Roger tilted his head, intrigued. “So how do we fix something as basic but great impact as that?” “That’s where Decision Domain Management comes in,” he explained. In the context of data governance and management, data domains are the high-level blocks that data professionals use to define master data. Simply put, data domains help data teams logically group data that is of interest to their business or stakeholders. “Think of it as the equivalent of SOPs—but for decision-making. Instead of prescribing exact steps for routine tasks, it helps classify decisions, assess their importance, and determine whether AI can support them—and if so, in what capacity.” They broke it down further. The Decision Types “First, we categorize decisions into three broad types: Once we correctly classify a decision, we get a clearer picture of how critical it is and whether it requires an AI agent (good at routine tasks) or an AI model (good at predictive and analytical tasks).” The Cynefin Framework The coworker then introduced the Cynefin Framework, explaining how it helps categorize decision contexts: By combining Decision Types with the Cynefin Framework, organizations can determine exactly where AI projects will be most beneficial. Putting It into Practice Seeing the spark of understanding in Roger’s eyes, the coworker provided some real-world examples: ✅ AI agents are ideal for simple SOP-based tasks like invoice validation or shipping notifications. ✅ AI models can support complicated decisions, like vendor negotiations, by analyzing performance metrics. ✅ Strategic AI modeling can help navigate complex decisions, such as predicting new market trends, but human judgment is still required. “Once we classify decisions,” the coworker continued, “we can score and prioritize AI investments based on impact and feasibility. Instead of throwing AI at random problems, we make informed choices.” The Lightbulb Moment Roger exhaled, visibly relieved. “So the problem isn’t just that we lack a single best AI approach—it’s that we don’t have a shared structure for decision-making in the first place,” he said. “If we build that structure, we’ll know which AI investments matter most, and we won’t keep debating in circles.” The coworker nodded. “Exactly. Decision Domain Management is the missing blueprint. We can’t expect AI to handle what even humans haven’t clearly defined. By categorizing

February 6, 2025in Data, Enterprise, Technology

05Feb

Deep Dive into DeepSeek

DeepSeek: The AI Lab Turned Controversial Global Player You know we have to write about anything AI related that is making waves. And DeepSeek is definitely doing that. On April 14, 2023, High-Flyer announced the launch of a dedicated artificial general intelligence (AGI) lab, focused on AI research independent of its financial business. This initiative led to the incorporation of DeepSeek on July 17, 2023, with High-Flyer as its primary investor and backer. DeepSeek’s Breakthrough and the Debate on AI Development DeepSeek quickly gained attention in the AI world, with former India IT Minister Rajeev Chandrasekhar highlighting its impact. He stated that DeepSeek’s success reinforced the idea that better datasets and algorithms—rather than increased compute capacity—are the key to advancing AI capabilities. National Security Concerns: Hidden Risks in DeepSeek’s Code Despite its technological achievements, DeepSeek is now at the center of global controversy. Cybersecurity experts have raised serious concerns about the tool’s potential data-sharing links to the Chinese government. According to a report by ABC News, DeepSeek contains hidden code capable of transmitting user data directly to China. Ivan Tsarynny, CEO of the Ontario-based cybersecurity firm Feroot Security, conducted an analysis of DeepSeek’s code and discovered an embedded function that connects user data to CMPassport.com—the online registry for China Mobile, a state-owned telecommunications company. Key Concerns Raised by Cybersecurity Experts: Global Backlash and Regulatory Actions DeepSeek’s security concerns have sparked international scrutiny. Several governments and organizations have moved swiftly to restrict or ban its use: John Cohen, a former acting Undersecretary for Intelligence and Analysis at the U.S. Department of Homeland Security, described DeepSeek as one of the most blatant cases of suspected Chinese surveillance. He emphasized that it joins a growing list of Chinese tech firms identified as potential national security threats. The Future of DeepSeek DeepSeek’s rapid rise and subsequent scrutiny reflect the broader tensions between AI innovation and national security. As regulators worldwide assess its risks, the company’s future remains uncertain—caught between technological breakthroughs and growing geopolitical concerns. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 5, 2025in AI Tools, Data

Einstein Service Agent

It’s been a little over a year since the global surge in GenAI chatbots, sparked by the excitement around ChatGPT. Since then, numerous vendors, both large and mid-sized, have invested heavily in the technology, and many users have already adopted AI-powered chatbots. The competition is intensifying, with CRM giant Salesforce releasing its own GenAI chatbot software, Einstein Service Agent. Einstein Service Agent, built on the Einstein 1 Platform, is Salesforce’s first fully autonomous AI agent. It interacts with large language models (LLMs) by analyzing the context of customer messages to determine the next actions. Utilizing GenAI, the agent generates conversational responses grounded in a company’s trusted business data, including Salesforce CRM data. Salesforce claims that service organizations can now significantly reduce the number of tedious inquiries that hinder productivity, allowing human agents to focus on more complex tasks. For customers, this means getting answers faster without waiting for human agents. Additionally, the service promises 24/7 availability for customer communication in natural language, with an easy handoff to human agents for more complicated issues. Businesses are increasingly turning to AI-based chatbots because, unlike traditional chatbots, they don’t rely on specific programmed queries and can understand context and nuance. Alongside Salesforce, other tech leaders like AWS and Google Cloud have released their own chatbots, such as Amazon Lex and Vertex AI, continuously enhancing their software. Recently, AWS updated its chatbot with the QnAIntent capability in Amazon Lex, allowing integration with a knowledge base in Amazon Bedrock. Similarly, Google released Vertex AI Agent Builder earlier this year, enabling organizations to build AI agents with no code, which can function together with one main agent and subagents. The AI arms race is just beginning, with more vendors developing software to meet market demands. For users, this means that while AI takes over many manual and tedious tasks, the primary challenge will be choosing the right vendor that best suits the needs and resources of their business. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

January 31, 2025in Data, Einstein 1 Platform, Google, Salesforce, Technology

23Jan

AI Market Heat

Alibaba Feels the Heat as DeepSeek Shakes Up AI Market Chinese tech giant Alibaba is under pressure following the release of an AI model by Chinese startup DeepSeek that has sparked a major reaction in the West. DeepSeek claims to have trained its model—comparable to advanced Western AI—at a fraction of the cost and with significantly fewer AI chips. In response, Alibaba launched Qwen 2.5-Max, its latest AI language model, on Tuesday—just one day before the Lunar New Year, when much of China’s economy typically slows down for a 15-day holiday. A Closer Look at Qwen 2.5-Max Qwen 2.5-Max is a Mixture of Experts (MoE) model trained on 20 trillion tokens. It has undergone supervised fine-tuning and reinforcement learning from human feedback to enhance its capabilities. MoE models function by using multiple specialized “minds,” each focused on a particular domain. When a query is received, the model dynamically routes it to the most relevant expert, improving efficiency. For instance, a coding-related question would be processed by the model’s coding expert. This MoE approach reduces computational requirements, making training more cost-effective and faster. Other AI vendors, such as France-based Mistral AI, have also embraced this technique. DeepSeek’s Disruptive Impact While Qwen 2.5-Max is not a direct competitor to DeepSeek’s R1 model—the release of which triggered a global selloff in AI stocks—it is similar to DeepSeek-V3, another MoE-based model launched earlier this month. Alibaba’s swift release underscores the competitive threat posed by DeepSeek. As the world’s fourth-largest public cloud vendor, Alibaba, along with other Chinese tech giants, has been forced to respond aggressively. In the wake of DeepSeek R1’s debut, ByteDance—the owner of TikTok—also rushed to update its AI offerings. DeepSeek has already disrupted the AI market by significantly undercutting costs. In 2023, the startup introduced V2 at just 1 yuan ($0.14) per million tokens, prompting a price war. By comparison, OpenAI’s GPT-4 starts at $10 per million tokens—a staggering difference. The timing of Alibaba and ByteDance’s latest releases suggests that DeepSeek has accelerated product development cycles across the industry, forcing competitors to move faster than planned. “Alibaba’s cloud unit has been rapidly advancing its AI technology, but the pressure from DeepSeek’s rise is immense,” said Lisa Martin, an analyst at Futurum Group. A Shifting AI Landscape DeepSeek’s rapid growth reflects a broader shift in the AI market—one driven by leaner, more powerful models that challenge conventional approaches. “The drive to build more efficient models continues,” said Gartner analyst Arun Chandrasekaran. “We’re seeing significant innovation in algorithm design and software optimization, allowing AI to run on constrained infrastructure while being more cost-competitive.” This evolution is not happening in isolation. “AI companies are learning from one another, continuously reverse-engineering techniques to create better, cheaper, and more efficient models,” Chandrasekaran added. The AI industry’s perception of cost and scalability has fundamentally changed. Sam Altman, CEO of OpenAI, previously estimated that training GPT-4 cost over $100 million—but DeepSeek claims it built R1 for just $6 million. “We’ve spent years refining how transformers function, and the efficiency gains we’re seeing now are the result,” said Omdia analyst Bradley Shimmin. “These advances challenge the idea that massive computing power is required to develop state-of-the-art AI.” Competition and Data Controversies DeepSeek’s success showcases the increasing speed at which AI innovation is happening. Its distillation technique, which trains smaller models using insights from larger ones, has allowed it to create powerful AI while keeping costs low. However, OpenAI and Microsoft are now investigating whether DeepSeek improperly used their models’ data to train its own AI—a claim that, if true, could escalate into a major dispute. Ironically, OpenAI itself has faced similar accusations, leading some enterprises to prefer using its models through Microsoft Azure, which offers additional compliance safeguards. “The future of AI development will require stronger security layers,” Shimmin noted. “Enterprises need assurances that using models like Qwen 2.5 or DeepSeek R1 won’t expose their data.” For businesses evaluating AI models, licensing terms matter. Alibaba’s Qwen 2.5 series operates under an Apache 2.0 license, while DeepSeek uses an MIT license—both highly permissive, allowing companies to scrutinize the underlying code and ensure compliance. “These licenses give businesses transparency,” Shimmin explained. “You can vet the code itself, not just the weights, to mitigate privacy and security risks.” The Road Ahead The AI arms race between DeepSeek, Alibaba, OpenAI, and other players is just beginning. As vendors push the limits of efficiency and affordability, competition will likely drive further breakthroughs—and potentially reshape the AI landscape faster than anyone anticipated. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

January 23, 2025in Data, Enterprise, Technology

09Jan

Generative AI Tools

Generative AI Tools: A Comprehensive Overview of Emerging Capabilities The widespread adoption of generative AI services like ChatGPT has sparked immense interest in leveraging these tools for practical enterprise applications. Today, nearly every enterprise app integrates generative AI capabilities to enhance functionality and efficiency. A broad range of AI, data science, and machine learning tools now support generative AI use cases. These tools assist in managing the AI lifecycle, governing data, and addressing security and privacy concerns. While such capabilities also aid in traditional AI development, this discussion focuses on tools specifically designed for generative AI. Not all generative AI relies on large language models (LLMs). Emerging techniques generate images, videos, audio, synthetic data, and translations using methods such as generative adversarial networks (GANs), diffusion models, variational autoencoders, and multimodal approaches. Here is an in-depth look at the top categories of generative AI tools, their capabilities, and notable implementations. It’s worth noting that many leading vendors are expanding their offerings to support multiple categories through acquisitions or integrated platforms. Enterprises may want to explore comprehensive platforms when planning their generative AI strategies. 1. Foundation Models and Services Generative AI tools increasingly simplify the development and responsible use of LLMs, initially pioneered through transformer-based approaches by Google researchers in 2017. 2. Cloud Generative AI Platforms Major cloud providers offer generative AI platforms to streamline development and deployment. These include: 3. Use Case Optimization Tools Foundation models often require optimization for specific tasks. Enterprises use tools such as: 4. Quality Assurance and Hallucination Mitigation Hallucination detection tools address the tendency of generative models to produce inaccurate or misleading information. Leading tools include: 5. Prompt Engineering Tools Prompt engineering tools optimize interactions with LLMs and streamline testing for bias, toxicity, and accuracy. Examples include: 6. Data Aggregation Tools Generative AI tools have evolved to handle larger data contexts efficiently: 7. Agentic and Autonomous AI Tools Developers are creating tools to automate interactions across foundation models and services, paving the way for autonomous AI. Notable examples include: 8. Generative AI Cost Optimization Tools These tools aim to balance performance, accuracy, and cost effectively. Martian’s Model Router is an early example, while traditional cloud cost optimization platforms are expected to expand into this area. Generative AI tools are rapidly transforming enterprise applications, with foundational, cloud-based, and domain-specific solutions leading the way. By addressing challenges like accuracy, hallucination, and cost, these tools unlock new potential across industries and use cases, enabling enterprises to stay ahead in the AI-driven landscape. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

January 9, 2025in AI Tools, Data, Enterprise, Generative AI, Google, Snowflake

03Jan

Speed to Launch of Agentforce

Agentforce isn’t just another AI platform that requires months of customization. At most customers, they quickly saw its power, launching transformative generative AI experiences in just days—no AI engineers needed. For companies with larger admin teams, the benefits can be even greater. Unlike other platforms, Agentforce places a strong emphasis on data privacy, building on the trust that Salesforce is known for, making these virtual assistants invaluable. We began with employee-facing use cases, saving our team several hours per week. Now, with Agentforce, we’re seeing even more opportunities to drive efficiencies and better serve our customers. “We’re excited to leverage Agentforce to completely overhaul recruitment and enrollment at Unity Environmental University. Instead of traditional forms or chatbots, our students will soon engage with an autonomous recruitment agent directly on our website, offering personalized support throughout the college application process.”– Dr. Melik Khoury, President & CEO, Unity Environmental University “For first-generation college students, the 1:385 coach-to-student ratio makes personalized guidance challenging. By integrating Agentforce into our platform, we’re deploying cutting-edge solutions to better support students. These agents enable our coaches to focus on high-touch, personalized experiences while handling vital tasks like sharing deadlines and answering common questions—24/7.”– Siva Kumari, CEO, College Possible “Agentforce offers organizations a unique opportunity to move beyond incremental improvements and achieve exponential ROI. By automating customer interactions, improving outcomes, and reducing costs, it integrates data, flows, and user interfaces to mitigate risks and accelerate value creation. This agent-based platform approach allows businesses to harness AI’s full potential, revolutionizing customer engagement and paving the way for exponential growth.”– Rebecca Wettemann, CEO and Principal Analyst, Valoir “Autonomous agents powered by Salesforce’s Agentforce are revolutionizing customer experiences by providing fast, accurate, and personalized support around the clock. With advanced AI making decisions and taking actions autonomously, businesses can resolve customer issues more efficiently, fostering deeper interactions and enhancing satisfaction. This innovation enables companies to reallocate human resources to more complex tasks, boosting individual productivity and scaling business growth. Agentforce is setting new standards for seamless sales, service, marketing, and commerce interactions, reinforcing its leadership in customer experience.”– Michael Fauscette, CEO and Chief Analyst, Arion Research LLC “The best way to predict the future is to invent it.” — Alan Kay, Computer Science Pioneer Technology progresses in what biologists call punctuated equilibrium, with new capabilities slowly emerging from labs and tinkerers until a breakthrough shifts the axis of possibility. These pioneering feats create new paradigms, unleashing waves of innovation—much like the Apple Macintosh, the iPhone, and the Salesforce Platform, which revolutionized the enterprise software-as-a-service (SaaS) model and sparked an entire industry. The Age of Agentforce Begins At Dreamforce 2024, Salesforce Futures reflected on the launch of Agentforce, inspired by visions like the Apple Knowledge Navigator. In 2023, we used this inspiration to craft our Salesforce 2030 film, which showcased the collaboration between humans and autonomous AI agents. Now, with Agentforce, we’re witnessing that vision come to life. Agentforce is a suite of customizable AI agents and tools built on the Salesforce Platform, offering an elegant solution to the complexity of AI deployment. It addresses the challenges of integrating data, models, infrastructure, and applications into a unified system. With powerful tools like Agent Builder and Model Builder, organizations can easily create, customize, and deploy AI agents. Salesforce’s Atlas Reasoning Engine empowers these agents to handle both routine and complex tasks autonomously. A New Era of AI Innovation At Dreamforce 2024, over 10,000 attendees raced to build their own agents using the “Agent Builder” experience, turning verbal instructions into fully functioning agents in under 15 minutes. This wasn’t just another chatbot—it’s a new breed of AI that could transform how businesses operate and deliver superior customer experiences. Companies like Saks, OpenTable, and Wiley have quickly embraced this technology. As Mick Costigan and David Berthy of Salesforce Futures explain, “When we see signals like this, it pushes us toward the future. Soon, we’ll see complex, multi-agent systems solving higher-order challenges, both in the enterprise and in consumer devices.” Shaping the Future Agentforce isn’t just a product—it’s a platform for experimentation. With hundreds of thousands of Salesforce customers soon gaining access, the full potential of these tools will unfold in ways we can’t yet imagine. As with every major technological shift, the real magic will lie in how people use it. Every interaction, every agent deployed, and every problem solved will shape the future in unexpected ways. Platform Evolution Adam Evans, Salesforce SVP of Product, notes that Agentforce builds on the company’s transformation over the past four years, following the pattern of Salesforce’s original disruption of enterprise software. Unlike traditional solutions, Agentforce eliminates the need for customers to build their own AI infrastructure, providing a ready-to-use solution. At the core of Agentforce is the Atlas Reasoning Engine, delivering results that are twice as relevant and 33% more accurate than competing solutions. This engine integrates Salesforce Data Cloud, Flow for automation, and the Einstein Trust Layer for governance. Early Customer Results Early Agentforce deployments highlight how organizations are using autonomous agents to enhance, rather than replace, human workers: George Pokorny, Senior VP of Global Customer Success at OpenTable, shared, “Just saving two minutes on a ten-minute call lets our service reps focus on strengthening customer relationships, thanks to seamless integration with Service Cloud, giving us a unified view of diner preferences and history.” 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

January 3, 2025in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Salesforce Data Cloud, Technology

26Dec

The Impact of AI on Jobs

The Impact of AI on Jobs: A Historical and Transformative Perspective For centuries, people have feared losing jobs to technological advancements. From the introduction of the printing press in 1440 to the widespread adoption of assembly lines in manufacturing, history has followed a familiar pattern: a wave of panic followed by a surge of innovation. Today, with AI in the spotlight, headlines warn of job-stealing robots. Yet, AI is not here to take jobs; it’s revealing new ones—and at an unprecedented pace. A Paradigm Shift: AI as a Job Creator Contrary to popular belief, AI is reshaping the job market for the better. Rather than replacing workers, it amplifies human potential, pushing society toward work that is creative, strategic, and uniquely human. Instead of asking, “Will AI take my job?” the better question is, “What new opportunities can AI unlock?” The answers are exciting and transformative. Lessons from the Past Technological disruption is far from new. The printing press, the weaving loom, and even the internet all provoked fears of mass unemployment. Yet, each time, these innovations sparked transformation rather than devastation. Consider the ATM, introduced in the 1960s. Initially, bank tellers feared redundancy. However, rather than replacing tellers, ATMs automated routine tasks, freeing human workers to focus on customer service and financial advising. In fact, the number of bank tellers increased in the decades following ATM adoption. AI follows the same trajectory. By handling repetitive tasks like sorting emails or managing schedules, AI frees workers to focus on areas requiring emotional intelligence, creativity, and problem-solving. AI: A Partner, Not a Competitor AI excels in areas that humans struggle with, such as processing vast datasets, recognizing patterns, and executing repetitive tasks with precision. However, it lacks empathy, context, and abstract thinking—traits that remain uniquely human. For example, IBM Watson can analyze millions of medical journals to suggest treatment options. Yet, a doctor’s role remains indispensable, as patients need empathy, understanding, and a human touch. Similarly, legal AI tools like CaseText can streamline research, but building persuasive arguments and negotiating terms require skills no algorithm can match. Rather than replacing professionals, AI enhances their productivity, enabling them to focus on higher-value tasks. The Birth of Entirely New Industries AI is not only reshaping existing jobs but also creating new roles and industries. The rise of generative AI has introduced positions like prompt engineers, who design effective queries to maximize AI’s output. Similarly, the need for unbiased algorithms has created the field of data ethics, where specialists ensure AI systems prioritize equity and fairness. These roles underscore an important reality: AI doesn’t eliminate opportunities—it redefines them. Addressing Ethical Challenges AI’s reliance on data is both its strength and its vulnerability. Algorithms trained on biased data can perpetuate harmful stereotypes, as seen in Amazon’s failed hiring algorithm, which penalized women. This challenge has given rise to data ethicists tasked with auditing algorithms and designing fair systems, further showcasing how AI disruption creates new fields and opportunities. Augmentation Over Replacement Fear of AI stems from misunderstanding its role. Machines are adept at repetitive and analytical tasks, but they lack the nuanced understanding required for roles in fields like art, music, and medicine. AI tools such as Adobe Sensei or AIVA enhance creativity, allowing artists and musicians to experiment, iterate, and push boundaries. Just as the printing press democratized writing rather than ending it, AI empowers workers to focus on what makes us uniquely human. A Future Worth Working Toward AI represents a profound shift in how society views work. It is not a destroyer of jobs but a catalyst for transformation. By automating inefficiencies and reinforcing human strengths, AI unlocks opportunities yet to be imagined. Rather than fearing the rise of AI, embracing its potential can lead to a future where work is more meaningful, creative, and impactful—an evolution worth striving for. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

December 26, 2024in AI Tools, Data, Generative AI, Salesforce

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