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18Dec

Marketing Agents for Campaigns

Marketing Agents: The Next Frontier in Campaign Creation and Optimization For years, content creation and distribution have been key challenges for marketers. According to recent research from the Content Marketing Institute, 54% of B2B marketers lack the resources needed to produce high-quality content at scale. Similarly, B2C marketers often struggle to create consistent, repeatable, and scalable processes. On top of these challenges, they must balance resource management to ensure campaigns are efficient, impactful, and engaging enough to stand out from the competition. The solution to these challenges lies in marketing agents, powered by data and AI. These intelligent tools streamline content creation, optimize campaigns, and make marketing processes more efficient. At this year’s Dreamforce, Salesforce introduced Agentforce, a suite of tools designed to create, customize, and deploy marketing agents across the Customer 360 platform. As part of this launch, Agentforce Campaigns is already gaining attention from customers eager to harness its potential. According to Salesforce’s 9th State of Marketing Report, 71% of marketers plan to integrate generative and predictive AI into their workflows within the next 18 months. This insight explores how marketing agents, like those offered in Agentforce, can transform customer engagement while enhancing internal team productivity and collaboration. Redefining Campaign Delivery with Marketing Agents Marketing agents are part of a broader suite of AI-powered virtual assistants that collaborate with humans to streamline workflows and optimize decision-making. These agents analyze data, interpret requests, and execute tasks such as content generation, campaign optimization, and performance analysis. Salesforce’s AI agents are designed to be partners, taking on time-consuming tasks so marketers can focus on high-value, strategic activities. With marketing agents, teams spend less time gathering and managing data and more time delivering personalized, impactful campaigns. A key mindset shift is required to maximize the value of these tools: instead of asking, “How can agents make business as usual more efficient?”, marketers should think, “How can agents transform the way we deliver exceptional customer experiences?” Streamlining Campaign Creation with Agentforce Campaigns 1. Intelligent Recommendations for Focused Action Einstein, Salesforce’s AI engine, analyzes data and provides contextual recommendations to help marketers achieve their goals. Agentforce Campaigns takes this a step further by turning these static recommendations into actionable steps, such as adjusting an audience segment or creating an entirely new campaign. 2. Rapid Campaign Brief Creation Agentforce Campaigns can draft campaign briefs in seconds using natural language prompts. These briefs incorporate organizational goals and marketing guidelines, making them ready for review and sharing within Salesforce. This saves time and ensures alignment from the start. 3. Contextual Content Creation Once a campaign brief is approved, marketing agents can generate campaign content such as emails and landing pages. Agentforce Campaigns automatically drafts subject lines, body copy, and calls to action within branded templates. Content can be refined using natural language commands or manual adjustments to fit the brand’s tone and strategy. 4. Simplified Audience Segmentation Marketers no longer need SQL expertise to build audience segments. With Agentforce Campaigns, they can describe their target audience in natural language, and the AI translates this into the necessary segment attributes. 5. Journey Activation at Scale Marketing agents simplify the process of activating multi-channel campaigns. Using natural language prompts, Agentforce Campaigns can configure a draft journey, complete with personalized content, ready for refinement and deployment. Unlocking New Opportunities with Marketing Agents 6. Unlimited Content Variations Time and budget constraints often limit the number of content variations marketers can produce. Agentforce Campaigns overcomes this limitation by generating multiple personalized content versions in seconds. This allows teams to deliver highly tailored messages for different audience segments without additional effort. 7. Nuanced Segment Exploration Traditional segmentation often depends on data science teams, which may have limited capacity. Marketing agents empower marketers to build their own nuanced segments using natural language. For example, separate churn segments can be created based on engagement scores, location, or purchase history, enabling more precise targeting. 8. A Culture of Testing and Learning Testing often falls by the wayside due to time constraints. Marketing agents make it easy to embed testing into campaigns by automating journey flows, enabling marketers to adopt a culture of continuous experimentation and improvement without increasing workloads. Transforming Marketing with Agentforce By integrating marketing agents into workflows, businesses can improve productivity, enhance personalization, and scale campaigns like never before. Agentforce Campaigns enables marketers to automate routine tasks, explore untapped opportunities, and deliver exceptional customer experiences. Marketers who embrace this shift will not only increase efficiency but also elevate their strategies, creating campaigns that are more targeted, impactful, and scalable. Note: Some features and services mentioned may not yet be available. Customers should base purchase decisions on currently available features. 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

December 18, 2024in Agentforce Platform, Agentic AI, Data0 Comments

15Dec

Transforming the Role of Data Science Teams

GenAI: Transforming the Role of Data Science Teams Challenges, Opportunities, and the Evolving Responsibilities of Data Scientists Generative AI (GenAI) is revolutionizing the AI landscape, offering faster development cycles, reduced technical overhead, and enabling groundbreaking use cases that once seemed unattainable. However, it also introduces new challenges, including the risks of hallucinations and reliance on third-party APIs. For Data Scientists and Machine Learning (ML) teams, this shift directly impacts their roles. GenAI-driven projects, often powered by external providers like OpenAI, Anthropic, or Meta, blur traditional lines. AI solutions are increasingly accessible to non-technical teams, but this accessibility raises fundamental questions about the role and responsibilities of data science teams in ensuring effective, ethical, and future-proof AI systems. Let’s explore how this evolution is reshaping the field. Expanding Possibilities Without Losing Focus While GenAI unlocks opportunities to solve a broader range of challenges, not every problem warrants an AI solution. Data Scientists remain vital in assessing when and where AI is appropriate, selecting the right approaches—whether GenAI, traditional ML, or hybrid solutions—and designing reliable systems. Although GenAI broadens the toolkit, two factors shape its application: For example, incorporating features that enable user oversight of AI outputs may prove more strategic than attempting full automation with extensive fine-tuning. Differentiation will not come from simply using LLMs, which are widely accessible, but from the unique value and functionality they enable. Traditional ML Is Far from Dead—It’s Evolving with GenAI While GenAI is transformative, traditional ML continues to play a critical role. Many use cases, especially those unrelated to text or images, are best addressed with ML. GenAI often complements traditional ML, enabling faster prototyping, enhanced experimentation, and hybrid systems that blend the strengths of both approaches. For instance, traditional ML workflows—requiring extensive data preparation, training, and maintenance—contrast with GenAI’s simplified process: prompt engineering, offline evaluation, and API integration. This allows rapid proof of concept for new ideas. Once proven, teams can refine solutions using traditional ML to optimize costs or latency, or transition to Small Language Models (SMLs) for greater control and performance. Hybrid systems are increasingly common. For example, DoorDash combines LLMs with ML models for product classification. LLMs handle cases the ML model cannot classify confidently, retraining the ML system with new insights—a powerful feedback loop. GenAI Solves New Problems—But Still Needs Expertise The AI landscape is shifting from bespoke in-house models to fewer, large multi-task models provided by external vendors. While this simplifies some aspects of AI implementation, it requires teams to remain vigilant about GenAI’s probabilistic nature and inherent risks. Key challenges unique to GenAI include: Data Scientists must ensure robust evaluations, including statistical and model-based metrics, before deployment. Monitoring tools like Datadog now offer LLM-specific observability, enabling teams to track system performance in real-world environments. Teams must also address ethical concerns, applying frameworks like ComplAI to benchmark models and incorporating guardrails to align outputs with organizational and societal values. Building AI Literacy Across Organizations AI literacy is becoming a critical competency for organizations. Beyond technical implementation, competitive advantage now depends on how effectively the entire workforce understands and leverages AI. Data Scientists are uniquely positioned to champion this literacy by leading initiatives such as internal training, workshops, and hackathons. These efforts can: The New Role of Data Scientists: A Strategic Pivot The role of Data Scientists is not diminishing but evolving. Their expertise remains essential to ensure AI solutions are reliable, ethical, and impactful. Key responsibilities now include: By adapting to this new landscape, Data Scientists will continue to play a pivotal role in guiding organizations to harness AI effectively and responsibly. GenAI is not replacing them; it’s expanding their impact. 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

December 15, 2024in Data, Generative AI0 Comments

04Dec

Google Gemini 2.0

Google Gemini 2.0 Flash: A First Look Google has unveiled an experimental version of Gemini 2.0 Flash, its next-generation large language model (LLM), now accessible to developers via Google AI Studio and the Gemini API. This model builds on the capabilities of its predecessors with improved multimodal features and enhanced support for agentic workflows, positioning it as a major step forward in AI-driven applications. Key Features of Gemini 2.0 Flash Performance and Efficiency According to Google, Gemini 2.0 Flash is twice as fast as Gemini 1.5 while outperforming it on standard benchmarks for AI accuracy. Its efficiency and size make it particularly appealing for real-world applications, as highlighted by David Strauss, CTO of Pantheon: “The emphasis on their Flash model, which is efficient and fast, stands out. Frontier models are great for testing limits but inefficient to run at scale.” Applications and Use Cases Agentic AI and Competitive Edge Gemini 2.0’s standout feature is its agentic AI capabilities, where multiple AI agents collaborate to execute multi-stage workflows. Unlike simpler solutions that link multiple chatbots, Gemini 2.0’s tool-driven, code-based training sets it apart. Chirag Dekate, an analyst at Gartner, notes: “There is a lot of agent-washing in the industry today. Gemini now raises the bar on frontier models that enable native multimodality, extremely large context, and multistage workflow capabilities.” However, challenges remain. As AI systems grow more complex, concerns about security, accuracy, and trust persist. Developers, like Strauss, emphasize the need for human oversight in professional applications: “I would trust an agentic system that formulates prompts into proposed, structured actions, subject to review and approval.” Next Steps and Roadmap Google has not disclosed pricing for Gemini 2.0 Flash, though its free availability is anticipated if it follows the Gemini 1.5 rollout. Looking ahead, Google plans to incorporate the model into its beta-stage AI agents, such as Project Astra, Mariner, and Jules, by 2025. Conclusion With Gemini 2.0 Flash, Google is pushing the boundaries of multimodal and agentic AI. By introducing native tool usage and support for complex workflows, this LLM offers developers a versatile and efficient platform for innovation. As enterprises explore the model’s capabilities, its potential to reshape AI-driven applications in coding, data science, and interactive interfaces is immense—though trust and security considerations remain critical for broader adoption. 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

December 4, 2024in Agentic AI, Data, Enterprise, Google0 Comments

20Nov

AI Productivity Paradox

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

November 20, 2024in AI Tools, Data, Einstein 1 Platform, Generative AI, Google, Technology

27Oct

AI Strategy for Your Business

How to Create a Winning AI Strategy for Your Business To maximize the value of AI, organizations must align their AI projects with strategic business objectives. Here’s a 10-step guide to crafting an effective AI strategy, including sample templates to support your planning. While AI adoption is on the rise, many companies still struggle to unlock its full potential. According to the 2024 IDC report Scaling AI Initiatives Responsibly, even organizations with advanced AI practices, termed “AI Masters,” face a 13% failure rate, while those still emerging in AI see a 20% failure rate. Challenges such as poor data quality and cultural resistance often contribute to these failures. To avoid these pitfalls, companies need to adopt a more deliberate and strategic approach to AI implementation. As Nick Kramer from SSA & Company states, “It’s not just about implementing the right technology; a lot of work needs to be done beforehand to succeed with AI.” What is an AI Strategy and Why is it Important? An AI strategy unifies all necessary components—such as data, technology, and talent—required to achieve business goals through AI. This includes: A well-designed AI strategy sets clear directions on how AI should be leveraged to achieve optimal outcomes within the organization. 10 Steps to Craft a Successful AI Strategy Resources for AI Strategy Templates If you’re ready to start building your AI strategy, here are several resources offering templates and guidance: By following these steps and utilizing the right resources, businesses can ensure they capture AI in ways that align with their strategic goals and maximize their competitive edge. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

October 27, 2024in Data, Generative AI, Technology

23Oct

Python Losing the Crown

For years, Python has been synonymous with data science, thanks to its robust libraries like NumPy, Pandas, and scikit-learn. It’s long held the crown as the dominant programming language in the field. However, even the strongest kingdoms face threats. Python Losing the Crown. The whispers are growing louder: Is Python’s reign nearing its end? Before you fire up your Jupyter notebook to prove me wrong, let me clarify — Python is incredible and undeniably one of the greatest programming languages of all time. But no ruler is without flaws, and Python’s supremacy may not last forever. Here are five reasons why Python’s crown might be slipping. 1. Performance Bottlenecks: Python’s Achilles’ Heel Let’s address the obvious: Python is slow. Its interpreted nature makes it inherently less efficient than compiled languages like C++ or Java. Sure, libraries like NumPy and tools like Cython help mitigate these issues, but at its core, Python can’t match the raw speed of newer, more performance-oriented languages. Enter Julia and Rust, which are optimized for numerical computing and high-performance tasks. When working with massive, real-time datasets, Python’s performance bottlenecks become harder to ignore, prompting some developers to offload critical tasks to faster alternatives. 2. Python’s Memory Challenges Memory consumption is another area where Python struggles. Handling large datasets often pushes Python to its limits, especially in environments with constrained resources, such as edge computing or IoT. While tools like Dask can help manage memory more efficiently, these are often stopgap solutions rather than true fixes. Languages like Rust are gaining traction for their superior memory management, making them an attractive alternative for resource-limited scenarios. Picture running a Python-based machine learning model on a Raspberry Pi, only to have it crash due to memory overload. Frustrating, isn’t it? 3. The Rise of Domain-Specific Languages (DSLs) Python’s versatility has been both its strength and its weakness. As industries mature, many are turning to domain-specific languages tailored to their specific needs: Python may be the “jack of all trades,” but as the saying goes, it risks being the “master of none” compared to these specialized tools. 4. Python’s Simplicity: A Double-Edged Sword Python’s beginner-friendly syntax is one of its greatest strengths, but it can also create complacency. Its ease of use often means developers don’t delve into the deeper mechanics of algorithms or computing. Meanwhile, languages like Julia, designed for scientific computing, offer intuitive structures for advanced modeling while encouraging developers to engage with complex mathematical concepts. Python’s simplicity is like riding a bike with training wheels: it works, but it may not push you to grow as a developer. 5. AI-Specific Frameworks Are Gaining Ground Python has been the go-to language for AI, powering frameworks like TensorFlow, PyTorch, and Keras. But new challengers are emerging: As AI and machine learning evolve, these specialized frameworks could chip away at Python’s dominance. The Verdict: Python Losing the Crown? Python remains the Swiss Army knife of programming languages, especially in data science. However, its cracks are showing as new, specialized tools and faster languages emerge. The data science landscape is evolving, and Python must adapt or risk losing its crown. For now, Python is still king. But as history has shown, no throne is secure forever. The future belongs to those who innovate, and Python’s ability to evolve will determine whether it remains at the top. The throne of code is only as stable as the next breakthrough. 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

October 23, 2024in Data, Laws

18Oct

Life of a Salesforce Admin in the AI Era

The life of Salesforce admins is rapidly evolving as artificial intelligence (AI) becomes integral to business operations. Let’s examine the Life of a Salesforce Admin in the AI Era. By 2025, the Salesforce admin’s role will expand beyond managing CRM systems to include leveraging AI tools to enhance efficiency, boost productivity, and maintain security. While this future offers exciting opportunities, it also comes with new responsibilities that require admins to adapt and learn. So, what will Salesforce admins need to succeed in this AI-driven landscape? The Salesforce Admin’s Role in 2025 In 2025, Salesforce admins will be at the forefront of digital transformation, helping organizations harness the full potential of the Salesforce ecosystem and AI-powered tools. These AI tools will automate processes, predict trends, and improve overall efficiency. Many professionals are already enrolling in Salesforce Administrator courses focused on AI and automation, equipping them with the essential skills to thrive in this new era. Key Responsibilities in Life of a Salesforce Admin in the AI Era 1. AI Integration and Optimization Admins will be responsible for integrating AI tools like Salesforce Einstein AI into workflows, ensuring they’re properly configured and tailored to the organization’s needs. Core tasks include: 2. Automating Processes with AI AI will revolutionize automation, making complex workflows more efficient. Admins will need to: 3. Data Management and Predictive Analytics Admins will leverage AI to manage data and generate predictive insights. Key responsibilities include: 4. Enhancing Security and Compliance AI-powered security tools will help admins proactively protect systems. Responsibilities include: 5. Supporting AI-Driven Customer Experiences Admins will deploy AI tools that enhance customer interactions. Their responsibilities include: 6. Continuous Learning and Upskilling As AI evolves, so too must Salesforce admins. Key learning areas include: 7. Collaboration with Cross-Functional Teams Admins will work closely with IT, marketing, and sales teams to deploy AI solutions organization-wide. Their collaborative efforts will include: Skills Required for Future Salesforce Admins 1. AI and Machine Learning Proficiency Admins will need to understand how AI models like Einstein AI function and how to deploy them. While not requiring full data science expertise, a solid grasp of AI concepts—such as predictive analytics and machine learning—will be essential. 2. Advanced Data Management and Analysis Managing large datasets and ensuring data accuracy will be critical as admins work with AI tools. Proficiency in data modeling, SQL, SOQL, and ETL processes will be vital for handling AI-powered data management. 3. Automation and Process Optimization AI-enhanced automation will become a key responsibility. Admins must master tools like Salesforce Flow and Einstein Automate to build intelligent workflows and ensure smooth process automation. 4. Security and Compliance Expertise With AI-driven security protocols, admins will need to stay updated on data privacy regulations and deploy tools that ensure compliance and prevent data breaches. 5. Collaboration and Leadership Admins will lead the implementation of AI tools across departments, requiring strong collaboration and leadership skills to align AI-driven solutions with business objectives. Advanced Certifications for AI-Era Admins To stay competitive, Salesforce admins will need to pursue advanced certifications. Key certifications include: Tectonic’s Thoughts The Salesforce admin role is transforming as AI becomes an essential part of the platform. By mastering AI tools, optimizing processes, ensuring security, and continuously upskilling, Salesforce admins can become pivotal players in driving digital transformation. The future is bright for those who embrace the AI-powered Salesforce landscape and position themselves at the forefront of innovation. 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

October 18, 2024in AI Tools, Artificial Intelligence, Data, Salesforce, Salesforce Einstein

10Oct

Build Launch and Track Campaigns

Revolutionizing Campaigns: How Marketing Agents Empower Your Marketing Team Marketing agents are transforming how businesses create, launch, and track campaigns—delivering better results while boosting internal team productivity and cohesion. With the power of AI and data, these agents act as collaborative partners, enhancing marketing efficiency and creativity in unprecedented ways. A Smarter Approach to Campaign Challenges Marketers have long faced the challenge of creating quality content at scale. According to the Content Marketing Institute, 54% of B2B marketers struggle to meet this demand, while B2C marketers often lack the resources to make their efforts scalable and consistent. On top of this, they must ensure campaigns are efficient, customer-centric, and stand out in a competitive landscape. Enter marketing agents—AI-powered tools that help teams manage and optimize campaigns, from strategy to execution. At Dreamforce 2024, Salesforce unveiled Agentforce, a suite of intelligent agents integrated across the Customer 360 platform, including Agentforce Campaigns. With 71% of marketers planning to adopt generative and predictive AI within the next 18 months, as per Salesforce’s State of Marketing report, tools like Agentforce are poised to redefine how campaigns are built and delivered. How Humans and AI Agents Work Together Marketing agents are AI-powered virtual assistants that collaborate with humans to analyze data, generate insights, and execute marketing plans. Unlike traditional tools, they understand the context behind your needs and suggest actionable solutions—whether that’s creating content, optimizing campaigns, or analyzing results. By automating time-consuming tasks, marketing agents free teams to focus on high-value activities like strategy and personalization. But the key to maximizing their potential lies in shifting your mindset: instead of simply seeking efficiency, aim to transform how you deliver exceptional customer experiences. 8 Ways Agentforce Campaigns Elevates Your Marketing 1. Intelligent Recommendations Agentforce Campaigns turns insights into actions. For example, Marketing Cloud’s Einstein not only tracks your goals but also suggests adjustments or new campaigns tailored to your objectives, helping you stay ahead. 2. Instant Campaign Briefs Building a campaign starts with a solid brief. With Agentforce, you can create one in seconds using natural language prompts. The AI-generated brief incorporates your goals and guidelines, making collaboration and approvals seamless. 3. Contextual Content Creation Agentforce generates emails, landing pages, and calls to action directly aligned with your brand’s tone and campaign goals. Marketers can refine outputs with natural language prompts, ensuring a perfect fit for their strategy. 4. Effortless Audience Segmentation No SQL skills? No problem. Describe your ideal audience in natural language, and Agentforce will translate that into actionable segments—helping you target precisely the right customers. 5. Automated Journey Activation Agentforce simplifies multi-channel journey creation by drafting personalized campaign flows. You can refine, approve, and activate these journeys with ease, saving time while enhancing impact. 6. Unlimited Content Variations AI eliminates content constraints, allowing you to generate multiple variations for personalized campaigns. Target high-value customers, newcomers, or loyal fans with tailored messages—all at scale. 7. Explore Nuanced Segments Agentforce enables marketers to create segments without relying on overburdened data science teams. Dive into deeper audience insights, such as churn rates based on location, age, or past behavior, with just a prompt. 8. Embed Continuous Testing Testing is often deprioritized due to time limitations. Agentforce automates testing workflows, making it easier to incorporate A/B testing and iterative learning into every campaign. Getting Started with Agentforce Campaigns Agentforce Campaigns is available in Marketing Cloud Growth and Advanced Editions, designed to empower businesses of all sizes. By integrating AI-driven tools into your workflow, you can elevate your marketing to new heights—enhancing creativity, efficiency, and customer engagement. Ready to revolutionize your campaigns? Explore how Agentforce can help you win customers and foster a more productive, cohesive marketing team. Salesforce Disclaimer: Unreleased features mentioned here are subject to change and may not become available as described. Make purchasing decisions based on currently available features. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 10, 2024in Agentforce Platform, Artificial Intelligence, Data, Google, Salesforce, Salesforce Personalization

05Oct

AI Revolution in Government

The AI Revolution in Government: Unlocking Efficiency and Public Trust As the AI boom accelerates, it’s essential to explore how artificial intelligence can streamline operations for government and public sector organizations. From enhancing data processing to bolstering cybersecurity and improving public planning, AI has the potential to make government services more efficient and effective for both agencies and constituents. AI Revolution in Government. The Role of AI in Public Sector Efficiency AI presents significant opportunities for government agencies to optimize their operations. By integrating AI-driven tools, public agencies can improve service delivery, boost efficiency, and foster greater trust between the public and private sectors. However, with these advancements comes the challenge of bridging the AI skills gap — a pressing concern as organizations ramp up investments in AI without enough trained professionals to support its deployment. According to a survey by SAS, 63% of decision-makers across various sectors, including government, believe they lack the AI and machine learning resources necessary to keep pace with the growing demand. This skills gap, combined with rapid AI adoption, has many workers concerned about the future of their jobs. Predictions from Goldman Sachs suggest that AI could replace 300 million full-time jobs globally, affecting nearly one-fifth of the workforce, particularly in fields traditionally considered automation-proof, such as administrative and legal professions. Despite concerns about job displacement, AI is also expected to create new roles. The World Economic Forum’s Future of Jobs Report estimates that 75% of companies plan to adopt AI, with 50% anticipating job growth. This presents a crucial opportunity for government organizations to upskill their workforce and ensure they are prepared for the changes AI will bring. Preparing for an AI-Driven Future in Government To fully harness the benefits of AI, public sector organizations must first modernize their data infrastructure. Data modernization is a key step in setting up a future-ready organization, allowing AI to operate effectively by leveraging accurate, connected, and real-time data. As AI automates lower-level tasks, government workers need to transition into more strategic roles, making it essential to invest in AI training and upskilling programs. AI Applications in GovernmentAI is already transforming various government functions, improving operations, and meeting the needs of citizens more effectively. The possibilities are vast: While AI holds immense potential, its successful adoption depends on having a digital-ready workforce capable of managing these applications. Yet, many government employees lack the data science and AI expertise needed to manage large citizen data sets and develop AI models that can improve service delivery. Upskilling the Government Workforce for AI Investing in AI education is critical to ensuring that government employees can meet the demands of the future. Countries like Finland and Singapore have already launched national AI training programs to prepare their populations for the AI-driven economy. For example, Finland’s “Elements of AI” program introduced AI basics to the public and has been completed by over a million people worldwide. Similarly, AI Singapore’s “AI for Everyone” initiative equips individuals and organizations with AI skills for social good. In the U.S., legislation is being considered to create an AI training program for federal supervisors and management officials, helping government leaders navigate the risks and benefits of AI in alignment with agency missions. The Importance of Trust and Data Security As public sector organizations embrace AI, trust is a critical factor. AI tools are only as effective as the data they rely on, and ensuring data integrity, security, and ethical use is paramount. The rise of the Chief Data Officer highlights the growing importance of managing and protecting government data. These roles not only oversee data management but also ensure that AI technologies are used responsibly, maintaining public trust and safeguarding privacy. By modernizing data systems and equipping employees with AI skills, government organizations can unlock the full potential of AI and automation. This transformation will help agencies better serve their communities, enhance efficiency, and build lasting trust with the people they serve. The Future of AI in Government The future of AI in government is bright, but organizations must take proactive steps to prepare for it. By unifying and securing their data, investing in AI training, and focusing on ethical AI deployment, public sector agencies can harness AI’s power to drive meaningful change. Ultimately, this is an opportunity for the public sector to improve service delivery, support their workforce, and build stronger connections with citizens. 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

October 5, 2024in Salesforce Government Cloud, Salesforce Optimization, Salesforce Public Sector Solutions

30Sep

Predictive Analytics in Salesforce

Predictive Analytics in Salesforce: Enhancing Decision-Making with AI In an ever-changing business environment, companies seek tools to forecast trends and anticipate challenges, enabling them to remain competitive. Predictive analytics, powered by Salesforce’s AI capabilities, offers a cutting-edge solution for these needs. In this guide, we’ll explore how predictive analytics works and how Salesforce empowers businesses to make smarter, data-driven decisions. What is Predictive Analytics? Predictive analytics uses historical data, statistical modeling, and machine learning to forecast future outcomes. With the vast amount of data organizations generate—ranging from transaction logs to multimedia—unifying this information can be challenging due to data silos. These silos hinder the development of accurate predictive models and limit Salesforce’s ability to deliver actionable insights. The result? Missed opportunities, inefficiencies, and impersonal customer experiences. When organizations implement proper integrations and data management practices, predictive analytics can harness this data to uncover patterns and predict future events. Techniques such as logistic regression, linear regression, neural networks, and decision trees help businesses gain actionable insights that enhance planning and decision-making. Einstein Prediction Builder A key component of the Salesforce Einstein Suite, Einstein Prediction Builder enables users to create custom AI models with minimal coding or data science expertise. Using in-house data, businesses can anticipate trends, forecast customer behavior, and predict outcomes with tailored precision. Key Features of Einstein Prediction Builder Note: Einstein Prediction Builder requires an Enterprise or Unlimited Edition subscription to access. Predictive Model Types in Salesforce Salesforce employs various predictive models tailored to specific needs: Building Custom Predictions Salesforce supports custom predictions tailored to unique business needs, such as forecasting regional sales or calculating appointment attendance rates. Tips for Building Predictions Prescriptive Analytics: Turning Predictions into Actions Predictive insights are only as valuable as the actions they inspire. Einstein Next Best Action bridges this gap by providing context-specific recommendations based on predictions. How Einstein Next Best Action Works Data Quality: The Foundation of Accurate Predictions The effectiveness of predictive analytics depends on the quality of your data. Poor data—whether due to errors, duplicates, or inconsistencies—can skew results and undermine trust. Best Practices for Data Quality Modern tools like DataGroomr can automate data validation and cleaning, ensuring that predictions are based on trustworthy information. Empowering Smarter Decisions with Predictive Analytics Salesforce’s AI-driven predictive analytics transforms decision-making by providing actionable insights from historical data. Businesses can anticipate trends, improve operational efficiency, and deliver personalized customer experiences. As predictive analytics continues to evolve, companies leveraging these tools will gain a competitive edge in an increasingly dynamic marketplace. Embrace the power of predictive analytics in Salesforce to make faster, more strategic decisions and drive sustained success. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 30, 2024in Data, Enterprise, Salesforce, Salesforce Einstein

22Sep

How to Achieve AI Democratization

AI democratization empowers non-experts by placing AI tools in the hands of everyday users, enabling them to harness the technology’s potential without requiring specialized technical skills. Today, IT leaders are increasingly focused on expanding AI’s benefits across the enterprise. The growing number of AI-based tools is making this more achievable. In some respects, democratization extends the concept of low- and no-code development—allowing non-developers to create software—into the realm of AI. However, it’s also about ensuring data is accessible and fostering data literacy throughout the organization. This doesn’t mean every employee needs to write machine learning scripts. Instead, it means business professionals should understand AI’s potential, identify relevant use cases, and apply insights to drive business outcomes. Achieving AI democratization is feasible, thanks to decentralized governance models and the emergence of AI-focused services. However, as with any new technology, democratization brings both benefits and challenges. How to Achieve AI Democratization AI is no longer reserved for experts. Tools like Google Colab and Microsoft’s Azure OpenAI Service have simplified AI development, enabling more employees to participate by writing and sharing code for various projects. To maximize the impact, enterprises must train business users on the basics of AI and how it can enhance their daily work. According to Arpit Mehra, Practice Director at Everest Group, decentralized governance models can help organizations build strategies for data and technology learning. Key strategies include: Arun Chandrasekaran, VP and Analyst at Gartner, also advises companies to focus on intelligent applications in areas such as customer engagement and talent acquisition, which can provide specialized training. Benefits and Challenges of AI Democratization AI democratization can significantly expand an organization’s capabilities. By placing AI in the hands of more employees, businesses reduce barriers to adoption, cut costs, and create highly accurate AI models. “Making AI more accessible broadens the scope of what businesses can achieve,” said Michael Shehab, PwC U.S. Technology and Innovation Leader. AI democratization also helps companies address IT talent shortages by upskilling employees and enabling them to integrate AI into their workflows. This approach improves productivity, allowing businesses to more easily spot trends and patterns within large data sets. However, challenges also arise. If AI is implemented without proper oversight, the technology is susceptible to bias. Poor training could lead to decision-making based on inaccurate or skewed data. Business leaders must ensure they understand who is using AI tools and establish standards for responsible use. Without careful testing, AI applications can automate mistakes that go unnoticed but may cause significant issues. Ed Murphy, SVP and Head of Data Science at 1010data, emphasizes the importance of testing to prevent these errors. To mitigate risks, organizations should invest in upskilling and reskilling employees. A well-defined training plan will enable nontechnical teams to participate in AI adoption and deployment effectively. Mehra from Everest Group also suggests exploring MLOps technologies to simplify AI development and streamline processes. Ultimately, AI democratization will benefit businesses that recognize AI’s potential beyond a small group of experts. While the benefits are clear, organizations must remain vigilant about the risks to ensure successful AI integration and reap the rewards of their efforts. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 22, 2024in Salesforce

15Aug

GLYNT.AI Launches Net Zero Hero

GLYNT.AI Launches Net Zero Hero for Salesforce Net Zero Cloud Net Zero Hero Sustainability Data + Net Zero Cloud GLYNT.AI, a leader in providing investor-grade sustainability data, has announced the launch of Net Zero Hero on the Salesforce AppExchange. This new offering enables sustainability and finance teams to access accurate, automated, and audit-ready data on energy, water, waste, and emissions, seamlessly integrated with Salesforce Net Zero Cloud. The service provides data within a week of implementation, with ongoing updates available daily, weekly, or monthly, all without requiring developer resources. Sustainability and finance teams often struggle with capturing, preparing, harmonizing, and validating key sustainability data. Traditionally, this data is buried in invoices and utility bills, making it difficult to extract and harmonize. Many companies rely on manual processes, such as spreadsheets, which can raise concerns during assurance and audit reviews and increase audit costs. There is also a growing pressure to manage sustainability data cost-effectively without expanding headcount. Net Zero Hero from GLYNT.AI addresses these challenges by providing SOC 1 compliant, audit-ready sustainability data. The service covers a wide range of input data, including landlord invoices, data center invoices, sub-billing, summary bills, renewables, utility site logins, vehicle ledgers, and direct emissions data. This comprehensive solution ensures that Salesforce Net Zero Cloud customers receive sustainability data that meets the rigorous standards of financial data preparation. GLYNT.AI leverages advanced AI technology to automate data preparation, drawing on years of experience in sustainability, finance, and data science. The system can deliver initial data in the first week, quickly scaling up to production-grade data services. This automation and customization result in significant cost savings, often exceeding 80% compared to traditional systems. Jeff Stienke, CEO of Third Eye Consulting, a leading Salesforce Summit Partner, expressed enthusiasm for the partnership, highlighting the benefits of automated systems and streamlined implementation. “Net Zero Hero breaks the data bottleneck, bringing costs down and simplifying the deployment of Net Zero Cloud. Our clients have been looking for the expertise in data, sustainability, and finance that GLYNT.AI provides,” Stienke said. Martha Amram, Ph.D., CEO of GLYNT.AI, emphasized the company’s commitment to supporting Salesforce customers in achieving sustainability goals. “Salesforce has a long-standing commitment to sustainability, and we’re excited to add Net Zero Hero data services to Salesforce AppExchange. GLYNT.AI unlocks the sustainability data stream, so that every Salesforce customer can be a force for good, a Net Zero Hero. And looking ahead, we’ve built in audit-readiness and AI-readiness into the offering, delivering Trusted Data for Trusted AI.” GLYNT.AI offers three bundles for Net Zero Hero on the Salesforce AppExchange: A 30-day trial of Net Zero Hero is available, allowing businesses to see their data in Net Zero Cloud within the first week. For more information about Net Zero Hero and to access the AppExchange listing, visit: Net Zero Hero on Salesforce AppExchange. 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

August 15, 2024in Salesforce Net Zero Cloud

01Aug

Snowpark Container Services

Snowflake announced on Thursday the general availability of Snowpark Container Services, enabling customers to securely deploy and manage models and applications, including generative AI, within Snowflake’s environment. Initially launched in preview in June 2023, Snowpark Container Services is now a fully managed service available in all AWS commercial regions and in public preview in all Azure commercial regions. Containers are a software method used to isolate applications for secure deployment. Snowflake’s new feature allows customers to use containers to manage and deploy any type of model, optimally for generative AI applications, by securely integrating large language models (LLMs) and other generative AI tools with their data, explained Jeff Hollan, Snowflake’s head of applications and developer platform. Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group, noted that Snowpark Container Services’ launch builds on Snowflake’s recent efforts to provide customers with an environment for developing generative AI models and applications. Sridhar Ramaswamy became Snowflake’s CEO in February, succeeding Frank Slootman, who led the company through a record-setting IPO. Under Ramaswamy, Snowflake has aggressively added generative AI capabilities, including launching its own LLM, integrating with Mistral AI, and providing tools for creating AI chatbots. “There has definitely been a concerted effort to enhance Snowflake’s capabilities and presence in the AI and GenAI markets,” Leone said. “Offerings like Snowpark help AI stakeholders like data scientists and developers use the languages they prefer.” As a result, Snowpark Container Services is a significant new feature for Snowflake customers. “It’s a big deal for the Snowflake ecosystem,” Leone said. “By enabling easy deployment and management of containers within the Snowflake platform, it helps customers handle complex workloads and maintain consistency across development and production stages.” Despite the secure environment provided by Snowflake Container Services, it was revealed in May that the login credentials of potentially 160 customers had been stolen and used to access their data. However, Snowflake has stated there is no evidence that the breach resulted from a vulnerability or misconfiguration of the Snowflake platform. Prominent customers affected include AT&T and Ticketmaster, and Snowflake’s investigation is ongoing. New Capabilities Generative AI can transform business by enabling employees to easily work with data to inform decisions and making trained experts more efficient. Generative AI, combined with an enterprise’s proprietary data, allows users to interact with data using natural language, reducing the need for coding and data literacy training. Non-technical workers can query and analyze data, freeing data engineers and scientists from routine tasks. Many data management and analytics vendors are focusing on developing generative AI-powered features. Enterprises are building models and applications trained on their proprietary data to inform business decisions. Among data platform vendors, AWS, Databricks, Google, IBM, Microsoft, and Oracle are providing environments for generative AI tool development. Snowflake, under Slootman, was less aggressive in this area but is now committed to generative AI development, though it still has ground to cover compared to its competitors. “Snowflake has gone as far as creating their own LLM,” Leone said. “But they still have a way to go to catch up to some of their top competitors.” Matt Aslett, an analyst at ISG’s Ventana Research, echoed that Snowflake is catching up to its rivals. The vendor initially focused on traditional data warehouse capabilities but made a significant step forward with the late 2023 launch of Cortex, a platform for developing AI models and applications. Cortex includes access to various LLMs and vector search capabilities, marking substantial progress. The general availability of Snowpark Container Services furthers Snowflake’s effort to foster generative AI development. The feature provides users with on-demand GPUs and CPUs to run any code next to their data. This enables the deployment and management of any type of model or application without moving data out of Snowflake’s platform. “It’s optimized for next-generation data and AI applications by pushing that logic to the data,” Hollan said. “This means customers can now easily and securely deploy everything from source code to homegrown models in Snowflake.” Beyond security, Snowpark Container Services simplifies model management and deployment while reducing associated costs. Snowflake provides a fully integrated managed service, eliminating the need for piecing together various services from different vendors. The service includes a budget control feature to reduce operational costs and provide cost certainty. Snowpark Container Services includes diverse storage options, observability tools like Snowflake Trail, and streamlined DevOps capabilities. It supports deploying LLMs with local volumes, memory, Snowflake stages, and configurable block storage. Integrations with observability specialists like Datadog, Grafana, and Monte Carlo are also included. Aslett noted that the 2020 launch of the Snowpark development environment enabled users to use their preferred coding languages with their data. Snowpark Container Services takes this further by allowing the use of third-party software, including generative AI models and data science libraries. “This potentially reduces complexity and infrastructure resource requirements,” Aslett said. Snowflake spent over a year moving Snowpark Container Services from private preview to general availability, focusing on governance, networking, usability, storage, observability, development operations, scalability, and performance. One customer, Landing AI, used Snowpark Container Services during its preview phases to develop LandingLens, an application for training and deploying computer vision models. “[With Snowflake], we are increasing access to AI for more companies and use cases, especially given the rapid growth of unstructured data in our increasingly digital world,” Landing AI COO Dan Maloney said in a statement Thursday. Future Plans With Snowpark Container Services now available on AWS, Snowflake plans to extend the feature to all cloud platforms. The vendor’s roadmap includes further improvements to Snowpark Container Services with more enterprise-grade tools. “Our team is investing in making it easy for companies ranging from startups to enterprises to build, deliver, distribute, and monetize next-generation AI products across their ecosystems,” Hollan said. Aslett said that making Snowpark Container Services available on Azure and Google Cloud is the logical next step. He noted that the managed service’s release is significant but needs broader availability beyond AWS regions. “The next step will be to bring Snowpark Container Services to general

August 1, 2024in Snowflake

13Jul

Where Will the Data Scientists Go

What Is to Become of the Data Scientist Role? This question frequently arises among executives, particularly as they navigate the changing roles of data teams, such as those at DataRobot. Where Will the Data Scientists Go may not be as relevant as what new places can they go with AI? The short answer? While tools may evolve, the core of data science remains steadfast. As the field of data science continues to expand, the role of the data scientist becomes increasingly vital. The need will grow, even as the role changes. Trust in AI is dependant upon human oversight. Beyond the Hype of Consumer AI The surge in consumer AI products has raised concerns among data scientists about the implications for their careers. However, these technologies are built on data and generate vast amounts of new data, presenting numerous opportunities. The real transformative potential lies in enterprise-scale automation. Enterprise-Scale Automation: The Data Scientist’s Domain Enterprise-scale automation involves creating large-scale, reliable systems. Data scientists are crucial in this effort, as they bring expertise in data exploration and systematic inference. They are uniquely positioned to identify automation opportunities, design testing and monitoring strategies, and collaborate with cross-functional teams to bring AI solutions from concept to implementation. As automation grows, the role of the data scientist is essential in ensuring these systems function effectively and safely, particularly in environments without human oversight. New Skills for Data Scientists: The Guardians of AI Applications Data scientists will need to acquire new skills to manage automation at scale, including securing the systems they build. Generative AI introduces new risks, such as potential vulnerabilities to prompt injections or other security threats. Governance and ensuring positive business impacts will become increasingly important, requiring a data science mindset. Building Great Data Teams in the Age of AI The future of data science will not be about automation replacing data scientists but about the evolution of roles and skills. Data scientists need to focus on the core foundations of their discipline rather than the specific tools they use, as tools will continue to evolve. Teams must be built intentionally, encompassing a range of skills and personalities necessary for successful enterprise automation. Business Leaders: Navigating the AI Landscape Business leaders will need to excel in decision-making, understanding the problems they aim to solve, and selecting the appropriate tools and teams. They will also need to manage evolving regulations, particularly those related to the design and deployment of AI systems. Data Scientists: Precision Thinkers at the Forefront Contrary to the belief that AI could replace coding skills, the essence of data science lies in precise thinking and clear communication. Data scientists excel in translating business needs into data-driven decisions and AI applications, ensuring that solutions are not only technically sound but also aligned with business objectives. This skill set will be crucial in the era of AI, as data scientists will play a key role in optimizing workflows, designing AI safety nets, and protecting their organization’s brand and reputation. The Evolving Role of Data Science The demand for precise, data-literate thinkers will only grow with the rise of enterprise AI systems. Whether they are called data scientists or another name, professionals who delve deeply into data and provide critical insights will remain essential in navigating the complexities of modern technology and business landscapes. 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

July 13, 2024in Data, Enterprise, Generative AI, Salesforce, Technology

01Jul

Ten Years of Data Lessons

Lessons Learned from a Decade in Data Science Over the past ten years, working in analytical roles at various companies—from a small fintech startup in Europe to high-growth pre-IPO scale-ups like Rippling and big tech firms such as Uber and Meta—has provided a wealth of insights. Each company had a unique data culture and view on data, and each role presented its own challenges and hard-learned lessons. Here are ten key ideas from this decade of experience, applicable to any company regardless of stage, product, or business model. Final Thoughts Some of these points may initially seem challenging, such as pushing back against cherry-picked narratives or adopting a more pragmatic approach over perfection. However, embracing these practices will ultimately help establish oneself as a true thought partner and a valuable asset to any organization. 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more

July 1, 2024in Google Analytics, Salesforce Data Cloud

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