, Author at gettectonic.com - Page 23
Employees Have Different Motivations

Employees Have Different Motivations

The workforce has undergone significant changes over the last two years. Today’s employees have different motivations, seeking more flexibility and purpose, while also expecting more from corporate leaders. Employees Have Different Motivations. Similarly, customers now demand high levels of personalization and exceptional experiences. How can C-suite executives keep up with these evolving expectations? Our research highlights emerging priorities for corporate leaders in these challenging times. In a recent webinar, we asked two Inc. 5000 CEOs about shifting priorities and the critical role of enhancing employee experiences to meet rising customer demands. The message was clear: efficient growth starts with your employees. Focusing on employee satisfaction, providing clear paths for growth, establishing strong values, and investing in the right tools are key drivers of success. However, for some leaders, old habits hinder progress. Today’s executives must not only be digitally proficient but also agile, with strong emotional intelligence to manage change and new relationships effectively. A prime example of this disconnect is seen in employee engagement. Salesforce’s recent report, The Experience Advantage, found that while 71% of C-suite executives believe their employees are engaged, only 51% of employees agree. Similarly, 70% of executives think their employees are happy, but only 44% of employees share that sentiment. How can companies enable their leaders to succeed in this era of heightened expectations? Let’s explore the top priorities for CEOs today. Top Priorities for Corporate Leaders In a world where CEOs are accountable to more stakeholders than ever, they must navigate an increasingly complex landscape. They’re expected to speak on social issues, advocate for sustainability, and ensure stability in times of rapid change. Adaptability is crucial for success. Here are some current top priorities for corporate leaders: At Salesforce, they’ve found success by operating with startup-style values—centering consumer trust, fostering constant innovation, and setting clear, simple goals. Marc Benioff’s V2MOM framework exemplifies this alignment in action. The New Skills Leaders Need After reviewing research and interviewing business leaders, several trends have emerged. The most successful executives today share the following traits: A 2021 IBM Institute for Business Value survey of 3,000 global CEOs revealed similar trends, highlighting purposeful agility and making technology a priority. The study found that 56% of CEOs emphasized the need for operational flexibility, and 61% were focused on empowering remote work. Key technologies driving results over the next few years include the Internet of Things (79%), cloud computing (74%), and AI (52%). A major shift on leader agendas is the growing focus on employee experience. As Salesforce’s chief growth evangelist, Tiffani Bova, noted, “Employees are now the most important stakeholder to long-term success.” Providing seamless, consumer-like experiences for employees is now essential for business growth. Our research also uncovered a key gap: 73% of C-suite executives don’t know how to use employee data to drive change. This disconnect between leadership perception and actual employee experience is undermining growth. Emotional Intelligence (EQ) Matters To close this gap, sharpening leaders’ emotional intelligence is essential. Last year, we conducted interviews with 10 CEOs across various sectors. Many revealed plans to replace C-suite team members with more digitally savvy and emotionally intelligent leaders better equipped to manage the modern workforce. Summit Leadership Partners’ 2020 research found that 80-90% of top-performing executives excelled because of their high EQ. In fact, EQ is twice as predictive of performance as technical skills or IQ. The Changing Role of Key Executives Who do CEOs rely on most? A decade ago, IBM’s Institute for Business Value found that 47% of CEOs considered the chief innovation officer critical. Today, only 4% of CEOs agree. The chief marketing officer and chief strategy officer roles have also seen significant declines in perceived importance. The positions that have gained prominence include the chief technology officer (CTO) and chief information officer (CIO), now ranked third in importance after the chief financial officer (CFO) and chief operating officer (COO). As Jeff McElfresh, COO of AT&T, observed, “Not all leaders are comfortable managing in a distributed model. We’ve got work to do to unlock the potential.” The rise in job titles related to the future of work—up 60% since the pandemic—reflects this shift, with hybrid work models becoming more common. Diversity Drives Innovation and Profitability Diversity in leadership has become essential for driving revenue and innovation. McKinsey’s 2020 report Diversity Wins found that companies with more gender-diverse executive teams were 25% more likely to achieve above-average profitability. Similarly, those with greater ethnic diversity outperformed their peers by 36%. Diverse management teams also deliver 19% higher revenues from innovation compared to less-diverse teams, according to research from BCG. As diversity becomes increasingly tied to executive compensation, companies must support a diverse leadership pipeline by developing inclusive talent strategies. Moving Forward To thrive in today’s business world, corporate leaders must plan for change, ensure all executives have both digital literacy and emotional intelligence, and redistribute power to drive success. The healthiest C-suites will include diverse leaders in key positions like COO, CFO, and CIO/CTO. Aligning the business around common goals—like those in Salesforce’s V2MOM framework—and eliminating barriers for employees are key to staying ahead. Innovation must remain a top priority. By investing in the right tools and connected platforms, companies can reduce costs and drive sustainable growth. Reach out to Tectonic for assistance in making the innovations that recognizes Employees Have Different Motivations. 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

Read More

How to Hire the Right Salesforce Developer

Maximizing Salesforce: How to Hire the Right Salesforce Developer Salesforce has revolutionized how businesses manage customer relationships, becoming a cornerstone for enhancing customer service and engagement. However, to fully leverage its potential, skilled professionals are needed to customize and develop the platform to meet specific business needs. Hiring the right Salesforce developer is crucial for ensuring your organization gets the most out of this powerful tool. In this guide, we’ll explore key considerations before hiring a Salesforce developer, the steps involved in the hiring process, and tips for effective Salesforce recruitment. How to Hire the Right Salesforce Developer. Understanding Salesforce Developer Roles Before diving into the hiring process, it’s essential to understand the different Salesforce roles: Your business needs may require a combination of these roles. Depending on the complexity of your Salesforce environment, you might need more than just a developer. Key Factors to Consider Before Hiring a Salesforce Developer The most critical factor when hiring a Salesforce developer is their skill set. Salesforce development requires deep knowledge of various technologies and tools. Key skills include: Assessing a candidate’s industry-specific experience can provide insight into their ability to address your business challenges. Salesforce certifications validate a professional’s expertise. Key certifications to look for include: These certifications provide assurance of the candidate’s competency. A strong Salesforce developer should not only possess technical skills but also understand business processes. Developers who can translate business requirements into technical solutions will drive business value. Salesforce development often involves troubleshooting complex issues. Assess a candidate’s problem-solving abilities through technical interviews or practical tests. Strong problem-solvers will be invaluable when unexpected challenges arise. Effective communication is vital for a Salesforce developer. They must explain technical concepts to non-technical stakeholders and document processes clearly for future maintenance. How to Hire the Right Salesforce Developer Start by defining the skills, experience, and certifications needed for the role. A well-defined job description will attract the right candidates. Create job postings that reflect the role’s responsibilities and qualifications. Use relevant keywords like “Salesforce job postings” and “Salesforce employment opportunities” to attract suitable candidates. Screen candidates based on their resumes and initial interviews, looking for a strong match between their experience and your job requirements. Evaluate candidates through practical tests to assess their Salesforce-specific coding skills and problem-solving abilities. Conduct multiple interview rounds: Salesforce Recruitment Tips Use platforms like LinkedIn and Salesforce-specific groups to find candidates and connect with the Salesforce community. Partnering with recruitment agencies that specialize in Salesforce roles can streamline the hiring process. Salesforce professionals are in high demand, so offering competitive salaries and benefits is key to attracting top talent. Identify candidates committed to professional development, as Salesforce is constantly evolving with new features and practices. Highlighting your company’s culture, values, and growth opportunities can attract top talent. Why Choose Tectonic – How to Hire the Right Salesforce Developer? Hiring the right Salesforce developer requires a thorough understanding of your business needs, a clear definition of the required skills, and a meticulous hiring process. By emphasizing these factors, you can ensure you recruit the right talent to drive your Salesforce initiatives. However many Salesforce projects don’t require a long term developer, business analyst, or project manager. Outsourcing these roles to Tectonic can provide a valuable savings in cost and improved project outcomes with a clean CRM in place from day one. At Tectonic, we take pride in being a leading Salesforce provider. Our team of certified Salesforce professionals is equipped with the skills and experience to meet your business demands. Whether you need a developer, administrator, or consultant, Tectonic’s rigorous recruiting process ensures we deliver the best talent. Partner with Tectonic to fully harness the potential of Salesforce and elevate your business to the next level. Contact us today to learn more about our Salesforce staffing solutions and how we can help you achieve your CRM goals. 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

Read More
AI Beneficial for Mental Healthcare

AI Beneficial for Mental Healthcare

Nearly half of the participants in a U.S. survey viewed AI as beneficial for mental healthcare, though concerns around incorrect diagnoses and reduced interaction with providers remain significant. A recent study from Columbia University School of Nursing highlighted that, while AI adoption in healthcare is growing, limited research has explored patient perspectives, especially in mental healthcare. Previous studies mainly focused on somatic healthcare issues like perinatal health and radiology, with patient trust hinging on the use case and clinician endorsement. The survey, which included 500 U.S. adults, revealed that 49.3% believed AI could be beneficial in mental healthcare, though opinions varied by demographic. Black respondents and those with lower health literacy were more likely to see the benefits, while women were less inclined to share that view. Major concerns included AI’s accuracy, risk of incorrect diagnoses, potential for inappropriate treatments, and fear of losing personal connection with providers. Additionally, most participants (81.6%) believed that mental health misdiagnoses involving AI would remain the provider’s responsibility. Key values identified by respondents included confidentiality, autonomy, and the ability to understand personal mental health risk factors. The researchers emphasized the need to communicate AI tool accuracy and ensure trust between patients and providers when implementing AI in mental healthcare. Lead researcher Dr. Natalie Benda emphasized the importance of understanding patient perspectives, as AI becomes more prevalent, to ensure safe and effective deployment of AI tools in mental health. 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

Read More
Benefits of AI in Banking

Benefits of AI in Banking

Artificial intelligence (AI) is rapidly gaining traction in the banking and finance sector, with generative AI (GenAI) emerging as a transformative force. Financial institutions are increasingly adopting AI technologies to automate processes, cut operational costs, and boost overall productivity, according to Sameer Gupta, North America Financial Services Organization Advanced Analytics Leader at EY. While traditional machine learning (ML) techniques are commonly used for fraud detection, loan approvals, and personalized marketing, banks are now advancing to incorporate more sophisticated technologies, including ML, natural language processing (NLP), and GenAI. Gupta notes that EY is observing a growing trend of banks using ML to enhance credit approvals, improve fraud detection, and refine marketing strategies, leading to greater efficiency and better decision-making. A recent survey by Gartner’s Jasleen Kaur Sindhu reveals that 58% of banking CIOs have either deployed or plan to deploy AI initiatives in 2024, with this number expected to rise to 77% by 2025. “This indicates not only the growing importance of AI but also its fundamental role in shaping how banks operate and deliver value to their customers,” Sindhu said. “AI is becoming essential to the success of banking institutions.” Here are five key benefits of AI applications in banking: Despite the benefits, concerns about AI in banking persist, particularly regarding data privacy, bias, and ethics. AI can inadvertently extract personal information and raise privacy issues. Regulatory challenges and the potential for AI systems to perpetuate biases are also major concerns. As AI technology evolves, banks are investing in robust governance frameworks, continuous monitoring, and adherence to ethical standards to address these risks. Looking ahead, AI is expected to revolutionize banking by delivering personalized services, enhancing customer interactions, and driving productivity. Deloitte forecasts that GenAI could boost productivity by up to 35% in the top 14 global investment banks, generating significant additional revenue per employee by 2026. 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

Read More
Salesforce Data Cloud and Zero Copy

Salesforce Data Cloud and Zero Copy

As organizations across industries gather increasing amounts of data from diverse sources, they face the challenge of making that data actionable and deriving real-time insights. With Salesforce Data Cloud and zero copy architecture, organizations can streamline access to data and build dynamic, real-time dashboards that drive value while embedding contextual insights into everyday workflows. A session during Dreamforce 2024 with Joanna McNurlen, Principal Solution Engineer for Data Cloud at Salesforce, discussed how zero copy architecture facilitates the creation of dashboards and workflows that provide near-instant insights, enabling quick decision-making to enhance operational efficiency and competitive advantage. What is zero copy architecture?Traditionally, organizations had to replicate data from one system to another, such as copying CRM data into a data warehouse for analysis. This approach introduces latency, increases storage costs, and often results in inconsistencies between systems. Zero copy architecture eliminates the need for replication and provides a single source of truth for your data. It allows different systems to access data in its original location without duplication across platforms. Instead of using traditional extract, transform, and load (ETL) processes, systems like Salesforce Data Cloud can connect directly with external databases, such as Google Cloud BigQuery, Snowflake, Databricks, or Amazon Redshift, for real-time data access. Zero copy can also facilitate data sharing from within Salesforce to other systems. As Salesforce expands its zero copy partner network, opportunities to easily connect data from various sources will continue to grow. How does zero copy work?Zero copy employs virtual tables that act as blueprints for the data structure, enabling queries to be executed as if the data were local. Changes made in the data warehouse are instantly visible across all connected systems, ensuring users always work with the latest information. While developing dashboards, users can connect directly to the zero copy objects within Data Cloud to create visualizations and reports on top of them. Why is zero copy beneficial?Zero copy allows organizations to analyze data as it is generated, enabling faster responses, smarter decision-making, and enhanced customer experiences. This architecture reduces reliance on data transformation workflows and synchronizations within both Tableau and CRM Analytics, where organizations have historically encountered bottlenecks due to runtimes and platform limits. Various teams can benefit from the following capabilities: Unlocking real-time insights in Salesforce using zero copy architectureZero copy architecture and real-time data are transforming how organizations operate. By eliminating data duplication and providing real-time insights, the use of zero copy in Salesforce Data Cloud empowers organizations to work more efficiently, make informed decisions, and enhance customer experiences. Now is the perfect time to explore how Salesforce Data Cloud and zero copy can elevate your operations. Tectonic, a trusted Salesforce partner, can help you unlock the potential of your data and create new opportunities with the Salesforce platform. Connect with us today to get started. 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

Read More
Strawberry AI Models

Strawberry AI Models

Since OpenAI introduced its “Strawberry” AI models, something intriguing has unfolded. The o1-preview and o1-mini models have quickly gained attention for their superior step-by-step reasoning, offering a structured glimpse into problem-solving. However, behind this polished façade, a hidden layer of the AI’s mind remains off-limits—an area OpenAI is determined to keep out of reach. Unlike previous models, the o1 series conceals its raw thought processes. Users only see the refined, final answer, generated by a secondary AI, while the deeper, unfiltered reasoning is locked away. Naturally, this secrecy has only fueled curiosity. Hackers, researchers, and enthusiasts are already working to break through this barrier. Using jailbreak techniques and clever prompt manipulations, they are seeking to uncover the AI’s raw chain of thought, hoping to reveal what OpenAI has concealed. Rumors of partial breakthroughs have circulated, though nothing definitive has emerged. Meanwhile, OpenAI closely monitors these efforts, issuing warnings and threatening account bans to those who dig too deep. On platforms like X, users have reported receiving warnings merely for mentioning terms like “reasoning trace” in their interactions with the o1 models. Even casual inquiries into the AI’s thinking process seem to trigger OpenAI’s defenses. The company’s warnings are explicit: any attempt to expose the hidden reasoning violates their policies and could result in revoked access to the AI. Marco Figueroa, leader of Mozilla’s GenAI bug bounty program, publicly shared his experience after attempting to probe the model’s thought process through jailbreaks—he quickly found himself flagged by OpenAI. Now I’m on their ban list,” Figueroa revealed. So, why all the secrecy? OpenAI explained in a blog post titled Learning to Reason with LLMs that concealing the raw thought process allows for better monitoring of the AI’s decision-making without interfering with its cognitive flow. Revealing this raw data, they argue, could lead to unintended consequences, such as the model being misused to manipulate users or its internal workings being copied by competitors. OpenAI acknowledges that the raw reasoning process is valuable, and exposing it could give rivals an edge in training their own models. However, critics, such as independent AI researcher Simon Willison, have condemned this decision. Willison argues that concealing the model’s thought process is a blow to transparency. “As someone working with AI systems, I need to understand how my prompts are being processed,” he wrote. “Hiding this feels like a step backward.” Ultimately, OpenAI’s decision to keep the AI’s raw thought process hidden is about more than just user safety—it’s about control. By retaining access to these concealed layers, OpenAI maintains its lead in the competitive AI race. Yet, in doing so, they’ve sparked a hunt. Researchers, hackers, and enthusiasts continue to search for what remains hidden. And until that veil is lifted, the pursuit won’t stop. 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

Read More
Data Governance for the AI Enterprise

Data Governance for the AI Enterprise

Salesforce Introduces Data Governance for the AI Enterprise Salesforce this month unveiled Data Governance for the AI Enterprise, a comprehensive suite of tools designed to help IT teams navigate the growing regulatory landscape surrounding generative AI. Why it matters: As governments worldwide work to implement stricter rules governing the use of AI, like the EU’s AI Act, data governance has become a top priority for businesses. According to Salesforce research, ensuring robust data security and governance is now the leading concern for Chief Data Officers. Cloud Data Security & Privacy SolutionsExplore the new suite: How Salesforce’s Data Governance for the AI Enterprise Can Help: Salesforce’s latest solution is designed to help companies proactively address both current and future regulations. Built on the Salesforce platform and integrated with Data Cloud, the suite offers advanced data management, enhanced security, and privacy features: Salesforce’s perspective:“Data governance is a top priority for every organization deploying AI, especially given the complexity of the regulatory landscape,” said Alice Steinglass, EVP and GM for Salesforce Platform. “Our Data Governance for the AI Enterprise suite equips businesses to tackle these challenges.” Customer success story:“Data encryption is essential to our data governance strategy,” said James Ferguson, Principal Security Architect at AWS. “With Salesforce’s flexible encryption solutions, we can maintain top-tier security while delivering innovative customer experiences.” Availability: For Data Cloud users: 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

Read More
New Technology Risks

New Technology Risks

Organizations have always needed to manage the risks that come with adopting new technologies, and implementing artificial intelligence (AI) is no different. Many of the risks associated with AI are similar to those encountered with any new technology: poor alignment with business goals, insufficient skills to support the initiatives, and a lack of organizational buy-in. To address these challenges, executives should rely on best practices that have guided the successful adoption of other technologies, according to management consultants and AI experts. When it comes to AI, this includes: However, AI presents unique risks that executives must recognize and address proactively. Below are 15 areas of risk that organizations may encounter as they implement and use AI technologies: Managing AI Risks While the risks associated with AI cannot be entirely eliminated, they can be managed. Organizations must first recognize and understand these risks and then implement policies to mitigate them. This includes ensuring high-quality data for AI training, testing for biases, and continuous monitoring of AI systems to catch unintended consequences. Ethical frameworks are also crucial to ensure AI systems produce fair, transparent, and unbiased results. Involving the board and C-suite in AI governance is essential, as managing AI risk is not just an IT issue but a broader organizational challenge. 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

Read More
Integrate Digital Delivery and Human Connection

Integrate Digital Delivery and Human Connection

Salesforce’s latest data reveals a complex challenge for banks: while digital excellence is now essential for customer satisfaction, a fully digital experience risks alienating customers who value human connections at critical moments. Banks often feel torn between scaling digital capabilities and preserving the personal touch that fosters customer loyalty. How can they strike the right balance?

Read More
Exploring Emerging LLM

Exploring Emerging LLM

Exploring Emerging LLM Agent Types and Architectures The Evolution Beyond ReAct AgentsThe shortcomings of first-generation ReAct agents have paved the way for a new era of LLM agents, bringing innovative architectures and possibilities. In 2024, agents have taken center stage in the AI landscape. Companies globally are developing chatbot agents, tools like MultiOn are bridging agents to external websites, and frameworks like LangGraph and LlamaIndex Workflows are helping developers build more structured, capable agents. However, despite their rising popularity within the AI community, agents are yet to see widespread adoption among consumers or enterprises. This leaves businesses wondering: How do we navigate these emerging frameworks and architectures? Which tools should we leverage for our next application? Having recently developed a sophisticated agent as a product copilot, we share key insights to guide you through the evolving agent ecosystem. What Are LLM-Based Agents? At their core, LLM-based agents are software systems designed to execute complex tasks by chaining together multiple processing steps, including LLM calls. These agents: The Rise and Fall of ReAct Agents ReAct (reason, act) agents marked the first wave of LLM-powered tools. Promising broad functionality through abstraction, they fell short due to their limited utility and overgeneralized design. These challenges spurred the emergence of second-generation agents, emphasizing structure and specificity. The Second Generation: Structured, Scalable Agents Modern agents are defined by smaller solution spaces, offering narrower but more reliable capabilities. Instead of open-ended design, these agents map out defined paths for actions, improving precision and performance. Key characteristics of second-gen agents include: Common Agent Architectures Agent Development Frameworks Several frameworks are now available to simplify and streamline agent development: While frameworks can impose best practices and tooling, they may introduce limitations for highly complex applications. Many developers still prefer code-driven solutions for greater control. Should You Build an Agent? Before investing in agent development, consider these criteria: If you answered “yes,” an agent may be a suitable choice. Challenges and Solutions in Agent Development Common Issues: Strategies to Address Challenges: Conclusion The generative AI landscape is brimming with new frameworks and fervent innovation. Before diving into development, evaluate your application needs and consider whether agent frameworks align with your objectives. By thoughtfully assessing the tools and architectures available, you can create agents that deliver measurable value while avoiding unnecessary complexity. 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

Read More
Salesforce Einstein Copilot Security

Salesforce Einstein Copilot Security

Salesforce Einstein Copilot Security: How It Works and Key Risks to Mitigate for a Safe Rollout With the official rollout of Salesforce Einstein Copilot, this conversational AI assistant is set to transform how sales, marketing, and customer service teams interact with both customers and internal documentation. Einstein Copilot understands natural language queries, streamlining daily tasks such as answering questions, generating insights, and performing actions across Salesforce to boost productivity. Salesforce Einstein Copilot Security However, alongside the productivity gains, it’s essential to address potential risks and ensure a secure implementation. This Tectonic insight covers: Einstein Copilot Use Cases Einstein Copilot enables users to: All of these actions can be performed with simple, natural language prompts, improving efficiency and outcomes. How Einstein Copilot Works Here’s a simplified breakdown of how Einstein Copilot processes prompts: The Einstein Trust Layer Salesforce has built the Einstein Trust Layer to ensure customer data is secure. Customer data processed by Einstein Copilot is encrypted, and no data is retained on the backend. Sensitive data, such as PII (Personally Identifiable Information), PCI (Payment Card Information), and PHI (Protected Health Information), is masked to ensure privacy. Additionally, the Trust Layer reduces biased, toxic, and unethical outputs by leveraging toxic language detection. Importantly, Salesforce guarantees that customer data will not be used to train the AI models behind Einstein Copilot or be shared with third parties. The Shared Responsibility Model Salesforce’s security approach is based on a shared responsibility model: This collaborative model ensures a higher level of security and trust between Salesforce and its customers. Best Practices for Securing Einstein Copilot Rollout Prepare Your Salesforce Org for Einstein Copilot To ensure a smooth rollout, it’s critical to assess your Salesforce security posture and ready your data. Tools like Salesforce Shield can help organizations by: By following these steps, you can utilize the power of Einstein Copilot while ensuring the security and integrity of your data. 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

Read More
Generative ai energy consumption

Growing Energy Consumption in Generative AI

Growing Energy Consumption in Generative AI, but ROI Impact Remains Unclear The rising energy costs associated with generative AI aren’t always central in enterprise financial considerations, yet experts suggest IT leaders should take note. Building a business case for generative AI involves both obvious and hidden expenses. Licensing fees for large language models (LLMs) and SaaS subscriptions are visible expenses, but less apparent costs include data preparation, cloud infrastructure upgrades, and managing organizational change. Growing Energy Consumption in Generative AI. One under-the-radar cost is the energy required by generative AI. Training LLMs demands vast computing power, and even routine AI tasks like answering user queries or generating images consume energy. These intensive processes require robust cooling systems in data centers, adding to energy use. While energy costs haven’t been a focus for GenAI adopters, growing awareness has prompted the International Energy Agency (IEA) to predict a doubling of data center electricity consumption by 2026, attributing much of the increase to AI. Goldman Sachs echoed these concerns, projecting data center power consumption to more than double by 2030. For now, generative AI’s anticipated benefits outweigh energy cost concerns for most enterprises, with hyperscalers like Google bearing the brunt of these costs. Google recently reported a 13% increase in greenhouse gas emissions, citing AI as a major contributor and suggesting that reducing emissions might become more challenging with AI’s continued growth. Growing Energy Consumption in Generative AI While not a barrier to adoption, energy costs play into generative AI’s long-term viability, noted Scott Likens, global AI engineering leader at PwC, emphasizing that “there’s energy being used — you don’t take it for granted.” Energy Costs and Enterprise Adoption Generative AI users might not see a line item for energy costs, yet these are embedded in fees. Ryan Gross of Caylent points out that the costs are mainly tied to model training and inferencing, with each model query, though individually minor, adding up over time. These expenses are often spread across the customer base, as companies pay for generative AI access through a licensing model. A PwC sustainability study showed that GenAI power costs, particularly from model training, are distributed among licensees. Token-based pricing for LLM usage also reflects inferencing costs, though these charges have decreased. Likens noted that the largest expenses still come from infrastructure and data management rather than energy. Potential Efficiency Gains Though energy isn’t a primary consideration, enterprises could reduce consumption indirectly through technological advancements. Newer, more cost-efficient models like OpenAI’s GPT-4o mini are 60% less expensive per token than prior versions, enabling organizations to deploy GenAI on a larger scale while keeping costs lower. Small, fine-tuned models can be used to address latency and lower energy consumption, part of a “multimodel” approach that can provide different accuracy and latency levels with varying energy demands. Agentic AI also offers opportunities for cost and energy savings. By breaking down tasks and routing them through specialized models, companies can minimize latency and reduce power usage. According to Likens, using agentic architecture could cut costs and consumption, particularly when tasks are routed to more efficient models. Rising Data Center Energy Needs While enterprises may feel shielded from direct energy costs, data centers bear the growing power demand. Cooling solutions are evolving, with liquid cooling systems becoming more prevalent for AI workloads. As data centers face the “AI growth cycle,” the demand for energy-efficient cooling solutions has fueled a resurgence in thermal management investment. Liquid cooling, being more efficient than air cooling, is gaining traction due to the power demands of AI and high-performance computing. IDTechEx projects that data center liquid cooling revenue could exceed $50 billion by 2035. Meanwhile, data centers are exploring nuclear power, with AWS, Google, and Microsoft among those considering nuclear energy as a sustainable solution to meet AI’s power demands. Future ROI Considerations While enterprises remain shielded from the full energy costs of generative AI, careful model selection and architectural choices could help curb consumption. PwC, for instance, factors in the “carbon impact” as part of its GenAI deployment strategy, recognizing that energy considerations are now a part of the generative AI value proposition. As organizations increasingly factor sustainability into their tech decisions, energy efficiency might soon play a larger role in generative AI ROI calculations. 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

Read More
Salesforce AI Evolves with the Generative AI Landscape

Salesforce AI Evolves with the Generative AI Landscape

Salesforce AI: Powering Customer Relationship Management Salesforce is a leading CRM solution that has long delivered cutting-edge cloud technologies to manage customer relationships effectively. In recent months, the platform has further advanced with the integration of generative AI and AI-powered features, primarily through its AI engine, Einstein. Salesforce AI Evolves with the Generative AI Landscape. To explore how AI operates within the Salesforce ecosystem and how various business teams can leverage these innovations, this guide delves into Salesforce’s AI capabilities, products, and features. Salesforce AI: Transforming CRM Capabilities Salesforce remains a top choice in the CRM software market, offering one of the most comprehensive solutions for managing relationships across departments, industries, and initiatives. Through dedicated cloud platforms, Salesforce enables teams to oversee marketing, sales, customer service, e-commerce, and more, with tools focused on delivering enhanced customer experiences supported by powerful data analytics. With the introduction of generative AI, Salesforce has significantly elevated its native automation, workflow management, data analytics, and assistive capabilities for customer lifecycle management. Einstein Copilot exemplifies this innovation, aiding internal users with tasks such as outreach, analysis, and improving external user experiences. What is Salesforce Einstein? Salesforce Einstein is an AI-driven suite of tools integrated natively into various Salesforce Cloud applications, including Sales Cloud, Marketing Cloud, Service Cloud, and Commerce Cloud. It also operates through assistive technologies like Einstein Copilot. Einstein is built on a multitenant platform and incorporates numerous automated machine learning features to unify organizational data with CRM capabilities. Designed to make intelligent, data-driven decisions, Einstein requires no additional installation, offering a seamless user experience when paired with a compatible subscription plan. 7 Key Features of Salesforce Einstein 7 Applications of Salesforce Einstein Future Trends in Salesforce AI Bottom Line: Salesforce AI Evolves with the Generative AI Landscape Salesforce continues to enhance its AI-powered features, keeping pace with advancements in generative and predictive AI. Whether new to the platform or a seasoned user, Salesforce offers innovative, AI-centric solutions to streamline customer relationship management and business operations. 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

Read More
Agentic AI is Here

AI Agent Myths

Myths About AI Agents Agents will transform how we work, but separating fact from fiction is essential. AI agents are revolutionizing business operations, yet misconceptions persist about their capabilities and value. Understanding these myths—and the truth behind them—can help your organization unlock their potential. Myth #1: AI Agents Are Just Glorified Chatbots While chatbots and AI agents both use artificial intelligence, their functionality and complexity differ significantly. For instance, a chatbot might provide an overview of your sales metrics, but an AI agent can analyze those metrics, forecast demand, adjust inventory levels, update marketing strategies, and even notify suppliers—all proactively and autonomously. This leap in capability allows agents to optimize workflows, make strategic recommendations, and dynamically respond to changing conditions. They’re not just answering questions—they’re driving outcomes. Myth #2: They’re unpredictable and uncontrollablePopular culture often paints AI as rogue systems—think 2001: A Space Odyssey or The Terminator—but in reality, modern AI agents are designed with safety, trust, and precision at their core. The most effective agents today use advanced techniques to prevent errors and ensure their actions stay within strict boundaries. At the heart of this is a reasoning engine. This engine doesn’t just execute tasks—it creates action plans based on the user’s goals, evaluates those plans, and refines them by pulling data from customer relationship management (CRM) systems and other platforms. It then determines the correct processes to execute and iterates until the task is completed successfully, improving with each interaction. When tasks fall outside an organization’s predefined guardrails—like user permissions or compliance rules—the reasoning engine automatically flags the task and escalates it for human oversight. “Helping an agent perform accurately while understanding what it is not allowed to do is a complex task,” says Krishna Gandikota, Manager of Solution Engineering at Salesforce. “The reasoning engine plans and evaluates the AI’s approach before it takes any action. It also assesses whether it has the necessary skills and information to proceed.” This process is further enhanced by continuous learning, enabling agents to refine their decision-making and actions over time. Grounded in DataThe best agents are contextually aware, leveraging relevant, up-to-date information to perform tasks accurately. Techniques like retrieval-augmented generation (RAG) help by sourcing the most relevant data, while semantic search ensures that agents retrieve the latest and most accurate information. Salesforce’s Agentforce employs these methods using Data Cloud, which enables agents to access real-time data without physically copying or modifying it—thanks to zero-copy architecture. This ensures speed, accuracy, and compliance across all agent-driven actions. Myth #3: They’re complicated, time-consuming, and expensive to set upIt’s easy to assume that deploying AI agents would require months of integration work and millions of dollars, but that’s no longer the case. Advances in generative AI and large language models (LLMs) have drastically simplified the process. Agents can now be deployed in minutes with prebuilt topics—specific areas of focus—and actions for common tasks in customer service, sales, and commerce. For more tailored needs, low-code tools make it easy to create custom agents. Using natural language processing (NLP), you simply describe what the agent needs to do, and the system builds it for you. For instance, Agent Builder automatically suggests guardrails and resources based on the task description. By scanning an app’s metadata, it identifies semantically similar processes, creating a smarter, context-aware agent that aligns with your business operations. “All the sophistication is already built into the platform,” Gandikota explains. “The Einstein Trust Layer, reasoning engine, and vector database for RAG and semantic search work seamlessly. With this foundation, you can build a team of agents quickly and confidently.” Myth #4: They’re always fully autonomousAI agents don’t need to operate completely autonomously to deliver value. Their autonomy depends on the complexity of their tasks and the industry they serve. “Agents don’t always need to take actions autonomously,” Gandikota explains. “They’re designed to understand requests, assess whether they can proceed independently, and involve humans when necessary.” Myth #5: They won’t deliver real business valueSome businesses using generic AI tools haven’t seen the ROI they expected. That’s because generic AI isn’t tailored to specific business needs. AI agents, on the other hand, are purpose-built to perform specialized tasks with precision. Whether it’s nurturing sales leads, brainstorming marketing campaigns, or resolving service tickets, targeted AI agents excel at solving specific problems. Unlike generic AI, they don’t just provide insights—they take action, driving measurable outcomes. For example, educational publisher Wiley improved support case resolution by over 40% after adopting AI agents. By handling routine tasks, the agents freed up Wiley’s service teams to focus on more complex cases. Similarly, early adopters like OpenTable and ADP have reported significant improvements in customer satisfaction and efficiency. According to MarketsandMarkets, AI agents are driving demand for automation by enhancing decision-making, scalability, and efficiency. The global market for AI agents is expected to grow from .1 billion in 2024 to billion by 2030. The Bottom LineUnderstanding the myths—and realities—of AI agents is critical for business leaders. Misconceptions can lead to missed opportunities, while clarity around their capabilities can help organizations work smarter, faster, and more efficiently. With trusted, adaptable, and purpose-built agents, the future of business automation is already here. 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

Read More
gettectonic.com