The concept of artificial intelligence, or synthetic minds capable of thinking and reasoning like humans, has been around for centuries. Ancient cultures often expressed ideas and pursued goals similar to AI, and in the early 20th century, science fiction brought these notions to modern audiences. Works like The Wizard of Oz and films such as Metropolis resonated globally, laying the groundwork for contemporary AI discussions.
10 Top AI Jobs in 2025 As we approach 2025, the demand for AI expertise is on the rise. Companies are seeking professionals with a strong background in AI, paired with practical experience. This insight explores 10 of the top AI jobs, the skills they require, and the industries that are driving AI adoption. If you are of the camp worrying about artificial intelligence replacing you, read on to see how you can leverage AI to upskill your career. AI is increasingly becoming an integral part of our lives, influencing various sectors from healthcare and finance to manufacturing, retail, and education. It is automating routine tasks, enhancing user experiences, and improving decision-making processes. AI is transitioning from data centers into everyday devices such as smartphones, IoT devices, and autonomous vehicles, becoming more efficient and safer thanks to advancements in real-time processing, lower latency, and enhanced privacy measures. The ethical use of AI is also at the forefront, emphasizing fairness, transparency, and accountability in AI models and decision-making processes. This proactive approach to ethics contrasts with past technological advancements, where ethical considerations often lagged behind. The rapid growth of AI translates to an increasing number of job opportunities. Below, we discuss the skills sought in AI specialists, the industries adopting AI at a fast pace, and a rundown of the 10 hottest AI jobs for 2025. Top AI Job Skills While many programmers are self-taught, the AI field demands a higher level of expertise. An analysis of 15,000 job postings found that 77% of AI roles require a master’s degree, while only 8% of positions are available to candidates with just a high school diploma. Most job openings call for mid-level experience, with only 12% for entry-level roles. Interestingly, while remote work is common in IT, only 11% of AI jobs offer fully remote positions. Being a successful AI developer requires more than coding skills; proficiency in core AI programming languages (like Python, Java, and R) is essential. Additional skills in communication, digital marketing strategies, effective collaboration, and analytical abilities are also critical. Moreover, a basic understanding of psychology is beneficial for simulating human behavior, and knowledge of AI security, privacy, and ethical practices is increasingly necessary. Industries Embracing AI Certain sectors are rapidly adopting AI technologies, including: 10 Top AI Jobs AI job roles are evolving quickly. Specialists are increasingly in demand over generalists, with a focus on deep knowledge in specific areas. Here are 10 promising AI job roles for 2025, along with their expected salaries based on job postings. As AI continues to evolve, these roles will play a pivotal part in shaping the future of various industries. Preparing for a career in AI requires a combination of technical skills, ethical understanding, and a willingness to adapt to new technologies. As we’ve seen with Salesforce a push for upskilling in artificial intelligence is 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 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
Organizations must acknowledge the risks associated with implementing AI systems to use the technology ethically and minimize liability. Throughout history, companies have had to manage the risks associated with adopting new technologies, and AI is no exception. Some AI risks are similar to those encountered when deploying any new technology or tool, such as poor strategic alignment with business goals, a lack of necessary skills to support initiatives, and failure to secure buy-in across the organization. For these challenges, executives should rely on best practices that have guided the successful adoption of other technologies. In the case of AI, this includes: However, AI introduces unique risks that must be addressed head-on. Here are 15 areas of concern that can arise as organizations implement and use AI technologies in the enterprise: Managing AI Risks While AI risks cannot be eliminated, they can be managed. Organizations must first recognize and understand these risks and then implement policies to minimize their negative impact. These policies should ensure the use of high-quality data, require testing and validation to eliminate biases, and mandate ongoing monitoring to identify and address unexpected consequences. Furthermore, ethical considerations should be embedded in AI systems, with frameworks in place to ensure AI produces transparent, fair, and unbiased results. Human oversight is essential to confirm these systems meet established standards. For successful risk management, the involvement of the board and the C-suite is crucial. As noted, “This is not just an IT problem, so all executives need to get involved in this.” 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
The EU AI Act is a complex piece of legislation, packed with various sections, definitions, and guidelines, making it challenging for organizations to navigate. However, understanding the EU AI Act is crucial for companies aiming to innovate with AI while staying compliant with both legal and ethical standards. Arnoud Engelfriet, chief knowledge officer at ICTRecht, an Amsterdam-based legal services firm, specializes in IT, privacy, security, and data law. As the head of ICTRecht Academy, he is responsible for educating others on AI legislation, including the AI Act. In his book AI and Algorithms: Mastering Legal and Ethical Compliance, published by Technics, Engelfriet explores the intersection of AI legislation and ethical AI development, using the AI Act as a key example. He emphasizes that while new AI guidelines can raise concerns about creativity and compliance, it’s quite necessary for organizations to grasp the current and future legal landscape to build trustworthy AI systems. Balancing Compliance and Innovation As of August 2024, the much-anticipated AI Act is in effect, with implementation timelines extending from six months to over a year. Many businesses worry that the regulations might slow down AI innovation, especially given the rapid pace of technological advancements. Engelfriet acknowledges this tension, noting that “compliance and innovation have always been somewhat at odds.” However, he believes the act’s flexible, tiered approach offers space for businesses to adapt. For instance, the inclusion of regulatory sandboxes allows companies to test AI systems safely, without releasing them into the market. Engelfriet suggests that while innovation might slow down, the safety and trustworthiness of AI systems will improve. Ensuring Trustworthy AI The AI Act aims to promote “trustworthy AI,” a term that became central to discussions after its inclusion in the first draft of the act in 2019. Although the concept remains somewhat undefined, the act outlines three key characteristics of trustworthy AI: legality, technical robustness, and ethical soundness. Engelfriet underscores that trust in AI systems is ultimately about trusting the humans behind them. “You cannot really trust a machine,” he explained, “but you can trust its designers and operators.” The AI Act requires transparency around how AI systems function, ensuring they reliably perform their intended tasks, such as making automated decisions or serving as chatbots. Ethics has gained even more prominence with the rise of generative AI. Engelfriet highlights the fragmented nature of AI ethics guidelines, which address everything from data protection to bias prevention. The EU’s Assessment List for Trustworthy AI provides a framework to guide organizations in applying ethical standards, though Engelfriet notes that it may need to be tailored to specific industry needs. The Role of AI Compliance Officers Given the complexity of AI regulations, organizations may find it overwhelming to manage compliance efforts. To meet this growing need, Engelfriet recommends appointing AI compliance officers to help companies integrate AI responsibly into their operations. ICTRecht has also developed a course, based on AI and Algorithms, to teach employees how to navigate AI compliance. Participants from various roles—particularly those in data, privacy, and risk functions—attend the course to expand their knowledge in this increasingly important area. Salesforce is developing Trailblazer content to address these challenges as well. As with GDPR, Engelfriet believes the AI Act will set the tone for future AI regulations. He advises businesses to proactively engage with the AI Act to ensure they are prepared for the evolving regulatory landscape. To get assistance exploring your EU risks, contact Tectonic today. 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
California’s new AI bill has sparked intense debate, with proponents viewing it as necessary regulation and critics warning it could stifle innovation, particularly for small businesses. Senate Bill 1047, known as the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, mandates that developers of advanced AI systems costing at least $100 million to train must test their models for potential harm and put safeguards in place. It also offers whistleblower protections for employees at large AI firms and establishes CalCompute, a public cloud computing resource aimed at startups and researchers. The bill is awaiting Governor Gavin Newsom’s signature by Sept. 30 to become law. Prominent AI experts, including Geoffrey Hinton and Yoshua Bengio, support the bill. However, it has met resistance from various quarters, including Rep. Nancy Pelosi and OpenAI, who argue it could hinder innovation and the startup ecosystem. Pelosi and others have expressed concerns that the bill’s requirements might burden smaller businesses and harm California’s leadership in tech innovation. Gartner analyst Avivah Litan acknowledged the dilemma, stating that while regulation is critical for AI, the bill’s requirements might negatively impact small businesses. “Some regulation is better than none,” she said, but added that thresholds could be challenging for smaller firms. Steve Carlin, CEO of AiFi, criticized the bill for its vague language and complex demands on AI developers, including unclear guidance on enforcing the rules. He suggested that instead of focusing on AI models, legislation should address the risks and applications of AI, as seen with the EU AI Act. Despite concerns, some experts like Forrester Research’s Alla Valente support the bill’s safety testing and whistleblower protections. Valente argued that safeguarding AI models is essential across industries, though she acknowledged that the costs of compliance could be higher for small businesses. Still, she emphasized that the long-term costs of not implementing safeguards could be greater, with risks including customer lawsuits and regulatory penalties. California’s approach to AI regulation adds to the growing patchwork of state-level AI laws in the U.S. Colorado and Connecticut have also introduced AI legislation, and cities like New York have tackled issues like algorithmic bias. Carlin warned that a fragmented state-by-state regulatory framework could create a costly and complex environment for developers, calling for a unified federal standard instead. While federal legislation has been proposed, none has passed, and Valente pointed out that relying on Congress for action is a slow process. In the meantime, states like California are pushing ahead with their own AI regulations, creating both opportunities and challenges for the AI industry. 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
Grammarly’s AI Regulatory Master Class: Key Insights on Navigating AI Compliance On August 27, 2024, Grammarly hosted an AI Regulatory Master Class webinar, featuring Scout Moran, Senior Product Counsel, and Alan Luk, Head of Governance, Risk, and Compliance (GRC). The event provided a comprehensive overview of the current and upcoming AI regulations affecting organizations’ AI strategies, along with guidance on evaluating AI solution providers, including those offering generative AI. While the webinar avoided deep legal analysis and did not serve as legal advice, Moran and Luk spotlighted key regulations emerging from both the U.S. and European Union (EU), highlighting the rapid response of regulatory bodies to AI’s growth. Overview of AI Regulations The AI regulatory landscape is changing quickly. A May 2024 report from law firm Davis & Gilbert noted that nearly 200 AI-related laws have been proposed across various U.S. states. Grammarly’s presentation emphasized the need for organizations to stay updated, as both U.S. and EU regulations are shaping the future of AI governance. The EU AI Act: A New Regulatory Framework The EU AI Act, which took effect on August 2, 2024, applies to AI system providers, importers, distributors, and others connected to the EU market, regardless of where they are based. As Moran pointed out, the Act is designed to ensure AI systems are deployed safely. Unsafe systems may be removed from the market, establishing a regulatory baseline that individual EU countries can strengthen with more stringent measures. However, the Act does not fully define “safety.” Legal experts Hadrien Pouget and Ranj Zuhdi noted that while safety requirements are crucial to the Act, they are currently broad, allowing room for further development of standards. The Act prohibits certain AI practices, such as manipulative systems, those exploiting personal vulnerabilities, and AI used to assess or predict criminal risk. AI systems are categorized into four risk levels: unacceptable, high-risk, limited risk, and minimal risk. High-risk systems—such as those in critical infrastructure or public services—face stricter regulation, while minimal-risk systems like spam filters have fewer requirements. Full enforcement of the Act will begin in 2025. U.S. AI Regulations Unlike the EU, the U.S. focuses more on national security than consumer safety in its AI regulation. The U.S. Executive Order on Safe, Secure, and Trustworthy AI addresses these concerns. At the state level, Moran highlighted trends such as requiring clear disclosure when interacting with AI and giving individuals the right to opt out of having their data used for AI model training. States like California and Utah are leading the way with specific laws (SB-1047 and SB-149, respectively) addressing accountability and disclosure in AI use. Key Considerations When Selecting AI Vendors Moran stressed the importance of thoroughly vetting AI vendors. Organizations should ensure vendors meet cybersecurity standards, such as SOC 2, and clearly define how their data will be used, particularly in training large language models (LLMs). “Eyes off” agreements, which prevent vendor employees from accessing customer data, should also be considered. Martha Buyer, a frequent contributor to No Jitter, emphasized verifying the originality of AI-generated content from providers like Grammarly or Microsoft Copilot. She urged caution in ensuring the ownership and authenticity of AI-assisted outputs. The Importance of Strong Third-Party Agreements Luk highlighted Grammarly’s commitment to data privacy, noting that the company neither sells customer data nor uses it to train models. Additionally, Grammarly enforces agreements preventing its third-party LLM providers from doing so. These contractual protections are crucial for safeguarding customer data. Organizations should also ensure third-party vendors adhere to strict guidelines, including securing customer data, encrypting it, and preventing unauthorized access. Vendors should maintain updated security certifications and manage risks like bias, which, while not entirely avoidable, must be actively addressed. Staying Ahead in a Changing Regulatory Environment Both Moran and Luk stressed the importance of ongoing monitoring. Organizations need to regularly reassess whether their vendors comply with their data-sharing policies and meet evolving regulatory standards. As AI technology and regulations continue to evolve, staying informed and agile will be critical for compliance and risk mitigation. In conclusion, organizations adopting AI-powered solutions must navigate a dynamic regulatory environment. As AI advances and regulations become more comprehensive, remaining vigilant and asking the right questions will be key to ensuring compliance and reducing risks. 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
Computing currently accounts for up to 3 percent of global power consumption, and Salesforce believes that AI could triple this figure. Lobbying for AI Rules on Environemental Impact. To address these concerns, Salesforce is initiating lobbying efforts to establish new regulations mandating emissions disclosure and efficiency standards for artificial intelligence. This announcement was made on Monday as part of Salesforce’s new “Sustainable AI Policy Priorities.” The company has previously taken stances on AI ethics and equity, a trend also seen among other major tech firms like Amazon, Google, and Microsoft. The move comes amidst rising apprehension about the energy-intensive nature of training and operating AI algorithms. Data centers already consume 2-3 percent of annual global power, a figure projected to triple by 2030 due to AI’s accelerating demand, according to Boston Consulting Group. This prospect has motivated tech giants like Amazon, Google, and Microsoft to explore alternative, non-fossil fuel energy sources such as nuclear and geothermal. Lobbying for AI Rules on Environemental Impact Salesforce has outlined six policy priorities aimed at supporting “sustainable AI” through regulations and incentives, categorized into two areas: Salesforce has already begun disclosing energy and environmental metrics related to its AI development activities. The company prioritizes the use of energy-efficient hardware and operates in data centers powered by lower-carbon sources, in collaboration with Google’s cloud services. Moreover, Salesforce, like other tech giants, supports organizations and startups developing AI applications with climate benefits. It recently announced support for five new nonprofits focused on climate initiatives: Climate Collective Foundation, Good360, Groundswell, Ocean Risk and Resilience Action Alliance, and WattTime. 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
Responsible AI and ethical AI are closely connected, with each offering complementary yet distinct principles for the development and use of AI systems. Organizations that aim for success must integrate both frameworks, as they are mutually reinforcing. Responsible AI emphasizes accountability, transparency, and adherence to regulations. Ethical AI—sometimes called AI ethics—focuses on broader moral values like fairness, privacy, and societal impact. In recent discussions, the significance of both has come to the forefront, encouraging organizations to explore the unique advantages of integrating these frameworks. While Responsible AI provides the practical tools for implementation, ethical AI offers the guiding principles. Without clear ethical grounding, responsible AI initiatives can lack purpose, while ethical aspirations cannot be realized without concrete actions. Moreover, ethical AI concerns often shape the regulatory frameworks responsible AI must comply with, showing how deeply interwoven they are. By combining ethical and responsible AI, organizations can build systems that are not only compliant with legal requirements but also aligned with human values, minimizing potential harm. The Need for Ethical AI Ethical AI is about ensuring that AI systems adhere to values and moral expectations. These principles evolve over time and can vary by culture or region. Nonetheless, core principles—like fairness, transparency, and harm reduction—remain consistent across geographies. Many organizations have recognized the importance of ethical AI and have taken initial steps to create ethical frameworks. This is essential, as AI technologies have the potential to disrupt societal norms, potentially necessitating an updated social contract—the implicit understanding of how society functions. Ethical AI helps drive discussions about this evolving social contract, establishing boundaries for acceptable AI use. In fact, many ethical AI frameworks have influenced regulatory efforts, though some regulations are being developed alongside or ahead of these ethical standards. Shaping this landscape requires collaboration among diverse stakeholders: consumers, activists, researchers, lawmakers, and technologists. Power dynamics also play a role, with certain groups exerting more influence over how ethical AI takes shape. Ethical AI vs. Responsible AI Ethical AI is aspirational, considering AI’s long-term impact on society. Many ethical issues have emerged, especially with the rise of generative AI. For instance, machine learning bias—when AI outputs are skewed due to flawed or biased training data—can perpetuate inequalities in high-stakes areas like loan approvals or law enforcement. Other concerns, like AI hallucinations and deepfakes, further underscore the potential risks to human values like safety and equality. Responsible AI, on the other hand, bridges ethical concerns with business realities. It addresses issues like data security, transparency, and regulatory compliance. Responsible AI offers practical methods to embed ethical aspirations into each phase of the AI lifecycle—from development to deployment and beyond. The relationship between the two is akin to a company’s vision versus its operational strategy. Ethical AI defines the high-level values, while responsible AI offers the actionable steps needed to implement those values. Challenges in Practice For modern organizations, efficiency and consistency are key, and standardized processes are the norm. This applies to AI development as well. Ethical AI, while often discussed in the context of broader societal impacts, must be integrated into existing business processes through responsible AI frameworks. These frameworks often include user-friendly checklists, evaluation guides, and templates to help operationalize ethical principles across the organization. Implementing Responsible AI To fully embed ethical AI within responsible AI frameworks, organizations should focus on the following areas: By effectively combining ethical and responsible AI, organizations can create AI systems that are not only technically and legally sound but also morally aligned and socially responsible. Content edited October 2024. 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
How can small businesses ensure compliance? Business in the New Age of Compliance with AI can be challenging. While larger corporations often allocate resources for extensive research and development to maintain compliance, smaller businesses may lack the means to conduct thorough due diligence. In such cases, it becomes crucial for them to pose the right questions to vendors and technology partners within their ecosystem. Even as Salesforce takes strides in creating trustworthy generative AI solutions for its customers, these customers also engage with other vendors and processors. It is imperative for them to remain vigilant about potential risks and not rely solely on trust. Salesforce and Tectonic suggest that smaller companies should inquire about: For smaller companies, depending on the due diligence of third-party service providers becomes essential. Evaluating privacy protocols, security procedures, identification of potential harms, and safeguarding measures are critical aspects that demand close attention. In this New Age of Compliance with AI everyone is responsible. Choosing an AI savvy Salesforce partner like Tectonic protects you and your company. The Einstein Trust Layer is your insurance that you are doing artificial intelligence right. 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
If you’re considering integrating generative artificial intelligence (GAI) tools into your business operations, you’re not alone. While these tools have the potential to boost employee productivity, concerns regarding their safety have been voiced by business leaders. Despite their utility in areas like marketing, customer service, and data insights, there is a growing demand for generative AI regulations due to apprehensions about its societal impact and potential risks. Key Points to Consider: Global AI Regulatory Response: A global response to AI regulation is taking shape, with U.S. lawmakers engaging tech leaders and expressing unanimous agreement on the necessity of AI regulation. In the EU, audits of AI algorithms and underlying data from major platforms meeting specific criteria have already begun. Business Decision Makers’ Role: As a decision-maker in your business, it is crucial to understand GAI and its implications for interactions with other companies and consumers. Countries worldwide are working to ensure that generative AI adheres to existing measures related to privacy, transparency, copyright, and accountability. What Your Company Can Do Now: Recent Developments: Concerns and Background on Generative AI Regulations: Considerations for Businesses: The rapidly evolving landscape of generative AI calls for ongoing awareness and adaptation, with the need for businesses to stay informed and engage in proactive discussions with trusted advisers. As regulatory efforts focus on privacy, content moderation, and copyright concerns, the conversations around generative AI regulations are crucial in navigating the ever-changing technological landscape. Like Related Posts 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 Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more
Consumer Trust and Responsible AI Implementation Ethical AI Consumer Trust vs Expectations Research indicates that while consumers have low trust in AI systems, they expect companies to use them responsibly. Around 90% of consumers believe that companies have a duty to contribute positively to society. However, despite guidance on responsible technology use, many consumers remain apprehensive about how companies are deploying technology, particularly AI. ethical ai consumer trust vs expectations A global survey conducted in March 2021 revealed that citizens lack trust in AI systems but still hold organizations accountable for upholding principles of trustworthy AI. To earn customers’ trust in AI and mitigate brand and legal risks, companies need to adopt ethical AI practices centered around principles such as Transparency, Fairness, Responsibility, Accountability, and Reliability. Developing an Ethical AI Practice Over the past few years, industry professionals like have focused on maturing AI ethics practices within companies like Salesforce. This journey toward ethical AI maturity often begins with an ad hoc approach. Ad Hoc Stage In the ad hoc stage, individuals within organizations start recognizing unintended consequences of AI and informally advocate for considering bias, fairness, accountability, and transparency. These early advocates spark awareness among colleagues and managers, prompting discussions on the ethical implications of AI. Some advocates eventually transition to full-time roles focused on building ethical AI practices within their companies. Organized and Repeatable Stage With executive buy-in, companies progress to the organized and repeatable stage, establishing a culture where responsible AI practices are valued. This stage involves: Achieve Ethical AI Consumer Trust vs Expectations During this stage, companies must move beyond superficial “ethics washing” by actively integrating ethical principles into their operations and fostering a culture of responsibility. Additionally, the independence and empowerment of individuals in responsible AI roles are crucial for maintaining integrity and honesty in ethical AI practices. Final Insight Thoughts As companies progress through the maturity model for ethical AI practices, they strengthen consumer trust and mitigate risks associated with AI deployment. By prioritizing transparency, fairness, and accountability, organizations can navigate the ethical complexities of AI implementation and contribute positively to society. ethical ai consumer trust vs expectations 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
What is data modelling in Snowflake? Snowflake and Customer 360 Data Model Data modeling is the process of organizing and mapping data using simplified diagrams, symbols, and text to represent data associations and flow. Engineers use these models to develop new software and to update legacy software. Data modeling also ensures the consistency and quality of data. What is Customer 360 Data Model? Snowflake and Customer 360 Data Model Customer 360 refers to a comprehensive view of a customer’s data, encompassing every interaction from website inquiries to product purchases to customer support tickets. It ensures that every department within a company accesses the same accurate information about the customer. Exploring Data Modeling in Snowflake Data modeling involves organizing and mapping data using simplified diagrams, symbols, and text to illustrate data associations and flow. Engineers utilize these models to develop new software and update existing ones, ensuring data consistency and quality. Unifying Business and Customer Data Break down silos and consolidate customer data into a unified platform using native-first and third-party connectors in Snowflake. These connectors securely ingest data from various sources, enriching customer profiles and resolving identities seamlessly. Enriching Profiles and Resolving Identities-Snowflake and Customer 360 Data Model Enhance customer profiles securely with a plethora of data sets available in Snowflake Marketplace. Resolve identities seamlessly using applications from leading providers native to the platform. Managing Consent and Responding to Data Subject Requests Adhere to customer data consent choices and comply with global privacy regulations by swiftly and effectively responding to data subject requests. Understanding the 360-Degree Customer View The 360-degree customer view is a coveted goal for B2C marketers, aiming to aggregate and unify customer interaction touchpoints. Achieving this comprehensive view enables businesses to better understand the customer journey, personalize experiences, and enhance engagement. Snowflake and Customer 360 Snowflake’s Data Cloud tackles the challenge of consolidating disparate data sources in Martech stacks. By collecting and unifying customer 360 data points into a SQL data warehouse, Snowflake empowers marketers to eliminate data silos and respond to customer behavioral trends promptly. Snowflake and Customer 360 Data Model: Evolution of Customer 360 The marketing technology landscape has evolved, with organizations shifting focus to centralized data and new marketing applications. This shift involves aggregating customer data centrally into Customer 360 profiles, enabling tailored marketing campaigns and improved customer experiences. CDPs: The Latest Incarnation of Customer 360 Customer Data Platforms (CDPs) have emerged as solutions to the Customer 360 challenge. By consolidating and organizing customer data, CDPs empower marketers to personalize customer journeys across channels effectively. Tectonic’s Role in Advancing Data Strategies-Snowflake and Customer 360 Data Model Tectonic assists businesses in embracing a composable architecture, future-proofing data structures, migrating data to the cloud securely, and creating a single source of truth for easy analysis of customer data. The Importance of Omnichannel Data in Marketing Understanding omnichannel data is crucial for marketers seeking to improve customer experiences and drive revenue. Omnichannel data encompasses every customer interaction, offering insights into behaviors, preferences, and values. Building a Customer 360 Crafting a holistic view of customers and their journey involves mapping touchpoints, identifying data sources, integrating data into a centralized location, and deriving insights to create customer profiles. Why a Robust Cloud Data Platform is Essential for Omnichannel A robust cloud data platform enables marketers to leverage a single source of truth, analyze streaming data, and facilitate collaboration across teams while ensuring compliance with privacy regulations. “Being customer-centric requires us to have a data-driven culture. We must ask ourselves how we can optimize the supply chain, product design, predictability and propensity of customers to buy our products. These are all data challenges.” Fabio Luzzi, VP of Data Science and Engineering, Tapestry The 360-degree customer view is the Holy Grail of B2C marketers: often sought but frequently unattainable. Companies that aspire to the Customer 360 view seek to aggregate and unify customer interaction touchpoints — from online transactions, social media, forums, and physical touches to third-party interactions, customer support, and service — to better understand the customer journey, meet demand, and personalize the customer experience. The challenge, of course, is that the customer data is spread across these numerous, various touchpoints. Without system synchronization, errors and duplicate records can become an ongoing problem. In the not-too distant past, interactions were largely limited to physical touch points or the phone. With the explosion of new interaction platforms, the job of aggreggating these disparate data sources has proven to be difficult. To really get a handle on all this data, modern B2C marketers need to engage with data management, data storage, data analytics, and often ML and BI tools to merge, unify, and then analyze these transactions to drive business insight. SNOWFLAKE AND CUSTOMER 360 The number of tools deployed in the average Martech stack has exploded from just a few to more than 90 SaaS applications. Marketers have struggled to consolidate these disparate data sources to both quickly react to customer behavior and also to report internally on performance. Snowflake’s Data Cloud solves this challenge, which collects and unifies all customer 360 data points — semi-structured or structured — into an easy-to-query SQL data warehouse that integrates with leading data analytics, BI, ML, and data management tools. This allows marketers to eliminate data silos and react to customer behavorial trends with timely and personalized campaigns. The world of marketing technology has evolved quickly. Although the quest for a single customer view remains, organizations are now focusing on how to create new marketing applications that centralize data. There’s a not-so-quiet (r)evolution going on in the world of marketing technology. Privacy-driven changes in the digital ecosystem are forcing companies to adopt a first-party data strategy with Customer 360 profiles at its center. But that’s not what this revolution is about; Customer 360 profiles have been part of marketing systems going back to Salesforce’s original Customer Relationship Management (CRM) platform. What is changing is not the “what” —a single customer view, but the “how”. What if Customer Data was aggregated centrally, organized into
AI Data Management is the strategic and systematic handling of an organization’s data assets through the integration of AI technology. The primary goal is to enhance data quality, analysis, and decision-making processes. This encompasses the implementation of procedures, guidelines, and technical methodologies for the efficient collection, organization, storage, and utilization of data. While Generative AI receives considerable attention, more established AI applications, such as predictive analytics and chatbots, have long proven beneficial for organizations. Technical leaders leveraging AI report notable improvements in decision-making speed and operational efficiency. Beyond speed, analytics and IT leaders find more time to address strategic challenges rather than being immersed in routine tasks. Customers also experience significant enhancements in satisfaction due to AI. With AI outcomes heavily reliant on data quality, nearly nine in 10 analytics and IT leaders prioritize data management as a high concern amidst new AI developments. Artificial Intelligence quietly contributes to data management by addressing aspects like quality, accessibility, and security. As organizations accelerate digital transformation, AI and Machine Learning are increasingly harnessed to maximize data value. Effective data management is pivotal in creating an environment where data becomes a valuable asset throughout the organization. It mitigates issues arising from poor data, such as friction, inaccurate predictions, and accessibility challenges, ideally preventing them proactively. The labor-intensive nature of data management involves cleaning, extracting, integrating, cataloging, labeling, and organizing data. AI plays a crucial role in organizing data by analyzing extensive datasets and identifying relevant and high-quality content based on predefined criteria. It assists in tagging, categorizing, and summarizing content, simplifying user access to needed information. AI significantly contributes to various data management areas, including classification, cataloging, quality improvement, security, and data integration. It excels in tasks such as obtaining, extracting, and structuring data, locating data, reducing errors, ensuring security, and building master lists. In the realm of database management systems, AI is integrated, particularly machine learning, for automatic diagnosis, monitoring, alerting, and protection of databases. This advancement allows software to manage these tasks autonomously. ML data management applies data quality practices and debugging solutions to machine learning processes. Techniques such as embeddings/similarity search, active learning, meta-learning, and reinforcement learning are utilized for understanding data. AI databases play a crucial role in meeting the complex querying needs of AI systems, providing flexibility and power to enhance innovation and progress. AI-powered solutions contribute to data management by analyzing access patterns, detecting anomalies, and ensuring compliance with privacy regulations through anonymization or pseudonymization of sensitive data. Like1 Related Posts 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 Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more
