Laws Archives - gettectonic.com - Page 3
Python Alongside Salesforce
23Oct

Python Losing the Crown

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

Read More
October 23, 2024in Data, Laws
AI Risk Management
22Oct

AI Risk Management

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 AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
October 22, 2024in AI Tools, Data, Enterprise, Generative AI, Laws, Technology
NYT Issues Cease-and-Desist Letter to Perplexity AI
16Oct

NYT Issues Cease-and-Desist Letter to Perplexity AI

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

Read More
October 16, 2024in Data, Generative AI, Google, Laws
collaboration between humans and AI
10Oct

Collaboration Between Humans and AI

The Future of AI: What to Expect in the Next 5 Years In the next five years, AI will accelerate human life, reshape behaviors, and transform industries—these changes are inevitable. Collaboration Between Humans and AI. For much of the early 20th century, AI existed mainly in science fiction, where androids, sentient machines, and futuristic societies intrigued fans of the genre. From films like Metropolis to books like I, Robot, AI was the subject of speculative imagination. AI in fiction often over-dramatized reality and caused us to suspend belief in what was and was not possible. But by the mid-20th century, scientists began working to bring AI into reality. A Brief History of AI’s Impact on Society The 1956 Dartmouth Summer Research Project on Artificial Intelligence marked a key turning point, where John McCarthy coined the term “artificial intelligence” and helped establish a community of AI researchers. Although the initial excitement about AI often outpaced its actual capabilities, significant breakthroughs began emerging by the late 20th century. One such moment was IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997, signaling that machines could perform complex cognitive tasks. The rise of big data and Moore’s Law, which fueled the exponential growth of computational power, enabled AI to process vast amounts of information and tackle tasks previously handled only by humans. By 2022, generative AI models like ChatGPT proved that machine learning could yield highly sophisticated and captivating technologies. AI’s influence is now everywhere. No longer is it only discussed in IT circles. AI is being featured in nearly all new products hitting the market. It is part of if not the creation tool of most commercials. Voice assistants like Alexa, recommendation systems used by Netflix, and autonomous vehicles represent just a glimpse of AI’s current role in society. Yet, over the next five years, AI’s development is poised to introduce far more profound societal changes. How AI Will Shape the Future Industries Most Affected by AI Long-term Risks of Collaboration Between Humans and AI AI’s potential to pose existential risks has long been a topic of concern. However, the more realistic danger lies in human societies voluntarily ceding control to AI systems. Algorithmic trading in finance, for example, demonstrates how human decisions are already being replaced by AI’s ability to operate at unimaginable speeds. Still, fear of AI should not overshadow the opportunities it presents. If organizations shy away from AI out of anxiety, they risk missing out on innovations and efficiency gains. The future of AI depends on a balanced approach that embraces its potential while mitigating its risks. In the coming years, the collaboration between humans and AI will drive profound changes across industries, legal frameworks, and societal norms, creating both challenges and opportunities for the future. Tectonic can help you map your AI journey for the best Collaboration Between Humans and AI. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
October 10, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Laws, Salesforce

Where Does AI Fit in Healthcare?

AI in Healthcare: Weighing the Promise Against the Pitfalls The rise of artificial intelligence in healthcare has been meteoric—sparking both enthusiasm and apprehension. From diagnosing diseases faster than human clinicians to parsing mountains of unstructured EHR data, AI’s potential seems limitless. But as adoption accelerates, so do concerns about privacy, ethics, and the risk of over-reliance on machines. Here’s a balanced look at the key benefits and challenges shaping AI’s role in modern medicine. The Case for AI: Efficiency, Insight, and Support 1. Reducing Clinician Burnout 2. Enhancing Diagnostics and Population Health 3. Restoring the Human Touch The Risks: Job Disruption, Bias, and Privacy Threats 1. Workforce Anxiety 2. Data Privacy and Security 3. Ethical Quagmires Navigating the Future: Collaboration Over Conflict The path forward demands guardrails, not gridlock: As National Academy of Medicine warns: “Unanswered questions aren’t a reason to stall—they’re a call to innovate responsibly.” The Bottom Line AI won’t replace doctors, but it will redefine their workflows. The stakes? Better care versus broken trust. Success hinges on balancing three imperatives: “The best healthcare AI doesn’t act alone—it empowers the people who heal.” Key Stats to Watch: Where do you stand? Is AI healthcare’s savior—or its next crisis? Content updated March 2025. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
October 9, 2024in AI Automated Decision Making, AI Tools, AI Voice Assistants, Artificial Intelligence, Data, Generative AI, Laws, Life Sciences Cloud, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Service Cloud, Technology
Promising Patient Engagement Use Cases for GenAI and Chatbots
06Oct

Promising Patient Engagement Use Cases for GenAI and Chatbots

Promising Patient Engagement Use Cases for GenAI and Chatbots Generative AI (GenAI) is showing great potential in enhancing patient engagement by easing the burden on healthcare staff and clinicians while streamlining the overall patient experience. As healthcare undergoes its digital transformation, various patient engagement applications for GenAI and chatbots are emerging as promising tools. Let’s look at Promising Patient Engagement Use Cases for GenAI and Chatbots. Key applications of GenAI and patient-facing chatbots include online symptom checkers, appointment scheduling, patient navigation, medical search engines, and even patient portal messaging. These technologies aim to alleviate staff workloads while improving the patient journey, according to some experts. However, patient-facing AI applications are not without challenges, such as the risk of generating medical misinformation or exacerbating healthcare disparities through biased algorithms. As healthcare professionals explore the potential of GenAI and chatbots for patient engagement, they must also ensure safeguards are in place to prevent the spread of inaccuracies and avoid creating health inequities. Online Symptom Checkers Online symptom checkers allow healthcare organizations to assess patients’ medical concerns without requiring an in-person visit. Patients can input their symptoms, and the AI-powered chatbot will generate a list of possible diagnoses, helping them decide whether to seek urgent care, visit the emergency department, or manage symptoms at home. These tools promise to improve both patient experience and operational efficiency by directing patients to the right care setting, thus reducing unnecessary visits. For healthcare providers, symptom checkers can help triage patients and ensure high-acuity areas are available for those needing critical care. Despite their potential, studies show mixed results regarding the diagnostic accuracy of online symptom checkers. A 2022 literature review found that diagnostic accuracy for these tools ranged from 19% to 37.9%. However, triage accuracy—referring patients to the correct care setting—was better, ranging between 48.9% and 90%. Patient reception to symptom checkers has also been varied. For example, during the COVID-19 pandemic, symptom checkers were designed to help patients assess whether their symptoms were virus-related. While patients appreciated the tools, they preferred chatbots that displayed human-like qualities and competence. Tools perceived as similar in quality to human interactions were favored. Furthermore, some studies indicate that online symptom checkers could deepen health inequities, as users tend to be younger, female, and more digitally literate. To mitigate this, AI developers must create chatbots that can communicate in multiple languages, mimic human interaction, and easily escalate issues to human professionals when needed. Self-Scheduling and Patient Navigation GenAI and conversational AI are proving valuable in addressing routine patient queries, like appointment scheduling and patient navigation, tasks that typically fall on healthcare staff. With a strained medical workforce, using AI for lower-level inquiries allows clinicians to focus on more complex tasks. AI-enhanced appointment scheduling systems, for example, not only help patients book visits but also answer logistical questions like parking directions or department locations within a clinic. A December 2023 literature review highlighted that AI-optimized scheduling could reduce provider workload, increase patient satisfaction, and make healthcare more patient-centered. However, key considerations for AI integration include ensuring health equity, broadband access, and patient trust. While AI can manage routine requests, healthcare organizations need to ensure their tools are accessible and functional for diverse populations. Online Medical Research GenAI tools like ChatGPT are contributing to the “Dr. Google” phenomenon, where patients search online for medical information before seeing a healthcare provider. While some clinicians have been cautious about these tools, research suggests they can effectively provide accurate medical information. For instance, an April 2023 study showed that ChatGPT answered 88% of breast cancer screening questions correctly. Another study in May 2023 demonstrated that the tool could adequately educate patients on colonoscopy preparation. In both cases, the information was presented in an easy-to-understand format, essential for improving health literacy. However, GenAI is not without flaws. Patients express concern about the reliability of AI-generated information, with a 2023 Wolters Kluwer survey showing that 49% of respondents worry about false information from GenAI. Additionally, many are uneasy about the unknown sources and validation processes behind the information. To build patient trust, AI developers must ensure the accuracy of their source material and provide supplementary authoritative resources like patient education materials. Patient Portal Messaging and Provider Communication Generative AI has also found use in patient portal messaging, where it can draft responses on behalf of healthcare providers. This feature has the potential to reduce clinician burnout by handling routine inquiries. A study conducted at Mass General Brigham in April 2024 revealed that a large language model embedded in a secure messaging tool could generate acceptable responses to patient questions. In 58% of cases, chatbot-generated messages required human editing. Promising Patient Engagement Use Cases for GenAI and Chatbots Interestingly, other research has found that AI-generated responses in patient portals are often more empathetic than those written by overworked healthcare providers. Nevertheless, AI responses should always be reviewed by a clinician to ensure accuracy before being sent to patients. Generative AI is also making strides in clinical decision support and ambient documentation, further boosting healthcare efficiency. However, as healthcare organizations adopt these technologies, they must address concerns around algorithmic bias and ensure patient safety remains a top priority. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
October 6, 2024in Agentforce Platform, AI Tools, Generative AI, Google, Laws, Life Sciences Cloud, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud
New Technology Risks
27Sep

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 AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
September 27, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Laws, Salesforce, Technology
A Company in Transition
26Sep

A Company in Transition

OpenAI Restructures: Increased Flexibility, But Raises Concerns OpenAI’s decision to restructure into a for-profit entity offers more freedom for the company and its investors but raises questions about its commitment to ethical AI development. Founded in 2015 as a nonprofit, OpenAI transitioned to a hybrid model in 2019 with the creation of a for-profit subsidiary. Now, its restructuring, widely reported this week, signals a shift where the nonprofit arm will no longer influence the day-to-day operations of the for-profit side. CEO Sam Altman is set to receive equity in the newly restructured company, which will operate as a benefit corporation (B Corp), similar to competitors like Anthropic and Sama. A Company in Transition This move comes on the heels of a turbulent year. OpenAI’s board initially voted to remove Altman over concerns about transparency, but later rehired him after significant backlash and the resignation of several board members. The company has seen a number of high-profile departures since, including co-founder Ilya Sutskever, who left in May to start Safe Superintelligence (SSI), an AI safety-focused venture that recently secured $1 billion in funding. This week, CTO Mira Murati, along with key research leaders Bob McGrew and Barret Zoph, also announced their departures. OpenAI’s restructuring also coincides with an anticipated multi-billion-dollar investment round involving major players such as Nvidia, Apple, and Microsoft, potentially pushing the company’s valuation to as high as $150 billion. Complex But Expected Move According to Michael Bennett, AI policy advisor at Northeastern University, the restructuring isn’t surprising given OpenAI’s rapid growth and increasingly complex structure. “Considering OpenAI’s valuation, it’s understandable that the company would simplify its governance to better align with investor priorities,” said Bennett. The transition to a benefit corporation signals a shift towards prioritizing shareholder interests, but it also raises concerns about whether OpenAI will maintain its ethical obligations. “By moving away from its nonprofit roots, OpenAI may scale back its commitment to ethical AI,” Bennett noted. Ethical and Safety Concerns OpenAI has faced scrutiny over its rapid deployment of generative AI models, including its release of ChatGPT in November 2022. Critics, including Elon Musk, have accused the company of failing to be transparent about the data and methods it uses to train its models. Musk, a co-founder of OpenAI, even filed a lawsuit alleging breach of contract. Concerns persist that the restructuring could lead to less ethical oversight, particularly in preventing issues like biased outputs, hallucinations, and broader societal harm from AI. Despite the potential risks, Bennett acknowledged that the company would have greater operational freedom. “They will likely move faster and with greater focus on what benefits their shareholders,” he said. This could come at the expense of the ethical commitments OpenAI previously emphasized when it was a nonprofit. Governance and Regulation Some industry voices, however, argue that OpenAI’s structure shouldn’t dictate its commitment to ethical AI. Veera Siivonen, co-founder and chief commercial officer of AI governance vendor Saidot, emphasized the role of regulation in ensuring responsible AI development. “Major players like Anthropic, Cohere, and tech giants such as Google and Meta are all for-profit entities,” Siivonen said. “It’s unfair to expect OpenAI to operate under a nonprofit model when others in the industry aren’t bound by the same restrictions.” Siivonen also pointed to OpenAI’s participation in global AI governance initiatives. The company recently signed the European Union AI Pact, a voluntary agreement to adhere to the principles of the EU’s AI Act, signaling its commitment to safety and ethics. Challenges for Enterprises The restructuring raises potential concerns for enterprises relying on OpenAI’s technology, said Dion Hinchcliffe, an analyst with Futurum Group. OpenAI may be able to innovate faster under its new structure, but the reduced influence of nonprofit oversight could make some companies question the vendor’s long-term commitment to safety. Hinchcliffe noted that the departure of key staff could signal a shift away from prioritizing AI safety, potentially prompting enterprises to reconsider their trust in OpenAI. New Developments Amid Restructuring Despite the ongoing changes, OpenAI continues to roll out new technologies. The company recently introduced a new moderation model, “omni-moderation-latest,” built on GPT-4o. This model, available through the Moderation API, enables developers to flag harmful content in both text and image outputs. A Company in Transition As OpenAI navigates its restructuring, balancing rapid innovation with maintaining ethical standards will be crucial to sustaining enterprise trust and market leadership. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
September 26, 2024in Data, Enterprise, Generative AI, Google, Laws, Technology
GitHub Copilot Autofix
14Aug

GitHub Copilot Autofix

On Wednesday, GitHub announced the general availability of Copilot Autofix, an AI-driven tool designed to identify and remediate software vulnerabilities. Originally unveiled in March and tested in public beta, Copilot Autofix integrates GitHub’s CodeQL scanning engine with GPT-4, heuristics, and Copilot APIs to generate code suggestions for developers. The tool provides prompts based on CodeQL analysis and code snippets, allowing users to accept, edit, or reject the suggestions. In a blog post, Mike Hanley, GitHub’s Chief Security Officer and Senior Vice President of Engineering, highlighted the challenges developers and security teams face in addressing existing vulnerabilities. “Code scanning tools can find vulnerabilities, but the real issue is remediation, which requires security expertise and time—both of which are in short supply,” Hanley noted. “The problem isn’t finding vulnerabilities; it’s fixing them.” According to GitHub, the private beta of Copilot Autofix showed that users could respond to a CodeQL alert and automatically remediate a vulnerability in a pull request in just 28 minutes on average, compared to 90 minutes for manual remediation. The tool was even faster for common vulnerabilities like cross-site scripting, with remediation times averaging 22 minutes compared to three hours manually, and SQL injection flaws, which were fixed in 18 minutes on average versus almost four hours manually. Hanley likened the efficiency of Copilot Autofix in fixing vulnerabilities to the speed at which GitHub Copilot, their generative AI coding assistant released in 2022, produces code for developers. However, there have been concerns that GitHub Copilot and similar AI coding assistants could replicate existing vulnerabilities in the codebases they help generate. Industry analyst Katie Norton from IDC noted that while the replication of vulnerabilities is concerning, the rapid pace at which AI coding assistants generate new software could pose a more significant security issue. Chris Wysopal, CTO and co-founder of Veracode, echoed this concern, pointing out that faster coding speeds have led to more software being produced and a larger backlog of vulnerabilities for developers to manage. Norton also emphasized that AI-powered tools like Copilot Autofix could help alleviate the burden on developers by reducing these backlogs and enabling them to fix vulnerabilities without needing to be security experts. Other vendors, including Mobb and Snyk, have also developed AI-powered autoremediation tools. Initially supporting JavaScript, TypeScript, Java, and Python during its public beta, Copilot Autofix now also supports C#, C/C++, Go, Kotlin, Swift, and Ruby. Hanley also highlighted that Copilot Autofix would benefit the open-source software community. GitHub has previously provided open-source maintainers with free access to enterprise security tools for code scanning, secret scanning, and dependency management. Starting in September, Copilot Autofix will also be made available for free to these maintainers. “As the global home of the open-source community, GitHub is uniquely positioned to help maintainers detect and remediate vulnerabilities, making open-source software safer and more reliable for everyone,” Hanley said. Copilot Autofix is now available to all GitHub customers globally. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
August 14, 2024in Data, Enterprise, Generative AI, Laws
Generative AI Replaces Legacy Systems
29Jul

Securing AI for Efficiency and Building Customer Trust

As businesses increasingly adopt AI to enhance automation, decision-making, customer support, and growth, they face crucial security and privacy considerations. The Salesforce Platform, with its integrated Einstein Trust Layer, enables organizations to leverage AI securely by ensuring robust data protection, privacy compliance, transparent AI functionality, strict access controls, and detailed audit trails. Why Secure AI Workflows Matter AI technology empowers systems to mimic human-like behaviors, such as learning and problem-solving, through advanced algorithms and large datasets that leverage machine learning. As the volume of data grows, securing sensitive information used in AI systems becomes more challenging. A recent Salesforce study found that 68% of Analytics and IT teams expect data volumes to increase over the next 12 months, underscoring the need for secure AI implementations. AI for Business: Predictive and Generative Models In business, AI depends on trusted data to provide actionable recommendations. Two primary types of AI models support various business functions: Addressing Key LLM Risks Salesforce’s Einstein Trust Layer addresses common risks associated with large language models (LLMs) and offers guidance for secure Generative AI deployment. This includes ensuring data security, managing access, and maintaining transparency and accountability in AI-driven decisions. Leveraging AI to Boost Efficiency Businesses gain a competitive edge with AI by improving efficiency and customer experience through: Four Strategies for Secure AI Implementation To ensure data protection in AI workflows, businesses should consider: The Einstein Trust Layer: Protecting AI-Driven Data The Einstein Trust Layer in Salesforce safeguards generative AI data by providing: Salesforce’s Einstein Trust Layer addresses the security and privacy challenges of adopting AI in business, offering reliable data security, privacy protection, transparent AI operations, and robust access controls. Through this secure approach, businesses can maximize AI benefits while safeguarding customer trust and meeting compliance requirements. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
July 29, 2024in Data, Generative AI, Laws, Salesforce, Technology
Building Trust in AI-Powered CRM
13Jun

Building Trust in AI-Powered CRM

Building Trust in AI-Powered CRM: The Key to Successful Adoption Why Education and Trust Matter in AI-Driven CRM To maximize the benefits of AI-powered CRM systems, organizations must focus on two critical pillars: Despite growing AI adoption, Forrester’s research reveals significant gaps: 1. The AI Knowledge Gap 2. Trust Barriers in Generative AI Top concerns delaying genAI adoption:🔒 Security risks – Fear of leaking customer PII or confidential data⚖️ Compliance risks – Potential violations of GDPR, CCPA, or copyright laws✍️ Output reliability – AI-generated content may be misleading or inaccurate “Deploying unchecked AI responses can damage brand credibility. Human oversight remains essential.” 3. AI-Powered CRM: Security as a Competitive Advantage Businesses prioritize vendors who proactively address: How to Build AI Confidence in Your Organization For Employees ✔ Role-based AI training – Tailor education to sales, service, and marketing teams✔ Hands-on sandbox environments – Let teams test AI tools risk-free✔ Clear guidelines – Define approved vs. restricted AI use cases For Customers ✔ Transparent data policies – Explain how AI improves their experience✔ Opt-in controls – Let users manage data-sharing preferences✔ Human-AI collaboration – Ensure sensitive interactions always have human review The Path Forward While AI-powered CRM adoption continues rising, trust remains the differentiator. Companies that:✅ Invest in AI education✅ Prioritize responsible data practices✅ Choose vendors with robust security …will gain a competitive edge—turning AI skepticism into customer and employee confidence. Ready to implement AI-powered CRM the right way? Let Tectonic lead the way! Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
June 13, 2024in AI Tools, Data, Generative AI, Laws
Technical Debt
01Jun

Understanding and Managing Technical Debt in Salesforce

Salesforce is a powerful and dynamic CRM platform with a vast array of tools and features. Given its complexity, users must make critical decisions daily—whether creating custom objects, automating workflows, nurturing leads, or developing applications. Each choice impacts how effectively Salesforce is utilized, influencing both short-term success and long-term sustainability. However, users often opt for the quickest solution rather than the most robust one. While this may provide immediate results, it can lead to inefficiencies and challenges over time. This is where technical debt comes into play. What Is Technical Debt in Salesforce? Technical debt refers to the hidden cost an organization incurs when prioritizing speed over quality in software development and system configuration. It results from taking shortcuts that may seem convenient at first but ultimately require additional work—often in the form of rework, maintenance, or system inefficiencies. A Real-World Analogy Imagine you’re on a trek and encounter two paths leading to the same destination. The shorter route is steep and exhausting, while the longer path includes rest stops and is easier on your body. Although the shorter path may seem efficient, it leaves you drained. Similarly, in Salesforce, quick fixes—such as writing redundant code, skipping documentation, or excessive customization—may seem efficient initially but create long-term complications, leading to technical debt. Common Causes of Technical Debt in Salesforce Types of Technical Debt in Salesforce Identifying and Measuring Technical Debt To assess technical debt, consider both business-related and technical questions: Business-Related Questions Technical Questions How to Avoid Technical Debt in Salesforce Final Thoughts Technical debt is an inevitable challenge in any complex system, but with proactive planning and best practices, it can be minimized. The key is to prioritize sustainability over speed—choosing well-structured, scalable solutions rather than quick fixes that may lead to costly rework in the future. By maintaining best practices, regular system reviews, and strategic planning, organizations can optimize their Salesforce environment for efficiency, scalability, and long-term success. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
June 1, 2024in Data, Laws, Salesforce
Data Management and Data Maturity
12Jan

DAMS for Lawyers

Streamlining Legal Document Management with Digital Asset Management (DAM) Systems Legal teams deal with vast amounts of digital documents, including contracts, agreements, and supporting files. Managing this volume of information can be overwhelming, even for the most organized teams. A Digital Asset Management (DAM) system simplifies the process, enabling your team to easily find, access, and collaborate on legal documents. What is DAM for Legal Teams? A DAM system is a centralized platform designed to organize, store, and retrieve digital assets such as documents, images, and videos. For legal teams, it ensures that critical information is securely managed, easily accessible, and shareable among team members, regardless of their location. DAM systems use metadata to catalog digital assets, making them searchable by attributes like document type, date, case relevance, or rights management information. By implementing a DAM system, legal teams can improve efficiency, ensure compliance with legal standards, and better manage intellectual property rights. With robust access controls and versioning features, sensitive legal information remains secure and up-to-date, reducing time spent on administrative tasks and allowing teams to focus on client needs and case strategies. Challenges in Managing Sensitive Legal Documents Traditional document management methods often fall short in protecting confidential information, leaving data vulnerable to unauthorized access, loss, or breaches. Physical storage poses risks like damage or misplacement, while unencrypted digital files can become targets for cyberattacks. Legal teams must also comply with strict regulations governing data privacy, records retention, and information sharing. Non-compliance can result in hefty fines, legal sanctions, and reputational damage. A DAM system addresses these challenges by safeguarding sensitive documents and ensuring compliance with regulatory requirements. Benefits of DAM for Legal Teams Key Features to Look for in a DAM Solution When selecting a DAM system for your legal team, prioritize the following features: Best Practices for Secure Legal Document Management To maximize the effectiveness of your DAM system, adopt these best practices: Frequently Asked Questions 1. What are the risks of not using a DAM system for legal document management?Without a DAM system, legal teams risk unauthorized access, data loss, and non-compliance with regulatory standards, potentially leading to fines and reputational damage. 2. How does a DAM system help legal teams comply with data privacy laws?DAM systems include features like encryption, access controls, and audit trails, which are essential for meeting data privacy regulations such as GDPR or HIPAA. 3. Why is encryption important in a DAM system?Encryption ensures that sensitive data is unreadable to unauthorized users, even if intercepted. It should be applied both during data transmission and while at rest. 4. How can legal teams train staff to use DAM systems effectively?Training programs should cover cyber hygiene, security protocols, and role-specific compliance requirements. Regular updates and hands-on practice can help staff become proficient in using the system securely. Conclusion For legal teams, managing sensitive documents securely and efficiently is critical. A Digital Asset Management (DAM) system provides the tools needed to organize, protect, and share legal documents while ensuring compliance with regulatory standards. By implementing a DAM solution and following best practices, legal teams can enhance security, streamline operations, and focus on delivering exceptional client service. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
January 12, 2024in Data, Laws
Government-Citizen Communication
05Nov

Government-Citizen Communication

Engaging Citizens and Influencing Behavior: A Public Sector Strategy Engaging citizens and influencing their behavior to achieve mission-critical outcomes follows a model similar to the traditional marketing funnel used in the private sector. By adapting this approach, government communicators can drive tangible results that contribute to the overall well-being of society. Government-Citizen Communication. Public Sector Communication Objectives: In today’s digital age, citizens expect timely, personalized communication. To meet this demand, government agencies must deliver the right message through the right channels at the right time. A failure to do so risks reduced engagement, which can negatively affect the success of public programs. Expanding Audience Reach To maximize citizen engagement, it’s crucial to focus on reaching a broader audience rather than narrowing it. A key question for communicators and their teams to ask is: “How broad is our audience?” This is an essential aspect of the funnel that ensures wider reach and greater impact. Communication Methods Public sector communication often utilizes a mix of channels, including radio, newspapers, television, and social media, to connect with the public. Collaboration is vital in this sector, requiring effective communication tools to coordinate across teams, departments, and agencies. As technology evolves, new tools are enhancing how public servants communicate and collaborate. Technology-Driven Collaboration Tools Several communication and collaboration tools are reshaping how the public sector operates: Best Practices for Government-Citizen Communication To foster effective engagement, government agencies should implement the following best practices: Secure, Customizable Citizen Communication Solutions Governments can benefit from a secure, open-source communication tool tailored to public sector needs. Such solutions ensure compliance with data protection laws and foster trust between citizens and government institutions, enhancing public service delivery and digital engagement. Tectonic’s Conclusion For optimal citizen engagement, government communicators must focus on expanding their audience reach and utilizing advanced communication tools. In doing so, they can enhance collaboration, drive citizen involvement, and ensure the success of critical public programs. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

Read More
November 5, 2023in Data, Google, Laws, Public Sector Solutions, Technology

Subscribe to our mailing list. Join our mail list to receive our newsletter

We'll keep you up to date with our latest news, insights, free resources, and much more.
Don't worry, we won't spam you.
gettectonic.com