Technology Archives - gettectonic.com - Page 10
Open AI Update
23Sep

Open AI Update

OpenAI has established itself as a leading force in the generative AI space, with its ChatGPT being one of the most widely recognized AI tools. Powered by the GPT series of large language models (LLMs), as of September 2024, ChatGPT primarily uses GPT-4o and GPT-3.5. This insight provides an Open AI Update. In August and September 2024, rumors circulated about a new model from OpenAI, codenamed “Strawberry.” Initially, it was unclear if this model would be a successor to GPT-4o or something entirely different. On September 12, 2024, the mystery was resolved with the official launch of OpenAI’s o1 models, including o1-preview and o1-mini. What is OpenAI o1? OpenAI o1 is a new family of LLMs optimized for advanced reasoning tasks. Unlike earlier models, o1 is designed to improve problem-solving by reasoning through queries rather than just generating quick responses. This deeper processing aims to produce more accurate answers to complex questions, particularly in fields like STEM (science, technology, engineering, and mathematics). The o1 models, currently available in preview form, are intended to provide a new type of LLM experience beyond what GPT-4o offers. Like all OpenAI LLMs, the o1 series is built on transformer architecture and can be used for tasks such as content summarization, new content generation, question answering, and writing code. Key Features of OpenAI o1 The standout feature of the o1 models is their ability to engage in multistep reasoning. By adopting a “chain-of-thought” approach, o1 models break down complex problems and reason through them iteratively. This makes them particularly adept at handling intricate queries that require a more thoughtful response. The initial September 2024 launch included two models: Use Cases for OpenAI o1 The o1 models can perform many of the same functions as GPT-4o, such as answering questions, summarizing content, and generating text. However, they are particularly suited for tasks that benefit from enhanced reasoning, including: Availability and Access The o1-preview and o1-mini models are available to users of ChatGPT Plus and Team as of September 12, 2024. OpenAI plans to extend access to ChatGPT Enterprise and Education users starting September 19, 2024. While free ChatGPT users do not have access to these models at launch, OpenAI intends to introduce o1-mini to free users in the future. Developers can also access the models through OpenAI’s API, and third-party platforms such as Microsoft Azure AI Studio and GitHub Models offer integration. Limitations of OpenAI o1 As preview models, o1 comes with certain limitations: Enhancing Safety with OpenAI o1 To ensure safety, OpenAI released a System Card that outlines how the o1 models were evaluated for risks like cybersecurity threats, persuasion, and model autonomy. The o1 models improve safety through: GPT-4o vs. OpenAI o1 Here’s a quick comparison between GPT-4o and OpenAI’s new o1 models: Feature GPT-4o o1 Models Release Date May 13, 2024 Sept. 12, 2024 Model Variants Single model Two variants: o1-preview and o1-mini Reasoning Capabilities Good Enhanced, especially for STEM fields Mathematics Olympiad Score 13% 83% Context Window 128K tokens 128K tokens Speed Faster Slower due to in-depth reasoning Cost (per million tokens) Input: $5; Output: $15 o1-preview: $15 input, $60 output; o1-mini: $3 input, $12 output Safety and Alignment Standard Enhanced safety, better jailbreak resistance OpenAI’s o1 models bring a new level of reasoning and accuracy, making them a promising advancement in generative AI. 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 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

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September 23, 2024in AI Tools, Data, Generative AI, Technology
Salesforce Success Story
23Sep

Case Study: Salesforce Advanced Forcasting and Streamline Operations Yields Big Change and Bigger Results

Case Study: Salesforce Advanced Forcsting and Streamline Operations Yields Big Change and Bigger Results

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September 23, 2024in Data, Enterprise, Google, Mulesoft, Public Sector Solutions, Salesforce, Salesforce Einstein, Salesforce Experience Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Marketing Cloud, Salesforce Success Stories, Technology
How to Achieve AI Democratization
22Sep

How to Achieve AI Democratization

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

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September 22, 2024in Salesforce
Is Agentforce Different?
22Sep

Is Agentforce Different?

The Salesforce hype machine is in full swing, with product announcements like Chatter, Einstein GPT, and Data Cloud, all positioned as revolutionary tools that promise to transform how we work. Is Agentforce Different? However, it’s often difficult to separate fact from fiction in the world of Salesforce. The cloud giant thrives on staying ahead of technological advancements, which means reinventing itself every year with new releases and updates. You could even say three times per year with the major releases. Why Enterprises Need Multiple Salesforce Orgs Over the past decade, Salesforce product launches have been hit or miss—primarily miss. Offerings like IoT Cloud, Work.com, and NFT Cloud have faded into obscurity. This contrasts sharply with Salesforce’s earlier successes, such as Service Cloud, the AppExchange, Force.com, Salesforce Lightning, and Chatter, which defined its first decade in business. One notable exception is Data Cloud. This product has seen significant success and now serves as the cornerstone of Salesforce’s future AI and data strategy. With Salesforce’s growth slowing quarter over quarter, the company must find new avenues to generate substantial revenue. Artificial Intelligence seems to be their best shot at reclaiming a leadership position in the next technological wave. Is Agentforce Different? While Salesforce has been an AI leader for over a decade, the hype surrounding last year’s Dreamforce announcements didn’t deliver the growth the company was hoping for. The Einstein Copilot Studio—comprising Copilot, Prompt Builder, and Model Builder—hasn’t fully lived up to expectations. This can be attributed to a lack of AI readiness among enterprises, the relatively basic capabilities of large language models (LLMs), and the absence of fully developed use cases. In Salesforce’s keynote, it was revealed that over 82 billion flows are launched weekly, compared to just 122,000 prompts executed. While Flow has been around for years, this stat highlights that the use of AI-powered prompts is still far from mainstream—less than one prompt per Salesforce customer per week, on average. When ChatGPT launched at the end of 2022, many predicted the dawn of a new AI era, expecting a swift and dramatic transformation of the workplace. Two years later, it’s clear that AI’s impact has yet to fully materialize, especially when it comes to influencing global productivity and GDP. However, Salesforce’s latest release feels different. While AI Agents may seem new to many, this concept has been discussed in AI circles for decades. Marc Benioff’s recent statements during Dreamforce reflect a shift in strategy, including a direct critique of Microsoft’s Copilot product, signaling the intensifying AI competition. This year’s marketing strategy around Agentforce feels like it could be the transformative shift we’ve been waiting for. While tools like Salesforce Copilot will continue to evolve, agents capable of handling service cases, answering customer questions, and booking sales meetings instantly promise immediate ROI for organizations. Is the Future of Salesforce in the Hands of Agents? Despite the excitement, many questions remain. Are Salesforce customers ready for agents? Can organizations implement this technology effectively? Is Agentforce a real breakthrough or just another overhyped concept? Agentforce may not be vaporware. Reports suggest that its development was influenced by Salesforce’s acquisition of Airkit.AI, a platform that claims to resolve 90% of customer queries. Salesforce has even set up dedicated launchpads at Dreamforce to help customers start building their own agents. Yet concerns remain, especially regarding Salesforce’s complexity, technical debt, and platform sprawl. These issues, highlighted in this year’s Salesforce developer report, cannot be overlooked. Still, it’s hard to ignore Salesforce’s strategic genius. The platform has matured to the point where it offers nearly every functionality an organization could need, though at times the components feel a bit disconnected. For instance: Salesforce is even hinting at usage-based pricing, with a potential $2 charge per conversation—an innovation that could reshape their pricing model. Will Agents Be Salesforce’s Key to Future Growth? With so many unknowns, only time will tell if agents will be the breakthrough Salesforce needs to regain the momentum of its first two decades. Regardless, agents appear to be central to the future of AI. Leading organizations like Copado are also launching their own agents, signaling that this trend will define the next phase of AI innovation. In today’s macroeconomic environment, where companies are overstretched and workforce demands are high, AI’s ability to streamline operations and improve customer service has never been more critical. Whoever cracks customer service AI first could lead the charge in the inevitable AI spending boom. We’re all waiting to see if Salesforce has truly cracked the AI code. But one thing is certain: the race to dominate AI in customer service has begun. And Salsesforce may be at the forefront. 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

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September 22, 2024in Agentforce Platform, Artificial Intelligence, Data, Enterprise, Google, Salesforce, Snowflake, Technology
State Loan Processing Software by Salesforce
21Sep

State Loan Processing Software by Salesforce

State Loan Processing Software: A Salesforce-Powered Solution Introduction In today’s fast-paced financial environment, efficient loan management is critical for lending institutions to succeed. Traditional loan processing methods are often inefficient, prone to errors, and unable to meet the demands of modern financial services. These outdated techniques lead to delays, compliance issues, and lost revenue. The answer lies in adopting advanced loan management software that leverages technology to streamline processes and enhance customer experiences. Current Challenges Many lenders continue to rely on outdated tools like spreadsheets and manual workflows, hindering productivity and increasing the potential for human error. A study by the National Association of Federal Credit Unions found that 60% of credit unions reported inefficiencies in their loan processes, negatively impacting member satisfaction. Key challenges faced by lending institutions include: Types of Loan Management Software To address these challenges, a variety of loan management software solutions have emerged, each designed to optimize specific aspects of the lending process. Loan Management Software Description: Automates essential loan processes like origination and payment processing. Main Features: Customer Relationship Management (CRM) Software Description: Platforms like Salesforce enable lenders to efficiently manage borrower relationships. Main Features: Compliance Management Software-State Loan Processing Software by Salesforce Description: Ensures lending practices adhere to state and federal regulations. Main Features: Analytics and Reporting Tools Description: Offers data-driven insights to guide strategic decision-making. Main Features: Integrated Payment Solutions Description: Streamlines payment processing across various channels. Main Features: Final Thoughts Adopting modern loan management software brings a host of advantages, including enhanced efficiency, improved compliance, and higher customer satisfaction. Platforms like Salesforce enable lenders to revolutionize their loan processing and management, making their operations more competitive in an evolving market. For lenders seeking to transform their approach to loan management, innovative solutions like Salesforce and Tectonic offer a path to operational excellence and business growth. 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

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September 21, 2024in Salesforce Financial Services Solutions, Salesforce Nonprofit Cloud, Salesforce Optimization
Agentforce and Thinking AI
21Sep

Agentforce and Thinking AI

Agentforce is how humans with AI drive customer success together, equips organizations with autonomous agents that boost scale, efficiency, and satisfaction across service, sales, marketing, commerce, and more New Agentforce Atlas Reasoning Engine autonomously analyzes data, makes decisions, and completes tasks, providing reliable and accurate results With Agentforce, any organization can build, customize, and deploy their own agents quickly and easily, with low-code tools New Agentforce Partner Network allows customers to deploy pre-built agents and use agent actions from partners like Amazon Web Services, Google, IBM, Workday, and more Customers like OpenTable, Saks, and Wiley are turning to Agentforce because it is integrated with their apps, works across customer channels, augments their employees, and scales capacity for business needs SAN FRANCISCO — September 12, 2024 – Salesforce (NYSE: CRM), the world’s #1 AI CRM, today unveiled Agentforce, a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction. Agentforce enables companies to scale their workforces on demand with a few clicks. Agentforce’s limitless digital workforce of AI agents can analyze data, make decisions, and take action on tasks like answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. With Agentforce, any organization can easily build, customize, and deploy their own agents for any use case across any industry. The future of AI is agents, and it’s here. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” MARC BENIOFF, CHAIR, CEO & CO-FOUNDER, SALESFORCE “Agentforce represents the Third Wave of AI—advancing beyond copilots to a new era of highly accurate, low-hallucination intelligent agents that actively drive customer success. Unlike other platforms, Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint,” said Marc Benioff, Chair and CEO, Salesforce. “While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability. With advanced security features, compliance with industry standards, and unmatched flexibility. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” In contrast to now-outdated copilots and chatbots that rely on human requests and struggle with complex or multi-step tasks, Agentforce offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention. Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organizations’ customized guardrails, ensuring every customer interaction is informed, relevant, and valuable. And when desired, Agentforce seamlessly hands off to human employees with a summary of the interaction, an overview of the customer’s details, and recommendations for what to do next. Industry leaders like OpenTable, Saks, and Wiley are already experiencing the transformative power of Agentforce. For example, Agentforce is helping organizations like Wiley provide customers with dynamic, conversational self-service. Agentforce is configured to answer questions using Wiley’s knowledge base already built into Salesforce so it can automatically resolve account access. It also triages registration and payment issues, directing customers to the appropriate resources. With Agentforce handling routine inquiries, Wiley has seen an over 40% increase in case resolution, outperforming their old chatbot and giving their human agents more time to focus on complex cases. Why it Matters An estimated 41% of employee time is spent on repetitive, low-impact work, and 65% of desk workers believe generative AI will allow them to be more strategic, according to the Salesforce Trends in AI Report. Every company has more jobs to be done than the resources available to do them. As a result, many jobs go unaddressed or uncompleted. Agentforce provides relief to overstretched teams with its ability to scale capacity on demand so humans can focus on higher-touch, higher-value, and more strategic outcomes. The future of work is a hybrid workforce composed of humans with agents, enabling companies to compete in an ever-changing world. Supporting Customer Quotes “Piloting Agentforce has made a noticeable difference during one of our busiest periods — back-to-school season. It’s been exciting to go live with our first agent thanks to the no-code builder, and we’ve seen a more than 40% increase in case resolution, outperforming our old bot. Agentforce helps to manage routine responsibilities and free up our service teams for more complex cases.” – Kevin Quigley, Senior Manager, Continuous Improvement, Wiley “Every interaction that restaurants and diners have with our support team must be accurate, fast, and reflective of the hospitality that restaurants show their guests. Agentforce has incredible potential to help us deliver that high touch attentiveness and support while significantly freeing up our team to address more complex needs.” – George Pokorny, SVP Customer Success, OpenTable “As we advance our personalization strategy, we believe Agentforce and its AI-powered capabilities have the potential to make a real impact on our approach to customer engagement, raising the bar in luxury retail. Agentforce will improve our effectiveness across customer touchpoints, empowering our employees and augmenting their ability to deliver the elevated and more individualized shopping experiences for which Saks is known.” – Mike Hite, Chief Technology Officer, Saks Global 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

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September 21, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology
Embedded Salesforce Einstein
21Sep

Embedded Salesforce Einstein

In a world where data is everything, businesses are constantly seeking ways to better understand their customers, streamline operations, and make smarter decisions. Enter Salesforce Einstein—a powerful AI solution embedded within the Salesforce platform that is revolutionizing how companies operate, regardless of size. By leveraging advanced analytics, automation, and machine learning, Einstein helps businesses boost efficiency, drive innovation, and deliver exceptional customer experiences. Embedded Salesforce Einstein is the answer. Here’s how Salesforce Einstein is transforming business: Imagine anticipating customer needs, market trends, or operational challenges before they happen. While it’s not magic, Salesforce Einstein’s AI-powered insights and predictions come remarkably close. By transforming vast amounts of data into actionable insights, Einstein enables businesses to anticipate future scenarios and make well-informed decisions. Industry insight: In financial services, success hinges on anticipating market shifts and client needs. Banks and investment firms leverage Einstein to analyze historical market data and client behavior, predicting which financial products will resonate next. For example, investment advisors might receive AI-driven recommendations tailored to individual clients, boosting engagement and satisfaction. Manufacturers also benefit from Einstein’s predictive maintenance tools, which analyze data from machinery to anticipate equipment failures. A car manufacturer, for instance, could use these insights to schedule maintenance during off-peak hours, minimizing downtime and preventing costly disruptions. Personalization is now a necessity. Salesforce Einstein elevates personalization by analyzing customer data to offer tailored recommendations, messages, and services. Industry insight: In e-commerce, personalized recommendations are often the key to converting browsers into loyal customers. An online bookstore using Einstein might analyze browsing history and past purchases to suggest new releases in genres the customer loves, driving repeat sales. In healthcare, Einstein’s personalization can improve patient outcomes by providing customized follow-up care. Hospitals can use Einstein to analyze patient histories and treatment data, offering reminders tailored to each patient’s needs, improving adherence to care plans and speeding recovery. Salesforce Einstein’s sales intelligence tools, such as Lead Scoring and Opportunity Insights, enable sales teams to focus on the most promising leads. This targeted approach drives higher conversion rates and more efficient sales processes. Industry insight: In real estate, Einstein helps agents manage numerous leads by scoring potential buyers based on their engagement with property listings. A buyer who repeatedly views homes in a specific area is flagged, prompting agents to prioritize their outreach, accelerating the sales process. In the automotive industry, Einstein identifies leads closer to purchasing by analyzing behaviors such as online vehicle configuration and test drive bookings. This allows sales teams to focus on high-potential buyers, closing deals faster. Automation is at the heart of Salesforce Einstein’s ability to streamline processes and boost productivity. By automating repetitive tasks like data entry and customer inquiries, Einstein frees employees to focus on strategic activities, improving overall efficiency. Industry insight: In insurance, Einstein Bots can handle routine tasks like policy inquiries and claim submissions, freeing up human agents for more complex issues. This leads to faster response times and reduced operational costs. In banking, Einstein-powered chatbots manage routine inquiries such as balance checks or transaction histories. By automating these interactions, banks reduce the workload on call centers, allowing agents to provide more personalized financial advice. Einstein Discovery democratizes data analytics, making it easier for non-technical users to explore data and uncover actionable insights. This tool identifies key business drivers and provides recommendations, making data accessible for all. Industry insight: In healthcare, predictive insights are helping providers identify patients at risk of chronic conditions like diabetes. With Einstein Discovery, healthcare providers can flag at-risk individuals early, implementing targeted care plans that improve outcomes and reduce long-term costs. For energy companies, Einstein Discovery analyzes data from sensors and weather patterns to predict equipment failures and optimize resource management. A utility company might use these insights to schedule preventive maintenance ahead of storms, reducing outages and enhancing service reliability. More Than a Tool – Embedded Salesforce Einstein Salesforce Einstein is more than just an AI tool—it’s a transformative force enabling businesses to unlock the full potential of their data. From predicting trends and personalizing customer experiences to automating tasks and democratizing insights, Einstein equips companies to make smarter decisions and enhance performance across industries. Whether in retail, healthcare, or technology, Einstein delivers the tools needed to thrive in today’s competitive landscape. Tectonic empowers organizations with Salesforce solutions that drive organizational excellence. Contact Tectonic today. 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

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September 21, 2024in Salesforce Einstein
Data Governance Frameworks
21Sep

Data Governance Frameworks

Examples of Data Governance Frameworks Data governance is not a one-size-fits-all approach. Organizations must carefully choose a framework that aligns with their unique goals, structure, and culture. Data is one of an organization’s most valuable assets, and proper governance is key to unlocking its potential. Without a well-designed framework, companies risk poor data quality, privacy breaches, regulatory noncompliance, and missed insights. A data governance framework provides a structured way to manage data throughout its lifecycle, including policies, processes, and standards to ensure data is accurate, accessible, and secure. By putting clear guidelines in place, organizations can increase trust in their data and improve decision-making. Key Pillars of a Data Governance Frameworks A robust data governance framework typically rests on four key pillars: 1. Center-Out Model The center-out model places a centralized team, such as a data governance council, at the core of the governance process. This group establishes policies and oversees data management across the organization, balancing consistency with flexibility for different departments. The Data Governance Institute’s framework is an example of this model. It focuses on creating a Data Governance Office responsible for managing key governance functions such as setting data policies, assigning data stewards, and monitoring compliance. The framework provides a clear structure while allowing business units some leeway in adapting governance practices to their needs. PwC’s model also adopts a center-out approach, with an emphasis on using data governance to monetize data assets. It highlights the importance of maintaining consistency while minimizing the risk of data silos. 2. Top-Down Model In the top-down model, data governance is driven by executive leadership, ensuring alignment with strategic goals. This model provides authority for enforcing governance standards but may face challenges if business units feel disconnected from the central governance team. McKinsey’s framework exemplifies this approach, focusing on integrating data governance with broader business transformation efforts. Executive leadership plays a key role in ensuring that governance initiatives receive the necessary attention and resources. 3. Hybrid Model The hybrid model combines centralized governance with flexibility for individual business units. It establishes an enterprise-wide framework while allowing departments to adapt governance practices to their specific needs. The Eckerson Group’s Modern Data Governance Framework represents a hybrid approach. It emphasizes the importance of people and culture, alongside technology and processes, and encourages organizations to create a roadmap for governance that evolves as needs change. This model provides a balance between centralized control and decentralized flexibility. 4. Bottom-Up Model In the bottom-up model, data governance is driven by subject matter experts and data stakeholders across the organization. This approach promotes collaboration and buy-in from the people closest to the data, ensuring that governance policies are practical and effective. The DAMA-DMBOK framework, developed by the Data Management Association, is a prime example. Although flexible, it often starts as a bottom-up initiative, driven by IT departments and data experts who later gain executive support. 5. Silo-In Model The silo-in model allows individual business units or departments to create their own governance practices. While this approach addresses localized data issues, it often leads to inconsistencies and challenges when the organization needs to integrate data across the enterprise. Though not widely recommended, the silo-in approach may emerge when specific business units take the initiative to establish governance due to regulatory requirements or data management needs within their domains. However, as organizations mature, they often transition to more holistic frameworks to support cross-functional collaboration and data integration. Choosing the Right Framework Selecting the right data governance framework involves evaluating the organization’s needs, structure, and culture. Whether an organization adopts a center-out, top-down, hybrid, bottom-up, or silo-in approach, success depends on involving key stakeholders, securing executive buy-in, and committing to continuous improvement. By treating data as a critical asset and implementing a governance framework that aligns with its business strategy, an organization can ensure that its data management practices support growth, innovation, and regulatory compliance. 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

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September 21, 2024in Data, Enterprise, Salesforce, Technology
Microsoft Copilot
21Sep

Microsoft Copilot

The fundamental capabilities of collaboration platforms have remained largely unchanged since the pandemic began. These platforms typically offer video conferencing, desktop sharing, and text chat, creating a virtual approximation of in-person meetings. While this setup effectively allows teams to collaborate across distances, it raises the question: Is this all there is to the collaboration experience? Enter Copilot. Microsoft is pioneering a new era of collaboration, where AI assistants help users prioritize meetings, manage follow-ups on action items, and integrate meeting outputs into future tasks. This evolution is particularly promising for knowledge workers who are overwhelmed by constant meetings. Copilot aims to redefine the collaboration experience, promising increased productivity and a more strategic approach to meetings. However, OpenAI, Microsoft’s prominent AI partner, is making moves to disrupt the enterprise space as well. OpenAI recently launched ChatGPT Enterprise, which now boasts 600,000 users, including clients from 93% of the Fortune 500. This week, OpenAI also acquired the videoconferencing startup Multi, sparking speculation that the company may integrate collaboration features directly into ChatGPT. Multi’s unique approach to videoconferencing—described as “multiplayer” and drawing parallels to gaming rather than traditional meetings—hints at a potential shift in how meetings are experienced. The Multi tool, set to be discontinued in July following the acquisition, was tailored for software developers, focusing on screen sharing and leveraging Zoom’s video capabilities. Yet, the concept of enhanced document collaboration extends beyond software developers. Integrating document collaboration with AI-driven features like summarization, and linking this to advanced language models, could revolutionize the collaboration experience. This approach promises to streamline the collaborative process, focusing on the work at hand with new functionalities. That said, not all meetings revolve around documents. Many are simply conversations—often the ones people prefer to avoid. Therefore, refining how meetings are managed and integrating them into users’ work lives will remain crucial, even as new technologies enhance screen sharing and video capabilities. So, where does this leave traditional video services? The quest for meeting equity and AI-enhanced directors will likely continue to refine the experience, striving for the “next best thing to being there.” As the collaboration platform evolves, any outdated elements will become more apparent. Ultimately, collaboration is a multifaceted experience, and technology will play a key role in its continued advancement. 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

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September 21, 2024in Enterprise, Salesforce, Technology
AI-Powered Contact Center Landscape
20Sep

AI-Powered Contact Center Landscape

Navigating the AI-Powered Contact Center Landscape: A Roadmap for Success With thousands of solutions in the contact center ecosystem, each claiming to offer “AI-powered, next-generation technology,” it’s easy to feel overwhelmed. Many of these claims are valid, as AI and machine learning are transforming contact centers and improving customer experiences. But with so many options and combinations of AI-powered solutions, how can you be sure you’re making the right decision? The answer is that it’s almost impossible without help. Trying to research and evaluate every solution on your own could take months or even years—by which time, the technology will have evolved. Plus, if you rely solely on information from manufacturers or software providers, you may only get a one-sided perspective that leads to “CCaaS FOMO” (Fear of Missing Out). A More Objective Approach to the Contact Center Journey While we can’t claim to be 100% unbiased, we take a unique approach. We start with your business, understanding your specific needs, culture, and processes before introducing solutions that fit. Not every top-rated solution is right for your business, and the roadmap below outlines how we help you navigate this complex landscape. 1. Involving Key Stakeholders The first step is ensuring you have the right people involved—those with a vested interest in the contact center‘s success. It’s helpful to break these roles into three categories: Having clear roles and expectations helps streamline the process and ensures everyone is on the same page. 2. Conducting a Contact Center Assessment This discovery phase is crucial for identifying the key drivers behind your business needs. Each contact center is different, even within the same industry. That’s why a one-size-fits-all scorecard won’t work. It’s beneficial to bring in a third-party consultant with broad industry knowledge to conduct an assessment, offering valuable insights that help create a clear vision. 3. Creating a Unique Scorecard Once you’ve completed your assessment, stakeholders can work together to establish a customized scorecard that reflects your business objectives. Whether customer service is your primary focus or you’re more telemarketing-heavy, this scorecard ensures that your solution is tailored to your specific needs. It’s also important to involve contributors and advocates in the process to gain widespread buy-in. 4. Scheduling Solution Demonstrations With a solid scorecard in hand, it’s time to identify and evaluate vendors. A contact center consultant can help streamline this process. Scoring each solution based on how well it aligns with your goals keeps the focus on substance over flash, ensuring the right solution for your business. 5. Analyzing Scorecard Data When reviewing the scorecard data, stakeholders should ask key questions: This analysis ensures that decisions are data-driven and aligned with business goals. 6. Finalizing Vendor Selection-AI-Powered Contact Center Landscape Once the data is compiled and a consensus is reached, it’s time to move forward with a contract proposal. Beyond the solution itself, discuss critical details like implementation timelines, ongoing support, and maintenance to set clear expectations and ensure accountability. Financial Modeling: Justifying the Investment Looking at your goals through a financial lens helps quantify the benefits of your contact center investment. For example, reducing average handling time by just 12 seconds across the company might result in cost-neutral savings. Similarly, reducing call abandonment by even half a percentage point can have a significant impact. These financial considerations help justify ROI and set expectations. Partnering with Tectonic: Expertise You Can Trust At Tectonic, we live and breathe contact centers. Our team of experts comes directly from this world, so we understand the challenges and opportunities. We’re here to help you navigate the complexities of the contact center ecosystem and bring clarity to your CCaaS journey. Contact us today to get started! For more resources, visit our blog or explore our AI solutions to elevate your customer experience. 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

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September 20, 2024in Salesforce Service Cloud
Critical Field Service Challenges with Connected Data and AI
20Sep

Critical Field Service Challenges with Connected Data and AI

Set Up for Success: Tackling Critical Field Service Challenges with Connected Data and AI Today’s customers demand faster, more personalized service, and field service is no exception. Research shows that 74% of mobile workers report that customer expectations have risen, with 73% noting an increased demand for a personal touch. This is shaping key trends in the field service industry. Trend #1: Rising Customer Expectations Amid a Shrinking Workforce Field service teams are grappling with rising customer expectations while dealing with a declining mobile workforce. In fact, 74% of mobile workers report increasing workloads. Given that mobile workers are often the only in-person company representatives, they face intense pressure to deliver exceptional service. At the same time, fewer young people are entering skilled trades, with applications dropping nearly 50% from 2020 to 2022, while seasoned technicians are retiring. This has led to high burnout rates, with 57% of mobile workers experiencing job-related fatigue. Trend #2: Connected Data Empowers Mobile Workers Mobile workers thrive when equipped with connected data. Yet, they spend only 32% of their time interacting with customers, as much of their time is consumed by manual tasks and disjointed systems. With the right technology, mobile workers can access up-to-date customer information through a CRM mobile app, streamlining processes and enabling more personalized service. Connected data also improves sustainability, with features like route optimization and drones reducing time on the road and minimizing worker stress. Trend #3: AI is Revolutionizing Field Service AI is rapidly transforming field service operations. Today, 79% of service organizations are investing in AI, and 83% of decision-makers plan to increase their AI investments next year. AI helps mobile teams save time and cut costs by analyzing customer data to generate personalized responses and streamline processes. By automating workflows with AI, mobile workers can deliver faster, more efficient service. AI-generated summaries of asset history and service interactions help prepare workers before they arrive at a job site, enabling better service and potential upsell opportunities. What’s Next in Field Service? Technologies like generative AI, augmented reality, and mobile solutions are shaping the future of field service. Companies that embrace these innovations now will gain a competitive edge. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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

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September 20, 2024in Generative AI, Salesforce Optimization, Salesforce Service Cloud
Salesforce Channel Order App
20Sep

Salesforce Channel Order App

Salesforce’s platform powers over 4.2 million apps, and Salesforce AppExchange offers more than 4,000 solutions. These numbers highlight Salesforce’s extensive ecosystem, with the Salesforce Channel Order App (COA) playing a crucial role for businesses managing complex partner relationships and order processes. This insight looks into the Salesforce Channel Order App, exploring its purpose, when and why you should use it, core features, who benefits from it, and best practices to maximize its potential. What is the Salesforce Channel Order App? The Salesforce Channel Order App is designed to streamline and automate order management across various sales channels, whether direct, through distribution partners, or a reseller network. It simplifies what would typically be a labor-intensive process by centralizing data, automating tasks, and providing real-time visibility into orders. This results in tighter control over order workflows and enhanced partner collaboration. When to Use the Salesforce Channel Order App The Salesforce Channel Order App is most effective for businesses that manage high volumes of orders from multiple channels. It’s especially useful in industries like technology, consumer goods, and manufacturing, where multi-channel sales are integral to operations. Key Use Cases: Core Features of the Salesforce Channel Order App Who Benefits from Salesforce Channel Order App? The Salesforce Channel Order App is particularly beneficial for industries where managing orders from multiple partners is crucial. Key beneficiaries include: Best Practices for Using Salesforce Channel Order App To get the most out of Salesforce Channel Order App, consider the following best practices: Final Take The Salesforce Channel Order App is an essential tool for businesses relying on channel partners to drive sales. By automating and streamlining the order management process, COA helps businesses improve efficiency, reduce errors, and ensure orders are fulfilled accurately and on time. Whether you’re a manufacturer, technology provider, or consumer goods company, adopting COA enables better order management and strengthens relationships with partners—setting your business up for long-term success. 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

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September 20, 2024in Salesforce
Bye Klarna
20Sep

Bye Klarna

Fintech firm Klarna is cutting ties with two major enterprise software providers, opting to automate its services using AI, and hints that more cuts could follow. Klarna co-founder and CEO Sebastian Siemiatkowski discussed the move during a recent conference call, as reported by Seeking Alpha. The company has already stopped using Salesforce, a platform that helps businesses manage sales and marketing data, and has also removed Workday, an HR and hiring platform, from its tech stack, according to a spokesperson from Klarna. This shift towards AI-driven automation is part of a larger strategy at Klarna. “We have multiple large-scale initiatives combining AI, standardization, and simplification that will allow us to eliminate several SaaS providers,” a company spokesperson said, though they did not specify which providers or services might be next. Founded in 2005, Klarna provides payment processing for e-commerce and reports over 150 million active users worldwide. Despite posting a net loss of $241 million last year—down from nearly $1 billion in 2022—the company reported a reduced loss of $32 million for the first half of 2024. With reports suggesting that Goldman Sachs has been tapped to underwrite Klarna’s potential IPO, the company’s focus on AI could strengthen its profitability prospects. This isn’t Klarna’s first AI initiative. Earlier in 2024, the company introduced an AI-powered customer service assistant in collaboration with OpenAI, which reportedly handled 2.3 million interactions in its first month and replaced the work of 700 agents. Klarna was among the early adopters of OpenAI’s enterprise ChatGPT package, and the company claims that 90% of its employees use the tool daily for process automation. Klarna’s decision to drop Salesforce and Workday is part of a broader effort to replace third-party SaaS solutions with internally developed applications, likely built on OpenAI’s infrastructure. Siemiatkowski stated in the August call, “We are shutting down a lot of our SaaS providers as we are able to consolidate.” However, not everyone is convinced. HR technology analyst Josh Bersin questioned whether Klarna could successfully replace a robust platform like Workday. “Workday systems have decades of workflows and complex data structures, including payroll and attendance,” he told Inc.. Bersin warned that developing an in-house system could lead Klarna into a “black hole of features,” with a poor user experience as a result. Many in the tech world share Bersin’s skepticism, with some suggesting the move is more of a PR tactic as Klarna gears up for its IPO. Investors and executives voiced doubts on social media, with financial insights account BuccoCapital posting on X, “Is it actually the best use of capital to rebuild in-house? Feels like a massive distraction,” while Ryan Jones, CEO of Flighty, called the move “free marketing.” Critics also point out that Klarna has downsized its workforce significantly, reducing its headcount by 1,200 over the past year, and Siemiatkowski has hinted at further reductions, suggesting the company could benefit from cutting staff from 3,800 to 2,000 employees. Siemiatkowski remains adamant that AI will allow Klarna to maintain growth despite these cuts. Bersin also noted that many tech giants have struggled to build their own HR platforms, citing examples like Google, which recently abandoned its internally developed HR software, and Amazon, which undergoes similar cycles regularly. “Microsoft,” Bersin added, “spends money on their own products but partners with SAP for HR software.” If Klarna does succeed in developing an in-house HR platform, it would be an achievement where even some of the biggest tech companies have fallen short. 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

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September 20, 2024in Salesforce
Collaborative Business Intelligence
20Sep

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. 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

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September 20, 2024in Data, Google, Salesforce, Technology
Apple New AI
19Sep

Apple New AI

Apple Unveils New AI Features at “Glowtime” Event In typical fashion, Apple revealed its latest product updates on Monday with a pre-recorded keynote titled “Glowtime,” referencing the glowing ring around the screen when Apple Intelligence is activated. Though primarily a hardware event, the real highlight was the suite of AI-powered features coming to the new iPhone models this fall. The 98-minute presentation covered updates to iPhones, AirPods, and the Apple Watch, with Apple Intelligence being the thread tying together user experiences across all devices. MacRumors has published a detailed list of all announcements, including the sleep apnea detection feature for the Apple Watch and new hearing health tools for AirPods Pro 2. Key AI Developments for Brand Marketers Apple Intelligence was first introduced at its WWDC event in June, focusing on using Apple’s large language model (LLM) to perform tasks on-device with personalized results. It draws from user data in native apps like Calendar and Mail, enabling AI to handle tasks like image generation, photo searches, and AI-generated notifications. The keynote also introduced a new “Visual Intelligence” feature for iPhone 16 models, acting as a native visual search tool. By pressing the new “camera control” button, users can access this feature to perform searches directly from their camera, such as getting restaurant info or recognizing a dog breed. Apple’s AI-powered visual search offers a strategic opportunity for brands. The information for local businesses is pulled from Apple Maps, which relies on sources like Yelp and Foursquare. Brands should ensure their listings are well-maintained on these platforms and consider optimizing their digital presence for visual search tools like Google Lens, which integrates with Apple’s search. The Camera as an Input Device and the Rise of Spatial Content The camera’s role as an input device has been expanding, with Apple emphasizing photography as a key feature of its new iPhones. This year, the iPhone 16 introduces a new camera control button, offering enhanced haptic feedback for smoother control. Third-party apps like Snapchat will also benefit from this addition, giving users more refined camera capabilities. More importantly, iPhone 16 models can now capture spatial content, including both photos and audio, optimized for the Vision Pro mixed-reality headset. Apple’s move to integrate spatial content aligns with its goal to position the iPhone as a professional creator tool. Brands can capitalize on this by exploring augmented reality (AR) features or creating immersive user-generated content experiences. Apple’s Measured Approach to AI While Apple is clearly pushing AI, it is taking a cautious, phased approach. Though the new iPhones will hit the market soon, the full range of Apple Intelligence features will roll out gradually, starting in October with tools like the AI writing assistant and photo cleanup. More advanced features will debut next spring. This measured approach allows Apple to fine-tune its AI, avoiding rushed releases that could compromise user experience. For brands, this offers a lesson in pacing AI adoption: prioritize quality and customer experience over speed. Rather than rushing to integrate AI, companies should take time to understand how it can meaningfully enhance user interactions, focusing on trust and consistency to maintain customer loyalty. By following Apple’s lead and gradually introducing AI capabilities, brands can build trust, sustain anticipation, and ensure they offer technology that genuinely improves the customer experience. 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

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September 19, 2024in Data, Google, Technology

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