July 2026 Summaries

A Zapier-commissioned survey of 1,005 U.S. employees at large companies found widespread use of sensitive business information in AI tools, with 79% reporting that they had entered data such as employee information, personally identifiable information, customer contacts, login credentials, or API keys. Although 85% say they consider data safety before using AI, 36% remain uncertain about what is permissible to share, and half have ignored workplace AI restrictions, sometimes by using personal accounts, devices, or workarounds to bypass approved tools. Employees often cite better usability, slower access to company tools, or poor fit with workplace-provided AI, while 43% of personal-account users prefer to keep their AI activity private from employers. The survey argues that organizations should address these risks through clear policies, formal employee training, suitable approved tools, access controls, monitoring, and workflow guardrails that keep credentials out of AI models and make compliant practices easier.

Damian Guzman, founder of FTE Legal, has significantly enhanced his solo legal practice by leveraging AI tools like Claude and automation platform Zapier MCP, resulting in a fivefold increase in productivity. This transformation allows him to manage tasks such as drafting documents and calendar management more efficiently, freeing up time to focus on advising others on optimizing their legal operations through his consultancy, Ergane. However, this efficiency raises questions about billing fairness in AI-augmented legal work, which led him to create Ariadni LLC, a software company offering counsel-oversight tools to assess the value of legal services. Guzman's approach illustrates a potential shift in the legal industry, emphasizing the integration of AI to improve productivity and the necessity for firms to adapt to these technological advancements. His work reflects a broader mission to ensure that both lawyers and clients can navigate the evolving landscape effectively.

An omnichannel customer experience seamlessly integrates all customer interactions across various channels, ensuring continuity and context are maintained throughout the customer journey. Unlike multichannel experiences, where each channel operates independently, omnichannel strategies connect channels to provide a cohesive experience, preventing customers from having to repeat themselves and enhancing satisfaction. This integration involves connecting technologies such as CRM systems, marketing automation software, and customer service platforms to share data and automate workflows, allowing for improved customer satisfaction, increased revenue, and boosted employee efficiency. Companies like Smith.ai and Toyota of Orlando and Clermont have successfully implemented these strategies using platforms like Zapier to streamline operations and maintain a consistent customer experience. By centralizing information and automating processes, businesses can deliver a unified customer experience that fosters loyalty and drives growth.

Product lifecycle management (PLM) is a comprehensive approach to managing a product from its inception to retirement, involving design, development, sales, and support stages. It acts as an operational discipline distinct from application lifecycle management (ALM) by focusing on managing product data and processes across various teams and functions, including design, engineering, and manufacturing. PLM software centralizes information and automates workflows, serving as a single source of truth to enhance collaboration, reduce costs, and improve time to market. Systems like SAP or Siemens Teamcenter offer deep integration with enterprise resource planning (ERP) tools, while comprehensive tech stacks connect dedicated PLM platforms with existing tools such as CAD software and project trackers. Challenges include data silos, legacy systems, and communication gaps, which can be mitigated by platforms like Zapier that automate and streamline data flow across systems. As PLM evolves, it increasingly incorporates AI for predictive maintenance and generative design, transitioning from a static information repository to an active, intelligent collaborator in the product development process.

AI agent skills function similarly to teaching a dog new tricks, allowing AI to perform specific, repeatable tasks efficiently by loading predefined instructions from a "skill" folder as needed. Introduced by Anthropic, these skills enable AI tools like Claude, Codex, and Cursor to automatically recognize relevant tasks and apply the correct processes without manual intervention, differentiating them from always-on instructions that provide persistent context. Skills are designed to streamline workflows by being stackable and portable, ensuring that they only activate when necessary, thereby preventing unnecessary system bloat. They are particularly useful for tasks that follow a repeatable pattern and require specific contextual references, such as CRM structures or marketing campaign analyses. While skills can be created from scratch, many users find it easier to utilize ready-made skills from directories like Claude's Skills Directory or Zapier's public repositories, which offer practical examples for various industries. Integrating these skills with tools your team already uses is facilitated by protocols like Zapier MCP, which connects AI agents to a wide array of applications, enhancing their functionality and efficiency across different environments.

Mach 1, co-founded by Chris Olson, is an AI operations platform that enables mid-market companies to deploy AI agents across various business functions such as customer success, sales, support, and finance without replacing existing software or forcing standardized workflows. The platform's core component, Tower, acts as an operational control layer, determining the appropriate systems and tools for agents, managing permissions, maintaining process context, and logging decisions for transparency. By leveraging Zapier MCP, Mach 1 provides a flexible interface that connects to existing customer tools, allowing agents to access necessary systems while customers retain control over actions and permissions. This approach enables the company to focus on refining AI intelligence and orchestration rather than building repetitive integration infrastructure, thus supporting AI-driven operations across 25 companies with diverse technology stacks. Mach 1 has successfully implemented this system internally, demonstrated by its significant task automation across various applications like Gmail and Microsoft tools, thereby enhancing operational efficiency and reducing the need for engineering support in process adjustments.

The text discusses the comparison between MuleSoft and Zapier, two integration platforms that cater to different needs within organizations. MuleSoft, owned by Salesforce, is an enterprise integration platform that emphasizes an API-led approach, allowing for deep, structured integrations across complex systems such as ERPs, CRMs, and databases, which require careful governance and compliance. It offers over 250 prebuilt connectors and the flexibility to create custom connectors, though this typically requires developer involvement and is tailored for environments where data integrity is critical. In contrast, Zapier provides a more user-friendly interface with over 9,000 integrations, designed for non-developers to quickly automate workflows across SaaS applications. It supports a wide range of tools, including CRMs, marketing platforms, and AI clients, allowing for rapid experimentation and deployment without extensive technical expertise. While MuleSoft is suited for high-complexity environments where precise control over integrations is necessary, Zapier offers accessible automation for teams seeking agility without the need for deep technical resources. Pricing structures also differ, with MuleSoft typically requiring custom enterprise agreements and Zapier providing transparent, tiered pricing options.

Automation software encompasses a variety of tools designed to handle repetitive, rule-based tasks, ranging from basic email confirmations to complex AI-powered workflows. The wide array of options includes no-code builders like Zapier, which offers extensive integrations and AI features, Microsoft Power Automate for seamless Microsoft ecosystem automation, and n8n for customizable self-hosted solutions. Other specialized platforms such as Boomi and MuleSoft cater to legacy systems and sensitive data management, respectively. Robotic Process Automation (RPA) tools like UiPath and Automation Anywhere are ideal for automating legacy systems without APIs, while Lindy provides personal AI assistance through text. The best choice depends on specific needs, including integration depth, ease of use, AI capabilities, and pricing models, with considerations for both solo entrepreneurs and large enterprise IT teams.

Relay is scheduled to shut down on August 15, 2026, for free users and September 14, 2026, for paid accounts, pushing users to transition their workflows to other tools, with Zapier being highlighted as the preferred alternative. Relay provides a streamlined process for exporting workflow data, which includes build prompts, run history, and JSON files detailing workflow structures, to facilitate rebuilding these in Zapier. Users are encouraged to utilize Zapier's Copilot, an AI assistant designed to replicate workflows using plain-language descriptions, which aligns with Relay's user-friendly approach. Zapier offers significant advantages over alternatives like n8n and Make, such as a broader range of app integrations, built-in error handling, and advanced governance features, making it well-suited for former Relay users. The transition process requires users to export their data promptly, as the download link is valid for only 48 hours, and to manually reconnect app credentials in Zapier, since Relay's JSON format is incompatible with Zapier's system.

Effective writing is a valuable skill that enhances communication by prioritizing clarity and conciseness, reducing the time readers spend interpreting content and allowing them to act more efficiently. The proliferation of AI-generated content that prioritizes length over clarity necessitates a shift towards more precise writing that eliminates unnecessary filler and uses simpler language, making it more accessible to non-native speakers and those using assistive technologies. Structuring long-form content through progressive disclosure—beginning with essential information and expanding for interested readers—improves readability and comprehension. Moreover, employing active voice and avoiding sensory language contribute to inclusivity and clarity. When utilizing AI for writing assistance, it is crucial to provide specific prompts and guidelines to maintain quality and adhere to best practices, ensuring that the final output aligns with the writer's standards and intentions.

Many companies are eager to implement AI, with 86% planning to increase their investment in the coming year, yet only 13% of AI projects have been fully deployed across businesses despite extensive pilot testing. A survey of 835 senior leaders found that AI pilots often stall due to IT infrastructure challenges, data quality issues, and system integration problems, with 41% of stalled projects affected by these factors. Successful AI deployment is more likely with dedicated ownership, early executive sponsorship, and sufficient budget allocation, with organizations spending between $250,000 and $499,999 annually reporting the highest success rates. While 81% of companies remain undeterred by failed pilots, emphasizing the importance of having robust data infrastructure, clearly defined use cases, and cross-functional buy-in, the survey highlights the critical role of executive sponsorship in advancing AI initiatives. Integration failures and compliance concerns are significant barriers, but tools like Zapier can enable secure connections between AI tools and existing company systems, facilitating faster deployment without losing control.

The three Zappy Award winners for June have innovatively utilized AI to improve workflow efficiency by turning scattered company knowledge into shared context. Eric McNulty at Mercari developed a support orchestration system using Zapier to handle customer service tickets more effectively by integrating AI into deterministic workflows, significantly reducing agent time and improving customer satisfaction. Blair Mishleau at Patreon created 'Treon,' a Slack-based knowledge assistant that consolidates internal resources for easier access, enhancing information transparency and oversight. Amber Sharp at Tivly designed a system to streamline the client onboarding process, reducing manual workload and improving the transition between sales and account management. Their efforts highlight the evolving role of AI in making company operations more efficient and employee workflows more interconnected, setting the stage for broader organizational adaptability in the AI era.

MuleSoft, a Salesforce-owned integration and API platform, is lauded for its ability to manage API-led connectivity and enterprise-level integrations with systems like SAP, Oracle, and Salesforce. However, it is often considered more suitable for IT teams with developers and integration architects due to its complex, architecture-first approach and lengthy implementation timelines. Alternatives to MuleSoft, such as Zapier, Boomi, Workato, Informatica, Microsoft Power Automate, and Celigo, offer various strengths, including ease of use for non-technical teams, quicker setups, extensive SaaS integrations, and specialized features for ERP and commerce integrations. Each alternative addresses specific needs, whether it's governance, data transformation, or hybrid connectivity, while also providing varying degrees of pricing transparency and user accessibility. Zapier, for instance, stands out for enabling anyone on a team to build automations across 9,000+ apps without needing a development team, contrasting MuleSoft’s developer-heavy model.

ChatGPT and Zapier serve distinct but complementary roles in AI and automation, with ChatGPT functioning as an AI chatbot and agent harness, and Zapier acting as a governed automation layer that incorporates AI where needed. While ChatGPT processes everything through AI, potentially increasing token costs, Zapier offers model flexibility, allowing users to switch between AI providers like OpenAI, Anthropic, and Google without rebuilding workflows, and only applying AI where it is necessary. Both platforms offer SDKs for developing agent capabilities, but Zapier's SDK provides access to over 9,000 apps, enabling a wide range of automated workflows without always relying on AI. Zapier supports stronger governance and offers more predictable pricing compared to ChatGPT, making it a cost-effective choice for businesses looking to automate workflows with or without AI. The optimal approach for maximizing functionality and cost-efficiency is to integrate both tools, using ChatGPT for AI-driven tasks and Zapier for structuring workflows and managing permissions, thus avoiding high token expenditure and providing robust automation solutions.

UiPath is a comprehensive robotic process automation (RPA) platform designed to automate repetitive tasks by mimicking human actions on a computer screen, making it particularly useful for legacy software lacking modern APIs. However, due to its niche focus on IT-led automation and enterprise-scale operations, several alternatives may better suit different needs. These alternatives include Zapier, known for its user-friendly approach allowing teams to build automation without IT intervention, and Automation Anywhere, which emphasizes agentic process automation using AI to integrate RPA, APIs, and intelligent document processing. Microsoft Power Automate is ideal for organizations already within the Microsoft ecosystem, offering seamless integration with Microsoft 365 tools. MuleSoft caters to regulated industries with its robust governance and compliance features, while Workato and Boomi provide strong integration capabilities, with Workato focusing on enterprise systems and Boomi excelling in hybrid cloud and on-premises integration. Ultimately, the best alternative depends on specific requirements such as system compatibility, team capability, budget constraints, and the degree of IT oversight needed.

API integration platforms have significantly streamlined both consumer and enterprise experiences by connecting diverse systems and automating workflows. While consumers enjoy seamless interactions like checking the weather or ordering food via APIs, enterprises face challenges integrating multiple SaaS tools with distinct data models and authentication processes, often leading to manual data entry and workflow disruptions. To address these complexities, various API integration tools have emerged, each catering to different needs and levels of technical expertise. Tools like Zapier, Tray, MuleSoft, Workato, Paragon, and Nango offer unique features, such as extensive connector ecosystems, data transformation capabilities, enterprise-grade security, AI integration readiness, and specialized governance functions. These platforms are evaluated based on criteria like connector availability, data mapping, security compliance, and AI capabilities, with options ranging from user-friendly, low-code builders to code-first infrastructures for developers. Ultimately, the choice of an API integration platform depends on the specific requirements and technical skills of the user or organization, with Zapier being highlighted for its extensive pre-built integrations and robust security measures.

The article explores the integration solutions offered by Zapier and Paragon, highlighting their distinct advantages and use cases for software businesses. Zapier provides access to over 9,000 app connections, allowing businesses to integrate with a vast array of applications without extensive engineering resources, and is particularly beneficial for early-stage SaaS companies aiming to offer immediate integrations. Powered by Zapier enables embedding these integrations directly into products, enhancing user experience and app stickiness. Paragon, on the other hand, focuses on offering a quicker development of native integrations with its 130+ prebuilt connectors, supporting real-time bidirectional data sync for critical applications, making it more suitable for businesses that require seamless core integrations. While Zapier is ideal for covering a broad range of integrations, Paragon is useful for building robust native integrations, with the article suggesting that many companies may benefit from using both platforms in tandem to maximize efficiency and coverage in their integration strategies.

Tokens have become a crucial aspect of AI models, serving as the primary unit for usage limits and billing, particularly in large language models (LLMs) like ChatGPT and Claude Code. These models interpret inputs as strings of tokens—units that can range from single characters to whole words—and generate outputs by predicting the next token in a sequence. The tokenization process, influenced by a model's training data, varies across languages and media, with more common patterns typically represented by fewer tokens. The context window, which limits the number of tokens processed at one time, is critical for tasks such as coding and reasoning, where complex thought processes consume more tokens. Recent shifts in AI economics have moved from fixed pricing to per-token billing, significantly impacting costs, especially for sophisticated reasoning models used in coding tasks. To manage these costs, strategies like combining AI with deterministic automation, such as Zapier, can optimize token usage by matching tasks to appropriate models, thus ensuring efficiency without excessive token consumption.

AI technology has advanced to the point where individuals without formal programming backgrounds can create, refine, and deploy apps using AI tools in a remarkably short time frame, such as hours or days. This process, sometimes referred to as "vibe coding," involves using AI-driven platforms like ChatGPT, GitHub Copilot, and Cursor to build software through plain language prompts. A significant portion of these non-traditional builders are creating applications for public or customer use, highlighting the democratization of software development. Despite the ease of creating functional tools, challenges remain, particularly regarding integration with company systems and maintaining security and data privacy. Tools like Zapier MCP address these challenges by facilitating secure app connections without exposing sensitive credentials. The accessibility of AI coding tools has not only empowered non-developers to innovate but has also provided career benefits, such as promotions and enhanced job confidence, for those who leverage these technologies. The survey conducted by Centiment for Zapier underscores the growing trend of AI-facilitated development, emphasizing the importance of governance and security in this evolving landscape.

Many teams use Microsoft Power Automate simply because it comes bundled with Microsoft 365, despite its limitations in integrating beyond the Microsoft ecosystem, complex pricing, and a less user-friendly interface for non-technical users. Alternatives to Power Automate include Zapier, which offers integration with over 9,000 apps and is geared towards non-technical users who need flexibility without overburdening IT resources. Make provides a visual, no-code workflow builder, while n8n caters to those requiring self-hosted, complex workflows with engineering resources. UiPath excels in desktop and RPA automation for legacy systems, whereas Workato is suited for enterprise-level orchestration with a focus on IT control. Boomi is ideal for enterprises needing iPaaS solutions with on-premises and hybrid deployment capabilities. Each alternative addresses specific weaknesses of Power Automate, offering diverse capabilities tailored to different organizational needs.

Conducting a security audit of AI agent workflows is crucial to manage the risks associated with autonomous operations, such as leaked PII, prompt injection attacks, and unauthorized actions. The audit process involves mapping the workflow to understand its tools, triggers, inputs, and data sensitivity, as well as reviewing user access to ensure adherence to the principle of least privilege. It also requires evaluating data handling by apps and AI, validating inputs against potential attacks, and implementing human-in-the-loop steps for critical decisions. Maintaining visibility into workflow failures and establishing a recurring review process are essential to adapting to changing risk profiles. Zapier provides tools like Canvas for workflow mapping, AI Guardrails for input inspection, and MCP for controlled app access, all of which help in maintaining a secure and compliant environment while enabling AI agents to perform effectively.

Power Automate and UiPath serve distinct purposes tailored to different business needs, despite their overlapping functionalities. Power Automate, deeply integrated within the Microsoft ecosystem, is ideal for teams already using Microsoft tools, offering a low-code, user-friendly approach with straightforward pricing and AI assistance geared towards simplifying routine tasks. It is designed for quick adoption without extensive training, making it suitable for automating everyday processes within Microsoft-heavy environments. Conversely, UiPath excels in handling complex, large-scale automation projects, particularly in environments with legacy systems, thanks to its robust orchestration capabilities and agentic AI that becomes part of the automation system itself. Its pricing reflects an enterprise-scale approach, supporting a more structured and technical automation framework. Zapier is presented as a versatile alternative, providing cross-platform, flexible automation that is easy to use across various applications, making it an attractive middle ground for teams seeking secure, AI-integrated workflows without the complexity of full RPA systems. The choice between these platforms ultimately hinges on a company's existing tech infrastructure and its specific automation needs.

UiPath is a robotic process automation (RPA) software designed to handle complex, high-volume, rule-based processes across various business systems, making it ideal for large enterprises with stringent compliance and security needs. It excels in automating tasks like data entry, invoice processing, and report generation by using bots that mimic human interactions with digital systems, often employing AI to process unstructured data and make simple decisions. While UiPath offers deep customization, governance, and security controls, making it suitable for industries like finance and healthcare, it requires RPA specialists for setup and maintenance, often involving detailed project plans and IT coordination. In contrast, Zapier caters to business users seeking quick, flexible automation for workflows within modern cloud apps, providing thousands of native integrations and an intuitive setup without requiring technical expertise. Zapier's approach is particularly beneficial for fast-moving enterprises looking for scalable automation across departments, while UiPath's strength lies in its ability to integrate with legacy systems and manage large-scale, structured automation programs.

B2B social media marketing involves the strategic use of social media platforms to engage businesses, build authority, and generate leads that support extended sales cycles. Unlike B2C marketing, B2B focuses on building relationships through content that informs and establishes trust over time rather than driving immediate purchases, as the decision-making process often involves multiple stakeholders. Effective B2B strategies require aligning content with different stages of the buyer's journey, from raising awareness to facilitating decision-making with case studies and testimonials. Utilizing various platforms like LinkedIn, YouTube, and TikTok, companies can leverage both organic and paid efforts to remain visible and credible, ultimately shortening sales cycles by engaging audiences early and consistently. Success hinges on understanding the audience, setting clear goals, and utilizing internal expertise to create meaningful content while employing tools like Zapier to streamline operations and enhance efficiency.

Demand generation is a marketing strategy designed to create awareness and interest in products or services, aiming to convert potential customers into leads by focusing on education and engagement rather than immediate sales pitches. This approach is particularly important in the modern business environment where buyers often self-educate and navigate longer sales cycles. Demand generation involves a blend of tactics such as content marketing, social media, thought leadership, and ungated educational campaigns to build trust and interest before transitioning to demand capture, which focuses on users already showing purchase intent with targeted ads and bottom-of-funnel content. The strategy differs from lead generation by emphasizing trust-building over immediate information capture, addressing the need for warm and qualified leads rather than high-volume but low-quality ones. Successful demand generation balances demand creation and capture, using various methods like account-based marketing, paid advertising, webinars, and interactive content to engage and nurture potential customers throughout their journey. The effectiveness of these efforts is measured over time through metrics like engagement, lead quality, and revenue attribution, supported by automation tools like Zapier to streamline processes and enhance efficiency in a cookie-less digital landscape.

Pipedream and Zapier offer distinct approaches to automation, catering to different user needs and technical expertise. Pipedream is an API-first platform ideal for developers comfortable with coding, providing extensive flexibility through webhooks, custom logic, and direct API calls, but requires understanding of runtime and memory efficiency due to its credit-based pricing model. This model, while rewarding efficient workflows, can lead to unpredictability in costs as it depends on compute usage. In contrast, Zapier offers a simpler, more predictable task-based pricing structure, appealing to users who prefer ease of setup and no-code automation workflows. It connects with over 9,000 apps, allowing users to set up end-to-end workflows without delving into code or infrastructure management, making it generally more cost-effective for those who prioritize functionality over performance optimization. Pipedream's credit-based system can be advantageous for highly optimized processes but also requires careful planning to manage unexpected expenses, whereas Zapier's model simplifies budgeting by charging only for completed actions, with a broader app integration range that supports comprehensive automation without technical overhead.

Workato and MuleSoft are two distinct automation platforms that cater to different integration needs within enterprise environments. Workato is known for its fast deployment, user-friendly low-code interface, and over 1,400 prebuilt connectors, making it ideal for organizations seeking rapid SaaS integration with minimal technical expertise required. Its cloud-native design allows for effortless scaling, although it can lead to sprawl without careful management. In contrast, MuleSoft offers a more structured approach with a focus on APIs, governance, and complex system integration, requiring more technical expertise and upfront planning but offering greater control and security for large-scale enterprises. MuleSoft's integration is more suitable for environments with legacy systems and strict governance needs, and its pricing model is infrastructure-based, offering predictable costs. Meanwhile, Zapier provides an alternative that emphasizes broad cloud integration with a no-code approach, allowing for fast deployment without the need for extensive technical resources. Both Workato and MuleSoft differ in their AI integration philosophies, with Workato focusing on dynamic, agent-driven workflows and MuleSoft emphasizing secure, governed AI use.