Each year, we’re honored to shine a light on an incredible student who embodies leadership, compassion, and a commitment to making a difference. This year, we’re thrilled to announce that Lucy Naiman, a senior at Sehome High School, has been selected as the 2025 RedCloud Consulting and Toys for Kids Seattle Scholarship Recipient. Now in its eighth year, this scholarship reflects RedCloud’s ongoing commitment to supporting the next generation of leaders who lift up their communities. Lucy is the definition of the spirit RedCloud looks for! A 4.0 GPA student and dedicated community volunteer, she has spent more than 150 hours serving as a juror and attorney in Teen Court — a peer-led program that gives young people the opportunity to take accountability, rebuild trust, and change their futures. “I had made a difference! Simply by speaking on behalf of my peer.” — Lucy Naiman Lucy’s story began nervously — hands shaking in front of a judge and jury — and grew into a powerful calling to advocate for others. Today, Lucy speaks with confidence, purpose, and compassion. “Speaking up fills me with joy, not anxiety, as I am able to give back to my community and give a voice to my peers.” Her mentors describe her as “compassionate, confident, and determined to lead with empathy.” Lucy hopes to continue her education with the goal of becoming a lawyer, turning her passion for advocacy into a career of service. “Each year, this scholarship reminds us why we do this work — to invest in our region’s most important resource, young people, who inspire us with their courage, leadership, and heart,” said Brett Alston, RedCloud Managing Partner and Toys for Kids Seattle Board Member. “Lucy represents everything this scholarship stands for, and we’re so proud to support her journey.” Through our partnership with Toys for Kids Seattle, RedCloud has proudly awarded scholarships to outstanding students for eight years running. These young leaders remind us that investing in education is one of the most meaningful ways we can give back. Read about our past scholarship recipients: 2024 Scholarship Winner  2023 Scholarship Winner  2022 Scholarship Winner  Congratulations, Lucy. We can’t wait to see all that you will accomplish — and the lives you’ll touch along the way.

At the recent Power Platform Conference in Las Vegas, Microsoft didn’t just preview new features. They signaled a seismic shift. Low-code is no longer the future — AI-generated code is. For years, low-code platforms promised simplicity and accessibility. That promise has evolved. Today, app creation is no longer about dragging and dropping. It’s about prompting and generating. AI is now the engine behind every modern Power Platform experience. The New Spectrum of App Creation App development now spans a continuum. On one end, fully AI-generated experiences. On the other hand, pro-code environments accelerated by AI. In between, hybrid tools that blend prompt-driven creation with manual flexibility. Here’s how the current Power Platform lineup maps to this new reality: Tool Status Experience App Type Data Access Coding Flexibility App Builder Frontier Program Single Page Copilot-Driven SharePoint Only No Code Editing Gen Pages GA Single Page Dataverse Apps Dataverse/SharePoint/SQL/Some Azure Limited Code Editing Code Agent Early Access Multipage Code App Dataverse Limited Code Editing Code Apps Preview Multipage Code App All Connectors Full Code Editing These tools aren’t just upgrades. They’re a redefinition of how apps are built. React is now the default architecture. Whether you’re creating a simple form or a complex enterprise solution, the code is AI-generated, React-native, and ready for scale. Agents Are the New Builders Microsoft’s agent ecosystem is reshaping development: Insight Agents:  Summarize dashboards, explain trends, and generate briefings from live data in tools like Power BI. Workflow Agent: Set up flows with simple prompts. Define instructions, actions, and connections in seconds. Productivity Copilots:  Draft content, interpret documents, and suggest actions inside Microsoft 365, Power Platform, and Dynamics. Governance & Evaluation Agents:  Ensure quality and compliance by checking accuracy, safety, and reliability, while surfacing oversight metrics. Autonomous & Self-Hosted Agents : Run on self-managed infrastructure using open-source models for full control over cost and privacy. These agents don’t just assist. They co-create. They interpret intent, generate code, and optimize logic in real time. Copilot Pricing: Credits Over Messages Copilot is shifting to a credit-based pricing model. Features within the studio now carry associated credit costs. For some, this will create challenges in accurately estimating project costs and usage. Microsoft encourages makers to use the Estimator, go here to view, to plan, and help understand spending for each agent.  Real-World Demos: From NASA to Network Ops The conference showcased stunning use cases: A solar system map tracking NASA missions via Gen Pages A nationwide outage dashboard for Lumen built with Code Agent These weren’t just prototypes. They were proof that AI-generated code can handle real complexity, scale, and performance. What This Means for RedCloud RedCloud thrives on anticipating what’s next. This is one of those moments. The future of app development is no longer low-code. It’s AI-generated, React-native, and agent-powered. For clients, this means faster delivery, smarter automation, and scalable architecture. For RedCloud, it’s a chance to lead the charge — architecting solutions that blend human creativity with machine precision. Low-code is dead. The future is already here. Ready to build smarter? Contact RedCloud’s Data & AI practice  to learn how we can help you harness AI-generated development, build and deploy Agents, optimize your Power Platform strategy, and future-proof your digital ecosystem.

After months of work, we are excited to introduce RedCloud’s “ChangeFlow”—a big step forward in how transformation offices operate. ChangeFlow is not just a new tool; it’s a game-changing capability designed to help organizations work smarter, faster, and with greater impact. The Journey: From Idea to Launch Our goal for ChangeFlow was clear: help transformation leaders move past scattered efforts and become true orchestrators of enterprise-wide change. We set out to tackle common roadblocks — siloed data, disparate delivery processes, overlapping projects, and limited insight into how initiatives connect and impact the business. Through real-world testing and feedback, we’ve shaped ChangeFlow into a solution that unifies change and impact assessments, stakeholder mapping, and operations in partnership with dynamic dashboarding to deliver a seamless experience. ChangeFlow is more than a platform. It is a new way of working that will elevate your team to become strategic partners for the enterprise. What This Tool Does ChangeFlow helps teams: Break down silos  by identifying where portfolios, programs, and projects overlap or compete, enabling leaders to make more informed decisions and avoid unidentified change collisions (Or at least proceed with eyes wide open). Support strategic conversations  by providing a clear view of the enterprise change horizon, enabling teams to adjust timelines and avoid conflicts, and partnering with P-andV-MOs and leaders to orchestrate change and avoid accidental stacking. Simultaneously Empower RedCloud Clients and consultants  to deliver programs smoothly by building cross-initiative transparency without building an environment where change managers compete for long-term stakeholder attention, or where clients are expected to trust consultants blindly with little visibility into their work. ChangeFlow solves for both. Speed up execution  by leveraging AI in early change assessments, eliminating manual busywork, allowing change-makers to focus on what matters most: Enabling the kinds of engagement that ensure business confidence through change remains high.  The results so far are promising. Bottlenecks are identified, and programs are being combined. Our partners are elevating the conversation while we continue to deliver excellence in change programming. Initiative overlaps and stakeholder impacts are clearer than ever. Real-time data is driving better planning and forecasting. Increasingly targeted, AI-enabled communications have helped users cut through the noise. Leaders have greater clarity about where they need to be, which project, and which teams they support. This isn’t just about efficiency; it’s about elevation. It is about helping transformation teams step up as trusted advisors who can confidently navigate complexity and lead change. What This Unlocks for You ChangeFlow isn’t just a smarter way to manage change — it’s a strategic lever for transformation leaders ready to lead with foresight. With automation and AI woven into the fabric of operations, you can: Surface hidden intersections across initiatives before they become bottlenecks. Reframe scattered efforts into unified programs that drive enterprise value. Navigate the change horizon with clarity, earning trust and buy-in from sponsors and delivery offices.  Step into a new role — not just managing change, but orchestrating it with precision, influence, and insight. Looking Ahead The launch of ChangeFlow is just the beginning. Our vision is to make transformation offices the central hub connecting departments, project teams, and leadership across the organization. With a shared view of impact and priorities, teams won’t compete blindly — they’ll coordinate smarter. We also plan to leverage AI agents to drive earlier insights and faster turns for assessments, planning, and development of change-related deliverables.  In short, ChangeFlow gives transformation leaders the insights, tools, and influence to shape the future with clarity, confidence, and cohesion.

At RedCloud, we understand that the interview process can sometimes feel overwhelming. That’s why our dedicated recruiting team is here to offer expert advice to help you navigate it with confidence. Drawing from our experience, we’ve compiled essential tips to set you up for success. Depending on the opportunity, your Recruiter may schedule one or more of the following interview types: Behavioral Interviews – Typically a 30-minute conversation with a RedCloud Account Manager who will assess your professional demeanor, communication style, and overall culture fit with our firm. Case Studies or Technical Interviews – These are usually 30–45-minute sessions with a RedCloud Account Manager where you’ll walk through a real-world scenario or technical challenge. We use these discussions to understand how you think, problem-solve, and approach client situations. Client Interviews – While not always required, these interviews can vary in duration and format, as each client has their own unique process and style. Your RedCloud Recruiter will ensure you’re fully prepared with all the details ahead of time. Before Your Interview Most interviews are conducted via video through Microsoft Teams. Please join the meeting five minutes before the scheduled start time. You can ask your RedCloud Recruiter to help test your Teams setup to ensure your video and audio are working properly. Choose a background that reflects professionalism. Make sure your space is clean, quiet, and organized. Prepare in advance to minimize distractions (such as pets, children, or background noise) during your call. If you’re using Teams on your desktop, consider selecting a RedCloud backdrop image from our gallery below for your background. Alternatively, you can blur your background for added focus. Dress professionally, as you would for an in-person interview. Please remove hats and sunglasses before joining the call. Avoid graphic t-shirts or clothing that could be interpreted as political or polarizing. Research your interviewer, the team, and the organization. Come prepared with thoughtful questions that demonstrate your interest and help you learn more about the role’s day-to-day responsibilities, deliverables, and team culture. Your RedCloud Recruiter will provide as much insight into the team and scope as possible, but if you have questions beforehand, please don’t hesitate to ask. During Your Interview Be yourself. We value authentic communication and want to hear your personal experiences and insights. Please avoid using AI tools or scripted responses during your interview, as we’re looking to understand your natural communication style and how you approach problem-solving. Use of AI tools during the interview may disqualify you from further consideration. Have your resume handy and be prepared to discuss your prior work experience in detail. Most interviewers prefer clear and concise answers. When responding to questions, consider this framework: The scope or challenge Your specific contribution The end impact or result Be respectful of your interviewer’s time. Provide complete responses but avoid going off on tangents. Pause occasionally to give your interviewer the opportunity to ask follow-up questions. Please do not discuss decision timelines or compensation details with the client interviewer. These topics should be addressed with your RedCloud Recruiter or Account Manager. After Your Interview When your interview wraps up, please let your recruiter know how it went. We’d love to hear your thoughts! Teams Backdrops If you’d like to use one of the RedCloud-branded backdrops for Teams featured below. Right-click on the image, select “Save As”, and save it to your preferred location before uploading it to your Teams app. Good luck! We’re rooting for you!

How to achieve enterprise-level capabilities with Self-Hosted Agentic AI This is the final post in our series on AI adoption. In Part 1, we examined how large enterprises have broken the traditional technology adoption curve, outpacing small businesses in AI deployment through substantial resource investments—deep talent pools, complex infrastructure, and organizational capabilities that small businesses struggle to match. Part 2 explored how autonomous architecture—systems that manage, optimize, and evolve themselves—could level this playing field and return the competitive advantage to smaller, more agile organizations. The critical question that remains is: Can small businesses actually access enterprise-level AI capabilities without enterprise-level budgets? Let’s Talk Technology Rather than relying on external AI service providers like OpenAI or Anthropic, a self-hosted Agentic AI model runs on your local device, managing the data on your own hardware, and maintaining full control over the entire technology stack. The reason self-hosting is best suited for autonomous architecture is to forgo all the recurring per-use costs and to keep your data contained within your environment. Why is this approach now viable? Just 18 months ago, this type of infrastructure would’ve required specialized vendors and six-figure budgets. Now, the same can be accomplished with open-source tools and some modest hardware. Here’s how we’ve done it. The Autonomous Architecture Stack Our solution requires three core components working together: The Orchestration Layer: n8n workflow automation, and a custom MCP Server n8n is a free open-source workflow automation platform that provides visual workflow design, system integration, and event-driven execution. Think of it as the nervous system—coordinating actions across different tools and systems, routing information, and managing complex multi-step processes. A custom MCP (Model Context Protocol) server extends n8n's capabilities by enabling AI agents to interact with its workflows dynamically. Instead of predefined automation paths, the MCP server allows agents to query available actions, execute workflows based on reasoning, and adapt behavior based on results. This is what transforms static automation into autonomous architecture. It is capable of managing itself. The AI Model Layer: Ollama and GPT-OSS Ollama provides a simple interface for running open-source language models locally, without external API calls. GPT-OSS — OpenAI's open-source release of advanced reasoning techniques — is our chosen open-source language model used for handling more sophisticated autonomous decision-making. These models power the reasoning and decision-making of your Agent. They interpret user requests, analyze data, generate responses, and make judgment calls about workflow execution. As mentioned, running these locally eliminates the per-token costs and keeps your sensitive data on-premises. These modern open-source models perform well enough for most business applications (e.g., customer support, document processing, data analysis, workflow automation), though they may not match Chat GPT-5 on every benchmark. Integration & Deployment: Docker and Web UI Docker containerizes each component, making deployment consistent and updates straightforward. The entire solution’s stack runs in containers that can move between development machines, production servers, or cloud infrastructure if needed. Web interfaces make the system accessible. Our workflows and models are configured through n8n's visual interface. We can monitor operations and adjust behavior, as needed, without writing any code. Code-level customization is still available though, should we desire it. An enterprise AI platform would typically require a dedicated DevOps team for this kind of deployment and maintenance. Our solution reduces this configuration management to a single individual. Bringing It Online You can implement a solution similar to ours using this 4-step process: Step 1: Deploy the core infrastructure Install Docker on your chosen hardware—this could be a dedicated server, a capable workstation, or a virtual machine. Deploy n8n in a container. Add Ollama with your chosen language models. Set up the custom MCP server to bridge n8n and your AI models. At this stage, you're not building production automation yet. The goal is a working environment where you can experiment with workflows and model interactions. Recommended Hardware: A modern workstation with 16-32GB RAM and a decent CPU can handle this workload. Time investment: 40-60 hours for someone with technical aptitude but no prior experience with these tools. Halve that if you have previous container and automation experience. Step 2: Your first Autonomous capability Implement one well-defined autonomous workflow. Good candidates for this might include: Customer inquiry routing and initial response Document processing and data extraction Routine workflow automation with decision points System monitoring and basic self-healing The key is choosing something with clear value, manageable scope, and tolerance for iteration. You're learning how the system behaves in production while delivering a measurable benefit. Here’s an example: Let’s say your customers contact you via email or chat. Your n8n autonomous workflow can receive them, pass content to your local AI model for classification and initial response generation. The AI model analyzes intent, checks relevant customer history from your CRM (API integration required), drafts a response, and either sends it automatically or routes to a human for approval. The system logs all the interactions for use in quality monitoring and continuous improvement. Step 3: Data mining & refining Monitor your workflow’s performance. Track accuracy, response quality, error rates, and edge cases. Adjust prompts, refine the workflow, and improve integration handling based on your actual usage. This step is for building confidence in your autonomous systems and helps to develop an intuition about what works. Your direction is instrumental in informing what will become the autonomous architecture’s ability to optimize & evolve itself. Step 4: Start to scale Add new capabilities systematically. Each new autonomous workflow builds up infrastructure and knowledge from previous implementations. The marginal cost of additional automation drops significantly once a foundation exists. Strategic Advantages Beyond the cost savings and data security benefits we’ve mentioned, self-hosted Agentic AI provides some other competitive advantages: Operational Leverage: Self-healing capabilities and automated workflows reduce maintenance costs and emergency responses. Rapid Iteration: Without vendor approval processes or API rate limits, you can experiment freely. Test new workflows, adjust agent behavior, and optimize performance on your timeline, not a vendor's roadmap. Customization Depth: Full control over models and workflows enables domain-specific optimization that cloud services can't match. Fine-tune your models on your specific use cases. Build integrations for proprietary systems. Optimize for your exact requirements. The Competitive Inflection Point In this series, we have traced a potential reversal in AI adoption patterns. Part 1 showed how resource intensity gave large enterprises the AI adoption advantage. Part 2 identified autonomous architecture—self-managing, self-optimizing systems—as the technology shift that could return competitive advantage back to small businesses and startups. In Part 3, we have demonstrated how self-hosted Agentic AI can make autonomous architecture accessible at a cost that small businesses can afford. A self-hosted Agentic AI solution changes the competitive equation. As a small business or startup, you can deploy autonomous systems that manage themselves, adapt to changing conditions, and operate at scale—with budgets measured in thousands rather than hundreds of thousands of dollars. As we’ve shown, the technology is cheap, and it is ready. The time to seize on this advantage is now, before a wave of acquisitions puts these cheap and easy AI services behind a barrier of premium licenses within enterprise suite applications. RedCloud Can Help If you’re ready to explore autonomous architecture, we can partner with you in a number of ways: Identify opportunities: Look for one process where autonomous capabilities would create clear, measurable value, and do a wholesale redesign of your work around it. Build the capability: Someone needs to understand these systems and train your technical staff on the initial setup and knowledge needed to expand and scale. Experiment with intent: Deploying your first autonomous capability will require you to learn and iterate. A strong foundation for your AI models will have a compounding benefit over time. Measuring impact: Establish the metrics to track the impact on your business. This data will guide your scaling decisions. The autonomous architecture shift is just beginning, and true to the traditional technology adoption curve, small businesses who recognize this inflection point and act as the early adopters have a tremendous opportunity. The opportunity to realize the productivity gains from successful AI deployments that will enable them to operate with fundamentally different economics and agility than their larger competitors. Contact us  today, and our experts will partner with you as you navigate this profound step forward in AI adoption.

Adoption of AI models has broken the traditional technology adoption cycle Artificial intelligence isn’t just another technology wave. It’s a fundamental shift in how businesses operate, compete, and grow. Unlike past innovation cycles, AI adoption has upended expectations. Enterprises are leading the charge, startups are scrambling to catch up, and every organization faces critical choices about cost, control, and capability. At RedCloud, we believe success in this era depends on cutting through the hype and building practical, future-ready strategies. This three-part blog series offers a perspective on AI adoption, including a look ahead to the next frontier—natural code, and a playbook for building agentic AI on your own terms. Along the way, we’ll highlight how RedCloud’s AI services help clients to unlock the potential of big data, gain valuable insights, automate processes, and accelerate transformation. Why Large Enterprises Are Leading the Charge For decades, the   technology adoption curve  followed a predictable pattern: scrappy startups and nimble small businesses would embrace new innovations first, while large enterprises lagged behind, weighed down by bureaucracy, legacy systems, and risk aversion. From personal computers to cloud computing, from social media to mobile-first platforms, the story was always the same—small businesses led, and large enterprises followed. But, so far, artificial intelligence is rewriting this narrative entirely. The Great Reversal Today's data reveals a striking paradox: when it comes to AI adoption, large enterprises are not just keeping pace with small businesses—they're leading the way. According to June 2025 transaction data in   Ramp’s AI Index , only 37% of small businesses have deployed AI in some form, compared to 49% of large enterprises. Looking a level deeper, a   2024 study by Boston Consulting Group  revealed only 26% of companies have developed the necessary capabilities to move beyond AI proofs of concept and generate tangible value, and they are predominantly large enterprises.  The AI adoption patterns we’ll discuss in this post may represent more than just a temporary market dynamic; it could be a signal of a permanent shift in how innovative new technologies proliferate throughout the world economy. Because AI offers such transformative potential, it seems the large enterprises that have pulled ahead of the pack are likely to stay there. According to   MIT’s Sloan School of Management , organizations in the first two stages of their AI maturity had financial performance below their industry’s average, while organizations in the last two maturity stages performed above their industry’s average. The result is a vicious cycle, where large enterprises that can afford to invest heavily in AI capabilities are creating distance from their small business competitors, which in turn enables them to only further increase that disparity over time. There are signs that small businesses are fighting to catch up.   According to Goldman Sachs , small business AI adoption has jumped to 17% from where it was two years ago. What this fails to capture, however, is that this growth is masking a crucial gap in the sophistication and maturity of implementation for AI solutions. Stated plainly, large enterprises are getting more from AI solutions because they can invest more and scale the value from their solutions further. The Resource Reality The primary driver of this technological reversal lies in the fundamental nature of AI adoption itself. Unlike previous technologies that could be implemented incrementally with modest investment, successful AI deployments have required substantial upfront resources and organizational capabilities. In practice, this means that large enterprises have had a natural advantage in building and implementing AI Solutions. They’ve invested in centralized AI governance models, established centers of excellence for risk and compliance, and created hybrid organizational structures that balance centralized oversight with distributed implementation across their business units. Smaller businesses simply lack the organizational complexity to support such sophisticated deployment efforts. We’ve also seen large corporations fiercely competing for people with AI skills.   Research from IBM  shows that 33% of companies cite limited AI skills and expertise as the top barrier to successful AI adoption. Large enterprises can afford to hire teams of AI specialists, data scientists, and implementation managers, whereas smaller businesses often rely on as few as one qualified professional or a handful of self-taught employees to guide their AI adoption journey. So, large enterprises have a superior infrastructure and a deeper talent pool to draw from.  What has been the result? The Implementation Maturity Gap Beyond raw resources, large enterprises are demonstrating superior strategic sophistication in their AI adoption approaches. The data shows clear differences taken by companies of different sizes. Asana’s report on the State of AI at Work 2025  finds "there’s a widening gap between the 29% of organizations we call AI Scalers and everyone else. AI Scalers don’t just “try AI.” They redesign work around it—and see a 91% productivity boost.". Further, less than one-third of companies report following most of the   12 key AI adoption and scaling best practices identified by McKinsey . Within that, however, large enterprises consistently outperform small businesses across these practices, including establishing dedicated AI teams, creating internal communications about AI value, and embedding AI solutions into business processes effectively. So, we’re seeing large enterprises focus on AI Solutions that affect their core business processes, rather than just experimenting with support functions, and putting the practices in place that are necessary to be successful. They identify and prioritize their most impactful use cases for AI transformation, whereas small businesses often scatter their limited resources across various experiments with AI solutions.  The Challenge for Small Businesses If you’re part of a small business or a startup, what can you do about this? You have good reason to be skeptical that you stand a chance of catching up and reclaiming your role as the innovators/early adopters. According to Goldman Sachs , about 42% of small businesses say they still don't have access to the resources and expertise necessary to successfully deploy AI. Of those businesses, 60% cite a lack of expertise in applying AI to their specific business context. So, it’s not just about hiring AI specialists; it's about understanding how to transform existing business processes effectively using AI solutions. As AI becomes increasingly more essential, the traditional startup advantage of speed and agility appears to have been neutralized by AI's complexity and resource requirements. Unlike deploying a mobile app or setting up a social media presence, implementing AI effectively seems to fundamentally favor large enterprises. The Generative AI Exception One notable exception to this trend is generative AI adoption, where small businesses have shown stronger initial adoption in certain areas. A   2024 survey from BizBuySell  showed that over 75% of respondents reported they use AI tools for Marketing functions. The reason is simple: AI excels at tasks like content creation, copywriting, social media management, email marketing, and analytics, and the tools available for performing these tasks can be used cheaply and immediately without an extensive technical infrastructure. Will we see more examples like this in the future?  Or perhaps we’ll see a wave of acquisitions of these cheap and easy AI services, putting them behind a barrier of premium licenses within enterprise suite applications? Don’t Give Up Yet The AI revolution has been fundamentally different from previous technology cycles in its initial phases. While the internet and mobile computing democratized access to powerful tools, AI has thus far consolidated its advantage among those with the resources to implement it effectively. Being a large enterprise hasn't been a disadvantage in AI adoption; it's been a prerequisite for early leadership. However, just as the internet eventually became accessible to garage startups, and mobile apps empowered individual developers to compete with established software giants, AI solutions may soon be approaching their own democratization inflection point. Coming in Part 2 The current corporate dominance in AI adoption has profound implications for entrepreneurship, competition, and economic opportunity for all of us. In Part 2 of this series , we’ll provide our point of view about how the pendulum may swing back in favor of the nimble innovators who have historically been the drivers of technological disruption. Small businesses and startups, it turns out, do have an opportunity to reclaim their traditional role as technology pioneers.

How Self-Managing Systems May Reshape the Competitive Landscape. This is the second post in our series on how AI is reshaping business. In Part 1, we studied the current state of AI Adoption across Large Enterprises versus Small Businesses. What we found is that, thus far, Large Enterprises have upended the traditional technology adoption curve and far outpaced Small Businesses with more complex and impactful deployments, bolstered by deep pools of talented AI researchers and professionals. Will Large Enterprises continue to widen their lead, or are there emerging trends that could shift the advantage back to Small Businesses and Startups? Evolving Beyond Traditional Development Recent developments in software creation suggest we may be approaching an inflection point. According to   SAP , low-code/no-code platforms are fundamentally transforming software development by allowing non-technical users to build applications without extensive programming skills, business users are now collaborating on more than 60% of low-code/no-code development projects. But the changes go deeper than simplified development tools. We're   beginning to see  the emergence of what could be called "autonomous architecture" — systems that can manage, optimize, and evolve themselves with reduced human intervention. Consider the current trajectory: we are already living in a world where applications can generate their own user interfaces on demand, scaffold backends in minutes, and even create and run their own test suites while still under construction. Developers can ask the system to add more mid-build tests and watch them execute instantly. We may soon be living in a world where Deployment processes can automatically handle scaling, security patches, and performance optimization, and systems monitor their own usage patterns, file their own backlog tickets, and even draft their next revision plan.  We’re heading for a world where productivity is no longer bound by typing speed or the number of engineers on a team — but bound by clarity of intent. Natural code, or the delivery of new software through sentences and structured explanations rather than syntax-driven code, is what can enable this future. In many ways, it’s a logical extension of what we already see that AI can do. Plain-language intent capture is becoming the new input for software. Living specifications — more than contracts, effectively, would become the new code base — anchored in quality and compliance. Golden paths turn best practices into default standards. Policy packs enforce governance automatically. Deployment bots smooth away infrastructure pain points. Self-observing systems transform usage data into Rev-2 plans. At some point, we’re not just using AI to write less code ourselves — we’re using AI to  change what software is . And that is, a continuously evolving partner in delivery rather than a static software artifact. The Resource Intensity Question In Part 1, we established that Large Enterprises have been AI Adoption leaders, because they can afford the substantial upfront resources and organizational capabilities that effective AI deployments require. The shift to autonomous architecture will challenge this status quo in several ways.   Reduced Talent Requirements:  When systems can handle more operational tasks independently, the need for large teams of specialists decreases. Small Businesses may find they can achieve sophisticated AI capabilities without competing directly with enterprises for scarce AI talent. Lower Operational Overhead: Large Enterprise solutions require ongoing management, integration work, and maintenance. Self-managing systems could reduce these demands significantly, perhaps enough to level the operational playing field. Simplified Infrastructure: Autonomous systems that handle their own scaling, security, and compliance could eliminate much of the infrastructure complexity that’s currently required. Agility Begets Ability When systems themselves can adapt and evolve, the organizational agility advantage that Startups and Small Businesses gain should become more pronounced. Examples of this could include: Deploying autonomous systems without extensive approval processes Allowing systems to make operational decisions without bureaucratic oversight Iterating and improving based on real-time feedback from autonomous monitoring Experimenting with new capabilities without complex change management efforts If autonomous architecture continues to mature and becomes more accessible, it could represent the democratization inflection point that returns the technology adoption advantage to smaller, more agile organizations. As we know, the traditional technology adoption curve has been:  startups innovate → enterprises eventually adopt and scale → enterprises dominate until the next disruption.  Autonomous architecture would compress this cycle significantly, allowing small businesses to achieve enterprise-scale capabilities without enterprise-scale complexity. Not So Fast Before we get too far down the road of making grandiose claims about the paradigm shift that autonomous architecture will bring, it’s important to note that this technology is still emerging, and as a result, some challenges remain.  Most critically, we’ve yet to see autonomous systems with built-in governance requirements for things like compliance, audits, and data classification.  Solving these challenges could very well disrupt the world of software development full stop.  Large Enterprises will also be trying to solve these challenges in parallel, and while their approach to this may differ due to the greater complexity of their requirements, that does not guarantee they will be less effective. It’s also important to consider the cost and availability of autonomous architecture tools.  The Large Enterprises that many Startups or Small Businesses are hoping to compete with may very well be the companies from which these tools originate.  Having the time to bring these systems online internally, before they ever even hit the open market, may be all the advantage that Large Enterprises need. Talk the Talk For business and technology leaders, the implications are significant. Speed no longer requires cutting corners. Smaller, sharper teams can accomplish what once required armies. Predictability improves as standards replace heroics. And strategically, the ability to move from “what if” to “here it is” faster than competitors becomes a defining advantage. Development will no longer just be coding. It will be conversation, automation, and continuous evolution. The future is software that ships itself, tests itself, and plans its own next revision. That is the power of natural code and autonomous architecture — and it’s closer than you might think. Coming in Part 3 In our final part of our AI Adoption series, we'll showcase specific use cases, tools, and strategies that organizations can employ to put themselves in a position for this seismic shift. And, we’re going to show you how to do this both quickly and cheaply.  Let’s get your adoption journey started with Self-hosted Agentic AI.

At RedCloud, we believe that preparing your organization for automation and AI is not just about technology. It is about setting up the right foundation across your people, processes, and tools. Our readiness model breaks down into five key areas that, together, help you understand your current standing and where to focus next. 1. Organizational Change Management (OCM) Process Maturity   Change can be tough, especially when new tech is involved. This area examines how effectively your organization’s processes facilitate smooth and predictable change. When your processes are solid and repeatable, it is easier to roll out automation and AI at scale without surprises. 2. Understanding AI and Its Potential   AI can sound mysterious, but it is crucial that your teams get what it can do, especially in their own day-to-day work. This means everyone should be able to spot good opportunities to apply AI that actually make a difference. 3. AI Tools and Infrastructure   Having the right tools matters. This is not just about owning cool software, but also about ensuring your teams have easy and safe access to the AI platforms they need, and knowing where gaps might be so you can close them. 4. Putting AI to Work   It is one thing to have AI tools, but are you truly utilizing them to enhance operations and informed decisions? This area examines the extent to which AI is integrated into your daily work and its impact on helping teams perform more effectively. 5. Treating Data as a Strategic Asset   Data is the fuel for AI. This area assesses how well your data is organized, collected, and utilized to generate insights. When data is handled strategically, it empowers your transformation teams to become trusted advisors and leaders in your business. What We’re Seeing in the Midwest and South Utilities Sector Our research across a dozen utilities shows strong potential but also some clear gaps: Data as a Strategic Asset:  Many use dashboards but still face fragmented data. Centralizing and real-time reporting can unlock smarter planning and better insights. OCM Process Maturity:  Organizations are starting to formalize change management. Focusing on core processes will help scale transformation with more consistency. Understanding AI:  Interest is growing, but basic knowledge is still low. Targeted education can help teams move from curiosity to confident AI use. Access to AI Tools:  Access is limited, and policies are outdated. Expanding availability will reduce the risk of experiments and accelerate adoption. AI in Action:  Early pilots show promise, but most teams are just getting started. Leading the integration of AI tools into daily work will drive real results. Looking Ahead For utilities ready to embrace AI and automation, the best approach is steady and intentional progress. Building basic AI literacy, ensuring safe access, and aligning efforts with clear strategy are the stepping stones to success. Transformation offices have a unique chance to lead this shift from isolated projects to company-wide coordination. New capabilities already demonstrate what is possible — connected tools that break down silos, reveal overlapping efforts, and turn data into actionable foresight. By integrating AI into everyday work and aligning changes with broader strategic objectives, organizations can enhance performance and unlock genuine value. The message is clear: those who start building now will lead confidently into a future powered by smart, intelligent transformation.Contact us today to learn more.

In America, perhaps more than any developed nation in the world, “consumer” habits drive our economy. Our nation’s Consumer Price Index  reports on the health of consumer spending, guiding many other aspects of the economy. The consumer sector is dynamic, with trends shifting constantly. What’s new today may be old news tomorrow. Here at RedCloud, we thrive on this dynamism as we help clients meet their customers where they are with their product or service's technology, go-to-marketing, and messaging.  We're not just observers but collaborators, innovators, and partners in progress, adapting strategies and solutions to meet the evolving needs of our clients and their customers.  From startups to established Fortune 50 enterprises, we embark on a journey of partnership, leveraging our expertise to refine strategies, enhance customer experiences, boost retention rates, and seamlessly integrate cutting-edge technologies. Our market research, which forms the bedrock of our approach, has led to significant improvements in our clients' businesses. We delve deep into consumer insights, deciphering trends, preferences, and behaviors to unearth actionable intelligence. Armed with this knowledge, we craft bespoke strategies tailored to each client’s unique positioning and aspirations. But it’s not just about understanding the market; it’s about crafting experiences that resonate profoundly. At RedCloud, we're champions of customer-centricity. We believe every touchpoint is an opportunity to delight, surprise, and forge lasting connections. Whether optimizing digital interfaces or revamping brick-and-mortar experiences, we strive to elevate every interaction, transforming customers into loyal advocates. With our expertise, passion, and dedication, we're not just shaping the future of consumer engagement; we're pioneering it. Take a closer look at our success stories in the consumer sector: Cyber Vulnerability Prevention & Information Security Management  and Boosting Sales​ Through Unified Support . These real-world examples showcase how RedCloud has helped businesses like yours navigate the dynamic landscape of consumer behavior, driving growth and fostering lasting customer relationships.

In the fast-paced world of enterprise technology, staying ahead is not just an advantage—it's a necessity we help our clients achieve. Companies in this sector must continually innovate and adapt to maintain competitiveness and drive growth. RedCloud’s team of enterprise tech experts helps our clients harness the power of today’s legacy and new tech platforms, ensuring they remain leaders in their industries. Our Tech-Forward Approach Our approach centers around leveraging the most advanced technologies to streamline processes, optimize IT infrastructure, and implement cutting-edge solutions. These technologies include artificial intelligence (AI), machine learning (ML), cloud computing, and advanced data analytics. By integrating these tools into our clients' operations, we not only enhance productivity and operational effectiveness but also deliver tangible, measurable results that drive business growth. Artificial Intelligence and Machine Learning : AI and ML are transforming enterprise technology by enabling automation and improving decision-making processes. RedCloud uses these technologies to help clients predict market trends, optimize supply chains, and personalize customer experiences. This results in more efficient operations and better business outcomes. Cloud Computing: We excel in the adoption of cloud computing. They assist enterprises in migrating to cloud platforms, which offer greater flexibility, scalability, and cost savings. By moving to the cloud, businesses can quickly adapt to changing demands and deploy new services faster. Data Analytics : In today's data-driven world, the ability to analyze and act on data is crucial. We provide advanced data analytics solutions that help clients turn raw data into valuable insights. These insights can drive strategic decisions, improve customer satisfaction, and uncover new business opportunities. Take a closer look at our success story in the enterprise technology sector: Sales Enablement Program Management and Modernization . This real-world example showcases how RedCloud has helped businesses like yours navigate the dynamic landscape of enterprise technology behavior, driving business productivity and effectiveness.

While big data and AI are key drivers of innovation in today’s economy, garnering a good deal of the current industry spotlight, marketing and sales remain crucial to the initial and long-term success of nearly any type of product or service.  To that end, over the last decade at RedCloud, we’ve developed a highly accomplished Marketing and Sales practice, providing our clients with proven strategies and innovative tactics to introduce, educate, and motivate customers. Leveraging that expertise, Account Director and Practice Lead Scott Morton hosted our recent internal “Lunch and Learn'' session, with a packed agenda covering RedCloud’s latest service offerings, industry trends, and insightful presentations on recent projects with our clients at Microsoft. Attended by a wide range of the RedCloud team, this session was a treasure trove of knowledge. Highlights of this Lunch and Learn session included:  A dive into RedCloud's Marketing and Sales offerings , providing an in-depth overview of the array of tools, strategies, and resources used by our team to bolster marketing and sales endeavors. RedCloud's offerings are finely tuned to address the multifaceted requirements of contemporary businesses, covering everything from lead generation and customer relationship management to data analytics and automation. An overview of RedCloud’s ongoing work with Microsoft Cloud Marketing MOSA (Marketing, Operations, Sales, and Analytics), a framework providing businesses with a holistic approach to cloud marketing through leveraging the full potential of Microsoft's cloud solutions for marketing and sales success. Our team also learned how RedCloud's tailored MOSA solutions can help businesses implement effective cloud marketing strategies, driving growth and profitability for our clients in today's competitive marketplace. The Lunch and Learn concluded with Scott providing a deep dive into strategy and planning, providing our team with essential tools and frameworks to develop and execute successful marketing and sales strategies. From setting SMART goals to conducting market research and competitor analysis, this “Strategy & Planning 101” equipped our consultants with a new set of skills, tools, and resources to expand current projects, adding more value to their clients.  As always, this session sparked cross-team collaborations and ideation within RedCloud’s team of experts, reminding us of the importance of pulling together from time to time to educate and inspire our teammates. We already can’t wait for the next one!

One of our most popular consultant-focused professional development opportunities is RedCloud’s regular “Lunch & Learn” sessions during which we bring relevant and practical learnings to our team. Our most recent L&L session in early May was no exception, as leadership and life coach Dat Tran focused on “Mental Resilience with Positive Intelligence” - skills and approaches that can help any professional in today’s quick-moving, pressure filled world! Having presented more than 30 times to a who’s who of Seattle-area leading companies, Dat’s Mental Resilience with Positive Intelligence session provides insights and practical exercises used to build mental resilience, which supports learning how to move towards peak performance and greater happiness. This allows professionals to create the mental space and type of response needed to best empower ourselves and others. During his session at RedCloud HQ in Bellevue, WA, Dat shared insights and walked us through several practical, 10-second exercises we can all use to build mental resilience - a key to success in a post-pandemic world that can feel hectic and disjointed at times. Our team also learned about the upsides and downsides of the “9 saboteurs”, how to better manage them, and finding gifts in things with a “Sage perspective” to achieve a more positive mindset overall. Overall, we had a great turnout and Dat’s presentation resonated with all of our team members who attended. We can’t wait to hear how these practical strategies are implemented by our team. What our team said: Consultant and Account Manager Melissa Wilson recommended Dat Tran as our guest speaker and said after attending his workshops she, “has benefitted from Dat’s Positive Intelligence offering and experienced greater peace, performance, and happiness as a result.” Consultant Christine Arnette commented, “Thank you for sharing your passion with us Dat Tran, what a lovely Lunch & Learn!” Thanks again Dat! About Dat Tran: Dat Tran has 13+ years of experience working with leaders across the world, and most recently as a global enablement leader at Microsoft. As a leadership and life coach, Dat focuses on balance, purpose, and authenticity. Dat has delivered over 30 coaching keynotes and workshops across Microsoft, Amazon, and Bain for affinity groups, teams, and leadership offsites since starting his Leader Within series in June 2022. His workshop was recommended to us by RedCloud consultant & account manager, Melissa Wilson, who attended a session he presented for Microsoft. More information is available at datpurposeinlife.com .