Artificial Intelligence in Business

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

AI didn't take my job... It helped me get promoted. It also came up with that hook. 🤖 Now that I know you are invested in learning about AI, I want to show you some tools I am using to maximize my efficiency in my new fractional remote role. Like most people, I used to juggle countless apps, lists, and notes, only to end my day feeling overwhelmed and underproductive. But integrating AI into my workflow has completely changed the game. Here's the 3 tools that I currently use the most: Notion.so : Organize & Streamline Notion: Effortlessly structures my ideas, projects, and plans in one cohesive space. UseMotion.com : Prioritize & Schedule Motion: Transforms chaotic task lists into clear, prioritized schedules, reducing stress and boosting productivity. The automatic scheduling and rescheduling of tasks, directly into my Google Calendar has been incredibly powerful. Here in the next couple weeks I plan on integrating several more members of my Heron Labs team into the app as well so that all of our projects and tasks are immediately visible to eachother. No more back and forth emails trying to schedule a call. (A note here, Google Calendar recently rolled out a new native feature for scheduling calls based on your calendar that works really well too) ChatGPT.com : Create & Inspire ChatGPT: Fuels my creativity, quickly turning rough ideas into polished content and captivating visuals. By delegating routine decisions and overcoming creative roadblocks with AI, I've been able to assume more professional responsibilities without sacrificing personal family time or neglecting my farm/homestead chores. Time is my most valuable resource. AI tools help me me spend it wisely. How are you leveraging AI to optimize your time?

The more I used AI to build, the less magical it felt. After spending weeks trying to rebuild our platform with Claude/ChatGPT, I realized a hard truth: What started as a productivity hack turned into a full-time prompt engineering job. Don't get me wrong, AI is mind-blowing at what it does. But you have to think of it the right way: Even with recent updates like Claude Code + Sonnet 3.7, AI still needs your architectural vision and continuous feedback to deliver production-quality results. I recently shared my experience with AI coding, and dozens of engineers and leaders jumped in with their own battle-tested insights (original post in comments). These were the strongest insights and recommendations: - Design the architecture first, then let AI do the implementation following YOUR architecture. - Instead of asking AI to build the whole car at once, ask it to build individual parts separately. - Iterate and guide: Aiming for perfection on the first prompt response will not work. - Start with AI-written unit tests to ensure your expectations align with the output (hello TDD 😉). - And finally, remember that AI can only code as well as you can architect and explain. These insights are from engineering professionals who are making AI work instead of creating more work. The gap between using AI and using it WELL is wider than most realize, and the teams closing this gap are the ones getting real results. What's your most effective technique for integrating AI into your development process? — Big thanks to Antons Kumkovs, Michael Rollins, Winner Emetuche, Drew Adams, Domingo Gómez García, Ryan Booth, Michael Fanous, David Cornelson, Youssef El Hassani, and Michael L. whose comments are included in the carousel 👇

𝘛𝘩𝘪𝘴 𝘸𝘢𝘴 𝘴𝘰𝘮𝘦𝘵𝘩𝘪𝘯𝘨 𝘐’𝘷𝘦 𝘣𝘦𝘦𝘯 𝘱𝘶𝘵𝘵𝘪𝘯𝘨 𝘵𝘰𝘨𝘦𝘵𝘩𝘦𝘳 𝘵𝘩𝘪𝘴 𝘸𝘦𝘦𝘬. 𝐍𝐨𝐭 𝐚𝐥𝐥 𝐀𝐈 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐚𝐫𝐞 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐞𝐪𝐮𝐚𝐥. Here’s how I integrate Microsoft Azure services to create AI that works for just about any business not the other way around. Want to know the secret sauce? 👇 7 Lessons from Building Scalable AI Solutions Customers Love: 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐧 𝐝𝐚𝐭𝐚. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐳𝐞𝐫 for structured ingestion. ↳ Automate preprocessing with 𝐀𝐳𝐮𝐫𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐀𝐩𝐩𝐬. ↳ Store data securely in 𝐀𝐳𝐮𝐫𝐞 𝐁𝐥𝐨𝐛 𝐒𝐭𝐨𝐫𝐚𝐠𝐞. 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐯𝐚𝐥𝐮𝐞. ↳ Focus on actionable insights, not noise. ↳ Leverage 𝐀𝐳𝐮𝐫𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 for advanced prep. ↳ Collaborate with end users for relevance. 𝐓𝐫𝐚𝐢𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 𝐭𝐡𝐚𝐭 𝐚𝐥𝐢𝐠𝐧 𝐰𝐢𝐭𝐡 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐠𝐨𝐚𝐥𝐬. ↳ Test multiple architectures, like custom LLMs. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐌𝐋 and Azure OpenAI to streamline experimentation. ↳ Optimize for speed and scalability. 𝐃𝐞𝐩𝐥𝐨𝐲 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐧𝐠 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬. ↳ Host on 𝐀𝐳𝐮𝐫𝐞 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 for reliability. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 for seamless integration. ↳ Monitor deployment with feedback loops. 𝐌𝐚𝐤𝐞 𝐝𝐚𝐭𝐚 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐛𝐥𝐞 𝐚𝐧𝐝 𝐚𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞. ↳ Index with 𝐀𝐳𝐮𝐫𝐞 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 Search. ↳ Store outputs in 𝐂𝐨𝐬𝐦𝐨𝐬 𝐃𝐁 for scalability. ↳ Ensure query optimization for real-time use. 𝐁𝐫𝐢𝐝𝐠𝐞 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐥𝐨𝐠𝐢𝐜. ↳ Use 𝐀𝐳𝐮𝐫𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬 to support decisions. ↳ Automate workflows for better efficiency. ↳ Integrate insights directly into operations. 𝐆𝐨𝐯𝐞𝐫𝐧 𝐰𝐢𝐭𝐡 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐚𝐠𝐢𝐥𝐢𝐭𝐲 𝐢𝐧 𝐦𝐢𝐧𝐝. ↳ Use 𝐆𝐢𝐭 𝐅𝐥𝐨𝐰 for version control. ↳ Secure pipelines with 𝐂𝐡𝐞𝐜𝐤𝐦𝐚𝐫𝐱. ↳ Automate infrastructure with 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦. Which step will move your business forward today? ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #ArtificialIntelligence #AzureCloud #InnovationInTech #AITransformation #MachineLearningPipeline

In my discussions with boards and CEOs on AI strategy, here are the 6 most common AI questions I hear and how I approach them: 1️⃣🤔 "How do we integrate AI into our existing business model?" Don't start with the technology. Start with your business goals and pain points. Identify areas where AI can enhance efficiency, improve customer experience, or create new value. Develop a roadmap that aligns AI initiatives with your overall strategy. 2️⃣🤔 "What are the risks, and how do we manage them?" Conduct a thorough risk assessment covering data privacy, security, ethical considerations, and potential operational disruptions. Develop a robust governance framework. Consider appointing an AI ethics board. Stay informed about evolving regulations and ensure compliance. 3️⃣🤔 "How do we measure ROI on AI investments?" Define clear, measurable objectives for each AI initiative. Track both quantitative metrics (cost savings, revenue growth) and qualitative outcomes (improved decision-making, customer satisfaction). Be patient – some benefits may take time to materialize. 4️⃣🤔 "Build in-house or partner with vendors?" Be wary of the common trap of overestimating in-house capabilities! Many companies instinctively lean towards building themselves, assuming it'll be "faster" and "cheaper." Reality check: it rarely works out that way. To make an informed decision: 👉Conduct an honest capability assessment. Do you truly have the expertise and bandwidth? 👉Calculate the total cost of ownership, not just initial development. Factor in ongoing maintenance, updates, and opportunity costs. 👉Consider time-to-market. 👉 Is this a core differentiator or a supporting capability? 👉 Assess the pace of innovation in the specific AI domain. Can you keep up with rapid advancements? For most companies, a hybrid approach works best. Build in-house for truly unique, core competencies. Partner for everything else. Remember, the goal is to create value, not to own every piece of technology. 5️⃣🤔 "Which AI use cases should we prioritize?" Focus on high-impact, low-complexity projects to start. Look for areas where you have quality data and clear business objectives. Prioritize use cases that align with your strategic goals and have potential for scalability. 6️⃣🤔 "How do we build an AI-capable workforce?" Don't silo AI in one tech team! Weave it into your entire organization's fabric. Remember, AI isn't just for tech—it's a business-wide transformation tool. Key strategies: 👉Company-wide AI training: From marketing to finance, everyone needs AI literacy. 👉Cross-functional teams: Blend tech experts with domain specialists. 👉Strategic partnerships & M&A: Quickly infuse AI capabilities across functions. 👉Foster an AI-first culture: Encourage all teams to apply AI in their work. 👉Continuous learning: Keep pace with AI advancements company-wide. What other AI-related questions are you grappling with? #AIStrategy #Innovation #DigitalTransformation

As a small business owner, I often find myself bogged down by limited resources. Over the past year, I have discovered multiple ways to use AI to streamline operations and reduce costs. I think now is good time to share some of these strategies with Linkedin network. 🧠 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 When facing a complex business decision, input key factors, constraints, and goals into an AI tool. Ask it to generate multiple solution scenarios, helping you break through mental blocks and consider fresh perspectives. 💡 𝐈𝐝𝐞𝐚𝐭𝐢𝐨𝐧 𝐟𝐚𝐜𝐢𝐥𝐢𝐭𝐚𝐭𝐢𝐨𝐧  A good question provides at least a half of the answer, right? Use AI to create thought-provoking questions and discussion topics based on your goals. This ensures a more focused and productive brainstorming sessions. 📅 𝐌𝐞𝐞𝐭𝐢𝐧𝐠 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲  Input meeting objectives and participant roles into an AI tool to generate a time-optimized agenda. Include instructions on specific discussion points to keep meetings on track and purpose-driven. 📊 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭  Feed project goals, list of tasks and deliverables, deadlines, and resource constraints into AI to generate a preliminary project roadmap, or analyze existing plans. Use this as a starting point for team discussions, refining the plan based on human expertise and insights. 🕵️♂️ 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞  Use AI to regularly scan and summarize competitor websites, news articles, press releases, and social media posts. This provides a very time-efficient and continuously updated snapshot of market trends and competitor activities. 🎯 𝐋𝐞𝐚𝐝 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 Input your ideal customer profile into AI to find matching companies. Adjust your search by having AI apply filters or explore similar businesses in other sectors or regions. Use the list to craft personalized outreach strategies tailored to each prospect. 🔍 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧  When tackling a new topic, technology, or market, use AI to quickly generate a comprehensive overview. This rapid synthesis offers a solid foundation for deeper human analysis and informed decision-making. 📈 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞  Use AI to analyze raw data. Have it identify bad data (empty cells or non-sensical values) to ensure cleanliness before analysis. Once verified, use AI to spot trends and anomalies. This ensures that your data-driven strategies and business decisions are based on reliable, high-quality information. ✍️𝐋𝐞𝐠𝐚𝐥 𝐬𝐮𝐩𝐩𝐨𝐫𝐭  For smaller teams, AI can serve as a valuable first line of defense by scanning contracts for potential risks and suggesting improved wording without changing the document’s essence. It ensures basic legal coverage and flags areas for professional review, helping balance protection with resource efficiency. 𝑫𝒐 𝒚𝒐𝒖 𝒉𝒂𝒗𝒆 𝒊𝒅𝒆𝒂𝒔 𝒕𝒐 𝒂𝒅𝒅 𝒕𝒐 𝒕𝒉𝒊𝒔 𝒍𝒊𝒔𝒕? 𝑷𝒍𝒆𝒂𝒔𝒆 𝒔𝒉𝒂𝒓𝒆 𝒕𝒉𝒆𝒎 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔!

Most people think the path to leading AI strategy at your company starts with a PhD or a job title with “data” in it. But here’s the truth: If you’ve been the #NoCode builder in your department — the one who actually solved problems, shipped automations, and connected tools to make things work — you’re already way ahead. You're not just “the ops person who builds Zaps.” You’re sitting on the exact skillset that makes someone qualified to lead AI adoption across an entire org. Here’s what that path can look like in 10 steps: 1. Own a painful problem – Automate a manual, messy process that affects real people. Get results. 2. Document what changed – How many hours did you save? What was the impact? Tell the story. 3.Share it internally – Build your internal brand. Present at a team meeting. Make noise. 4. Repeat across teams – Run small pilot projects with Sales, CS, HR, Finance. Start stitching systems together. 5. Layer in AI – Use AI to improve those automations. Draft messages, generate reports, classify data. 6. Create frameworks – Don't just build Zaps. Build repeatable processes. Start thinking like a platform. 7. Start teaching – Host lunch & learns. Run internal demos. Write internal playbooks. 8. Partner with IT – Get buy-in. Learn the guardrails. Build trust. Speak both languages. 9. Make it safe to experiment – Create a sandbox where other teams can play, test, and learn. 10. Propose a formal AI enablement role – You’ve got receipts. Now pitch the job: AI Innovation Lead, Automation Strategist, or even Head of AI Citizen Development. This isn’t a hypothetical. I’ve seen it happen. I’ve helped people do it. The future of AI at your company won’t be owned by one brilliant prompt engineer. It’ll be owned by the person who knows how work actually gets done. That might just be you.

85% of AI projects don’t succeed when it comes to customer success It’s no surprise. Most companies run without clear processes or choose out of the box solutions not fine tuned for their business. They try to force AI into their current systems without a plan. This leads to: → AI implementation failing over operational issues → Believing AI is overrated or can't deliver results → Results that are all over the place with no clear ROI Your big idea to change your industry never fully takes off. But it doesn’t have to be this way. Many clients come to me when their initial AI plans fall short. Here’s why cleaning up your data and processes makes all the difference: 1. Spot existing automation opportunities and out of the box wins: ↳ It’s best to find easy tasks for AI to take over initially. Avoid complex flows like the plague. 2. Map data sources and flow: ↳ Map how information flows and an updated process. A lot of skeletons in this area when a business does not factor in how to keep the AI up to date with business logic. 3. Reveal inefficiencies in your current flows: ↳ Pinpoint areas where AI can fix delays and speed up slow processes. This could mean getting more information from users or simply triaging tickets to start off with. 4. Create standard workflows: ↳ Keep things consistent, making AI integration smoother. The more custom and complicated your business processes are the harder it is to automate 5. Clarify decision points: ↳ Decide where AI can assist, and where humans need to step in. Always have fallbacks in place where an AI agent can hand off to a human and document it clearly. 6. Simplify the transition: ↳ Make switching from manual to AI-supported processes smoother. Start with using AI internally for your teams before allowing your customers to use it. 7. Enable constant improvement: ↳ Keep measuring and improving AI’s impact on your workflows and its ROI. Only at this point look at the more complex use cases that AI can help with The better you clean up your data and processes, the easier it will be for AI to step in and deliver big wins for you customers.

Here are four things to turn your team into an "AI-first team". They're pretty simple but require putting in the work. It's worth it: I'm seeing first-hand at Augment Code the agility of a team that uses AI all the time. I didn't expect they'd have this much fun doing it. AI-first teams start with "How can I use AI for this?", from mundane tasks to strategic projects. They: - automate their work and that of others - move much faster, - produce better work, - bring more value to customers and… - enjoy their work more! 1️⃣ Equip and empower your team. Even starve it a little - Find and elevate your AI champions—they'll pull the team along - Create dedicated time for sharing AI techniques and learnings: offsites, afternoon blocks, a “demo my AI workflow” section at every all-hands - Unlock significant budgets for AI tools and training. Get them approved by IT & Security. Your team should not fear using them - Gradually reduce resources for tasks AI can handle. This creates pressure to adopt new approaches 2️⃣ Enable your primary LLM, as you enable Sales - Whether ChatGPT, Claude*, or Google Gemini, set one up as the primary tool with your knowledge bases. Create GPTs, gems, on top of it - Task Product Marketing with adding and maintaining foundational knowledge as essential context to these models, so the rest of the team benefits and is on message. Include messaging docs, persona cards, ICP, competitor intel, battle cards, case studies, brand voice and tone style guide... The output of the team’s work will be on-brand and relevant to customers. 3️⃣ Hiring: assess for AI-first skills and mindset. Go deep - Dedicate a whole focus interview to assessing AI skills and mindset. Ask these questions: - Innovation: What are three effective ways you have leveraged AI recently? - AI leadership: What role did you play in enabling your extended team? - Continuous learning: How do you learn and get new ideas about the latest AI tools and best practices? What resources do you recommend I use and why? If you give an assignment, explicitly encourage using AI. Require that candidates share their process. See if and how they use it. 4️⃣  Inspire, assess, and reward your team - Ask “How will you / have you used AI for that?” in every one-on-one (put a sticky note by your/their desk) - Make “Acting AI-first” a key chapter of performance reviews - Recognize, give more scope to, and promote your AI champions. Fast and visibly - Lead by example, of course This list is not exhaustive. If you did other things that worked well, don't hesitate to add them in comments. 

Are you a product manager looking to find how AI can help you be more productive and innovative? Recently, I've been researching the art of getting quality responses from AI using well-structured prompts. In many tasks, it's increased my productivity and creativity significantly. I'm certain it can do the same for you. Here are five tips that might help you in your daily work as a product manager or in product development: ✅ Drafting User Stories: Provide clear instructions and let ChatGPT draft detailed user stories for your product features. This saves you time and ensures consistency. ✅ Creating Questionnaires: Simplify your interview preparations by using ChatGPT to generate tailored questionnaires. Well-crafted questions lead to insightful user feedback. ✅ Predicting Trends: Explore future possibilities by asking ChatGPT to analyze data and predict trends. This helps you stay ahead and make data-driven decisions. ✅ Brainstorming Features: Use ChatGPT to generate new ideas for features or product extensions. Fresh perspectives can spark innovative solutions. ✅ Enhancing User Experience: Analyze the customer journey with ChatGPT to identify pain points and suggest improvements. Enhance user satisfaction with actionable insights. The key is in providing clear, detailed instructions. The more context you give, the better the output. While AI can’t read minds (yet!), it can certainly help you work smarter, not harder! Found this insightful? Give this a thumbs up and follow me as I share my insights around digital transformation, product development and being an AI Power User. 😎