How to Implement AI Tools in Organizations

Step 3 of 7 for AI Enablement: Identify and Prioritize AI Use Cases See full 7-step breakdown here: https://lnkd.in/g3t7MiZb In setting up AI for success, we’ve covered the foundations: Step 1 defined clear business objectives. Step 2 assessed team readiness, revealing gaps to achieve outcomes. Now for Step 3: Identify and Prioritize AI Use Cases. This step isn’t just about knowing where AI could fit; it’s also about evaluating tools to ensure they meet essential requirements—and testing the top choices with trial runs. First: Explore What AI Tools Are Out There Before diving into specific use cases, it’s important to understand the types of AI tools available that could support your goals. If you’re unsure where to start, here are two valuable resources: • Theresanaiforthat.com – A searchable directory of AI tools across industries. • GTM AI Tools Demo Library – A curated list of go-to-market AI tools from the GTM AI Academy (l^nk in comments). Identify AI Opportunities with the PRIME Framework With a better understanding of AI options, use the PRIME Framework to identify use cases that directly address your most critical business gaps: • Predictive: Can AI help forecast outcomes? • Repetitive: Are there time-consuming, repeated tasks? • Interactive: Could AI enhance customer engagement? • Measurable: Can AI provide useful metrics? • Empowering: Can AI support creativity or productivity? Evaluate Tools with a Checklist Once you’ve outlined use cases, evaluate potential tools to ensure they meet critical requirements before trialing them: • Security & Compliance: Does the tool meet company standards? • Governance: Does it support data governance and accountability? • Cost & ROI: Is it cost-effective based on expected value? • Scalability: Can it grow with your team’s needs? • Integration: Will it fit with your current systems? Evaluate Tools: Make sure selected tools meet security, compliance, and integration needs before trial runs. Pilot Testing Once you’ve prioritized and evaluated, move into a pilot phase. Select top tools to trial with a small pilot team. This phase helps test effectiveness, build internal champions, and refine any processes before rolling out to the larger team in Step 4. Your Checklist for Step 3 1. Explore AI Options: Start with Theresanaiforthat.com and GTM AI Tools Demo Library. 2. Identify Use Cases with PRIME: Target high-impact areas. 3. Evaluate Tools with the Checklist: Confirm tools meet security, compliance, and integration needs. 4. Pilot Test: Trial top tools with a small team to validate effectiveness. By following this approach, you’ll set your team up for measurable, AI-driven success with tools that are tested and proven valuable. Ready to PRIME your AI Enablement? Check out free resources in the GTM AI Academy: • PRIME Use Case Guide • Impact-Feasibility Template • AI Critical Requirements Assessment Up next.. Step 4 of 7 for AI Enablement..

Missing the Agentic AI Revolution? Here's Your Roadmap to Get Started If you're not exploring Agentic AI yet, you're missing the biggest paradigm shift since the emergence of LLMs themselves. While others are still perfecting prompts, forward-thinking teams are building systems that can autonomously plan, reason, and execute complex workflows with minimal supervision. The gap between organizations leveraging truly autonomous AI and those using basic prompt-response systems is widening daily. But don't worry—getting started is more accessible than you might think. Here's a practical roadmap to implementing your first agentic AI system: 1. 𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗮 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 – Choose a specific task with clear boundaries where automation would provide immediate value. Document research, competitive analysis, or data processing workflows are excellent starting points. 2. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁'𝘀 𝘁𝗼𝗼𝗹 𝗯𝗲𝗹𝘁 – An agent's power comes from the tools it can access. Start with simple tools like web search, calculator functions, and data retrieval capabilities before adding more complex integrations. 3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 – The ReAct (Reasoning + Acting) pattern dramatically improves reliability by having your agent think explicitly before acting. This simple structure of Thought → Action → Observation → Thought will transform your results. 4. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝗮𝗿𝗹𝘆 – Don't overlook this critical component. Even a simple vector store to maintain context and retrieve relevant information will significantly enhance your agent's capabilities. 5. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 – LangGraph, LlamaIndex, and CrewAI provide solid foundations without reinventing the wheel. They offer battle-tested patterns for orchestration, memory management, and tool integration. The most important step? Just start building. Your first implementation doesn't need to be perfect. Begin with a minimal viable agent, collect feedback, and iterate rapidly. What specific use case would you tackle first with an autonomous agent? What's holding you back from getting started?

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

Buying Clay won’t get you more leads. Buying Gong won’t make your sales team better on calls. Just like: Buying a set of Wüsthofs won’t make you a better chef. Buying that new Titleist driver? Yeah… it’s not going to magically straighten your slice. Too often we buy tools hoping they’ll solve our problems. But tools don’t solve problems. Processes do. And the best Revenue and Rev Ops leaders I know all follow a playbook when it comes to tooling: 1. Start with the problem, not the tool You need a list—not of tools you want to try, but of business problems you need to solve. Some common ones I hear: "We need to improve our pipeline conversion rate" "We need better forecasting data" "We need to stay in closer touch with customers post-sale" Then you can go hunting for tools that solve those problems. But if you’re just chasing every shiny new AI-powered tool? You’re going to waste time, budget, and team attention. Trust me, the 100th AI SDR tool still sounds pretty cool but it might not be what you need for your business at the current time. 2. Use a structured, data-driven evaluation process “I can see us using this” is not a business case. You need a scorecard. How easy is it to implement? How hard will it be to drive adoption? What’s the expected ROI? Does it integrate with our current workflow and tech stack? The best teams run their tooling like procurement pros. Gut feel isn’t enough, especially when budgets are tight and the stakes are high. 3. No process = no payoff Let’s say you buy the tool. Now what? Without enablement, accountability, and integration into daily workflows, that tool is going to sit on the shelf (just like that $500 driver in your garage). At minimum, you need: -Training plans -Change management -Clear documentation -Leadership support -An incentive or consequence to drive usage If you don’t have a process to make the tool work, you’ve bought shelfware. 4. Continuously re-evaluate your stack We’re in an era where AI is creating entirely new categories almost overnight. Point solutions are becoming features. New platforms are emerging weekly. And you can’t afford to run the same stack just because it worked last year. Great revenue leaders are constantly pruning and optimizing, aligning tools with the evolving needs of the team and the business. The bottom line is software doesn’t make you better. Process does. So before you pull the trigger on the next tool, ask yourself: “Do we have the infrastructure, alignment, and plan to make this successful?” Because trust me, your new Titleist is still going to slice 20 yards right unless you’ve put in the reps (or booked some lessons).

How do you actually get AI adoption over 90% across the organization? At Credal.ai, we’ve seen how companies succeed and how companies fail in making this adoption happen. 6 lessons lessons from our experience so far: 1. Success in adoption starts with clear ownership to push the initiative to completion. While AI is still new and having dedicated teams is not yet mainstream, for smaller companies (<1000), IT usually leads the charge, while larger enterprises typically assign it to dedicated AI/ML Platform teams. 2. Give your people access & license to experiment (with guardrails)! AI adoption works best when top down meets bottom up. We encounter many organizations who want to “drive AI adoption”, but aren’t willing to let users experiment early on. Around 10% of employees will be early adopters, and then evangelists as they ultimately share production-ready AI applications. Fun fact: Credal users are 16% more likely to be promoted and 27% less likely to face lay-offs vs. their peers. 3. Create a governance program, and make sure people know about it. It’s counterintuitive, but in practice, users who are not sure what they are allowed to do will bias towards doing nothing at all, for fear of breaking rules. Announcing a governance program actually empowers employees. 4. Meet users where they are. One thing will never change - users hate learning new tools. The more that users are given access to AI tooling inside platforms they already use - like Slack, Microsoft Teams, Salesforce, etc, the faster adoption will be. (For Credal, we make it seamless to deploy into Slack). These existing platforms will act as a “gateway” to realizing how useful AI can be, but the key is helping them discover the tooling organically. 5. Pick AI-first partners/vendors. Since the technology is going to move fast, you want your partners and solution providers to move fast as well. Legacy enterprises or tech companies that pivoted into AI products are stuck maintaining legacy codebases and unable to ship basic features that users want. Take Glean for example, which still doesn't allow users to switch between models for their use case. Meanwhile, AI-first companies like Credal support new models on the day they come out. 6. Teach your employees, and even better: let them teach each other. Ultimately, a decentralized education system that lets your employees discover new use cases and teach each other drives much more real world value, much faster. One hackathon hosted by our customer almost single-handedly converted 32% of the invitees into builders and an additional 50% into users of AI. There’s a *lot* more in the blog (link in comments). As ever, please send this to any of your colleagues and friends who are thinking of deploying generative AI in the enterprise, and feel free to email us if we can help at founders@credal.ai. We’re also curious to hear YOUR lessons and takeaways. Comment below if you’re using gen AI in your company, and tell us how!

Most leaders fail at getting their teams to adopt AI tools. Here's what actually works: This framework comes from Claire Vo (Creator of ChatPRD and 3X CPO) who shared it with the Surpa community in a recent event we did. The playbook is simpler than you'd think: 1/ Open up experimentation budgets Don't lock down tools behind approvals. Let people try things. ↳ Want to test Cursor? Go for it ↳ Interested in Deep Research? Try it ↳ New AI tool catching your eye? Experiment The only rule? Share what you learn. 2/ Create a public "Building with AI" channel Make it the central hub for AI experiments ↳ Share your wins AND failures ↳ Post prompts that worked ↳ Ask questions freely ↳ Document unexpected use cases 3/ Document winning recipes Create a central playbook that includes: ↳ Successful use cases ↳ Exact prompts that worked ↳ Common pitfalls to avoid ↳ ROI calculations 4/ Lead by example Be the most active experimenter yourself Share your own failures openly Show what "good" looks like Pro tip: Add your AI workflow to docs ↳ Include prompts you used ↳ Share how you got to the output ↳ Help others learn by example The goal isn't perfect adoption. It's creating a culture where AI experimentation becomes the norm. What strategies have worked in your organization? 

You don't need more AI tools → You need an AI strategy. Everyone's rushing to "use AI in their business." But randomly testing tools isn't a strategy. Here's how to actually implement AI effectively 👇 First, work backwards: → What tasks consume most of your time? → Where do you need faster output? → What could be improved with automation? Then, audit your workflow: → What requires human creativity? → What's repetitive but necessary? → What needs a human final touch? Now choose your AI tools based on needs: Low-complexity tasks: → Email drafts → Social media captions → Basic research → Meeting summaries High-complexity tasks: → Content strategy → Market analysis → Customer insights → Product development Implementation approach: → Start with one process → Test and measure results → Document what works → Scale gradually Pick 2-3 use cases maximum. Master them before adding more. Remember: AI is a tool, not a solution. The key is knowing where it fits in YOUR business. Success comes from strategy first, tools second. #AIStrategy #BusinessGrowth #Productivity P.S. Want my tested AI workflows? Drop a "+" below.

I've watched companies crash and burn. Duolingo is a prime example. The company thought AI was the answer. But they got it all wrong. Their "AI-first" strategy blew up in their faces. They lost 6.7 million TikTok followers and 4.1 million on Instagram. That's a $7 billion lesson in what happens when you replace people instead of partnering with them. CEO Luis von Ahn decided to cut contractors. He claimed they would only hire if teams couldn't automate their work. Predictably, this led to chaos. Employees revolted. Users were furious. Social media went silent. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: • They tossed out human expertise instead of building on it. • They saw AI as a way to save money, not as a partner. • They spread fear, not hope. • They ignored that culture and creativity can't be replaced by machines. 𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁 𝗸𝗻𝗼𝘄 𝘁𝗵𝗶𝘀: AI is rewriting the rules of business, but it should only be harnessed when it is integrated with human skills, not when it replaces them. They tackle biases in AI to make sure their systems serve everyone. Microsoft found that teams using AI perform better than those that don't. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘀𝗺𝗮𝗿𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗔𝗜 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆: • Treat AI agents like new team members, onboard them, assign ownership, measure performance. • Set clear human-agent ratios for each function. • Invest in AI literacy training across all levels. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 • Use AI for 24/7 availability and processing power, things humans can't provide • Keep humans in charge of judgment, creativity, and high-stakes decisions • Create "thought partner" relationships where AI challenges thinking leads to ideas 𝗦𝗰𝗮𝗹𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆 • Move beyond pilots to organization-wide adoption • Start with functions farthest from your competitive edge • Continuously evaluate and adjust your AI tools The truth is clear. Companies that fail to integrate AI smartly will be left behind. This concerns how AI will change your workforce and how you will lead that change. Will you lift your team up with AI, or will you create fear like Duolingo did? What's your experience with AI integration? Are you seeing partnership or replacement in your industry? The future belongs to those who master human-AI collaboration. Those who don't risk becoming the next cautionary tale. #AIvsEI #BetterTogetherAgency #Duolingo #HumanCentric  

Just read a fascinating piece by Tetiana S. about how our brains naturally "outsource" thinking to tools and technology - a concept known as cognitive offloading. With AI, we're taking this natural human tendency to a whole new level. Here's why organizations are struggling with AI adoption: They're focusing too much on the technology itself and not enough on how humans actually work and think. Many companies rush to implement AI solutions without considering how these tools align with their teams' natural workflow and cognitive processes. The result? Low adoption rates, frustrated employees, and unrealized potential. The key insight? Successful AI implementation requires a deep understanding of human cognition and behavior. It's about creating intuitive systems that feel like natural extensions of how people already work, rather than forcing them to adapt to rigid, complex tools. Here are 3 crucial action items for business leaders implementing AI: 1) Design for Cognitive "Partnership": Ensure your AI tools genuinely reduce mental burden rather than adding complexity. The goal is to free up your team's cognitive resources for higher-value tasks. Ask yourself: "Does this tool make thinking and decision-making easier for my team?" 2) Focus on Trust Through Transparency: Implement systems that handle errors gracefully and provide clear feedback. When AI makes mistakes (and it will), users should understand what went wrong and how to correct course. This builds long-term trust and adoption. 3) Leverage Familiar Patterns: Don't reinvent the wheel with your AI interfaces. Use established UI patterns and mental models your team already understands. This reduces the learning curve and accelerates adoption. Meet them where "they are"" The future isn't about AI thinking for us - it's about creating powerful human-AI partnerships that amplify our natural cognitive abilities. This will be so key to the future of the #employeeexperience and how we deliver services to the workforce. #AI #FutureOfWork #Leadership #Innovation #CognitiveScience #BusinessStrategy Inspired by Tetiana Sydorenko's insightful article on UX Collective - https://lnkd.in/gMxkg2KD

The more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership