Best Practices for Implementing AI in Business Intelligence

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

The rush to implement AI solutions can lead to significant pitfalls. Here's a provocative thought: the greatest risk in AI isn't just inaction. It's implementing without understanding. Let’s unravel why AI implementation demands careful thought and expertise. The promise of AI is undeniable. But when businesses leap without looking, the consequences can be dire. → Mismanaged data leads to flawed predictions. ↳ Garbage in, garbage out—AI doesn't magically fix bad data. → Overreliance can breed complacency. ↳ AI is a tool, not a crutch. → Lack of understanding can result in ethical oversights. ↳ Algorithms must be checked for bias and fairness. → Insufficient expertise can stall projects. ↳ Proper training and a clear strategy are essential. AI implementation isn't just about tech. It's about aligning with business goals and ethics. So, how do we get it right? Prioritize data quality → Clean, accurate data is nonnegotiable. Invest in education → Equip your team with the knowledge to leverage AI effectively. Engage multidisciplinary teams → Combine tech expertise with business acumen. Embed ethical considerations → Regularly audit models for bias and fairness. Iterate and refine → Continuous learning and adaptation are key. Remember, AI isn't a onesizefitsall solution. It's a journey that requires thoughtful planning and execution. Done right, AI can transform businesses, enabling them to act with foresight and agility. Yet, it's the careful, calculated steps that ensure this transformation is both successful and sustainable. What steps have you taken to ensure AI success in your organization? Share your thoughts below.

You’re Probably Not Ready for AI Transformation I’ve helped organizations implement AI strategies that scaled revenue and transformed operations, but I’ve also seen teams collapse under the weight of poorly executed AI initiatives. AI is a game-changer, but if you rush in unprepared, it can sink your business. Here are the 5 biggest lies companies tell themselves about AI strategy, implementation, and transformation (and how to truly unlock AI’s potential): 1. “We’ll Just Add AI to What We’re Already Doing” AI isn’t a bolt-on feature—it’s a fundamental shift in how you operate. It demands new workflows, infrastructure, and mindsets. Sure, you can use out-of-the-box solutions, but true transformation means aligning AI to your unique business challenges. If you’re not ready to rethink processes, AI won’t deliver transformative results. 2. “Our Current Team Can Handle AI” AI implementation requires cross-functional expertise in data science, engineering, and business strategy. Even with great talent, most teams aren’t ready to bridge the gap between AI’s potential and its practical application. Without proper enablement, adoption will falter, and the shiny new tool will collect dust. 3. “We’ll Just Hire AI tech to Lead the Charge” Good luck. Hiring AI tech specialists isn’t enough—especially if they don’t understand your industry or business model. These hires will spend months ramping up, navigating legacy systems, and explaining concepts to teams unfamiliar with AI. Transformation requires leaders who can marry technical expertise with a deep understanding of your business. 4. “AI Will Solve Our Big Problems Quickly” Not so fast. AI projects live or die on data quality, and most companies’ data is messy, siloed, or incomplete. Before you can expect results, you’ll need to clean, structure, and enrich your data—a slow, unglamorous process that determines whether AI succeeds or fails. 5. “We Just Need to Buy the Right AI Tools” Tools are only as good as the strategy behind them. AI success isn’t about flashy tech—it’s about embedding intelligence into your business processes. Without a clear plan to use AI for specific outcomes, you’ll waste time and money on solutions that fail to deliver meaningful impact. 2025 AI Transformation Plan: Instead of diving headfirst, take an intentional, step-by-step approach: •Start with a clear AI strategy tied to business outcomes •Audit and prepare your data for AI use •Train teams on AI-powered workflows •Build cross-functional alignment for smooth implementation •Invest in AI tools that solve specific problems •Set realistic KPIs and measure progress incrementally AI isn’t just a trend. It’s a paradigm shift. But it’s not a magic bullet. Approach it strategically, and it will unlock new growth, efficiency, and innovation. Rush in without preparation, and you’ll burn time, resources, and credibility. Learn what AI transformation really requires—then execute thoughtfully. No shortcuts.

🚨 95% of GenAI pilots are failing, but not for the reasons you think. Stop blaming the AI. Start fixing the rollout. Too often, we launch AI like it’s plug-and-play. But success isn’t about the tool . It’s about the system you build around it. Here’s your AI Launch Readiness Checklist 👇 ☐ 1. Start with Strategy ↳ AI without a business outcome is just an expensive science project. ↳ Define the “why” before you buy. ☐ 2. Build Human Readiness ↳ Employees don’t fear AI they fear being left behind. ↳ Upskill, reskill, and explain the why at every step. ☐ 3. Resist the Vendor Hype ↳ Leaders often chase market buzz instead of checking internal readiness. ↳ Buying tools before defining use cases = expensive underuse. ☐ 4. Fix the Foundations ↳ Bad data in = bad insights out. ↳ Data quality, governance, and access matter more than models. ☐ 5. Rethink Workflows, Not Just Tools ↳ AI must slot into the way people already work. ↳ Otherwise, adoption stalls. ☐ 6. Pilot with Purpose ↳ “Test everything” = wasted time. ↳ Pick 1–2 high-impact use cases and scale only what works. ☐ 7. Establish AI Guardrails ↳ Clear policies on risk, compliance, & ethics build trust. ↳ No guardrails = no scale. ☐ 8. Lead from the Top ↳ Culture follows leadership. ↳ If execs treat AI like a gadget, employees will too. ☐ 9. Measure What Matters ↳ Set KPIs that connect to business impact, not vanity metrics. ↳ If you can’t prove ROI, you can’t scale. ☐ 10. Keep Iterating ↳ AI isn’t a “set it and forget it” project. ↳ Continuous feedback and tuning separate pilots from success stories. The lesson? AI doesn’t fail because it’s weak tech. It fails because we built weak systems around it. ♻️ Repost if you’re investing in people, not just tech. Follow Janet Perez for Real Talk on AI + Future of Work --- Source: MIT report via Fortune

You don’t need more AI. You need better strategy. Eight steps to drive real impact with AI. 1. Align AI with business goals. AI is only valuable when tied to strategy. Start by asking what you want to achieve. Then link each use case to a real outcome. 2. Engage leadership early. C-suite buy-in drives clarity and speed. Leaders must model adoption and own the “why.” Without this, teams stall or resist the change. 3. Evaluate readiness for change. Fear - not tech - is the biggest blocker. Assess confidence, trust, and communication. Prepare change agents across the business. 4. Assess your tech infrastructure. Legacy tools slow AI to a crawl. Check for speed, scale, and integrations. Strong foundations lead to strong results. 5. Define the right KPIs. What you measure drives what you improve. Set goals around adoption, speed, and impact. Track consistently - and iterate often. 6. Ensure your data is ready. AI is only as good as your data is clean. Fix silos, tag documents, and validate sources. Governance and compliance matter too. 7. Build a phased roadmap. Start with one clear, high-value use case. Test it. Learn fast. Build trust with wins. Then scale thoughtfully with feedback loops. 8. Monitor and adapt constantly. AI strategy is never “one and done.” Review performance, listen to users, adjust. The best teams evolve their playbook often. P.S. Want my free L&D strategy guide? 1. Scroll to the top 2. Click “Visit my website” 3. Download your free guide.

"We need an AI strategy!" 𝘙𝘦𝘤𝘰𝘳𝘥 𝘴𝘤𝘳𝘢𝘵𝘤𝘩 Hold up. That's the wrong question. The right question? "What business problem are we actually trying to solve?" I've sat in countless board meetings where executives demand AI initiatives – not because they've identified a problem AI can solve, but because they're afraid of being left behind. This FOMO-driven approach is precisely how companies end up in what I call "perpetual POC purgatory" – running endless proofs of concept that never see production. Here's the uncomfortable truth: Your goal isn't to use AI for the sake of AI. Your goal is to solve real business problems. Sometimes the best solution is a regular hammer, not a sledgehammer. So when leadership pushes AI without purpose, redirect the conversation: → "What business outcome are we trying to drive?” → “What’s the actual problem we’re solving?” → “Is AI the most effective tool for that — or just the most exciting one?” Next, how do you determine if AI is the right solution? I recommend this straightforward approach that keeps business problems at the center: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗽𝗿𝗲𝗰𝗶𝘀𝗲𝗹𝘆 - What specifically are you trying to solve? The more precisely you can articulate the problem, the easier it becomes to evaluate whether AI is appropriate. 2. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗶𝗿𝘀𝘁 - Could existing technology or processes handle this faster, cheaper, and more reliably? 3. 𝗟𝗲𝗮𝗻 𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 - If the problem seems AI-suitable, validate it with people who’ve delivered outcomes — not just hype. 4. Be brutally realistic about your organization's maturity - Do you have the data infrastructure, talent, and risk tolerance necessary for an AI implementation? Remember this fundamental truth: AI is not a silver bullet. Even seemingly simple AI projects require time, focus, alignment, and resilience to implement successfully. The companies winning with AI aren't the ones with the flashiest technology. They're the ones methodically solving pressing business challenges with the most appropriate tools—AI or otherwise. 𝗜’𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂: What business problem are you trying to solve that might (or might not) actually need AI?

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

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.

Demandbase has used AI to score 38B accounts, predict 4M opportunities, and launch 20k outcome-based advertising campaigns. Here are 3 best practices for using AI in your account-based GTM: 1. Start with data AI strategy should start with data cleansing and enrichment. Not all data is equal, it’s important to understand what signal matters most and to focus on quality over quantity – you don’t need 150M contacts weighing down CRM, you need 100k highly accurate contacts from your ICP. 2. Build healthy models There are three best practices here too: (i) Know what the strongest signals are. For example, for tech companies generally technographics, industry, and revenue ranges are strong signals for ICP models, while campaign responses, sales activities, website engagement, and intent are strong signals for pipeline prediction models. (ii) Build specialized models for different products, regions, and aspects of your GTM. For example, models focused on acquisitions of new logos, models focused on customer retention, and models focused on gross retention. (iii) Models need to be re-trained frequently to avoid following behind your GTM evolution. 3. Avoid black boxes AI models have to be transparent. Without transparency you can’t tell if the AI model is making a recommendation that you know for obvious reasons is flawed. Transparency enables Marketing and Sales to improve their messaging and activation by learning directly from model recommendations. And transparency is critical for data science teams at your company driving AI strategy across the enterprise. There’s a lot of hype and promise in AI. What’s working best for account-based GTM’s is focusing on the strongest signal, prioritizing quality of data over quantity, using specialized models, re-training models frequently, and making sure AI is transparent.

The companies that dominate AI over the next 2 years won't be the ones with the fanciest tools. They'll be the ones who mastered the basics first. Here's what everyone gets wrong about AI implementation: Most businesses are chasing shiny objects. → Complex custom agents → Multi-layered automation workflows → Bleeding-edge AI tools nobody's heard of Meanwhile, they're ignoring the massive gap right in front of them. ‼️ The ChatGPT Reality Check Let's be honest about something: The difference between a team that uses ChatGPT daily vs. one that doesn't is ENORMOUS. We're talking about: • 10x faster content creation • Instant research and analysis • Error-free first drafts • Complex problem-solving in seconds That gap alone is bigger than most "advanced" AI implementations. The Winning Formula: Basics First. The companies crushing it with AI aren't building rocket ships. They're getting their fundamentals rock-solid: 1. Master the Core Tools Before you build custom agents, can everyone on your team effectively use: • ChatGPT for writing and analysis • AI-powered research tools • Basic automation platforms 2. Optimize What You Have Stop buying new tools until you've maximized the ones you already own. Most businesses use 10% of their current software's capabilities. 3. Build Simple Systems The best AI implementations are boring: • Automated data entry • Smart email responses • Basic report generation Nothing fancy. Just fundamentals done flawlessly. ‼️ Why "Basics First" Wins Here's the truth about AI transformation: Complexity kills adoption. Your team won't use a 47-step AI workflow. But they WILL use ChatGPT if you show them how. ROI comes from consistency, not complexity. A simple automation used daily beats a complex system used monthly. Foundations enable everything else. You can't build advanced AI systems on broken processes. Fix the basics, then scale up. ‼️ The Action Plan If you want to win with AI over the next few years: Week 1-2: Audit your current AI usage → Who's using what tools? → Where are the biggest time-wasters? → What basic tasks could AI handle today? Month 1: Master the fundamentals → Train everyone on ChatGPT → Implement basic automations → Create simple AI-powered workflows Month 2+: Build from there → Connect your tools → Automate repetitive processes → Scale what's already working The AI revolution is about who executes the basics flawlessly. While your competitors are chasing the latest AI trend, you'll be dominating with fundamentals they overlooked. Don't overcomplicate it. Master the basics. Win the long game. Follow me, Luke Pierce, for more AI and Automation insights like this.