Evaluating Productivity Tools for Teams

Vendors say, “AI coding tools are writing 50% of Google’s code.” I say, “Autocomplete or IntelliSense was writing about 25% of Google’s code, and AI made it twice as effective.” When it comes to measuring AI’s ROI, real-world benchmarks are critical. Always compare the current state to the future state to calculate value instead of just looking at the future state. Most companies are overjoyed to see that AI coding tools write 30% of their code, but when they realize that vanilla IDEs with basic autocomplete could do 25%, the ROI looks less impressive. 5% rarely justifies the increased licensing and token costs. That’s the reality I have found with about half of the AI tools I pilot with clients. They work, but the improvement over the current state isn’t worth their price. I have used the same method to measure ROI for almost a decade. 1️⃣ Benchmark the current process performance using value outcomes. 2️⃣ Propose a change to the current process that introduces technology/new technology into the workflow. 3️⃣ Quantify the expected change in outcomes and value delivered with the new process/workflow. 4️⃣ Make the update and measure actual outcomes. If there’s a difference between expected vs. actual, find the root cause and fix it if possible. Measuring AI ROI is simple with the right framework. It’s also easier to help business leaders make better decisions about technology purchases, customer-facing features, and internal productivity initiative selection. I would rather see a benchmark like, percentage of code generated from text prompts vs. the percentage of code recommended by autocomplete. That benchmarks the reengineered process against the old one. AI process reengineering (AI tools augmenting people performing an optimized workflow) is where I see the greatest ROI. Shoehorning AI tools into the current process typically delivers a fraction of the potential ROI.

It's well understood that AI has the ability to impact individual productivity. But most critical work is done in teams. What's AI role within a team? A new HBS paper studies how AI acting as a Teammate impacts knowledge work. The study tracked hundreds of professionals (business & technical) at P&G and analyzed the impact of using AI on individuals and teams measured by time savings and output. (Link to paper in comments) * Big Takeaway: AI often functions as more of a teammate than a tool, democratizing expertise, improving quality of output, and even improving emotional experiences. * Big Productivity Gains:  Individuals and Teams using GPT-4 completed tasks 12-16% faster and produced work 0.37-0.39 standard deviations higher in quality.   * Blurring Expertise Boundaries: AI helped both R&D and Business specialists produce balanced technical and commercial solutions, erasing traditional knowledge silos.    * AI as a Teammate Equivalent: Individuals using AI performed on par with two-person teams without AI, demonstrating the AI as a teammate concept is real. * AI Teammates + Human Teammates Work Best: Teams using AI were significantly more likely to produce top-tier solutions, suggesting that there is extra value in having human teams working on a problem + AI. * Enhanced Emotional Experience: Participants using AI reported significantly more positive emotions (excitement, energy) and fewer negative emotions (anxiety, frustration). The author (Ethan Mollick) provides prescient guidance to companies:  “To successfully use AI, organizations will need to change their analogies. Our findings suggest AI sometimes functions more like a teammate than a tool. While not human, it replicates core benefits of teamwork—improved performance, expertise sharing, and positive emotional experiences.” AI founders would do well to remember AI should be more than a tool and seek to be a teammate.

Adopting new technology requires what I call “foundational”work. Here are three such key tasks: 1) Conduct a Thorough Needs Assessment -Evaluate existing tools and workflows: Are they meeting your needs, or are inefficiencies and manual tasks slowing you down? -Pinpoint pain points: Identify recurring challenges such as data silos, integration issues, or compliance gaps. -Engage your team: Host discussions or surveys to uncover their everyday challenges and gain insights from those closest to the work. 2) Map and Analyze Workflows -Document end-to-end processes: Map each step of key workflows, from intake to output. -Spot inefficiencies: Look for bottlenecks, redundant steps, and high-risk areas where errors commonly occur. -Visualize opportunities: Use these insights to identify areas ripe for automation or enhancement. 3) Set Clear, Data-Driven Goals -Tie goals to business outcomes: Define objectives that align with broader organizational priorities—e.g., "Reduce contract review time by 30%" or "Achieve a 15% increase in team productivity." -Define metrics of success: Establish KPIs that will help you track progress and assess ROI over time. 4) Build Cross-Functional Buy-In -Engage early with stakeholders: Collaborate with legal, IT, finance, and operations teams to ensure the chosen solution addresses both tactical needs and strategic objectives. -Promote transparency: Share the rationale behind adopting new technology and the benefits for each stakeholder group to build trust. #legaltech #innovation #law #business #learning

New research from Harvard Business School explores a big question: What if AI isn’t just a tool but a teammate? In a large-scale field experiment with Procter & Gamble, researchers tested how GPT-4 affected performance when used by individuals versus teams of experienced professionals working on real product development challenges. Some key findings: - AI-enabled individuals performed as well as teams without AI - Teams using AI produced the best and most exceptional results overall — not only did they outperform others, but they were significantly more likely to generate top 10% solutions - AI helped bridge expertise gaps and broke down professional silos - Participants using AI had better emotional experiences — more excitement, less frustration The takeaway? AI isn't just about individual productivity — it’s reshaping how we collaborate, think, and solve complex problems. It’s acting more like a cybernetic teammate, not just a more efficient tool. The working paper — “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise” — is worth a read. As someone interested in the future of work, this raises important questions: 1. How do we design teams when AI levels the playing field? 2. What happens to traditional boundaries between roles? 3. How do we rethink collaboration when AI enhances both performance and emotional engagement? Curious what you all think — especially if you’re leading teams or exploring how to integrate AI meaningfully into your org. #FutureOfWork #LinkedInWorkplace #LinkedInLife #WorkplaceResearch

Top-performing AI users are twice as likely to quit. Here's why - and how to stop it: While AI tools are dramatically increasing productivity, They're also dramatically increasing burnout. According to new findings from The Upwork Research Institute,   88% of top AI performers report burnout, And they are 2x as likely to leave their jobs. The reality is that AI is quietly eroding ↳Human connection ↳Trust ↳Purpose Which are all critical to long-term success. But it doesn't have to be that way. Proactive leaders can take advantage of AI's massive contributions, While still supporting motivated, engaged, and thriving teams. Here's how to start: 1. Make it okay to log off ↳AI tools are always available, which makes people feel like they should be too ↳Ex: Say, "You don't need to respond outside work hours," and back it up 2. Make time for real connection ↳People now say they trust AI more than coworkers, putting connection at risk ↳Ex: Start team meetings with shoutouts and appreciation, not just metrics 3. Notice the good stuff out loud ↳AI delivers quick results, but people need to feel appreciated to stay engaged ↳Ex: Ask, "What's something you're proud of this week?" in 1:1s 4. Give people time to think ↳AI increases speed, but without space to think, quality suffers ↳Ex: Block two hours daily for deep work across the team 5. Remind people why their work matters ↳AI can make work feel transactional, draining the meaning that fuels motivation ↳Ex: Share a customer story to remind people what they're part of 6. Make it safe to speak up ↳AI moves fast, but without safety, speed leads to silence, not innovation ↳Ex: Encourage questions even when things are moving quickly 7. Share the full picture ↳AI tools give fast answers, but people need context to feel connected ↳Ex: Share how and why key decisions were made 8. Celebrate what only humans can do ↳AI optimizes for output - humans thrive when their unique gifts are seen ↳Ex: Recognize creativity, empathy, or leadership, not just speed or output 9. Show it's okay to struggle ↳AI can make it seem like everyone's cruising, making people hide their own challenges ↳Ex: Share your confusion or frustrations so others feel safe doing the same 10. Slow down when it matters ↳AI can answer fast, but it takes human judgment to ask the right questions ↳Ex: Praise someone who pauses to think critically instead of rushing 11. Find moments to build trust ↳Trust is built through action, but AI has made human follow-through less visible ↳Ex: Send the message, make the call, follow up - show them they can count on you AI's short-term gains are real and impressive. But only the leaders who invest in people will make those gains last. Are you seeing burnout rise as AI use increases? --- ♻️ Repost to help more teams thrive with AI. And for even more tips on how to strengthen workplaces in the AI era, Check out Upwork’s complete report: http://spr.ly/GeorgeStern #UpworkPartner #FutureOfWork #AI

Most enterprises waste millions on tech without seeing real impact. I learned this the hard way. Early in my career, I saw companies invest in cutting edge tools only to struggle with adoption, integration, and ROI. That’s when I developed a smarter, outcome-driven approach. Here’s the exact method I use to maximize ROI from technology investments:  Start with Business Outcomes, Not Features ↳ Define the measurable impact before picking the tech. What problem are you solving? What KPIs will prove success?  Ensure Alignment Across Teams ↳ IT, finance, and business leaders must be on the same page. Misalignment leads to wasted budgets and underutilized tools.  Adopt in Phases, Not All at Once ↳ Test, refine, and scale. A phased rollout prevents disruptions and maximizes adoption.  Measure, Optimize, Repeat ↳ Regularly assess ROI. What’s working? What needs adjustment? Continuous refinement drives long-term value. Tech alone doesn’t drive transformation—strategy does. How do you ensure your technology investments deliver real business impact? Let’s discuss. 👇 🔹 Follow me for more insights on digital transformation. 🔹 Connect with me to explore strategies that drive real impact. ♻️ Repost this to help your network. P.S.: Thinking about how to maximize your tech investments? Let’s chat. I’m happy to share insights on what works (and what to avoid).

Every VC-backed SaaS company out there is trying to convince you their tool is "mission-critical." My LinkedIn feed looks like a SaaS party where everyone's fighting to be the next Salesforce. (Spoiler: They won't be.) Today, when conversations can feel like an Olympics of 𝘞𝘩𝘰’𝘴 𝘣𝘶𝘴𝘪𝘦𝘳? 𝘞𝘰𝘳𝘬𝘦𝘥 𝘭𝘢𝘵𝘦𝘳? 𝘐𝘴 𝘮𝘰𝘳𝘦 𝘦𝘹𝘩𝘢𝘶𝘴𝘵𝘦𝘥? And things being “hard” has become some sort of badge of valor like— ”Wow you actually made it to [insert wedding, networking happy hour, or pickleball hang here] when things are so difficult. Good on you!” It’s easy to fall into the trap of measuring 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆, but what about 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀? 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗺𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝘁𝗿𝗮𝗰𝗸: - Number of emails sent - Connection requests made - Calls logged - Tasks completed 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: - Time from lead to meeting - Meeting show rate - Pipeline-to-close ratio - Customer acquisition cost The difference?  The first set tells you if people are busy.  The second tells you if they're effective. Most tools don’t deliver ROI because they just create more work than they solve. Here's how to evaluate if a tool is worth it: 1. 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗧𝗶𝗺𝗲 𝗦𝗮𝘃𝗲𝗱   - Track hours spent on tasks the tool would automate   - Multiply by hourly cost of sales reps   - Factor in onboarding and training time 2. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗜𝗺𝗽𝗮𝗰𝘁   - Track pipeline influenced by the tool   - Calculate conversion rate improvement   - Monitor deal velocity changes 3. 𝗛𝗶𝗱𝗱𝗲𝗻 𝗖𝗼𝘀𝘁𝘀 𝘁𝗼 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿   - Integration time with existing tools   - Ongoing maintenance   - Team adoption challenges   - Context switching overhead I fall for this all the time as well - towards shiny new tools that look like they’ll solve the problem. until they reveal themselves after I’ve wasted hours of my own and my team’s time.

Need help justifying an AB tool switch/implementation? Use cases: • 𝗧𝗼𝗼𝗹 𝗝𝘂𝘀𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗕𝘂𝗱𝗴𝗲𝘁 𝗔𝗽𝗽𝗿𝗼𝘃𝗮𝗹 • 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 • 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 𝗳𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗕𝘂𝘆-𝗜𝗻 • 𝗕𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗳𝗼𝗿 𝗩𝗲𝗻𝗱𝗼𝗿 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗼𝗿 𝗥𝗙𝗣 Here's my template we're starting to use with clients and vendors (this one was for an edge case, don't use as a template but rather a guiding framework): 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗖𝗮𝘀𝗲 𝗕𝗿𝗶𝗲𝗳: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗥𝗢𝗜 𝗼𝗳 𝗮𝗻 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗼𝗼𝗹 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 Implement an experimentation analysis platform integrated with the data warehouse to improve test analysis efficiency, ensure data reliability, and support scalable experimentation across teams. 𝗞𝗲𝘆 𝗥𝗢𝗜 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀: 1. 𝗧𝗶𝗺𝗲 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 & 𝗖𝗼𝘀𝘁 𝗦𝗮𝘃𝗶𝗻𝗴𝘀    𝘊𝘶𝘳𝘳𝘦𝘯𝘵 𝘪𝘯𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺: Analysts spending ~4–8 hours/week manually aggregating and formatting test data.    𝘗𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭 𝘨𝘢𝘪𝘯: Automating this process could save ~200–400 hours/year per analyst.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Value of reclaimed time × analyst cost (e.g., $60–$100/hour) = $12K–$40K per analyst/year. 2. 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 & 𝗧𝗿𝘂𝘀𝘁    𝘐𝘴𝘴𝘶𝘦: Sample Ratio Mismatch (SRM) in GA4, attribution discrepancies with current tool.    𝘐𝘮𝘱𝘳𝘰𝘷𝘦𝘮𝘦𝘯𝘵: Direct integration with the warehouse removes reliance on biased or sampled tools, fostering confidence in test outcomes.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Reduced decision risk, improved test quality, fewer invalid tests. 3. 𝗧𝗲𝘀𝘁 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆    𝘊𝘶𝘳𝘳𝘦𝘯𝘵 𝘧𝘳𝘪𝘤𝘵𝘪𝘰𝘯: Manual processes and tool limitations slow down testing cycles.    𝘉𝘦𝘯𝘦𝘧𝘪𝘵: A dedicated tool accelerates experiment cycles through auto-generated reports, and easy-to-share insights.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Increase in tests run/year × average test impact = greater cumulative business impact. 4. 𝗖𝗿𝗼𝘀𝘀-𝗧𝗲𝗮𝗺 𝗘𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁 & 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻    𝘊𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦: Disparate methods, siloed reporting, misalignment across functions.    𝘉𝘦𝘯𝘦𝘧𝘪𝘵: Shared platform = standardized test logging, clear version control, consistent metrics, better governance.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Time saved in coordination, increased collaboration, fewer redundant or conflicting tests. 5. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗜𝗺𝗽𝗮𝗰𝘁    𝘓𝘰𝘯𝘨-𝘵𝘦𝘳𝘮: Empowers decision-making at higher fidelity, underpins a culture of experimentation, and aligns with business OKRs.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Higher win rate from better experiments + institutional knowledge retained via a centralized source of truth. 𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀 • Conduct a pilot with 1–2 teams. • Baseline current effort, accuracy, and velocity metrics. • Define KPI targets: time saved, test throughput, SRM reduction, stakeholder satisfaction.

When “free” tools end up costing founders millions.   (A cautionary tale for SaaS CEOs chasing fast growth)   In the early days of your business, picking tools that "just work" feels like a win.     - Freemium CRMs. - Lightweight analytics. - Quick signups. - Minimal setup.   But here’s what no one tells you:     🚫 No RBAC   🚫 No SSO   🚫 No usage analytics   🚫 No security compliance   By the time you hit Series B, those “growth hacks” become scaling anchors.   You can’t:   - Manage teams without access controls.   - Onboard enterprise clients without audit logs.   - Forecast usage-based costs without visibility.   And replacing legacy tools isn’t cheap.     - 62% of Series B+ companies end up replacing their early-stage tools.   - Salesforce migrations alone cost an average of $487K. - Unpredictable OPEX from usage-based pricing keeps CFOs up at night.   The industry is divided:     → Some founders rip and replace their entire stack.   → Others keep stacking tools on top of shaky foundations.   Here’s my take after helping dozens of SaaS founders navigate this:     You don’t need a full overhaul. Just smarter decision-making.   Ask these before committing to any tool:   ✅ Will this scale with my team size and client needs in 12 months?     ✅ Does it offer enterprise-ready features I "might" need sooner than I think?     ✅ Is the pricing predictable enough to keep my burn in check?   Choosing software isn’t just a tech decision.   It’s a business decision.   Make it wisely.   If you’re a SaaS founder struggling with tool chaos, let’s talk.     I help teams cut through the noise and build a future-proof stack, without the expensive detours.   #SaaS #Founders #StartupTools #ScalingSaaS #TechStack #Productivity  

🧠 Workers using AI performed just as well as full teams while working 16% faster and reporting more excitement, energy, and enthusiasm. This isn’t speculation...it’s what 776 professionals at Procter & Gamble just proved in a study. The latest research reveals something we’re only beginning to grasp: AI isn’t just a tool. It’s a teammate. Here’s what People Analytics leaders need to know: 1️⃣ AI boosts individual performance to team-level outcomes 🔹 Individuals using GenAI improved performance by +0.37 standard deviations, matching the effectiveness of human teams. 🔹 They also worked 16.4% faster, producing longer, more detailed solutions. 📌 Takeaway: One AI-enabled employee can now match the output of a traditional 2-person team. 2️⃣ AI breaks down expertise silos 🔹 Commercial specialists started suggesting technical solutions. 🔹 R&D pros brought forward customer-facing ideas. 🔹 AI leveled the playing field across specialties. 📌 Takeaway: GenAI is becoming the great equalizer in cross-functional collaboration. 3️⃣ AI improves emotional experience at work 🔹 Participants reported more energy, excitement, and enthusiasm. 🔹 They also saw lower frustration and anxiety when AI was in the loop. 📌 Takeaway: AI isn’t just changing how we work—it’s changing how we feel at work. 4️⃣ AI helps surface breakthrough ideas 🔹 AI-enabled teams were 3x more likely to generate top 10% solutions. 🔹 Even less experienced employees delivered ideas on par with veterans. 📌 Takeaway: AI is democratizing creativity and unlocking hidden potential across the org. 💡 Bottom line for People Analytics teams: AI isn’t just enhancing productivity. It’s reshaping how teams form, how they collaborate, and how individuals experience their work. Check the comments for the full research paper and Ethan Mollick’s excellent breakdown. How is your organization measuring the real impact of AI on collaboration, expertise, and experience? #GenAI #AIAdoption #PeopleAnalytics #FutureOfWork #WorkforceTransformation