Evaluating Productivity Tools for Teams

By now, the "95% failure rate" of GenAI financial returns (ref MIT's Project NANDA) is part of all consulting decks. The report blames the incorrect approach as the primary reason, rather than model maturity, etc. The key is to understand what #ROI metrics are used to determine the financial returns. I asked #Copilot on this, and here's what it told me: --- Here are three examples of ROI frameworks that enterprises are using to evaluate and scale GenAI adoption effectively: 1. Business Outcome-Based ROI Framework (Gartner) Summary: Gartner recommends aligning GenAI initiatives with measurable business outcomes such as cost reduction, revenue growth, or productivity gains. For example, a retail company using GenAI for automated product descriptions tracked a 22% increase in conversion rates and a 15% reduction in content creation costs. The framework emphasizes setting baseline metrics before deployment and tracking improvements post-implementation. 🔗 https://lnkd.in/dER7cTeF 2. Time-to-Value and Efficiency Metrics (BCG) Summary: Boston Consulting Group suggests using time-to-value (TTV) and operational efficiency as key ROI indicators. In one case, a logistics firm used GenAI to optimize routing, reducing delivery times by 18% and fuel costs by 12%. BCG’s framework includes pre/post comparisons, automation impact, and employee productivity metrics to quantify GenAI’s contribution. 🔗 https://lnkd.in/da2zcSfW 3. Model Performance vs. Business KPIs (McKinsey) Summary: McKinsey advocates for linking GenAI model performance directly to business KPIs. For instance, a financial services firm used GenAI for customer support automation and tracked resolution time, customer satisfaction scores, and call deflection rates. The framework includes continuous monitoring of model accuracy, relevance, and business impact. 🔗 https://lnkd.in/dA6zEGuS 🔑 Key Message Summary Effective GenAI ROI frameworks combine technical performance metrics with business impact indicators. Leading approaches include tracking cost savings, productivity gains, time-to-value, and alignment with strategic KPIs. Enterprises that define success upfront and monitor outcomes continuously are more likely to scale GenAI successfully. --- The direction taken seems to be well-intentioned. However, the measure of success is not quite what might lead to real solid business outcomes! Individual productivity improvements are just that! They don't scale across the organization unless "vertically scaled" top-to-down an entire process delivering bottomline improvements, which then need to be further "horizontally scaled" end-to-end across the entire value chain of the firm to deliver topline value! My forthcoming book on Cognitive Chasm provides actionable guidance to practitioners on this.

Businesses don’t care if you built your DAG with Airflow or Mage or Databricks workflows! They care that it produces a high return on investment! ROI can be measured on two sides: - how much it impacts the business? What decisions are informed with this data? What products are enhanced with this data? How much revenue is generated or cost saved? - how expensive it is to maintain and operate? How often are data quality issues and other on call issues impacting engineering time? How difficult is it to add more fields and enhance the data sets? How much cloud costs is the pipeline using? If you can maximize the first number and minimize the second, you’ll be well on your way to creating massive impact for businesses!

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.

𝐓𝐞𝐚𝐦 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐮𝐫𝐧𝐬 𝐚𝐦𝐛𝐢𝐭𝐢𝐨𝐧 𝐢𝐧𝐭𝐨 𝐡𝐢𝐠𝐡 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐮𝐬𝐭𝐚𝐢𝐧𝐞𝐝 𝐫𝐞𝐯𝐞𝐧𝐮𝐞 𝐠𝐫𝐨𝐰𝐭𝐡 ! 📈 Employees whose productivity has improved since the 2020 pandemic are 2× more likely to view their workplace as highly supportive compared to those whose productivity has declined. 🤝 Most employees come into the office to collaborate with their teams — yet the experience isn’t the same for everyone. 📉 Middle managers are over 3× more likely to say their productivity has dropped compared to senior management. 🔄 Among employees facing productivity decline, 53% want more personalized and flexible workplace experiences. 🤖 Today, 74% of senior management use AI & automation to get work done — compared to just 40% of junior staff, according to a new interesting research published by SBS-Global - Enabling Business Transformation in partnership with WORKTECH Academy using data from a survey of 570 workers spanning seven countries - the US, UK, Germany, Switzerland, Hong Kong, Singapore and Japan – and eight industries. Researchers found that difficulty focusing ranks as the top frustration with current office setups — followed closely by these other challenges: ❌ Limited flexibility in office attendance ❌Time wasted finding the right people or resources ❌Lack of available meeting rooms/collaboration space 5 ❌ Difficulty accessing technology & support ❌ Poor collaboration tools ☝️ 𝙈𝙮 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡 𝙫𝙞𝙚𝙬: Technology, flexible work design, and organisational culture aren’t just “nice to have” — they are decisive performance enablers. The data makes it clear: when employees feel supported, equipped with the right tools, and trusted with flexibility, productivity soars. When those elements are missing, even the smartest tech can’t fill the gap. My recommendations:🌟 𝐅𝐨𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: ➡️ Invest in thoughtful workplace design ➡️ Pair digital and AI tools with the training, policies, and culture ➡️ Preserve a hybrid work environment to give employees the flexibility they need to focus and collaborate effectively 🌟 𝐅𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬: ➡️ Model active, confident use of AI and automation to inspire adoption ➡️ Build a culture of trust ➡️ Create open channels for employees 🌟 𝐅𝐨𝐫 𝐫𝐞𝐜𝐫𝐮𝐢𝐭𝐞𝐫𝐬: ➡️ Highlight your organisation’s digital enablement and flexibility as key talent attractors ➡️ Seek candidates who are adaptable, tech‑curious, and comfortable with change 🙏Thank you SBS and WORKTECH Academy researchers team for sharing these insightful findings: Philip Ross Kursty Groves Markus Albers Kate Lister Elizabeth Leath 🔑 What does a truly “supportive workplace” look like to you, and how would you know you’ve found it? #ProductivityMatters #HighPerformanceCulture #WorkplaceCulture #EmployeeExperience

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.

If I had to pick and roll out a PM tool today, Here's what I'd do: Step 1: Start with the pain, not the platform ↳ Interview PMs, sponsors, and execs on what's broken today ↳ Map current workarounds (Excel chaos, email threads, status guessing) ↳ Define 3-5 must-haves the tool needs to solve immediately Step 2: Evaluate with real work, not demos ↳ Shortlist 1-2 tools that fit your maturity and budget ↳ Run scripted demos for apples-to-apples comparison ↳ Involve multiple relevant stakeholders in the evaluation ↳ Have basic objective scoring mechanisms ↳ Run a 2-week pilot with actual projects and real users ↳ Test adoption friction - if PMs resist in week 1, they'll resist forever Step 3: Configure light, launch fast ↳ Set up core workflows only (intake, status, risks, decisions) ↳ Avoid customizing everything - start with 80% out-of-the-box ↳ Build templates that save time, not add steps Step 4: Run pilots before full rollout ↳ Start with 1-2 teams who are eager to try ↳ Test workflows with real projects for 30-60 days ↳ Gather feedback weekly and adjust configuration ↳ Document what works and what needs fixing Step 5: Drive adoption like a product launch ↳ Train in small groups with real scenarios, not feature tours ↳ Set up a command center with core team and champions ↳ Assign tool champions in each business unit ↳ Provide hand-holding initially for hesitant users ↳ Celebrate early wins publicly and share quick-win stories Step 6: Scale thoughtfully across the organization ↳ Roll out in waves by team or business unit ↳ Use lessons learned from pilots to refine approach ↳ Keep training tight and scenario-based ↳ Monitor adoption closely in each new group Step 7: Measure what matters ↳ Track adoption rate and time saved per PM weekly ↳ Tie tool usage to portfolio visibility and decision speed ↳ Iterate based on feedback - drop what doesn't stick ⚠️ What I'd avoid at all costs: ↳ Buying a tool before fixing your process ↳ Rolling out to everyone at once without champions 💡 What's the biggest mistake you've seen with PPM tool rollouts? ♻️ Repost to help PMOs succeed! 🔔 Follow me (Hussain Bandukwala) for more content like this. 🗝 To get a jumpstart to review PM/PPM tools, use resources like Gartner / Capterra, TechnologyAdvice & Panoramic Solutions.

ROI in Industry 4.0: It’s Not the Tech, It’s the Fit That Matters (10 Underrated Drivers of Real ROI from Transform Partner) 1.    Use Case Fit -Alignment with a real operational pain point. -Clear KPIs defined from the start (e.g., downtime reduction, defect rate, energy savings).   2.    Process Integration Complexity -Ease or difficulty in integrating with legacy systems (ERP, MES, SCADA). -Disruption to existing workflows during deployment.   3.    Data Readiness -Availability of clean, structured, contextualized data. -Latency and accessibility of real-time data streams.   4.    Change Management and Workforce Buy-In -Training, adoption speed, and frontline engagement. -Resistance from operators or middle management can delay ROI.   5.    Scale of Deployment -ROI on a pilot line vs. enterprise-wide rollout differs drastically. -Proof-of-value efforts deliver faster ROI than end-to-end transformations.   6.    Organizational Agility -Speed of decision-making, budget reallocation, and vendor coordination. -Bureaucratic friction adds months to ROI timelines.   7.    Regulatory and Industry Constraints -In industries like aerospace, pharma, or energy, ROI is slowed by compliance, certification cycles, and safety validation.   8.    Vendor Capability and Accountability -Partner’s ability to deliver measurable outcomes (e.g., SLA-driven delivery). -Strong vendor-client collaboration accelerates time-to-value.   9.    Infrastructure Maturity -Whether the digital backbone (cloud, edge, connectivity) is already in place. -Tech upgrades may be needed before value realization starts.   10. Business Ownership -Strong executive sponsorship and cross-functional alignment. -ROI stalls when the initiative sits in a tech silo without business accountability.   This checklist can serve as a diagnostic lens before any investment—to pressure-test expected ROI timelines and set realistic expectations at the leadership table.   Do subscribe to our Premium Content Newsletter for such insightful notes. Image Source: Databricks   Transform Partner – Your Digital Transformation Consultancy

Why expecting immediate ROI from AI assistants is the wrong question.. What’s the right way to think about ROI for tools like Microsoft Copilot and ChatGPT in the enterprise? From what I’m seeing, this isn’t a binary decision. It’s a maturity curve. 1. Access (Utility phase) Deploy broadly. Build skills. Learn fast. At this stage, you’re making an investment, chasing hard ROI too early will dissapoint and slows you down. 2. Productivity (Accountability phase) Start measuring impact: time saved, throughput, quality. This is where signals emerge, but not full ROI. If AI is only helping with emails and summaries, you’re not moving the needle. 3. Transformation (ROI phase) This is where it gets real. At some point, the constraint shifts from the individual to the system. Productivity gains at the individual level start to expose constraints in the workflow. That’s the trigger to redesign how work gets done: – Compress workflows and reduce handoffs – Evolve roles and expand scope – Increase span of control – Actively reallocate capacity This is where ROI shows up, not from the tools themselves, but from how work is fundamentally restructured. The mistake I’m seeing.. Companies expect Stage 3 outcomes while still operating in Stage 1 or 2. They deploy widely… but never change how work actually gets done. The result? A very expensive convenience layer.

You're about to launch an AI initiative. The board approved the budget. The vendor is selected. The team is excited. But when someone asks "How will we measure success?" the room goes quiet. This is where most AI investments fail. Not because the technology doesn't work. Because no one defined what "working" actually means. Here are 10 steps to measure real ROI: 𝟭/ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗶𝗿𝘀𝘁 AI is not the goal. Solving a problem is. → What specific pain point are you addressing? → What's the cost of this problem today? If you can't articulate the problem in one sentence, you're not ready. 𝟮/ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗕𝗮𝘀𝗲𝗹𝗶𝗻𝗲𝘀 You can't measure improvement without knowing where you started. → How long does this process take today? → What's the error rate? The cost per transaction? No baseline, no ROI story. 𝟯/ 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲 𝗛𝗮𝗿𝗱 𝗮𝗻𝗱 𝗦𝗼𝗳𝘁 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗛𝗮𝗿𝗱: Cost reduction, time savings, revenue impact, volume handled 𝗦𝗼𝗳𝘁: Employee experience, customer experience, innovation speed, decision quality Track both. Don't pretend soft metrics don't count. 𝟰/ 𝗦𝗲𝘁 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗧𝗮𝗿𝗴𝗲𝘁𝘀 "Improve efficiency" is a wish, not a target. → Reduce handling time from 12 minutes to 4 → Cut document review costs by 40% Specific targets create accountability. 𝟱/ 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗧𝗼𝘁𝗮𝗹 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 The license fee is the down payment, not the investment. Include: implementation, training, maintenance, internal time. Underestimating cost is the fastest way to negative ROI. 𝟲/ 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗤𝘂𝗶𝗰𝗸 𝗪𝗶𝗻𝘀 𝗮𝗻𝗱 𝗟𝗼𝗻𝗴-𝗧𝗲𝗿𝗺 𝗩𝗮𝗹𝘂𝗲 Quick wins (0-90 days) build confidence and stakeholder support. Long-term value (6-18 months) delivers compounding gains. You need both. 𝟳/ 𝗕𝘂𝗶𝗹𝗱 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 If measurement requires extra effort, it won't happen. Automate collection. Build real-time dashboards. Make ROI visible to the teams doing the work. 𝟴/ 𝗧𝗿𝗮𝗰𝗸 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝗹𝘆 𝗳𝗿𝗼𝗺 𝗜𝗺𝗽𝗮𝗰𝘁 High adoption with low impact is a warning sign. A tool everyone uses but nobody benefits from is still a failed investment. 𝟵/ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗥𝗲𝘃𝗶𝗲𝘄 𝗖𝗮𝗱𝗲𝗻𝗰𝗲 → 30 days: Early signals → 90 days: Quick-win targets → 6 months: Actual vs. projected ROI → 12 months: Scale, pivot, or stop Regular reviews catch problems early. 𝟭𝟬/ 𝗧𝗶𝗲 𝗥𝗢𝗜 𝘁𝗼 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Someone has to own the number. If no one is accountable for ROI, no one will deliver it. AI ROI isn't magic. It's math. Define the problem. Establish baselines. Set specific targets. Track relentlessly. Hold someone accountable. Do this before you launch, not after you've spent the budget. Get my 10-step AI ROI Measurement Framework (free): https://lnkd.in/gACFJFT8 Save this for your next AI initiative.

𝗔𝗜 𝘄𝗼𝗻'𝘁 𝘀𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺, 𝗯𝘂𝘁 𝘆𝗼𝘂𝗿 𝘀𝘆𝘀𝘁𝗲𝗺 𝗺𝗶𝗴𝗵𝘁 DORA's 2025 report (5,000 professionals) confirms: 95% of developers use AI. 80% say it increased productivity. But 30% don't trust the code it generates. Teams are moving faster while breaking more things. AI now improves throughput, a reversal from 2024. Here are the main findings: 𝟭. 𝗦𝗲𝘃𝗲𝗻 𝘁𝗲𝗮𝗺 𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝘀 DORA identified seven archetypes: "harmonious high-achievers" to teams in a "legacy bottleneck." The pattern: AI amplifies your existing system. Strong teams get better. Dysfunctional teams accelerate chaos. 𝟮. 𝗧𝗵𝗲 𝘀𝗲𝘃𝗲𝗻 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 🔹 𝗖𝗹𝗲𝗮𝗿 𝗔𝗜 𝘀𝘁𝗮𝗻𝗰𝗲. Ambiguity makes people either underuse AI (fear) or overstep boundaries (ignorance). 🔹 𝗛𝗲𝗮𝗹𝘁𝗵𝘆 𝗱𝗮𝘁𝗮 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀. High-quality, accessible, and unified data multiplies the organizational impact of AI. 🔹 𝗔𝗜-𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗮𝘁𝗮. Connect AI to your repos and docs—context turns plausible into useful. 🔹 𝗦𝘁𝗿𝗼𝗻𝗴 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗰𝗼𝗻𝘁𝗿𝗼𝗹. Frequent commits amplify individual effectiveness. Rollback use amplifies team performance. 🔹 𝗦𝗺𝗮𝗹𝗹 𝗯𝗮𝘁𝗰𝗵𝗲𝘀. More critical in the AI era. Amplifies product performance, reduces friction. 🔹 𝗨𝘀𝗲𝗿-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗳𝗼𝗰𝘂𝘀. Most striking finding: without this, AI actively harms team performance. 🔹 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀. 90% adoption. Platforms scale AI benefits from individual gains to organizational advantages. 𝟯. 𝗩𝗮𝗹𝘂𝗲 𝗦𝘁𝗿𝗲𝗮𝗺 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗮𝘀 𝗳𝗼𝗿𝗰𝗲 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿 Teams that visualize workflow from idea to customer direct AI toward actual constraints. Without this, gains get absorbed into bottlenecks. VSM ensures local improvements translate to team and product performance. 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘄𝗲 𝗹𝗲𝗮𝗿𝗻? Stop treating AI as a tool problem. It's a systems problem. Ask: Can we draw our value stream? Do developers know which tools are permitted and why? Is data accessible or siloed? Can our platform handle velocity? Winners aren't using the best models. They redesigned the systems to capitalize on the gains. Your choice: 𝗙𝗶𝘅 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺, 𝗼𝗿 𝘄𝗮𝘁𝗰𝗵 𝗔𝗜 𝗲𝘅𝗽𝗼𝘀𝗲 𝗲𝘃𝗲𝗿𝘆 𝗰𝗿𝗮𝗰𝗸 𝘆𝗼𝘂'𝘃𝗲 𝗶𝗴𝗻𝗼𝗿𝗲𝗱.