Change Management Metrics And KPIs

ADC Success Rates, Timelines & Strategic Implications Despite significant advances in antibody-drug conjugate (ADC) platform technologies, even promising preclinical data, translation into the clinic is tough, time-consuming, expensive, and often unsuccessful. In the infographic below, I’ve used data from the Beacon ADC database to analyse the rates of phase transition for all ADCs, then by payload mechanism, linker, conjugation site specificity, and target, and mapped the time in clinic for approved ADCs. ADC vs Oncology vs All Drugs – Success Rates Phase 1 → 2: ADCs 46% | Oncology 58% | All drugs 66% Phase 2 → 3: ADCs 24% | Oncology 33% | All drugs 49% Phase 3 → Approval: ADCs 82% | Oncology 36% | All drugs 59% Overall approval: ADCs 9%, vs 3.4% in oncology, and 14% across all drugs Why ADC success rates differ from other drugs: 🟪 Early risk: ADCs have lower Phase 1–2 (46%) and Phase 2–3 (24%) success due to off-target toxicity, complex PK, and biomarker variability. 🟪 Late-stage strength: Once in Phase 3, ADCs achieve 82% approval, more than 2x oncology's average (36%) thanks to smarter trial designs and biomarker strategies. 🟪 Takeaway: ADCs are high-risk upfront, but high-reward when well-designed; precision, conjugation, and CMC and regulatory strategies are everything. Accelerating Development Timeline Implications The compression from 9 to 7 years in development reflects not just improved regulatory pathways but evolving trial design strategies. Recent approvals demonstrate successful implementation of biomarker-driven patient selection and novel endpoints that better capture ADC-specific response patterns. Investment Implications The success rate differentiation by design elements provides a framework for valuing ADC platforms beyond target selection. Portfolios balancing established design elements (cytotoxic payloads, validated targets) with novel approaches (dual-payload ADCs, ISACs, DACs), may optimise risk-adjusted returns. It’s important to note that manufacturing capabilities and clinical development expertise may be as important as innovations in ADC constructs in determining commercial success. For ADC developers, investors, and service providers, these benchmarks provide essential context for pipeline valuation, resource allocation, and strategic planning. Understanding these industry averages helps teams set realistic expectations and identify programs that are outperforming the norm. It is important to note that this analysis doesn't capture all variables affecting success, specific payload characteristics, antibody properties, and disease indication selection. Ultimately, success is determined by specific combinations of these components working in concert, not any single element in isolation. Which ADC subsets would you like to see analysed for transition rates? What are you expecting the results to show? #adcs #antibodydrugconjugates #drugdevelopment #pharmainvestment #pharmaintelligence

We worked on a programme during a period when youth unemployment was one of the most pressing social challenges the country faced. One in five young people was out of work. In 2020, DWP launched the Kickstart Scheme as one of the most ambitious youth employment interventions the UK had seen in years. Most programmes counted placements. We measured what happened after them. The brief was straightforward in principle and complex in practice. Identify young people who were out of work, match them with meaningful employment opportunities, support them through the transition, and track what happened next. The difficulty was not finding willing employers or willing candidates. It was building a process that could move at scale without losing the individual care that made placements actually stick. Here's what we delivered. 152 placements across the programme, each one requiring coordination between candidates, employers, and support structures that had to work together without a shared system to connect them. We built the workflow to manage this from intake through to outcome tracking, ensuring every placement had the oversight it needed without creating bureaucracy that slowed the human work down. The results told the story more clearly than any process document could. 80 percent of individuals transitioned into full time roles after their placements. That is not a programme statistic. That is 120 people who entered the workforce with skills, references, and a track record where they had none before. The insight that applies beyond this programme. Workforce transformation succeeds when it is designed around the person making the transition, not the organisation administering it. The placements that led to full time employment were the ones where the match was right, the support was genuine, and the employer understood they were investing in potential, not just filling a role. Process matters. But what actually changes outcomes is whether the people running the programme believe the work is worth doing properly. What does your organisation do to ensure employment programmes create lasting transitions rather than temporary statistics?

In every conversation with project/procurement leaders, the same frustration arises: 𝐍𝐨 𝐨𝐧𝐞 𝐬𝐭𝐢𝐜𝐤𝐬 𝐭𝐨 𝐭𝐢𝐦𝐞𝐥𝐢𝐧𝐞𝐬, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐬𝐮𝐟𝐟𝐞𝐫. I’ve seen this happen firsthand—delays don’t happen in isolation. It’s never just the vendor, the client, or the procurement team. It’s one of those collective contributions! Some of the many reasons: - Albeit under pressure, Vendors commit to terms without 100% clarity. - Low focus on planning at MSMEs adds to the noise. - Vendors portray on-ground situations much better than they really might be. - Any mid-way changes by the clients, shifting expectations and complicating the problem statement for the vendors further. - Vendors scramble with last-minute acceleration and resource constraints. - Internal teams juggle misalignments, leading to reactive decisions. In project procurement from MSME vendors, in my view, the biggest aspect that leads to delays is a lack of transparency and visibility of how the work is progressing on the vendor side. For instance, on the vendor side— any gaps in planning for the procurement of raw materials and bought-out items lead to chaos at the last minute. Inefficiencies in capturing real inputs in current formats—spreadsheets, emails, scattered approvals—only add to the chaos. Further, the lack of authentic data makes it difficult to address real issues. What happens next? 𝐅𝐢𝐫𝐞𝐟𝐢𝐠𝐡𝐭𝐢𝐧𝐠, 𝐜𝐨𝐬𝐭 𝐨𝐯𝐞𝐫𝐫𝐮𝐧𝐬, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐝𝐞𝐥𝐚𝐲𝐬 𝐭𝐡𝐚𝐭 𝐧𝐨 𝐨𝐧𝐞 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐞𝐝 𝐟𝐨𝐫! At Venwiz, we are leveraging technology and have developed a Milestone Management Tool (MMT) to capture real-time information and reduce human dependency, tracking jobs at multiple vendor locations. The on-ground team is responsible for capturing raw data from different sites. However, all the metrics used for project tracking are calculated using our Milestone Management Tool (MMT)—which adds to the authenticity and reliability of the data. Our core focus is on actively preventing (and reducing) delays by understanding the root causes. In my opinion, the best procurement leaders don’t just manage vendors—they orchestrate the entire project ecosystem with data and transparency. How do you tackle shifting timelines in your projects? #Manufacturing #CapEx #Procurement #VendorManagement #Automation

A few months ago, I spoke to a project manager who had just wrapped up a client project. Or rather, should have wrapped it up. The project was originally going to be for 8 weeks. Everyone agreed on the timeline upfront, shook hands, and dove in. But then the delays started: • The client needed more time to approve designs. • The vendor supplying key software missed their deadline. • Halfway through, a critical feature needed to be reworked. Suddenly, the "8-week" project stretched to 12 weeks. And the Contract? It had strict deadlines and no room for adjustments. This caused: • Frustration on both sides. • The client was unhappy about delays. • The project manager was penalized for missed deadlines. • The relationship? Completely soured. Deadlines look great in contracts. Because they are clear, concise, and seemingly immovable. But projects don’t exist in a vacuum. That's why things often go wrong: 1. Dependencies Get Overlooked Deadlines often rely on third parties - client approvals, vendor deliveries, or team availability. One missed milestone, and the entire timeline collapses. 2. No Cushion for the Unexpected Tech hiccups, team illness, or surprise feature requests can derail progress. Without a buffer, small issues snowball fast. 3. Rigid Timelines Create Tension When deadlines slip (and they almost always do), the blame game begins. Trust erodes, and disputes become inevitable. 4. The Risk of Penalties Missed deadlines can trigger financial penalties or harm your reputation - even when delays are beyond your control. 5. Misaligned Expectations Rigid deadlines assume everything will go perfectly - which rarely happens. Without clarity on flexibility, both sides end up frustrated. Let’s go back to that project manager’s situation. What if the contract had been different? Because a good contract would have: a) Buffer Periods Built Into the Timeline Adding a 1-2 week buffer to each milestone allows for delays without derailing the project. b) Clear Contingency Plans Specify how delays will be managed - who’s responsible, what adjustments are made, and how costs or timelines shift. c) Defined Flexibility Mention that deadlines may shift due to dependencies or unforeseen issues. d) Shared Accountability Be clear on mutual responsibility - clients delivering approvals on time, vendors meeting commitments, and the team staying on schedule. Imagine that same project manager with a flexible contract: • When the vendor delays delivery, the buffer period absorbs the impact. • When the client needs extra time, the contingency plan kicks in. • And when the project wraps at week 12 instead of week 8, no one is surprised. No penalties. No disputes. No burned bridges. Deadlines are important. But assuming they won’t change? Now you are asking for disaster. —— 📌 If you need my help with drafting flexible contracts for your high-ticket projects, then DM me "Contract". #Startups #Founders #Contract #Law #Business

🚀 Defining Metrics to Track GenAI's Impact on Your Business 🚀 As businesses embrace Generative AI, it's crucial to track the right metrics to understand its impact on key areas like operational efficiency, productivity, and revenue growth. It's not just about having AI in place; it's about ensuring AI is driving measurable outcomes that matter. 📊 Operational Efficiency 🤖 Task Automation: Measure how many repetitive, manual tasks AI automates. ⏱️ Cycle Time Improvement: Track how much faster core processes are after AI implementation. ⚡ Productivity Gains 🧠 Speed of Decision-Making: Measure how quickly AI enables teams to make informed decisions. 👥 Employee Utilization: Monitor how AI frees up employees to focus on higher-value work. 📈 Revenue Growth 💰 Customer Lifetime Value (CLV): Track how CLV trends post-AI adoption. 🛍️ Upsell and Cross-Sell Opportunities: Assess the increase in basket size and cross-sell success. 😊 Customer Satisfaction: Measure changes in customer satisfaction scores. ✅ First Contact Resolution: Evaluate the percentage of issues resolved on the first contact. 😊 Customer Satisfaction: Measure changes in customer satisfaction scores before and after implementing GenAI. ⏱️ Response Time: Track the speed of customer inquiries and issue resolution. Additional Metrics to Consider: 👍 Employee Satisfaction: Assess how employees feel about using GenAI tools. 📊 Data Quality: Ensure data used to train and feed AI models is accurate and reliable. ⚖️ Ethical Considerations: Monitor for any unintended biases or negative consequences. By focusing on these key performance indicators, you can gain a clear picture of how AI is moving the needle on efficiency, productivity, and growth—and ensure your AI investments are translating into real business results. #GenerativeAI #OperationalEfficiency #RevenueGrowth #DataDriven #AITransformation Image by wahyu_t on Freepik

There is a point in most transformations where you can feel things starting to move. Energy builds, new behaviours emerge, and there is a sense that the organisation is shifting, but whether that momentum sustains rarely comes down to strategy or even execution. It comes down to something far less visible: how the organisation is being measured. The Finance pillar within the Transformation Leaders Body of Knowledge (TLBoK) plays a far more active role in transformation than most programmes acknowledge. Not as reporting or control, but as a system that shapes behaviour. What gets measured, funded, and rewarded defines what the organisation will actually do, regardless of what has been communicated. One consistent pattern in more mature transformation environments is that KPIs are not treated as fixed. They are deliberately redesigned as the transformation progresses. Because introducing new processes or operating models is not enough on its own, those ways of working need to be viable inside the system and that viability is determined by the measurement framework. Performance metrics are not passive indicators of success. They influence decision-making, shape trade-offs, and signal what “good” looks like across the organisation. When those metrics remain anchored in the legacy model, the organisation does not resist the transformation; it adapts around it (if your lucky). This is where the tension quietly builds. You see process change without a real behavioural shift. New operating models constrained by old cost logic. Strategic intent diluted by incentives that still reflect the past. Not because people are unwilling, but because the system has not been fully redesigned to support the future state. In stronger transformation environments, KPI evolution is treated as part of the architecture. Early on, metrics are used to create visibility and expose constraints. As the transformation progresses, they begin to reinforce new behaviours and build capability. Later, they anchor value realisation, scalability, and resilience. Measurement moves with the transformation, not behind it. This is often where programmes lose integrity without realising it. Not in the ambition or the effort, but in the gap between what the organisation says it wants and what it continues to measure. That gap is where drift begins. This is why the Finance pillar is not positioned as oversight. It sits inside the execution architecture, alongside value logic, incentives, and governance design, because transformation does not happen through intent alone. It happens through the systems that make certain behaviours possible and others impossible. If behavioural change is slower than expected, it is rarely a communication issue. It is usually a sign that the organisation is still being structurally rewarded for the past. Do you have examples of this that you are prepared to share?

The difference between success and failure in change management is slim. Statistics show that 70% of change initiatives fail. If we dig deep, there is more than the luck factor. Initial employee resistance and lack of management support could make or break the campaigns. Organizations with effective change management strategies see 73% to 88% success rates in meeting objectives. In our experience, here’s what companies should focus on:    •   Employee Engagement: Only 34% of change initiatives succeed without active employee participation. Involving employees in decision-making increases success by 15%.    •   Leadership Support: Visible leadership commitment is crucial. 25% of employees believe change management is a significant strength of their organization’s senior leaders.    •   Adaptability: 79.7% of organizations need to adapt their business strategies every 2-5 years. Effective change management can drive 264% revenue growth compared to those with subpar strategies. Would love to hear strategies that you have adopted for successful change management. #ChangeManagement #Leadership #Innovation #Collaboration

The system worked. The transition failed. Cloud is live. Code is bug-free. Data migrated successfully. Project status: Complete. Six weeks later - teams are back in spreadsheets. Adoption rate: 15%. McKinsey 2024: 70% of digital transformations fail to meet objectives. In 85% of those failures, the technology worked perfectly. Here's what the radar chart reveals: Technical System Readiness: 98% Leadership Role-Modeling: 35% Shared Meaning & Buy-In: 27% Skills & Behavioral Mastery: 22% Incentive & KPI Alignment: 18% The budget imbalance mirrors this perfectly. 90% allocated to systems. 10% to people. Yet 70% of ROI depends on adoption. Four mechanisms guarantee failure: ❌ The Hypocrisy Gap ↳ Only 1 in 3 leaders change their habits ↳ CEO asks for the old spreadsheet once - transition dies ❌ The Training Fallacy ↳ Most users reach basic awareness, stop there ↳ Only 20% achieve mastery ↳ The rest build workarounds ❌ The Structural Sabotage ↳ New system launched ↳ Bonuses tied to old behaviors ↳ People choose the bonus every time ❌ The Engagement Exodus ↳ 70% of staff feel change is "done to them" ↳ Not "for them" or "with them" ↳ Resistance becomes their identity The 48-hour test predicts everything. If leadership modeling sits below 50%, teams revert to shadow processes within 48 hours of launch. Then the pattern completes: System gets labeled "broken." Transition gets ignored. Change lead gets fired. Document this before your next launch: ↳ Leadership modeling score (target: 70%+) ↳ Incentive alignment assessment (currently 18%) ↳ User engagement in design process ↳ Behavioral mastery milestones beyond training Your technology budget was never the problem. Your people budget was. -------- 🔔 Follow Justin R. for more Transformation insights ♻️ Share with someone launching a system next quarter

Small companies are 2.7x more likely to succeed at transformation than large ones. The data is brutal but clear. • Small team transformation success rate: ~80% when done right • Large team transformation success rate: ~30% industry average Why small teams win: • Faster decisions • 5 people in a room vs. 50 people across departments. • Less bureaucracy • 'Let's try this' vs. 'Let's form a committee to evaluate forming a committee.' • Direct communication • Changes get explained once, not filtered through 6 layers. • Skin in the game • Everyone feels the impact immediately. Why large teams struggle: • Change fatigue • By the time the plan reaches everyone, priorities have already shifted. • Competing initiatives • Transformation project #47 competing with transformation projects #1-46. • Legacy thinking • 'We've always done it this way' multiplied by hundreds of people. The founder's dilemma: You want to scale, but scaling makes transformation harder. The solution: Transform in small batches. Build transformation capability as you grow. Don't wait until you're big to figure out how to change. Small moves, consistently applied, beat big plans, poorly executed. How big is your transformation team, and is that helping or hurting your success?

𝗬𝗼𝘂𝗿 𝗖𝗠𝗠𝗦 𝘀𝗵𝗼𝘄𝘀 𝟵𝟱% 𝗣𝗠 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲. 𝗦𝗼 𝘄𝗵𝘆 𝗱𝗶𝗱 𝘆𝗼𝘂𝗿 𝗚𝗿𝗶𝗻𝗱𝗶𝗻𝗴 𝗠𝗶𝗹𝗹 𝗷𝘂𝘀𝘁 𝗳𝗮𝗶𝗹 ? Here's the uncomfortable truth: PM compliance is a vanity metric when your maintenance intervals or tactics don't match your failure patterns. I see this scenario play out constantly. Operations celebrate hitting PM targets. Finance is happy with the maintenance budget. Then catastrophic failure strikes, and everyone scrambles for answers. The Root Cause Nobody Talks About Your PM intervals were set: • Based on OEM recommendations (designed for warranty, not reliability) • Using generic industry standards • From "that's how we've always done it" • Without analysing YOUR actual failure data Meanwhile, your Grinding mill was following its own degradation curve, completely disconnected from your calendar-based maintenance. What 95% PM Compliance Actually Means 𝗜𝘁 𝗺𝗲𝗮𝗻𝘀 𝘆𝗼𝘂'𝗿𝗲 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝘁 𝗮𝘁: • Following a schedule • Ticking boxes • Creating maintenance records • Spending money on time 𝙄𝙩 𝙙𝙤𝙚𝙨𝙣'𝙩 𝙢𝙚𝙖𝙣 𝙮𝙤𝙪'𝙧𝙚 𝙥𝙧𝙚𝙫𝙚𝙣𝙩𝙞𝙣𝙜 𝙛𝙖𝙞𝙡𝙪𝙧𝙚𝙨. The Data-Driven Alternative Transform your maintenance from time-based to condition-based: 1. Collect Real Failure Data • Document every failure mode • Track time between failures • Record operating context • Capture early warning signs 2. Perform Weibull Analysis • Calculate your actual P-F intervals • Identify wear-out vs random failures • Optimize PM intervals based on YOUR data • Build confidence intervals for planning 3. Integrate Condition Monitoring • Vibration analysis for bearings • Oil analysis for gearboxes • Thermography for motors • Thickness testing for liners 4. Create Dynamic PM Strategies • Adjust intervals based on operating hours • Factor in throughput and ore hardness • Use real-time data to trigger maintenance • Move from calendar to condition The Measurable Impact When you align maintenance with actual failure patterns: • 30-40% reduction in catastrophic failures • 20-25% decrease in maintenance costs • 15-20% improvement in availability • ROI typically within 12 months Your Next Steps Stop celebrating PM compliance. Start measuring: • Mean Time Between Failures (MTBF) • P-F interval accuracy • Condition-based maintenance effectiveness • Failure prediction accuracy At Holistic Asset Management, we help teams move beyond compliance theatre to reliability that delivers results. Your CMMS has the data. Let's unlock its predictive power. Ready to prevent your next SAG mill failure? Let's review your maintenance strategy. #ReliabilityEngineering #PredictiveMaintenance #AssetManagement #MaintenanceOptimization