Innovation Portfolio Management

Are we measuring the wrong things in drug innovation? Some of the most valuable therapies might never show up on our innovation radar. The typical view in US #biopharma has long equated “innovation” with patents, new drug approvals, and R&D spend. They're easy to count and look good in investor decks. However, these metrics often reward volume more than total value. They don't tell us whether a therapy meaningfully improves patient lives, strengthens public health, or delivers returns beyond the financial metrics. A new six-dimensional framework published in The Incidental Economist offers another option. Drawing from over 600 interdisciplinary studies, the authors propose a more rigorous definition of #innovation: - Scientific and Technological Advances: Captures innovation and productivity using metrics such as new molecules, new drug applications, and patents. Emerging indicators, such as AI-enabled R&D and digital biomarkers, offer forward-looking insights. - Clinical Outcomes: Highlights therapeutic impact through metrics such as safety, efficacy, and patient-reported outcomes, emphasizing real-world patient benefits and delays in disease progression. - Operational Efficiency: Measures efficiency in development and production using trial success rates, R&D timelines, supply chain resilience, and adaptive trial designs. - Economic and Societal Impact: Evaluates economic returns and societal benefits through cost-effectiveness analyses, budget impacts, and productivity improvements. - Policy and Regulatory Effectiveness: Assesses how regulatory frameworks support innovation through approval speed, breakthrough designations, and surrogate endpoint integration. - Public Health and Accessibility: Examines broader health impacts, including reduced disease incidence, healthcare access improvements, and equitable geographic distribution, ensuring innovations meet widespread public health needs. This doesn't have to just be academic. It could change what gets funded, approved, and reimbursed. Some examples mentioned in the article: -An Alzheimer's therapy might look risky on paper, but when viewed through long-term productivity gains and reduced caregiver burden, it becomes a more attractive, high-risk/high-reward bet. -A platform technology (e.g., mRNA) may not boost new molecule counts today, but could enable faster, more precise drug development in the future. -A one-time gene therapy with high upfront cost could prove more valuable than chronic treatments when lifetime adherence and hospitalizations are factored in (if payers can afford the upfront investment). Of course, expanding how we define innovation introduces trade-offs. Complexity increases. Metrics will compete against each other. The question is whether the upside of greater alignment with ALL stakeholders is worth the operational complexity and potential reductions in value for some individual stakeholders. Would you be in favor of evaluating innovation more holistically?

Why product roadmaps should be outcome based not feature-driven We do sprints to ship features, and they don’t always work out. Why? Because features alone don’t move the needle -outcomes do. A practice that I usually follow is to ask myself: What problem are we solving, and how will we measure success?” And that’s how we pivot from feature factories to outcome-driven roadmaps with actionable steps to make it stick. 𝗪𝗵𝘆 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 > 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Outcome-based roadmaps focus on measurable results (e.g., “Increase free-to-paid conversion by 15%” vs. “Build a pricing calculator”). This shift: - Aligns teams around business goals, not just deliverables. - Empowers creativity (solve the problem, don’t just check a box). - Reduces waste by killing initiatives that don’t drive impact. But how do you actually make this work? Here’s My Practical Playbook 👇🏻 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 “𝗪𝗵𝘆” - Define outcomes tied to business goals: Partner with leadership to align on 1-2 KPIs per quarter (e.g., “Reduce churn by 10%”). - Ask this question: “If we deliver X feature, what outcome does it enable?”. If there’s no clear answer, rethink it. 2️⃣ 𝗕𝗿𝗲𝗮𝗸 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗶𝗻𝘁𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 Outcomes are broad—break them into testable hypotheses. - Example: To “Increase user engagement by 20%,” run:   - A/B test push notification timing.   - Pilot a gamified onboarding flow.   - Measure DAU/WAU ratios weekly. 3️⃣ 𝗔𝗱𝗼𝗽𝘁 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 - OKRs: Link Objectives (outcomes) to Key Results (metrics). - Impact Mapping: Visualize how features connect to goals. - RICE Scoring: Prioritize initiatives by Reach, Impact, Confidence, Effort. 4️⃣ 𝗚𝗲𝘁 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗕𝘂𝘆-𝗜𝗻 - Frame outcomes as ROI: Show how “Reduce support tickets by 25%” cuts costs. - Prototype outcomes first: Share a mock roadmap with leadership, highlighting gaps in current feature-centric plans. 5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Track leading indicators (e.g., user behavior changes) alongside lagging metrics (e.g., revenue). - Celebrate “failures”: Killing a feature that didn’t drive outcomes is a win. 𝟯 𝗧𝗵𝗶𝗻𝗴𝘀 𝘁𝗼 𝗔𝘃𝗼𝗶𝗱 - - Vague outcomes: “Improve UX” → ❌ | “Reduce checkout abandonment by 20%” → ✅. - Overloading the roadmap: Focus on 1-2 outcomes per quarter. - Ignoring feedback loops: Revisit outcomes bi-weekly—adapt as data comes in. This week, try this: Audit your roadmap. For every feature, ask: “What outcome does this serve?” If it’s unclear, reframe it, or cut it. I believe outcome-based roadmaps is a survival tactic. Let’s build products that matter. 👉 How are you bridging the gap between features and impact? Would love to know your process.

𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 𝐌𝐚𝐤𝐞 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐏𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝙄𝙣𝙣𝙤𝙫𝙖𝙩𝙞𝙤𝙣 𝙘𝙖𝙣’𝙩 𝙩𝙝𝙧𝙞𝙫𝙚 𝙞𝙣 𝙘𝙝𝙖𝙤𝙨. 𝙄𝙩 𝙜𝙧𝙤𝙬𝙨 𝙬𝙝𝙚𝙧𝙚 𝙮𝙤𝙪 𝙣𝙪𝙧𝙩𝙪𝙧𝙚 𝙞𝙩. Enterprise Architects rarely get credit for creativity — but without structure, creative ideas don’t scale. • Without governance, emerging tech stays in the lab. • Without capability maps, every hackathon win gets lost in translation. 𝐋𝐞𝐭’𝐬 𝐟𝐥𝐢𝐩 𝐭𝐡𝐞 𝐬𝐜𝐫𝐢𝐩𝐭: 🎯 Architects don’t stifle innovation. ✅ We 𝐞𝐧𝐚𝐛𝐥𝐞 it — with clarity, connection, and guardrails that give teams room to run. 𝟑 𝗪𝐚𝐲𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 𝐄𝐦𝐩𝐨𝐰𝐞𝐫 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: 𝟏️ | 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐟𝐨𝐫 𝐅𝐥𝐨𝐰 Innovation needs rhythm, not randomness. 💡 𝐇𝐨𝐰? Establish “innovation runways”, spaces to test ideas inside architecture cycles. 𝟐️ | 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬, 𝐍𝐨𝐭 𝐆𝐚𝐭𝐞𝐬 Creativity dies in bottlenecks. 💡 𝐇𝐨𝐰? Co-create standards with delivery teams, so adoption feels like enablement, not overhead. 𝟑️ | 𝐅𝐚𝐬𝐭 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐋𝐨𝐨𝐩𝐬 Great ideas scale when they evolve quickly. 💡 𝐇𝐨𝐰? Embed architects in product teams to connect experimentation to enterprise vision. This Friday, let’s celebrate the real engine behind organizational creativity: 🎉 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 𝐰𝐡𝐨 𝐜𝐥𝐞𝐚𝐫 𝐭𝐡𝐞 𝐫𝐮𝐧𝐰𝐚𝐲 𝐟𝐨𝐫 𝐢𝐝𝐞𝐚𝐬 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 𝐢𝐦𝐩𝐚𝐜𝐭. 🛫 What’s one thing you’ve done to unlock innovation this year? Share and inspire others. — ➕ 𝐅𝐨𝐥𝐥𝐨𝐰 Kevin Donovan 🔔 ♻️ 𝐑𝐞𝐩𝐨𝐬𝐭 | 💬 𝐂𝐨𝐦𝐦𝐞𝐧𝐭 | 👍 𝐋𝐢𝐤𝐞 🚀 𝐉𝐨𝐢𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬’ 𝐇𝐮𝐛 – our newsletter & community to enhance skills, meet peers, and level‑up your architecture career! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 ➔ https://lnkd.in/dgmQqfu2

GCC Leaders: Are You Measuring What Truly Matters? To measure the real impact of your Global Capability Center (GCC), you must go beyond traditional operational KPIs like cost savings or headcount. Those are hygiene. What truly matters is how your GCC moves the needle for the business. Here are 5 strategic metrics every GCC leader should track: 1. Value Delivered per Dollar Spent Why it matters: Shows how effectively the GCC converts investment into business outcomes. How to measure: • Business value (e.g., product revenue, productivity gains, IP created) / Total GCC cost • Can be benchmarked against alternative models (outsourcing, onshore) 2. Time to Market Acceleration Why it matters: Reflects the GCC’s ability to improve speed of execution for product development, support, or operations. How to measure: • % improvement in release velocity or cycle times after GCC involvement • Lead time from idea to launch before vs. after GCC enablement 3. Innovation Output Why it matters: Indicates contribution toward competitive advantage and future growth. How to measure: • Patents filed, features launched, automation use cases deployed • Number of AI/GenAI initiatives incubated and scaled • New product ideas or MVPs driven from GCC 4. Business Function Ownership & Accountability Why it matters: Measures the maturity and strategic importance of the GCC. How to measure: • % of global business function fully owned or co-owned by GCC (e.g., platforms, support functions, analytics COEs) • Strategic roles (Directors, VPs) based in the GCC • Participation in global decision-making forums 5. Customer or Stakeholder NPS / Satisfaction Score Why it matters: This metric reflects how well the GCC is delivering value—both through the products it helps build and the support it provides to global stakeholders. How to measure: • NPS from external customers using products or services developed by GCC teams • NPS from internal stakeholders on the GCC’s responsiveness, collaboration, and strategic alignment • Qualitative feedback on product quality, innovation, speed of execution, and business understanding If your GCC isn’t driving the business forward, it’s just another offshore team. And in 2025, that’s not enough. Rethink how you measure. Reframe how you lead. Redefine what your GCC stands for. Zinnov Amita Goyal Karthik Padmanabhan Amaresh N. Mohammed Faraz Khan Namita Adavi Dipanwita Ghosh Sagar Kulkarni Hani Mukhey ieswariya Rohit Nair Komal Shah Saurabh Mehta

AI in Healthcare: What We Measure Determines What We Scale In healthcare, innovation isn’t just about what we build. It’s about what we measure. Because what we choose to measure is what gets resourced, defended, scaled, and institutionalized. Too often, we fall in love with performance metrics without asking whether we’re solving the right problem or whether the benefits actually reach patients and providers in the real world. Here’s how I break down the four stages of responsible AI adoption and the metrics that matter most at each: IDEA – Does the problem matter? We often over-index on technological possibilities and under-index on problem clarity. Key metrics here aren’t precision or recall. They are: • Problem significance (How big is the gap or harm?) • Workflow relevance (Is this aligned with real clinical or operational bottlenecks?) • Strategic fit (Does it support institutional goals or health equity outcomes?) PROOF OF CONCEPT (PoC) – Can it work technically and operationally? At this stage, metrics help reduce uncertainty: • Model performance: sensitivity, specificity, AUC • System integration: latency, uptime, backend compatibility • Early user signals: perceived usefulness, usability, acceptability PoC tells us if it can work, not if it should. PROOF OF VALUE (PoV) – Does it matter enough to justify adoption? This is where many projects stall. And rightly so, because the bar gets higher: • Clinical impact: outcomes improved, risks reduced • Operational value: time saved, throughput increased • Economic justification: cost-effectiveness, ROI • User experience: trust, burden, intent to reuse • Equity: Does it serve diverse populations equally? If PoC is about internal validity, PoV is about external consequences. MAINSTREAMING – Can it scale safely, sustainably, and equitably? Scaling AI isn't a technical task. It’s a systems leadership challenge. Key metrics shift toward: • Implementation fidelity • Training and adoption rates • Safety triggers and override behavior • Equity audits: performance across demographics, comorbidities, language • Governance readiness: procurement, documentation, feedback loops Mainstreaming means moving beyond what works in pilot to what survives and improves in practice. As a clinician trained in medicine (MBBS), public health (MPH), and business strategy (MBA), I’ve come to see metrics not as technical detail but as ethical choice. We don’t scale what’s possible. We scale what we measure and what we reward. What metrics have helped you decide when an AI tool was ready to move forward or when to walk away? #AIinHealthcare #PoC #PoV #Mainstreaming #ClinicalAI #HealthInnovation #MBBSMPHMBA #HealthEquity #DigitalHealth #Enneagram5 #INTP #StrategicDesign #ResponsibleAI #HealthSystems #InnovationGovernance

Architects don't just draw boxes. They make change safer and faster When strategic priorities change, new regulation hits, a merger lands, or AI hype enters the roadmap, there is always a risk of misalignment across business, technology and what you deliver to your customers; that misalignment is a major driver of failed transformation. That is what architecture exists to reduce and contain. An Enterprise Architect designs the organisation as a system: operating model, capabilities, information, the technology landscape that enables them and the architectural integration of the enterprise and its environment; customers, partners, vendors. They map the current state, articulate the target state and more importantly, define options. Then create a transformation path built on clear principles and apply governance to realise value usually under pressure. Enterprises exist to create value, which is why ‘architect’ is not just one job. The specialisation reflects which part of the system you are designing: Solution architects translate business needs into end‑to‑end solution designs across domains, working closely with enterprise architects and technical architects. Technical architects design the technical foundations: guardrails, platforms, and engineering standards so teams can build quickly and safely. Understanding this matters because architecture creates coherence that shows up later as: - Better decisions: Leaders see dependencies and trade-offs before committing spend. - Business agility: Clearer interfaces, fewer reworks, and fewer delivery surprises. - Lower risk and cost: Less duplication with security and compliance designed in. - Managed innovation: New models and tech adopted while protecting and stabilising core operations. If you want one test: When your ‘transformation’ roadmap is just a disconnected list of projects; you need architecture. When it is a transformation path with principles, options, and dependencies made explicit; architecture is doing its job. Where is misalignment costing you most: strategy, operating model, or technology? As a leader, make room for your architect to architect. Source: Related Areas adapted from SFIA v9

Enterprise Architecture 2.0: From Blueprint Function to Business Growth Engine For the C-Suite: Your Enterprise Architecture isn’t a documentation function anymore — it’s your organization’s hidden lever for agility, speed, and scalable innovation.   To unlock this value, EA must transform. It's no longer about enforcing standards but about enabling growth. These are the four shifts every C-suite should champion to make EA a true strategic powerhouse.   1. Challenge: The Old Command-and-Control EA Model.
 As organizations decentralize into federated models, a centralized, governance-heavy EA function becomes a bottleneck to speed and autonomy.
   The Pragmatic Shift: Move from enforcement to orchestration.
 Adopt a federated operating model that embeds architects within business teams, with a lean central EA setting strategic guardrails and a common North Star. This builds alignment without sacrificing agility.   2. Challenge: A Bloated, Legacy-Heavy Tech Portfolio.
 Outdated systems and redundant applications create massive technical debt, which directly diverts capital from growth initiatives and cripples time-to-market.
   The Pragmatic Shift: Treat tech modernization as a continuous discipline.
 Implement a disciplined, iterative cycle Assess → Define → Prepare → Execute → Learn to systematically rationalize applications, reduce debt, and free up resources for competitive advantage.   3. Challenge: An EA Team Lacking Business and AI Credibility.
 If your architects can't model the financial ROI of a tech investment or speak credibly about AI's risks and opportunities, they can’t earn a seat at the strategic table.   The Pragmatic Shift: Equip architects with business and AI fluency.
 Arm your EA team with financial modeling skills to build compelling business cases and develop deep AI competencies to guide safe, effective, and strategic adoption.   4. Challenge: A Static and Poorly Communicated Value Proposition.
 When EA is seen as a cost center that only says "no," its value erodes. Its relevance must be constantly demonstrated and tied to evolving business priorities.
   The Pragmatic Shift: Proactively manage the EA value narrative.
 Embed EA leaders directly in business-led change teams. Consistently articulate and demonstrate how EA enables key outcomes: accelerating product launches, de-risking investments, and enabling scalable growth.   The Bottom Line:
  The question isn’t “Do you have an EA team?” It’s “Have you empowered them to lead your transformation?”   For leadership teams already tackling modernization or operating model redesign, the next critical step is architectural alignment — ensuring every investment ties to measurable business value.   If you’re assessing how to reposition your EA function for speed, credibility, and ROI impact, reach out. I can share what’s working — backed by real enterprise outcomes, not theory.   Transform Partner – Your Strategic Champion for Digital Transformation Image Source: Gartner

The number of ideas is a weak innovation KPI. It looks productive. But for corporate innovation leaders, idea count mostly rewards volume. It says little about whether the team is reducing uncertainty, making better decisions, or moving closer to a business result. A better measurement set tracks progress, not just input: 1. What was learned 2. What was validated 3. What decision was made 4. What moved forward That is the difference between vanity metrics and useful metrics. Idea count can still have a place. Only if it is connected to what happens next. - How many ideas reached a clear evaluation? - How many assumptions were tested and resolved? - How many concepts earned a decision to advance, stop, or reshape? More ideas do not mean better innovation. Better measurement does. Better measurement changes behavior.

Your programme works. You have data to prove it. Then the hard questions came: 'How do you KNOW it was YOUR intervention?' 'Which parts must stay the same when we replicate this in 12 countries?' 'Why did it work in the first place?' Silence. You're not alone in not having the answers. Most programme (innovative or traditional) can't answer these questions because they collected activity data, not evidence for scale. Here's what you should be measuring at each stage instead: 📍 Early stage (Pilot): Don't just count participants. Measure: Did it work? Was it feasible? Do users actually want this? 📍 Mid-stage (Acceleration): Don't just report more numbers. Measure: What are the core elements that CAN'T change? What CAN flex for different contexts? 📍 Scale stage: Don't just show reach. Measure: Can you prove YOUR intervention caused the change? Can others sustain it without you? UNICEF's Innovation MEL Toolbox breaks down exactly what evidence you need at each stage (from ideation to scale) including practical tools like: →Theory of Change for different stages →Contribution Analysis (when RCTs aren't possible) →Fidelity & Adaptation Monitoring →Scaling Approach frameworks Whether you're testing something new, expanding what works, or adapting proven approaches to new contexts, this document is for you. 🔥 If this resonated, follow me. I break down Monitoring and Evaluation (M&E) concepts daily with practical, implementable tips that are grounded in facilitation experience across sectors. #MonitoringAndEvaluation

KPIs and innovation are natural enemies. Innovation is uncertain on purpose. KPIs, too often, pretend certainty is available — neat targets, clean lines from action → impact, tidy attribution. But in cities (and the public sector more broadly), attribution is almost always messy. Outcomes move because of multiple actors, shifting contexts, political trade-offs, and timing. Which is why KPIs can be a pretty poor tool for managing innovation — we end up rewarding simplistic outcomes and retrofitting stories that look “measurable”. In our work at UCL Institute for Innovation and Public Purpose (IIPP) on public sector capabilities in cities, we argue that when we are dealing with innovations by and in city governments, we shouldn't just ask “what did we deliver?”, but also “what did we learn?” Did an innovation leave the city more capable for the next challenge? In my new piece for Bloomberg Philanthropies Bloomberg Cities, I suggest adding a simple “capabilities check” alongside the usual KPIs: - Did we build partnerships we’ll actually reuse? - Did we learn/iterate and make those lessons portable? - Did we change a routine/template/rule so future change is easier? The point isn’t more reporting. It’s making sure innovation compounds rather than resets with every pilot. Read on https://lnkd.in/epCwRRnk