Project Management

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - ... [Reached length limit; full text: https://lnkd.in/geQBWz6s ]

It’s easy as a PM to only focus on the upside. But you'll notice: more experienced PMs actually spend more time on the downside. The reason is simple: the more time you’ve spent in Product Management, the more times you’ve been burned. The team releases “the” feature that was supposed to change everything for the product - and everything remains the same. When you reach this stage, product management becomes less about figuring out what new feature could deliver great value, and more about de-risking the choices you have made to deliver the needed impact. -- To do this systematically, I recommend considering Marty Cagan's classical 4 Risks. 𝟭. 𝗩𝗮𝗹𝘂𝗲 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗦𝗼𝘂𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 Remember Juicero? They built a $400 Wi-Fi-enabled juicer, only to discover that their value proposition wasn’t compelling. Customers could just as easily squeeze the juice packs with their hands. A hard lesson in value risk. Value Risk asks whether customers care enough to open their wallets or devote their time. It’s the soul of your product. If you can’t be match how much they value their money or time, you’re toast. 𝟮. 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗨𝘀𝗲𝗿’𝘀 𝗟𝗲𝗻𝘀 Usability Risk isn't about if customers find value; it's about whether they can even get to that value. Can they navigate your product without wanting to throw their device out the window? Google Glass failed not because of value but usability. People didn’t want to wear something perceived as geeky, or that invaded privacy. Google Glass was a usability nightmare that never got its day in the sun. 𝟯. 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗣𝗼𝘀𝘀𝗶𝗯𝗹𝗲 Feasibility Risk takes a different angle. It's not about the market or the user; it's about you. Can you and your team actually build what you’ve dreamed up? Theranos promised the moon but couldn't deliver. It claimed its technology could run extensive tests with a single drop of blood. The reality? It was scientifically impossible with their tech. They ignored feasibility risk and paid the price. 𝟰. 𝗩𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝗶𝘀𝗸: 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗲𝘀𝘀 𝗚𝗮𝗺𝗲 (Business) Viability Risk is the "grandmaster" of risks. It asks: Does this product make sense within the broader context of your business? Take Kodak for example. They actually invented the digital camera but failed to adapt their business model to this disruptive technology. They held back due to fear it would cannibalize their film business. -- This systematic approach is the best way I have found to help de-risk big launches. How do you like to de-risk?

Getting Started with Git- 𝗴𝗶𝘁 𝗶𝗻𝗶𝘁 : This is the very first command you'll need to use when starting a new project. It initializes a new Git repository in your current directory. 𝗴𝗶𝘁 𝗰𝗹𝗼𝗻𝗲 <𝗿𝗲𝗽𝗼> : To work on an existing project, you'll want to clone (copy) it to your local machine. This command does that. 𝗠𝗮𝗸𝗲 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗴𝗶𝘁 𝘀𝘁𝗮𝘁𝘂𝘀 : Before making or after making changes, it's good practice to check the status of your files. This command will show you any changes that are currently unstaged. 𝗴𝗶𝘁 𝗮𝗱𝗱 <𝗳𝗶𝗹𝗲𝗻𝗮𝗺𝗲> : After you've made some changes to your files, you'll want to stage them for a commit. This command adds a specific file to the stage. 𝗴𝗶𝘁 𝗮𝗱𝗱 . 𝗼𝗿 𝗴𝗶𝘁 𝗮𝗱𝗱 -𝗔 : Instead of adding files one by one, you can add all your changed files to the stage with one command. 𝗴𝗶𝘁 𝗰𝗼𝗺𝗺𝗶𝘁 -𝗺 "𝗖𝗼𝗺𝗺𝗶𝘁 𝗺𝗲𝘀𝘀𝗮𝗴𝗲" : Now that your changes are staged, you can commit them with a descriptive message. 𝗕𝗿𝗮𝗻𝗰𝗵𝗶𝗻𝗴 𝗴𝗶𝘁 𝗯𝗿𝗮𝗻𝗰𝗵 : This command will list all the local branches in your current repository. 𝗴𝗶𝘁 𝗯𝗿𝗮𝗻𝗰𝗵 <𝗯𝗿𝗮𝗻𝗰𝗵𝗻𝗮𝗺𝗲> : This command creates a new branch. 𝗴𝗶𝘁 𝗰𝗵𝗲𝗰𝗸𝗼𝘂𝘁 <𝗯𝗿𝗮𝗻𝗰𝗵𝗻𝗮𝗺𝗲> : If you want to switch to a different branch, use this command. 𝗴𝗶𝘁 𝗺𝗲𝗿𝗴𝗲 <𝗯𝗿𝗮𝗻𝗰𝗵𝗻𝗮𝗺𝗲> : Once you've finished making changes in a branch, you'll want to bring those changes into your main branch (usually master). This command does that. 𝗥𝗲𝗺𝗼𝘁𝗲 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝗴𝗶𝘁 𝗽𝘂𝘀𝗵 𝗼𝗿𝗶𝗴𝗶𝗻 <𝗯𝗿𝗮𝗻𝗰𝗵𝗻𝗮𝗺𝗲> : This command sends your commits to the remote repository. 𝗴𝗶𝘁 𝗽𝘂𝗹𝗹 : If other people are also working on your project, you'll want to keep your local repo up-to-date with their changes. This command fetches and merges any changes from the remote repository. 𝗴𝗶𝘁 𝗿𝗲𝗺𝗼𝘁𝗲 -𝘃 : To check which remote servers are connected with your local repository. 𝗞𝗲𝘆 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗴𝗶𝘁 𝗳𝗲𝘁𝗰𝗵 𝘃𝘀 𝗴𝗶𝘁 𝗽𝘂𝗹𝗹: Both download data from a remote repository. However, git fetch just downloads it without integrating it, while git pull also merges it into your local files. 𝗴𝗶𝘁 𝗺𝗲𝗿𝗴𝗲 𝘃𝘀 𝗴𝗶𝘁 𝗿𝗲𝗯𝗮𝘀𝗲: Both incorporate changes from one branch to another. git merge combines the source and target branches via a new commit, whereas git rebase moves or combines commits to a new base, making a cleaner history. 𝗴𝗶𝘁 𝗿𝗲𝘀𝗲𝘁 𝘃𝘀 𝗴𝗶𝘁 𝗿𝗲𝘃𝗲𝗿𝘁: Both are used to undo changes. git reset discards local changes completely, while git revert undoes public changes by creating a new reversing commit, thereby preserving history. Git is an extremely powerful tool with plenty more commands and options. However, this post gives you a good start and reference point as you continue to explore and leverage Git for your version control needs.

RFP responses can be a real challenge. They’re often slow and inconsistent due to scattered knowledge and manual processes. This was the case for a global consultancy that wanted to speed up how it brought its offerings to market. Sales teams struggled to access past proposals, relevant case studies, and client-specific context. This customer was an early Glean Agent adopter, and we’re thankful for their feedback along the journey. To address this challenge, they deployed a suite of Glean agents. The goal was to unify content discovery and streamline proposal workflows, pulling from their company knowledge bases, CRM systems, and external research to support end-to-end RFP generation. This was paired with a methodical approach to enablement and adoption. Some examples of agents they built: • A Client Need Triage agent that maps client requirements to standard service offerings • A Research agent to pull together industry and company-specific insights • A Historian agent to surface past engagements and account activity right from the CRM • A Proposal Helper agent to accelerate proposal creation with standardized, offering-aligned drafts This foundation delivered real business value: • Proposal development time dropped from 4 weeks to just a few hours. That’s a 97% productivity gain. • A heuristic metric of deflecting over $150K if a single point enablement Saas solution was chosen. By embedding agents directly into the sales workflow, the consultancy improved both speed and precision in proposal development. Now, they’re looking to apply the same agent-driven approach to other parts of the business, like managed services and engineering, to bring that same efficiency and intelligence everywhere.

8 Examples where Pharma is Using AI to Enhance Clinical Trials >> Pharma’s greatest use of AI is in drug development, but optimising clinical trials is an important and growing focus, as these recent examples illustrate 🔘 Bristol Myers Squibb extended its partnership with Medidata Solutions to enhance clinical trial management. BMS will adopt Medidata Clinical Data Studio and explore AI, advanced analytics, and data tools to optimize trial efficiency. This builds on their 2016 collaboration supporting cancer and other trials 🔘 Eisai US also partnered with Medidata Solutions on an AI-driven platform to streamline clinical trial management, reduce errors by 80%, and accelerate treatment development for cancer and Alzheimer's. The platform replaces spreadsheets with integrated data sources, aiming to improve patient experience and data accuracy 🔘 Eli Lilly and Company's Digital Health Hub in Singapore leverages AI tools like Magnol.AI to advance drug discovery for Alzheimer’s, autoimmune diseases, and cancer, while supporting Phase 1 clinical trials and real-time monitoring 🔘 AstraZeneca partnered with Immunai to enhance cancer drug trials using its AI platform, which maps the immune system. The collaboration leverages Immunai's machine learning and single-cell biology to improve clinical decision-making and accelerate immunotherapy development 🔘 AstraZeneca's new business Evinova launched, offering AI and health-tech solutions to enhance clinical trials, with support from Accenture and AWS 🔘 AbbVie collaborated with ConcertAI and Caris Life Sciences to enhance precision oncology by utilizing AI for clinical trials and patient enrollment. 🔘 Sanofi partnered with COTA to use real-world data and AI to enhance clinical trials for multiple myeloma, aiming to speed up the development and improve the design of future studies 🔘 Sanofi in collaboration with OpenAI and Formation Bio introduced Muse, an AI tool to streamline patient recruitment for clinical trials by identifying ideal profiles, generating materials, and ensuring regulatory compliance 👇Links to source articles in comments #DigitalHealth #Pharma #AI #ClinicalTrials

New product initiatives within large companies often fail to achieve their potential because they have too much rather than too little. They have too much: 1) Headcount You are now under pressure to come up with something for all these people to do. Especially in cultures where “engineers must always be coding” and a PM is seen as failing if engineers are even briefly “blocked on requirements.” 2) Democratic decision making Creative ideas get killed (or watered down) by groups — yet this is the default in most big companies, even those that claim to use RAPID or similar frameworks. 3) Optics requirements You must now manufacture metrics and milestones to show straight-line progress and demonstrate certainty — during what is, by its very nature, an uncertain journey. 4) Involvement of the “core” product group To appease the leaders of the company’s cash cow, you make compromises that weaken your product. These leaders have the most power within the company and some may even try to confuse the CEO or quietly sabotage your initiative. 5) Reliance on the company’s distribution Due to the mirage of distribution, you won’t be incentivized to deeply understand your customer like a real startup would. Your initial traction is misleading — you get a usage spike, but: (a) those users are scattered across segments, not your core segment (have you even identified that core segment?) (b) what’s given will be taken away — that homepage slot for your new product will disappear next quarter due to VP jealousy or shifting OKRs (with some hand-wavy “metrics neutral” excuse). So if you are leading a new initiative within a larger company and your CEO/CxO asks you what you need to succeed, do not default to the answer that everyone in this situation gives: “I need more resources”. Instead, consider asking for less — less reporting, less certainty, less consensus-driven decision making, less meddling, and less pressure to build out a “full team” & great operations early on. If your CEO is competent, they’ll respect it. (clearly, this entire post is only for the intrepid product leaders who want to make winning products, it is not for everyone 🙂)

As head of our product organization at Chase, I often think about how and what we’re delivering to customers, but I recently reflected on the vital role of product managers. While some may view it as merely administrative, in my opinion this couldn't be further from the truth. Product managers are the driving force behind strategy and exceptional experiences, whether for external customers or internal users. Our role demands a deep connection to both the product and its users. Three essential qualities we all have: Customer Obsession: Go beyond empathy by diving into data and insights to understand user behavior, pain points, and opportunities. Decisions should be data-driven, ensuring the product evolves with user needs. Strategic Leadership: Product managers must define and drive the product vision, setting strategies that align with company goals. This involves fostering alignment across cross-functional teams and building strong relationships with stakeholders to ensure everyone is working toward a shared vision. Accountability: Own the outcomes, whether good or bad. Exceptional product managers embrace challenges, learn from mistakes, and continuously iterate to improve. They step into gray areas, connecting the dots to drive cohesive and successful outcomes. This role is strategic and high-impact, requiring us to lead with intention, push boundaries, and always advocate for the user. #productmanagers #productdevelopment

6 proven techniques to increase productivity, And reclaim your time: This sheet highlights the  ↳What ↳When ↳Why ↳And how So you can start putting these to work today: 1) Eisenhower Matrix What it is - A system to prioritize When to use it - You feel busywork is keeping you from "real" work Why it works - The least important tasks keep rising to the top because they're the easiest How to use it - Sort your tasks into quadrants: ↳Important and urgent: do it now ↳Important but less urgent: schedule it ↳Not important but urgent: delegate it ↳Not important and not urgent: delete it 2) 80/20 Rule What - A rule for focusing only on the most impactful work When - You feel over-capacity, and you need to cut things Why - 80% of outcomes come from 20% of causes, and then results diminish quickly after that How - Focus on just the most critical 20%: ↳20% of effort → 80% of results ↳20% of products → 80% of sales ↳20% of habits → 80% of impact ↳20% of innovations → 80% of growth 3) 1-3-5 Method What - A tool for simplifying your to-do list so you can actually complete it When - Your list is never-ending, and it's hard to know what to tackle Why - In reality, committing to work on less lets you finish more How - The night before or morning of, choose for the day just:   ↳1 key project (only 1!) ↳3 medium items ↳5 smaller items ↳Leave everything else off 4) Eat Your Frog What - A commitment to do your most critical item first When - You keep putting off an important (but scary or intimidating) task Why - Doing it likely won’t be as bad as you thought, and it builds momentum How - Follow these 4 simple steps: ↳Identify the big task you're avoiding ↳Schedule time for it early in the day ↳Eat your frog: actually complete the task ↳Celebrate an early win and progress 5) Deep Work What - A block of distraction-free time to work on a key item When - You constantly get interrupted and can't focus Why - Multitasking doesn't work - you dramatically increase productivity by focusing on just one thing How - Create a deep work environment: ↳Schedule a block on your calendar ↳Put away your phone, exit your email, close Slack, shut the door ↳Focus on just 1 task for at least an hour (and preferably 2 to 3) 6) Pomodoro Technique What - A style of working in intervals When - Your feel your energy fade over time or your work seems too big Why - Short bursts paired with breaks keeps your energy and productivity up How - Alternate medium work, short break: ↳Typical: work for 25 minutes, break for 5 ↳Experiment to find what’s best for you ↳Your break should be restful (breathing, time outside) not staring at your phone or answering email The most productive people you know aren't superhuman, They're simply using these strategies. Put these to work, And you'll soon get much more done AND have more time. Any you'd add to this list? --- ♻️ Repost to help your network reclaim their time. And follow me George Stern for more productivity content

There ya have it... A little to close for comfort. A close call, near miss, or accident without injury is easy to shrug off and forget. But there is a danger in brushing off accidents that don’t hurt, harm, or damage. In order to learn from close calls, the incidents must be reported and investigated. Employees need to understand that the purpose of studying near misses is not to punish them or assign blame; it is to improve workplace safety and reduce injuries. Reporting close calls leads to improvements in work areas and job procedures while allowing the correction of unsafe conditions before an injury occurs. Failing to report even a small incident allows hazards to escalate into more serious situations. When a close call happens, it should immediately send up a red warning flag that something was wrong, unplanned, unexpected, and could happen again. The next time it happens, it could result in serious damage, injury, or death.

The FDA’s latest complete response letter to Genentech signals a significant shift in how global oncology trials will be evaluated in Washington. Columvi + GemOx failed to secure a U.S. label for second‑line DLBCL. The FDA stated that the STARGLO study did not demonstrate a benefit for American patients and returned the application for additional U.S. data. Last September, the FDA published draft guidance stating that sponsors of multiregional oncology trials must include a “robust” U.S. cohort or prespecified bridging analyses. New Commissioner Marty Makary M.D., M.P.H., has since reiterated this stance, emphasizing diversity and domestic relevance in every advisory meeting. The U.S. data from STARGLO was weak: 🔹  Only 9% of the 364 patients were treated in the United States. 🔹 In a subgroup analysis, the Columvi combo increased the risk of death by 6 percent in non‑Asian regions, even though the global read‑out looked positive. Analysts had pencilled in up to $2 billion in peak sales for the drug. A typical launch would have delivered $150‑200 million in year one. That's now pushed to 2026-2027 while the team gathers more U.S. data. The takeaway? If a pivotal study looks world‑class everywhere except the United States, assume it is no longer approvable. Bake a ~20 percent U.S. accrual target, or a rigorous bridging plan, into every protocol from day one. That adjustment may raise budgets in the short term, but it is cheaper than a missed launch and a surprise CRL. Article: https://lnkd.in/e-GEBUYe