Project Management

Last week, I heard from a super impressive customer who has cracked the code on how to give salespeople something they’ve always wanted: more selling time. Here’s how he transformed their process. This customer runs the full B2B sales motion at an awesome printing business based in the U.S. For years, his team divided their time across six key areas: 1. Task prioritization 2. Meeting prep 3. Customer responses 4. Prospecting 5. Closing deals 6. Sales strategy Like every sales leader I know, he wants his team to spend most of their time on #5 and #6 — closing deals and sales strategy. But together, those only made up about 30% of their week. (Hearing this gave me flashbacks to my time in sales…and all that admin tasks 😱) Now, his team uses AI across the sales process to compress the amount of time spent on #1-4: 1. Task prioritization → AI scores leads and organizes daily tasks 2. Meeting prep → AI surfaces insights from calls and contact records before meetings 3. Customer responses → Breeze Customer Agent instantly answers customer questions 4. Prospecting → Breeze Prospecting Agent automatically researches accounts and books meetings The result? Higher quantity of AI-powered work: More prospecting. More pipeline.  Higher quality of human-led work: More thoughtful conversations. Sharper strategy. This COO's story made my week. It's a reminder of just how big a shift we're going through – and why it’s such an exciting time to be in go-to-market right now.

The Irish Government has just announced plans to introduce the Regulation of Artificial Intelligence Bill in its Spring 2025 legislative programme, a pivotal piece of legislation aimed at giving full effect to the European Union’s Artificial Intelligence Act (EU Regulation 2024/1689). Even though the AI Act as a regulation has direct effect, this move is set to shape the national regulatory framework for AI governance in Ireland and establish national enforcement mechanisms in line with the EU’s approach. At the heart of the bill is the designation of Ireland’s National Competent Authorities: the entities that will be responsible for enforcing compliance with the AI Act. These authorities will oversee risk classification, conduct market surveillance, and impose penalties for violations. Given Ireland’s role as the EU base for major technology firms including Google, Anthropic, Meta, and TikTok, the effectiveness of its enforcement regime will be closely scrutinised across the EU and beyond. The Irish Government’s approach will be particularly significant due to the country’s track record in regulating the digital sector. Ireland’s Data Protection Commission (DPC) has wielded considerable influence over EU-wide enforcement of the GDPR, given the presence of multinational tech firms within the state. The DPC was designated as one of ireland’s nine fundamental rights authorities under the AI Act in November 2024. The bill will include provisions for penalties, though details remain unspecified. Under the EU AI Act, non-compliance can result in fines of up to €35 million or 7% of a company’s global annual turnover, whichever is higher. For Ireland, the challenge will be ensuring its enforcement framework has sufficient resources and expertise to oversee AI systems deployed within its jurisdiction. Tech industry leaders and legal experts will be closely monitoring how Ireland structures its national framework. The AI Act imposes strict obligations on high-risk AI applications, including those used in healthcare, banking, and recruitment. Companies will be required to maintain transparency, conduct impact assessments, and ensure that their AI systems do not lead to unlawful discrimination or harm. Ireland’s legislative initiative comes at a time of growing regulatory scrutiny over AI’s impact on society, innovation, and human rights. The AI Act represents the world’s most comprehensive attempt to regulate artificial intelligence, at a time other jurisdictions such as the USA are moving in the opposite regulatory direction. The Regulation of Artificial Intelligence Bill is still in its early stages, at the “Heads in Preparation” point. In the Irish legislative process, the Heads of a Bill serve as a blueprint for the eventual legislation. As Ireland moves toward full implementation of the AI Act, the government’s decisions on AI oversight will have significant implications for businesses, consumers, and the broader EU regulatory landscape.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Some of you guys are throwing around the job title "Producer" like it's synonymous with "could be anything, but definitely will be everything". Let me break down the different kinds of producers that work in the creative space so that you can update your job postings to catch the right candidates. I'm leaving TV production titles out of this btw otherwise this post will never end (plus it's not my area of expertise). Sound off in the comments if ya'll have something to say. ↳ Creative Producer - They help develop ideas, shape the creative vision, collaborate with writers, directors and editors, and ensure the final product maintains its intended artistic quality. They're generally involved in decisions about story, talent, and overall creative direction. Think of them as the bridge between the creative vision and its execution. ↳ Line Producer - These folks manage the day-to-day practical operations of production for photo and motion shoots. They create and oversee budgets, hire crew, coordinate schedules, handle logistics, and solve problems as they arise on set. They're essentially the project manager who makes sure everything runs on time and on budget. If there's a practical question about how to get something done, the line producer figures it out. ↳ Content Producer - This is a broader, more modern term used in digital media. They might create content themselves or oversee its creation for platforms like websites, social media, or streaming services. Their role can blend creative and practical elements—developing ideas, producing the actual content, and sometimes managing distribution and performance metrics. If you're looking for someone to shoot, edit, concept, and strategize, that's a Content Producer. Of course there are other roles like Head of Production, Post-Producer, or Executive Producer as well, but for the most part I've seen those job postings enjoying the correct distinction. I've seen these three roles smooshed into on uncomfy "Producer" too many times. Hiring managers would catch more flies with better titles, and it would save a lot of people the trouble of applying to jobs that aren't actually a fit. 🤍

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?

🇺🇸 Terrific reference doc on Irregular Warfare, shedding light on U.S doctrine &challenges in defining irregular warfare. 116 p by the Center for Army Lessons Learned, June 2025 🔹Irregular Warfare (IW) considered central to modern conflict &recognized as more common than conventional warfare 🔹IW + conventional warfare considered complementary -NOT separate/ hybrid: can be woven together across the competition continuum 🔹“using military and nonmilitary means—overt, clandestine, or covert—to achieve policy objectives without seeking outright domination“ Key doctrine: 🔹irregular activities included during competition below armed conflict to create &exploit strategic advantages to win without fighting. 🔹During armed conflict IW adds lethal force to compel enemies, at levels that prevent escalation & help avoid high risk of conventional warfare 🔹Ongoing work emphasizes integrating irregular activities into joint campaigns, combining conventional & special ops forces with multinational, interagency& private sector actors 🔹 IW considered a core competency in National strategies: essential in countering great-power competition. DOD directives mandate equal proficiency in conventional and irregular warfare 🔹Allied perspectives: misconceptions around IW &underinvestment 🔹Information domain is decisive: ie: Ukraine’s social media strategy and Israel’s contested X narratives 1️⃣2️⃣ Irregular Warfare Operations: 1. Unconventional Warfare Support resistance or insurgent groups (covertly, overtly, indirectly) to coerce, disrupt, overthrow hostile regimes. 2. Foreign Internal Defense (FID) Assist host nations in countering internal threats (insurgency, terrorism, lawlessness) through whole-of-gov support 3. Counterinsurgency (COIN) Blend mil& civilian efforts to defeat insurgencies &address root causes, strengthening gov legitimacy 4. Counterterrorism (CT) Neutralize terrorist networks to prevent them from using violence to coerce 5. Stability Activities Restore/maintain safe environments, essential services, governance, humanitarian relief after crises 6. Security Cooperation (SC) Build partner &ally defense capacity, interoperability while advancing U.S security interests 7. Security Force Assistance (SFA) Train, equip, advise foreign security forces to develop capacity for long-term stability 8. Counter Threat Finance (CTF) Deny adversaries ability to fund operations by disrupting illicit & licit financial flows 9. Counter Threat Networks (CTN) Identify &neutralize adaptive adversarial networks that threaten U.S goals. 10. Military Information Support Operations (MISO) Influence foreign audiences’ attitudes &behaviors through tailored messaging to achieve U.S objectives 11. Civil-Mil Operations (CMO) Coordinate with civil authorities to reduce friction,build trust, support mil ops 12. Civil Affairs Operations (CAO) Conduct specialized engagements with civi populations/institutions to address instability,governance&recovery needs

Day 4 of teaching A to Z of LinkedIn .Today is 'D' for DMs :the underrated power move of LinkedIn growth. When people think of LinkedIn growth, they often think: 📌 Viral posts 📌 Aesthetic profiles 📌 Hashtags, hooks, and headlines But what they MISS is something that doesn’t make noise publicly... DMs. YESssssssss the Direct Messages. The real game of LinkedIn. So, Let’s break this down. 📌 Why are DMs so underrated? Because they don’t bring instant dopamine like a viral post. Because they’re private. Not flashy. Not performative. But if you’re serious about career growth, networking, and personal branding then DMs are your shortcut to depth. ➡️ According to LinkedIn, InMail messages are 300% more likely to get a response than cold emails. ➡️ I personally got 50+ speaking opportunities, different clients, and 100+ crazyyyy yet hardworking people from DMs in the last 6 months. 🍪 Brownie Points: The Types of DMs that WORK : 1. The Gratitude DM “Hi Riya, just read your post on personal branding: such a fresh take! Thank you for sharing. I would love to stay connected and learn more from your journey.” ✅ Low-pressure ✅ Builds trust ✅ Starts a warm connection 2. The Curiosity DM “Hi Riya! Loved your recent talk on Gen Z marketing. Quick Q: What do you think is one emerging trend most marketers are missing in 2025?” ✅ Invites conversation ✅ Makes the other person feel valued ✅ Opens a loop 3. The Collaboration DM “Hi Riya, I run a small series on marketing meets psychology. Your recent breakdown of consumer bias was ! Wondering if you'd be open to a small collab or feature?” ✅ Personalized ✅ Value-forward ✅ Converts followers into collaborators ⚠️ Common mistakes people make in DMs: “Hi” (and nothing else) 📌 Pitching without permission 📌 Copy-paste mass messages 📌 No context = No reply Every week, I do this: ✅ DM 3 people whose content I genuinely liked ✅ Follow up with 2 people who engaged on my post ✅ Introduce 2 people who can benefit from knowing each other ✅ Reply to every DM with authenticity (even if it’s late) Here’s your DM Framework (save this): D.M.A. Discover. Message. Align. Is this series helping you? Any guesses for tomorrow 's post of E of Linkedin? #linkedin #linkedinlearning #riyagadhwal #marketing #brands #ceos

We lost a $50M deal because of a small investor (6% ownership) from 5 years ago, killed it. Here's what happened. His corporate investor - who he barely remembered from their seed round had just weaponized years of confidential information to kill his company's biggest partnership. Strategic investors are notorious for buying small pieces of equity just to get these rights. The issue is, there was this concept of "information rights" in an investor agreement. This is a seemingly innocent "information rights" clause he never thought twice about. When you give corporate VCs visibility into your: - Product roadmaps - Financial weaknesses - Customer complaints - Technical limitations You're handing ammunition to an entity that may someday compete with you, buy your competitors, or block your partnerships. Especially, if you have a strategic investor on your cap table. This founder gave quarterly updates for years without concern. Then watched helplessly as that same investor shared his company's vulnerabilities with a potential customer, torpedoing a deal that would have 10x'd his business. Strategic investors can bring incredible value, but protect yourself: - Limit information to high-level metrics only - Add 24-month expiration to information rights - Exclude sensitive customer and product data Your information asymmetry is your only advantage. Guard it carefully. #CorporateVC #StartupStrategy #VentureCapital

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

The distributor of my last feature film guessed that our budget was AT LEAST $1M.... Let me tell you how very much it was NOT even close to that budget. (hint: it was WAY less). Why did they assume the movie was at this budget level? We were smart. Here are the top 5 things I prioritize when making a micro-budget movie: 1. Locations. Try not to keep your audience stuck in one place, it screams low budget. Move around, get outside, get BIG sweeping views of something beautiful. I push a majority of our budget into getting a location that looks epic, an outdoor location that makes it feel big, and then you can concentrate your story in a primary setting. But, get creative, make sure everything has character. 2. Production design. Don't skimp. My art team is scrappy and smart. They can take $5 and some cardboard and create some epic illusions. Find someone who understands how to work with a low budget, and has a very special eye for making something simple - turn into something beautiful. 3. Happy team = good movie. Keep your team happy, get their favorite crafty snacks, feed them a good lunch, break on time. I have had top-notch filmmakers working on my films and they agree to friend rates because they know my set is going to be respectful and comfortable (as comfortable as possible). The results are the incredible images they create. 4. A GOOD script. By the time we shot Regression, I was working on draft 23. Don't be precious, ask trusted and successful peers in the industry to give you honest feed back - AND TAKE IT. Listen to the majority. If too many people don't understand something in your script - it's probably because it doesn't make sense. You can get away with a lot in low budget filmmaking if your story is compelling and unique. Listen, learn, rewrite. 5. Be picky about your actors. The actors I cast in my movie are one-take-wonders. All of them. It's hard making an indie movie, sometimes you get one take and 5 minutes - get actors who respect your story, trust you, show up prepared, and can NAIL it on the first take. I have some Oscar worthy performances in some of those one-takes that I am very proud of. You don't always need a huge crew, expensive lenses and a fancy camera. You need talented people that you trust, an epic story, and a good environment. It's a lot easier to make a movie these days than it used to be. Don't let big studio budgets scare you - go make it happen.