Using Tech To Enhance Competitive Intelligence

💣 Most companies start looking for acquisition targets after their competitors do. But what if AI could help you spot the next big opportunity before anyone else even knows it exists? I just published a new article in my series “The Future of AI in Mergers & Acquisitions”: 👉 AI-Powered Competitive Intelligence: Detecting Acquisition Opportunities Before the Competition In it, I explore how AI is transforming deal sourcing by monitoring patent filings, hiring trends, and product updates to reveal emerging competitors worth acquiring. From my experience advising corporates and startups, the best deals rarely come from public listings, they come from seeing the signals early: 📊 a sudden wave of patents, 💼 new strategic hires, 🚀 product features quietly launched under the radar. AI can now connect these dots in real time, giving you an edge in detecting the next breakout player before the market catches up. As always, I also address the ethical side because using AI for competitive intelligence requires transparency and respect for privacy boundaries. Read the full article here 👇 And stay tuned as this Thursday, I’ll release the next piece in the series: ➡️ “Real-Time Due Diligence with AI” where we’ll explore how AI can validate acquisition targets as fast as it finds them.

Continuing with my series on Gen AI, we had recently assisted a leading global company in unlocking cognitive insights generation at scale. The client faced significant obstacles in accessing and analysing critical performance metrics and market intelligence. They relied on disparate data sources—including multiple tables, external datasets, and competitor insights from websites and news articles—which made the process slow and complicated. Business leaders spent significant time gathering data and insights, often requiring help from tech teams leading to delays in decision-making and reduced agility.   Recognising the need for transformation, we collaborated closely with the client to design, deploy, and scale a GenAI-driven platform, empowering business leaders to track the performance of business divisions. The platform was based on a module with two kinds of datasets: structured KP datasets and unstructured textual datasets. Our GenAI solution enabled the client to conduct real-time computations, extract insights, and generate visual answers from both structured tabular data and unstructured text—allowing users to “converse” with the data. Leveraging advanced LLM models and text embeddings, the system performs at least eight distinct computations in response to queries, while summarising information from multiple sources seamlessly.   The impact of this solution has been significant. Leaders can now access critical information in seconds, changing their decision-making process from reactive to proactive. The client realised key benefits such as:   - Rapid access to critical insights: The solution reduced the effort for business managers to generate insights by 90%, while also minimising the risk of missed insights, enabling accurate and timely data-driven decisions. - Accelerated decision-making: The rapid analysis of data augmented by textual insights has led business leaders to make timely decisions, enabling them to respond to market dynamics instantly - Significantly improved operational efficiency: By automating routine tasks such as calculations and data summarisation, operational efficiency has improved significantly, with a reported 30% reduction in time spent on manual data gathering - Conversational interface: By enabling users to interact directly with the underlying data and insights, the organisation has fostered a self-service culture, significantly improving access to information across all levels   This case is a compelling case of how Generative AI could transform the insights generation process, delivering business decision support. Currently, the solution supports business leadership and has been scaled up across almost all global business units, with plans to cover most of the organisation in the future.   #GenAI #GenAISeries #Innovation #Consumer #GenAIInnovation #InsightGeneration #ConversationalAI

The Strategic Imperative: Build Your AI GTM Moat Before Competitors Do GTM teams slow to leverage AI's content generation and data synthesis capabilities will be systematically outmaneuvered by competitors in their market space that do. Is your competitors' use of AI keeping you up at night? Are they building unfair advantage: Sales reps armed with POV battle cards for discovery calls, Customer Success teams with real-time Customer Account health alerts highlighting likelihood to churn before the customer signals an issue, Marketing generating personalized campaigns highly curated to Target ICP and Personas, while your team debates single campaign messaging. They're not just working faster—they're playing a completely different game where they see opportunities, patterns, and solutions invisible to traditional approaches. Competitors outmaneuvering you aren't just using AI tools—they're combining AI's content and data capabilities with their proprietary customer data, industry insights, and process knowledge to increase the quality of Outreach motions, Discovery Calls, and Customer QBR's, creating defensible competitive advantages that cannot be replicated. They're not automating existing processes; they're inventing entirely new categories of delivering customer value to differentiate themselves from you in sales cycles. Your 90-Day Action Plan: Audit Data Assets: What unique customer insights, market intelligence, and operational data do you possess that competitors cannot access? This is your AI differentiation foundation. Implement Dual-Engine AI Strategy: Deploy content generation for scale (personalized outreach, health scores, curated proposals, real-time competitive positioning) AND data synthesis for intelligence (predictive qualification, account prioritization, churn prevention). Create AI-Native Customer Experiences: Design interactions that would be impossible without AI—real-time deal coaching, predictive customer success interventions, and dynamic pricing optimization. The Competitive Reality Check: Are you up at night, worried that your sales team is flying blind or spending valuable time trying to get to the data needed to be effective in sales cycles, while competitors have synthesized content enriched in real-time? Are your AE's and SDR's guessing at pain points while AI-powered competitors arrive armed with data-driven insights about each persona's specific challenges, decision-making patterns, and preferred communication styles? Are your Customer Success managers surprised by churn notifications while your competitors deliver dynamically generated QBRs that speak directly to usage health, value delivered, and new use cases that align with stakeholders' priorities? Modernize core GTM processes and motions with AI. Competitive advantage depends on how quickly you can combine AI's dual capabilities with existing documented processes, data-driven insights, and market position to create defensible differentiation.

Not every AI initiative moves the P&L. I’ve found it helpful to bucket AI opportunities into three categories; each with a different impact on revenue and margin. 1️⃣ Productivity Add-ons Tools like Microsoft Copilot, Adobe Firefly, and ChatGPT deliver quick wins. Most SaaS platforms now include AI features; and you’re already paying for them. These tools are valuable, and their gains compound over time; but they’re table stakes. Every enterprise should be deploying them to unlock hidden capacity and insight. As CIOs, our job is to ensure the right tools are in place and adopted; so we see real margin expansion, not just impressive demos. 2️⃣ Reimagined Workflows This is where meaningful business impact happens. Your business processes are your capabilities; they define how you create value. With machine learning, we can upgrade those capabilities and strengthen our competitive position. Today, many customer interactions depend on an employee’s individual experience and intuition. But what if every team member could make decisions with the collective intelligence of your best people? Imagine a new customer service agent: as they answer a call, their system recommends the Next Best Action; drawn from machine learning models trained on your historical data and customer patterns. The agent decides whether to follow the suggestion, and the outcome is captured. Over time, the system keeps learning from every decision and result. This creates augmented intelligence; continuously upskilling every employee, improving consistency, and driving measurable revenue uplift through smarter upselling and cross-selling. As confidence grows, you can safely automate lower-risk scenarios, freeing people to focus where relationships truly matter. And when you start blending internal and external datasets, the system becomes even more powerful; a self-learning engine that turns every interaction into a competitive advantage. That’s what it means to move from human experience → machine-augmented performance. 3️⃣ New Business Models At the far end of the spectrum are entirely new models born from AI; like Shopify’s dynamic micro-stores or Netflix’s content optimization. They’re transformative but rare, and often require a willingness to reinvent or even cannibalize parts of your core business. As a CIO, I focus on two or three business areas where re-imagining workflows and applying machine learning can materially move the P&L. That means diving deep into the business model; understanding how value is created and where we can strengthen our competitive advantage. Only then can we translate AI potential into real financial outcomes. That’s where AI becomes more than hype; it becomes a growth engine that differentiates the business. Curious how to identify the best workflows to reimagine with AI? 👉 I’ll explore that in my next post. #AI #DigitalTransformation #CIOLeadership #EnterpriseAI #BusinessGrowth

Your competitor just predicted your Q2 product launch. They didn't hack your servers. They didn't bribe an employee. They spent $47 and 4 hours analyzing your LinkedIn job posts. Every time you post for "blockchain architects" or "ML engineers with recommendation system experience," you're broadcasting your strategy to anyone with basic pattern recognition skills. Samsung and TSMC predict each other's chip capabilities 18 months out just by tracking equipment purchases. Amazon knew Target's same-day delivery plans the moment they started hiring from Flexport. This isn't paranoia. It's math. My latest newsletter breaks down: → The 3 ways you're leaking intelligence right now → Why your job postings are a strategic roadmap → What a quarterly "signal audit" should actually look like → Why speed beats secrecy in a transparent world The uncomfortable truth? Most companies can't even clean their CRM data, let alone run intelligence operations. But in your industry, at least one competitor is watching. And that's all it takes. The question isn't whether you're broadcasting intelligence (you are). The question is whether your strategy depends on secrecy or execution speed. What strategic move would still work even if competitors knew about it today?Those are your strongest plays. #AgenticAI #CompetitiveIntelligence #TheAgentWars #DigitalStrategy #ExecutiveLeadership P.S. - After reading this, you might want to review those job postings you just approved... J.D. Meier, Oliver Aust, Kerri-Lynn Primmer Morris

Companies are swimming in data, but it's disconnected. So how do companies leverage the comprehensive digital twin and AI to contextualize this data and turn it into gold, i.e. actionable insight? In the second episode of my conversation with Jim Brown, President of Tech Clarity, we talked about why companies struggle to use the data they already have, how artificial intelligence and semantic conditioning unlock its value, and why the comprehensive digital twin is becoming a critical enabler for the true digital enterprise. Most organizations have more data than they know what to do with, but the real problem isn’t volume - it’s fragmentation. Data lives in silos across engineering, manufacturing, supply chain, quality, and operations systems, preventing teams from understanding the full context around decisions. Advanced analytics struggle when the information lack's structure or alignment, and companies often struggle to connect or trust the data they already possess. When this happens, the data becomes a liability instead of an asset. Data only becomes valuable when companies can contextualize it. To address this, AI-driven semantic conditioning can be used to clean and organize the data, apply consistent meaning to different data fields, and make the data consumable and analyzable. Once the data is contextualized, that same data can be used to produce actionable insights the company can trust, turning raw data into usable intelligence. The comprehensive digital twin acts as the backbone of a digital enterprise. The digital twin enables companies to organize and contextualize data from across the lifecycle by providing a structured environment for analytics, simulation and modeling. The digital twin creates the right foundation for AI to generate insights. While AI alone is powerful, AI with a comprehensive digital twin is transformative, accelerating how quickly companies can turn data into understanding and, ultimately, into better decisions. Companies need digital transformation to stay competitive — and that transformation is driven by data, AI, simulation, modeling, and the comprehensive digital twin. Organizations that connect and contextualize their data unlock new insights, operate more efficiently, and innovate faster. Those that can’t fall behind. This conversation with Jim underscored a powerful truth: Data becomes an asset only when you give it meaning, and AI and the digital twin are how modern enterprises make that happen. Listen to the second episode of my conversation with Jim on the Industry Forward Podcast at the link in the comments. I look forward to hearing your thoughts!

The gap between 'Competitor Launches' and 'your team knows about it' should be Minutes, not Days. Here’s how AI-Powered Agents can Automate the entire Competitive Intelligence process, from collecting signals to delivering insights: 𝟏. 𝐏𝐮𝐬𝐡 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 𝐟𝐫𝐨𝐦 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: Monitor diverse sources like news, press, competitors, and social media for real-time updates. These updates are sent to an event bus (SNS, SQS, Kafka) or a webhook queue. 𝟐. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐓𝐢𝐞𝐫𝐬: Classify updates based on priority focusing on high-priority sources like pricing, launches, and funding. Medium-priority updates include blogs and case studies, while low-priority updates focus on reviews and trends. 𝟑. 𝐒𝐢𝐠𝐧𝐚𝐥 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐨𝐫 𝐀𝐠𝐞𝐧𝐭: Aggregates, filters, deduplicates, and enriches signals by adding metadata, reducing noise by up to 90%. 𝟒. 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐀𝐠𝐞𝐧𝐭: Retrieves competitor history and contextualizes each signal, categorizing it by urgency, impact, and relevance. This agent looks for patterns in competitor behavior. 𝟓. 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐬𝐭 𝐀𝐠𝐞𝐧𝐭: Generates draft updates, suggests objection handlers, and creates win/loss matrices. It pulls insights from CRM data and produces content for reports or battle cards. 𝟔. 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐒𝐜𝐨𝐮𝐭 𝐀𝐠𝐞𝐧𝐭: Monitors competitor activities, identifies opportunities, and surfaces vulnerabilities. It matches competitor movements with your sales pipeline to suggest talking points for sales teams. 𝟕. 𝐇𝐮𝐦𝐚𝐧-𝐢𝐧-𝐭𝐡𝐞-𝐋𝐨𝐨𝐩: Provides oversight, ensuring AI-driven insights are validated and approved before use. 𝟖. 𝐌𝐨𝐝𝐞𝐥 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐋𝐚𝐲𝐞𝐫 AI models (like Amazon Bedrock, GPT, and Claude) analyze and enhance the intelligence gathered by agents. 𝟗. 𝐌𝐞𝐦𝐨𝐫𝐲 𝐚𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Store insights and historical data in systems like Redis, Upstash, and Amazon S3. Use analytics tools like Google Analytics and Mixpanel to measure usage and performance. This is Agnetic AI at its best automating data collection, signal filtering, analysis, and decision-making processes for more efficient competitive tracking. Is your organization ready to move from manual competitive analysis to intelligent automation? ♻️ Repost this to help your network get started ➕ Follow Sandipan for more #AIAgents #AgenticAI #GenAI #BusinessStrategy

Most companies track 10-30 competitors manually. We built an AI system that monitors 2,563 companies in real-time. The difference? Everything. Here's what we learned building enterprise competitive intelligence at scale with AI: 1. Personalization is the new battleground Generic news alerts are dead. AI enables personalized write-ups and CI alerts tailored to each stakeholder—at scale. Your development team sees clinical trial changes. Your commercial team sees messaging changes. Your CEO sees earnings call alerts. Same intelligence. Different lens. Automatic delivery. 2. Language barriers have disappeared We're tracking competitors across across the globe in different languages. AI translation isn't just accurate—it's instantaneous. This means your competitive scope isn't limited by the languages your team speaks. A Japanese competitor's press release? A German patent filing? A Brazilian market entry? You'll be able to know about it instantly. 3. Speed is the only moat that matters Manual monitoring creates delays and inbox noise. When news breaks, teams scramble with "Did you see this?" emails across departments. AI delivers one authoritative alert before the confusion starts. Our clients consistently tell us they're beating their manual providers—often by a full business day. The companies winning today aren't the ones with the most analysts. They're the ones using AI to see further, faster, and with greater precision than ever before. What are you still tracking manually? Don't hesitate to get in touch with me if you are interested in learning more about our AI solutions for competitive intelligence in pharma and biotech.

You don't need a competitive intelligence team or fancy tools. You need a system. Here's the scrappy competitive intel stack: 👉 Google Alerts (Free): Set up alerts for competitor names, your category keywords, and key executives. Takes 5 minutes 👉 LinkedIn (Free): Follow competitor companies and key executives. Watch for hires, posts, and engagement patterns 👉 F5Bot or Visualping (Free): Track specific URLs (competitor pricing pages, homepages). Get alerted on changes 👉 Meta Ads Library + LinkedIn Ad Library (Free): See every ad your competitors are running. Steal their messaging frameworks 👉 Feedly or Newsletters (Free): Aggregate competitor blogs and industry news in one place. Check weekly 👉 G2/Capterra (Free): Read reviews monthly. Set calendar reminders. The 3-star reviews are gold 👉 Job boards (Free): Check competitor job postings. Indeed, LinkedIn, their careers page. Do this quarterly 👉 One Google Sheet (Free): Track everything in one place. Date, competitor, observation, implication, action taken The system: 30 minutes every Monday morning. Update the sheet. Share insights with your team. Actually use what you learn. You don't need budget. You need discipline. What's in your scrappy competitive intel stack? --- I love talking about marketing strategy and product marketing. If you’re running a marketing team, a founder, or a small business owner, let’s connect! I’m honestly just here to meet cool people and talk about nerdy marketing stuff. 🤓

Sparks is here 🌟✨🚀‼️ In the early days of competitive intelligence, the biggest problem was figuring out what competitors were up to. The internet was young, most companies were barely online, and the sales tech stack wasn’t there yet. Boy has that changed! The “digital footprint” of your competitors has grown exponentially, and the sales tech stack now records almost every buyer-seller interaction. Getting your hands on competitor data isn’t the problem anymore. Data overwhelm is! With thousands of signals pouring in every hour, how are compete teams supposed to keep up? And if compete teams can’t keep up, how are sales teams supposed to win competitive sales deals, which now make up the majority of serious opps in the enterprise? We spent years at Crayon innovating to solve data overwhelm for customers. We made great strides, but they were never enough. And the bar raised every year b/c there was always more data pouring in. Our customers suffered: both the compete teams who couldn’t deliver the level of enablement they held in their hearts, and the sellers who found themselves with out-of-date competitive intel or no intel at all. Imagine how happy we were when LLMs came on the scene. Finally a solution! But then we realized it’s easier said than done. How do you harness the power of LLMs to help compete teams turn data overwhelm into actionable enablement? One day last year, we spent 10 hours brainstorming all the combinations of data types, prompts and outputs that we’d like to solve with AI: - Analyze all the win-loss interviews and seller win-loss notes from the last 30 days in comparison to our “Objection Handling”’ battlecard tile, and produce a list of new objections coming up that our sellers should be prepared for - Analyze EVERYTHING that happened with this competitor over the past quarter across news, social, content marketing, website changes, reviews, and internal slack messages, and create a summary report that can be shared with the sales team - etc. etc. Awesome exercise right? The problem is we ended up with 238 ideas. 238! We left the day with mixed emotions. We were bullish on the 238 ideas, but how long would it take to build 238 features? Our customers needed this now! Then innovation happened. Our engineering, product & design team figured out how to build all 238 features (and a bunch more we hadn’t even thought of yet) ALL AT ONCE. Announcing Crayon Sparks. It’s an enterprise grade application of LLMs to the signal-to-noise problem in compete: enabling not just one analysis on one data source, but enabling any analysis on over 100 data types in Crayon from the “online digital footprint” as well as intel from buyers, sellers & deals. Sparks means freedom from the resource-constrained tyranny of manually sifting through data. Sparks means compete teams can finally reach their full potential! The future is bright!