Competitor Benchmarking Methods

Lately I’ve been struck by how different AI adoption feels depending on where you sit. In my day-to-day, especially being based in Seattle, it often feels like AI is already part of everyday life. But the data tells a more uneven story. AI adoption in the US is growing, but not evenly. Our latest AI diffusion report shows that more than 30% of working-age Americans are using AI today, with usage in metro areas at about 33% compared to just over 16% in rural communities.    What stands out to me is how quickly these gaps can shape everyday experiences, from access to skills and jobs to broader economic opportunity.    We’re also seeing some early signals of what can help close that gap. Places with strong ties to higher education, especially communities with larger populations of young people, are seeing higher levels of adoption. It’s a reminder of the role that education and local talent play in how new technologies take hold.    As AI continues to evolve, understanding where adoption is happening and where more support is needed will be key to making sure more communities can benefit.    Brad Smith shares more in his latest blog, along with a closer look at the data across states and counties. The interactive report is also worth exploring. https://lnkd.in/gcJ8Y75g

AI adoption is failing at most companies. (it's not the technology) You use ChatGPT daily. Your team has random AI tools. No unified strategy. No measurement. Your VP keeps asking: "What's our AI plan?" You need frameworks, not more tools. 9 AI Adoption Frameworks: 1/ Workflow Audit Before Tool Selection → Map your team's top 10 daily tasks first → Flag repetitive work worth automating → Identify judgment calls for AI augmentation 2/ Build vs Buy Decision Matrix → Buy for standard ops (scheduling, emails) → Build only for competitive differentiation → Partner for specialized expertise gaps 3/ Pilot Program That Actually Scales → One department, one use case, 90 days → Define success metrics before you start → Document every lesson for VP presentation 4/ Executive-Ready Training Strategy → VP briefing: ROI projections and risks → Manager training: implementation roadmaps → User training: hands-on, role-specific 5/ ROI Measurement That VPs Care About → Track hours saved per employee per week → Measure quality improvements and accuracy → Calculate revenue impact, not just savings 6/ Data Governance Framework → Audit what data touches AI tools now → Create approval process for new platforms → Set data retention rules before scaling 7/ Change Management for AI Rollouts → Address "will AI replace me?" fears early → Show augmentation wins before automation → Create AI champion roles for career growth 8/ Smart Automation vs Augmentation Rules → Automate: data entry, report generation → Augment: strategy, creative work, decisions → Never automate: customer relationship calls 9/ VP-Level Adoption Mistakes to Avoid → Don't chase every shiny new AI tool → Never skip the governance foundation step → Stop letting AI adoption happen randomly AI adoption isn't a technology problem. It's a leadership strategy problem. Twice a week I send frameworks like this to 15,000+ operators in Tactical Memo. Join free: https://lnkd.in/eFNHsxmh

AI adoption in enterprises rarely follows a straight line. You can build a capable agent that solves a real problem and still find no one using it. One extra click from the usual process can become an inhibitor. A new window, and your DAU/WAU/MAU can tank. Adoption isn’t just about rolling out a tool; it’s about reshaping ingrained habits. Teams grow so comfortable with existing workflows that AI tools can initially feel like a liability rather than a productivity enhancer. The journey moves through three stages: adoption, adaptation, and transformation. Strategy often starts with the end state (transformation), but execution must begin with the first step: adoption. Each stage requires building trust, lowering friction, and proving value in small, tangible increments. Without that, even the most well-designed AI solutions risk becoming "shelfware". AI isn’t a solo game. It’s a team sport. One weak link, one reluctant user, can cause the whole purpose to fall flat. Success depends not just on technology but on shared conviction. Real transformation happens when every click, every process, and every team member feels like AI isn’t an extra step but the obvious next one. #ExperienceFromTheField #WrittenByHuman

There is a growing gap I am observing with technology. Tools are advancing. People are hesitating. → Leaders underestimate behavioural resistance. → Teams lack shared literacy. → Governance feels heavy rather than enabling. → Success is measured in pilots, not decision quality. The result? Impressive demos. Limited enterprise impact. A Digital strategy is NOT a technology roadmap. It is an adoption and trust agenda. Boards and executive teams that recognise this early avoid the cycle of excitement followed by disillusionment. Transformation occurs when capability, culture, and accountability evolve in tandem. Anything else remains surface-level. If you are seeing adoption friction despite strong investment, there is usually a deeper structural reason.

As I reflect on my journey in marketing, I can't help but think about the clarity that comes from a well-defined strategy. In my experience, a comprehensive marketing strategy framework revolves around four interconnected phases. First, aligning branding, marketing, and sales is crucial. It requires us to really listen. What problems do our customers face? For instance, understanding their pain points helped us redefine our product as the ideal solution. Then comes positioning. Differentiation is key. By checking with specific target markets, we can clearly define the outcomes we aim to achieve. Next, effective content marketing can’t be overlooked. It's about understanding our audience's goals and preferences, crafting content that speaks directly to them. We saw increment in engagement just by tweaking our messages. Lastly, a feedback mechanism is essential. Tracking metrics and analyzing engagement ensures we’re always improving. Reflecting on trends, it’s clear we’re moving towards more personalized experiences. How are you adapting to these changes? PS: Embrace the struggle; it often leads to the greatest insights. Also I have attached an image which talks about this framework in detail. Let me know your thoughts. #MarketingStrategy #CustomerEngagement

The article "Updating the Balanced Scorecard for Triple Bottom Line Strategies" by Robert Kaplan and David McMillan explores how the Balanced Scorecard (BSC) should be upgraded to fit today’s triple-bottom-line approach—financial, environmental, and societal performance. Here are the key takeaways: ☑ Triple Bottom Line Focus: ↳ It’s not just about financial results anymore. ↳ Companies must consider their environmental and societal impacts too. ↳ Success in this area means collaborating across sectors and the supply chain. ☑ Evolving the Balanced Scorecard: The original BSC focused on maximizing profits. But for companies balancing shareholder returns with sustainability goals, the perspectives need an update: ↳ Financial becomes Outcomes: covering financial, environmental, and societal performance. ↳ Customers become Stakeholders, involving all players in the ecosystem. ↳ Learning & Growth becomes Enablers: focusing on collaboration and alignment capabilities. ↳ Processes remain unchanged. ☑ Examples of Triple Bottom Line Strategies: ↳ Amanco: A Latin American company integrating eco-efficiency and social responsibility. ↳ Ben & Jerry’s & Patagonia: Balancing profitability with social and environmental goals. ☑ Stakeholder Capitalism: ↳ Moving beyond shareholder primacy (Milton Friedman style) towards stakeholder inclusion. ↳ Businesses are expected to help solve environmental and social challenges. 🔍 Multi-stakeholder ecosystems are key: ↳ Collaboration with stakeholders like suppliers, communities, and governments drives greater results. ↳ Example: Palladium’s health impact bond in India is a powerful multi-sector partnership that delivers social and environmental impact. ☑ Strategic Planning Evolution: ↳ Sustainability goals should be integrated into the core strategy, not siloed. ↳ Engage stakeholders in co-creating strategies and objectives—this builds alignment and trust. ☑ Inclusive Growth: ↳ Pursue “win-win” strategies that deliver financial returns and positive societal outcomes. ↳ Example: Improving skills of marginalized groups to enhance labour supply and socio-economic conditions. This framework is designed for today’s complex, multi-stakeholder business environments. Full article here https://lnkd.in/eZRWZjGb Ps. If you like content like this, please follow me 🙏

Benchmarking in the context of internal audit involves comparing an organization’s processes, performance metrics, and practices to industry standards or best practices from other organizations. Here’s how benchmarking through internal audit can help in cost saving: 1. Identifying Performance Gaps: By comparing the organization’s performance with industry standards, internal auditors can identify areas where the organization is underperforming and suggest improvements. Closing these performance gaps can lead to cost savings. 2. Adopting Best Practices: Benchmarking allows internal auditors to identify best practices from other organizations that can be adopted to improve efficiency and reduce costs. This could include process improvements, technological advancements, or organizational changes. 3. Setting Realistic Targets: Benchmarking helps set realistic and achievable performance targets based on industry standards. Achieving these targets can improve efficiency and reduce costs over time. 4. Improving Resource Utilization: By understanding how other organizations utilize resources efficiently, internal auditors can recommend ways to optimize the use of resources, leading to cost savings. 5. Enhancing Productivity: Benchmarking can reveal opportunities to enhance productivity by comparing labor, materials, and overhead costs against those of competitors or industry leaders. Improved productivity often results in lower costs. 6. Encouraging Innovation: By exposing the organization to innovative practices and technologies industry leaders use, benchmarking can inspire internal changes that improve efficiency and reduce costs. 7. Negotiating Better Terms: Benchmarking vendor contracts and pricing against industry standards can help negotiate better terms, reducing costs for goods and services. Conclusion: Overall, benchmarking enables internal auditors to provide actionable insights and recommendations that can lead to substantial cost savings by ensuring the organization operates as efficiently and effectively as possible. #IA #Internalaudit Alkit Jain

Disruption is fast. Adoption isn’t. In health care, truly disruptive tech rarely “goes viral.” Morris, Wooding & Grant (https://lnkd.in/ga6Xb2Pm) reviewed 23 studies that measured translational time lags. Results varied widely by method and stage, but the most repeated figure was ~17 years from research to routine clinical practice. Measuring from product launch, it is not uncommon for widespread commercial adoption to unfold over a decade. The first 2–3 years belong to pioneers running pilots; clinician "friends and family" are the primary adopters. This is the moment the management team realizes that all their launch forecasts are wildly optimistic and it's time to sheepishly inform the VCs that a Series D is in their near future. The cycle can only be broken by reimbursement. Only when payment arrives is sustainable growth possible.  Years 4–7 are mostly linear: training, workflow fit, financial clarity, an established revenue model, progressive product improvements, and early clinical evidence accumulate. These are the "hard slogging" years: one-on-one meetings educating and instructing care teams about an evolving value proposition. The product doesn't "sell itself" - the sales and clinical teams sell the product. This pattern isn’t just anecdotal. Foundational work in health-care diffusion shows that translating discoveries into routine care is slow and social, not purely technical. What you put in is what you get out. Years 8–10 are where the S-curve steepens, as guidelines catch up, KOLs normalize use, and publications coalesce into clinical consensus. The resulting run through the bell curve leads pundits to comment that widespread product uptake was "inevitable" - it wasn't. If you’re building or adopting disruptive tech: pilot early, publish relentlessly, design for workflow (not just efficacy), and make reimbursement & training first-class features. Two high profile examples: • Intuitive Surgical (da Vinci robotic surgery): FDA clearance arrived in 2000 for general laparoscopy. Adoption then compounded over the 2000s and 2010s, with robotic techniques ultimately capturing a dominant share in procedures like radical prostatectomy—illustrating a long, stepwise shift from early pilots to mainstream practice. • Dexcom (continuous glucose monitoring): First FDA-approved system in 2006 (STS). A major inflection came with Medicare coverage in 2017 for “therapeutic” CGM and continued guideline endorsement by the ADA—moving CGM from early adopters to the standard toolkit for insulin-treated patients. When it comes to disruptive product adoption in medicine, I try to remember Atul Gawande's wise words from “Slow Ideas” (The New Yorker): “We yearn for frictionless, technological solutions. But people talking to people is still the way that norms and standards change.”

I see people make this mistake all the time. Marketing Strategy ≠ Marketing Plan. They aren't the same thing, but most people treat them like they are. And it's costing them time and money. Let me break down the real differences. A marketing strategy is your positioning framework. It's the foundational decisions about who you serve, how you're different,  And why people should choose you over competitors. A marketing plan is your execution system. It's a tactical roadmap that turns strategy into daily actions And measurable results you can track. Your strategy should answer: - Who is our customer?  - What's our unique position?  - Which channels fit our brand?  - What's our core offer? While your plan will lay out: - What campaigns you'll run  - When you'll launch  - How you plan to measure success - Your daily workflow and tasks This is how it looks across multiple channels: Paid Advertising ↳ Strategy: Target high-intent buyers with offer-focused messaging ↳ Plan: Launch Facebook campaigns, test creative variations, optimize for ROAS, scale winning ads Email Marketing   ↳ Strategy: Build trust through educational content and expert positioning  ↳ Plan: Create welcome sequence, send weekly newsletters, segment by engagement, A/B test subject lines Content Marketing ↳ Strategy: Establish authority in our niche by sharing frameworks ↳ Plan: Publish LinkedIn posts, create carousel graphics, engage with comments, repurpose across platforms The strategy guides every decision you make.  The plan executes those decisions systematically. Most brands will jump straight to planning,  Without establishing clear strategic positioning. They're optimizing tactics without understanding the bigger picture. At Ad Pros, we start with strategy first.  We define positioning, then build the execution system around it. That's how you get consistent results instead of random wins. Ready to add $1m/month to your eComm business? Join the waitlist: https://lnkd.in/e-Av-tdY Which one do you struggle with the most?  Share your thoughts in the comments. ♻️ Repost to teach others in your network the difference.  Follow Nehal Kazim Kazim for more systems-first marketing strategies. 

This paper investigates the determinants and performance outcomes of AI adoption in U.S. hospitals, emphasizing how factors like market share influence adoption and assessing the operational and financial impacts. 1️⃣ Hospitals with larger market shares are significantly more likely to adopt AI due to better financial and human resource capabilities to handle complexity and uncertainty. 2️⃣ AI adoption enhances key performance metrics, including outpatient revenue (+8.6%), inpatient revenue (+7.5%), productivity (+7.9%), and occupancy (+5.2%). 3️⃣ Nonprofit, system-affiliated, metro-area, and teaching hospitals are more likely to adopt AI compared to standalone, for-profit, or rural hospitals. 4️⃣ Despite the positive impact on operational performance, financial returns (ROA) remain insignificant in the short term, suggesting benefits may materialize over time. 5️⃣ The study analyzed 1,882 hospital-year observations across 40 U.S. states (2000–2020), providing a large, diverse dataset for robust empirical analysis. 6️⃣ Longitudinal regression and instrumental variable methods addressed endogeneity, confirming that AI adoption causally improves hospital performance. 7️⃣ Initial investments and the learning curve pose barriers to immediate financial benefits, underlining the need for strategic implementation and patience. 8️⃣ Complexity of care (measured by case mix index) and total expenses strongly influence AI adoption, as these factors reflect operational demands and resource availability. 9️⃣ A difference-in-difference analysis validated the findings, showing consistent improvements in performance for AI-adopting hospitals compared to non-AI hospitals. 🔟 Smaller hospitals face challenges in adopting AI due to limited economies of scale and resource constraints, making targeted support crucial for broader AI integration. ✍🏻 Phuoc Pham, Huilan Zhang, Wenlian Gao, Xiaowei (Linda) Zhu. Determinants and performance outcomes of artificial intelligence adoption: Evidence from U.S. Hospitals. Journal of Business Research. 2024. DOI: 10.1016/j.jbusres.2023.114402