How Companies Are Advancing AI Strategy

The more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership

One in three companies are planning to invest at least $25m in AI this year, but only a quarter are seeing ROI so far. Why? I recently sat down with Megan Poinski at Forbes to discuss Boston Consulting Group (BCG)'s AI Radar reporting, our findings, and my POV.   Key takeaways below for those in a hurry. ;-)   1. Most of the companies have a data science team, a data engineering team, a center of excellence for automation, and an IT team; yet they’re not unlocking the value for three reasons:   a. For many execs, the technologies that exist today weren't around during their school years 20 years ago. As silly as it is, but there was no iPhone and for sure no AI at scale deployed at people’s fingertips.   b. It's not in the DNA of a lot of teams to rethink the processes around AI technologies, so the muscle has never really been built. This needs to be addressed and fast...   c. A lot of companies have got used to 2-3% continuous improvement on an annual basis on efficiency and productivity. Now 20-50% is expected and required to drive big changes. 2. The 10-20-70 approach to AI deployment is crucial. Building new and refining existing algorithms is 10% of the effort, 20% is making sure the right data is in the right place at the right time and that underlying infrastructure is right. And 70% of the effort goes into rethinking and then changing the workflows. 3. The most successful companies approach AI and tech with a clear focus. Instead of getting stuck on finer details, they zero in on friction points and how to create an edge. They prioritize fewer, higher-impact use cases, treating them as long-term workflow transformations rather than short-term pilots. Concentrating on core business processes is where the most value lies in moving quickly to redesign workflows end-to-end and align incentives to drive real change.   4. The biggest barrier to AI adoption isn’t incompetence; it’s organizational silos and no clear mandate to drive change and own outcomes. Too often, data science teams build AI tools in isolation, without the influence to make an impact. When the tools reach the front lines, they go unused because business incentives haven’t changed. Successful companies break this cycle by embedding business leaders, data scientists, and tech teams into cross-functional squads with the authority to rethink workflows and incentives. They create regular forums for collaboration, make progress visible to leadership, and ensure AI adoption is actively managed not just expected to happen.

Half life of a AI plan seems to be two weeks. I had at least three conversations with enterprise leaders in the past week. The pace of change in AI seems dizzying for them. Big companies are used to plan in quarters and year-long roadmaps. But the terrain keeps shifting: by the time they have consulted experts, crafted their approach, and aligned internal stakeholders, the tech landscape has shifted, rendering their careful thinking irrelevant. Standard enterprise risk frameworks stop working. In such a scenario startup speed is essential. In military strategy terms, this is about operating within the OODA loop Observe: Watch the demos, notice changes in model capabilities, price points, orchestration methods, or integration cost reductions like MCP. Orient: Adjust your use cases from Co-Pilot applications to Agentic ones. Decide: Align everyone on the adjusted direction. Act: Get your team to schedule the release for the next shipping date. Startups, with their weekly shipping cadence and teams agile enough to iterate through tech stacks, use AI above API level, and pivot between approaches (RAG, fine-tuning, or revising their model building), find this OODA rhythm natural. But enterprises, despite their distribution advantages, find themselves like oil tankers in a speedboat race: impressive in scale but impossibly slow to turn. Companies who are getting ahead are doing the following few things. Create small, autonomous teams outside normal approval chains, embrace imperfection with minimum viable products that can be in users' hands next week, and use metrics that reward speed rather than completeness. I've seen companies cut approval layers from six to one, release weekly beta features, and measure teams on days to feedback rather than feature coverage, all with dramatic results

𝗧𝗟;𝗗𝗥: As per McKinsey, success of AI depends 𝗽𝗿𝗶𝗺𝗮𝗿𝗶𝗹𝘆 𝗼𝗻 𝗖𝗘𝗢 𝗹𝗲𝘃𝗲𝗹 𝘀𝗽𝗼𝗻𝘀𝗼𝗿𝘀𝗵𝗶𝗽 and the ability to 𝗿𝗲𝘄𝗶𝗿𝗲 𝗮𝗻 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻’𝘀 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 (vs just deploying intelligent chatbots). Interestingly as per METR, AI performance in terms of the 𝗹𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘁𝗮𝘀𝗸𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆 𝗲𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝘁𝗵𝗲 𝗽𝗮𝘀𝘁 𝟲 𝘆𝗲𝗮𝗿𝘀, 𝘄𝗶𝘁𝗵 𝗮 𝗱𝗼𝘂𝗯𝗹𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 𝗼𝗳 𝗮𝗿𝗼𝘂𝗻𝗱 𝟳 𝗺𝗼𝗻𝘁𝗵𝘀. This will have a huge impact on business rewiring and faster time to outcomes. Some key points from McKinsey & Company State of AI report (https://mck.co/4hMale0): • 78% of organizations now use AI in at least one business function, up from 55% last year. • 𝗟𝗮𝗿𝗴𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗲𝗮𝗱 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝘀 𝗮𝗻𝗱 𝗱𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗮𝗺𝘀. • CEO oversight of AI governance shows strongest correlation with positive financial impact. • Organizations increasingly mitigate AI risks around accuracy, security, and IP infringement. • Companies are both hiring AI specialists and reskilling existing employees. • Over 80% of organizations still see no material enterprise-level EBIT impact from AI. On a related topic to workflow redesign, METR did some great work (https://bit.ly/4hCk2LQ) where they showed AI's ability to complete tasks (measured by equivalent human time required) has been doubling approximately every 7 months for the past 6 years which means that 𝘄𝗶𝘁𝗵𝗶𝗻 𝟮-𝟰 𝘆𝗲𝗮𝗿𝘀, 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗼𝘂𝗹𝗱 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘄𝗲𝗲𝗸-𝗹𝗼𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗼𝗻𝗲 𝗯𝘆 𝗵𝘂𝗺𝗮𝗻𝘀! (hat tip to Ethan Mollick for the METR link) Organizations that strategically reimagine their operations 𝗮𝗿𝗼𝘂𝗻𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆 𝗰𝗮𝗽𝗮𝗯𝗹𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀—centralizing risk and data governance while distributing tech talent in hybrid models as the McKinsey survey suggests—will capture greater value. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗘𝗢𝘀 𝗮𝗻𝗱 𝗖𝗔𝗜𝗢𝘀: Rather than waiting for AI to demonstrate enterprise-wide EBIT impact, 𝗳𝗼𝗿𝘄𝗮𝗿𝗱-𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗺𝗮𝗽𝗽𝗶𝗻𝗴 𝗼𝘂𝘁 𝘄𝗵𝗶𝗰𝗵 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘁𝗮𝘀𝗸𝘀 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗵𝗮𝗻𝗱𝗹𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗼𝗻𝘁𝗵𝘀 𝗮𝗻𝗱 𝘆𝗲𝗮𝗿𝘀, allowing them to proactively restructure roles, retrain employees, and redesign processes to leverage this exponential growth in AI task completion capabilities.

Stop asking "what's our AI strategy?" and start asking "what's slowing us down?" Everyone's hiring Chief AI Officers and building committees to create their "AI strategy." Meanwhile their salespeople are struggling with the same inefficiencies they had last year. Your IT department doesn't know what your AI strategy should be for your SDRs, AEs, or marketing team. AI isn't a strategy. It's a role and use case-specific deployment. It's exactly like the internet in '95. We didn't need a "Chief Internet Officer" to tell marketing how to use email or sales how to research prospects online. The companies winning right now are ignoring the hype cycle and instead asking: ➡️ Where are my teams spending too much time? ➡️ Which tasks are reps avoiding but have high impact? ➡️ What parts of our process frustrate customers? ➡️ Where do we lose deals because we're too slow? Find your bottlenecks first. Then apply AI tactically to eliminate them. Your competitive edge isn't having an "AI strategy", it's solving real problems faster than everyone else still stuck in committee meetings.

From The Information today: There has been a significant shift in enterprise AI adoption: CFOs are now driving AI purchasing decisions, with ROI becoming the primary focus. According to Writer co-founder Waseem Alshikh, CFO involvement in AI purchase conversations has jumped from less than 5% a year ago to over 70% today. This represents a dramatic shift from the early AI adoption phase when companies were experimenting broadly with little concern for immediate returns. Key takeaways: ▪️ Focus has shifted from model specs to tangible business outcomes ▪️ Customers now prioritize secure, scalable solutions that deliver measurable ROI ▪️ Many companies view AI primarily as a cost-cutting tool rather than revenue generator ▪️ Some organizations are investing in AI specifically to increase worker productivity instead of hiring The enterprise AI market is clearly entering a more mature, pragmatic phase. As tech leaders, we need to be prepared to demonstrate clear financial benefits when advocating for AI investments. Read the full article: https://lnkd.in/ew7eHTUj What's your experience with AI purchasing decisions in your organization?

𝗠𝘆 𝗔𝗜+𝗛𝗜 𝗥𝗮𝗱𝗮𝗿: 𝗧𝗵𝗶𝘀 𝗪𝗲𝗲𝗸'𝘀 𝗠𝘂𝘀𝘁-𝗥𝗲𝗮𝗱𝘀 Four key developments in AI integration and human factors this week. Links in comments. 𝗧𝗵𝗲𝗺𝗲: 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗺𝗼𝘃𝗲 𝗳𝗿𝗼𝗺 𝗔𝗜 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (NVIDIA) What to Know: California launches $500M initiative to train 100,000 AI-ready workers by 2025. Gwinnett County implements the first K-16 AI curriculum in Georgia, focusing on practical development skills. Why it Matters: States compete for AI talent as tech companies expand operations. Early education programs aim to close the 60% skills gap identified in NVIDIA's workforce assessment. 𝗔𝗜 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (Stanford University/Google DeepMind) What to Know: Study of 1,000 participants shows AI can replicate individual decision-making patterns with 85% accuracy using two-hour structured interviews and behavioral analysis. Why it Matters: This technique reduces AI personality modeling from months to hours, enabling practical applications in personalized AI assistants and decision support systems. JPMorganChase's 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 What to Know: JPMorgan deployed its "LLM Suite" to 200,000 employees in Q4 2023. The bank identified the top 20% of early adopters to serve as AI implementation leaders. Why it Matters: Initial data shows a 40% reduction in routine tasks among early adopters, with the wealth management division reporting 70% faster client documentation processing. 𝗔𝗜 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 (Salesforce) What to Know: Benioff challenges current chatbot limitations, presenting Salesforce's shift toward autonomous agents that can execute multi-step business processes without constant human prompting. Why it Matters: Early adopters report autonomous agents completing sales qualification processes in minutes versus hours while maintaining human oversight for final decisions. Bottom Line: Organizations succeeding with AI focus on measured implementation, clear metrics, and structured human oversight rather than rushing to adopt every new capability. #AIImplementation #WorkforceTransformation #BusinessAI