Assessing the Economic Viability of AI

𝑵𝒆𝒘 𝒑𝒐𝒔𝒕 𝒔𝒆𝒓𝒊𝒆𝒔 -- 𝑮𝒆𝒏 𝑨𝑰 𝒇𝒐𝒓 𝑵𝒆𝒕𝒘𝒐𝒓𝒌𝒔. 𝑷𝒐𝒔𝒕 6/7 Setting Clear Objectives for AI Integration = Measuring ROI When implementing AI initiatives, it's crucial to ➡ establish clear, measurable objectives, ➡seamlessly integrate AI into existing processes, ➡ continuously measure ROI to ensure alignment with business goals. 𝐂𝐥𝐞𝐚𝐫 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 𝐨𝐟 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬 To ensure that AI initiatives are successful, start by setting clear, measurable objectives that align with your overall business goals: ✅ Setting targets for cost reduction through automation and optimization. For instance, a McKinsey report indicates that AI-driven predictive maintenance can reduce maintenance costs by up to 20% and cut unplanned downtime by 50%. ✅Enhancing customer experience by leveraging AI for personalized recommendations, chatbots, and 24/7 support. Gartner predicts that by 2025, 80% of customer service interactions will be handled by AI, leading to faster response times and higher customer satisfaction. ✅Generating new revenue streams by using AI to identify market opportunities and develop innovative products. PwC studies show that AI could contribute up to $15.7 trillion to the global economy by 2030, highlighting its potential for creating new business opportunities. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 For AI to deliver its maximum value, it needs to be seamlessly integrated into existing business processes: ✅Mapping out existing workflows to identify areas where AI can be most beneficial, such as repetitive, time-consuming tasks that can be automated. ✅Designing a strategic integration plan that minimizes disruptions while maximizing the benefits of AI technologies. Start with pilot projects to test the integration process and refine your approach based on feedback and initial results. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭 𝐨𝐟 𝐑𝐎𝐈 To justify AI investments, it's essential to establish and continuously monitor metrics that measure the return on investment: ✅Tracking direct financial gains, such as cost savings from automation, increased sales from personalized marketing, or new revenue streams from AI-driven products. ✅Measuring indirect benefits like improvements in customer satisfaction, operational efficiency, and employee productivity. For example, AI can streamline customer service operations, leading to faster response times and higher customer satisfaction ratings. ✅Implementing a robust monitoring system to continuously track these metrics, regularly evaluating the success of AI implementations, and making necessary adjustments to optimize performance and outcomes. This structured methodology helps organizations harness the full potential of AI, driving both innovation and efficiency. What would you add?

The mood I’m feeling at Davos is different. In 2023 and 2024, speakers who brought hype packed their sessions. Now, CEOs are only interested in speakers who bring receipts.   2025 is the year of ‘Prove It.’ According to BCG research, AI is a top-three strategic priority for 75% of firms. Still, only 25% say their AI initiatives return significant value. https://on.bcg.com/4g21VhI Two changes to a firm’s AI mindset boost AI initiative ROI. BCG’s findings align with what I’ve been seeing with clients for almost a decade.   The 25% of companies getting significant value from AI invest in inventing new products and services and innovating their operating models by reshaping critical business functions.   The other 75% of companies that aren’t generating value with AI greenlit too many AI initiatives and focused on smaller, incremental gains. Not every use case is an AI use case.   One of the first things I do is take “incremental” initiatives off the AI team’s plate so they have the bandwidth to focus on the highest-value opportunities.   Equally critical is removing use cases when traditional software can deliver 90% or more of the value. Leaders must evaluate the additional value AI delivers vs. conventional software.   The extra 10% of the use case that AI can support is rarely enough to offset the additional costs of developing, integrating, and maintaining an AI solution. Costs typically scale faster than returns.   Significant value comes from vertical depth rather than horizontal breadth. In my experience, the highest-value use cases are ones that no other technology can serve.   Business units’ operations must fundamentally change to get the full value from AI agents. Human-AI collaboration is a novel paradigm, not an incremental change. #ArtificialIntelligence #AIStrategy #BCGPartner

In most cases, the real economic impact of #AI is missing from the investment decisions. Nobel Laureate Acemoglu tempers all the overhyped claims about AI, and in his scientific assessment, AI might only automate 5% of all tasks, and add just 1% to the global GDP over the entire next decade. This is indeed quite a concern for all the billions of dollars being invested by corporations. My doctoral research on Cognitive Chasm, the failure modes of #GenAI adoption reveals that the majority of AI initiatives fail to deliver the ROI at a firm level, because they have a fundamentally incorrect understanding of what problem they are solving. In most cases, they naively think of applying AI as yet another equivalent of the digitalization of existing processes. Unless the organizations are willing to fundamentally transform their ways of thinking around how AI could be leveraged, their efforts will end up self-limiting. https://lnkd.in/g8YpJ7y7

A big question when building AI Agents for the enterprise is where the greatest amount of economic value is in AI Agents, which often ties directly to how differentiated your AI Agent is and your ability to monetize it. 1. For the most basic AI query or assistant experiences, the economic potential will mostly correlate to how proprietary the data is that your Agents are working off of. For pure public data this is harder to differentiate on and the productivity can be squishier; but the value can be expanded when the Agent has access to domain specific information, data from tools, or corporate knowledge, and especially where there are direct productivity gains that can be measured. 2. As AI Agents can execute narrow tasks, like reading documents and extracting data, typing ahead as you generate code for a project, or generating new content, the economic potential goes up quite a bit. These AI Agents will often need access to corporate data, have access to tools, and be able to work across multiple platforms. These Agents start to approximate the value of a discrete task inside of a business process, and thus their productivity can be directly measured. 3. Then, we'll have AI Agents that can execute entire workflows, like helping with client onboarding processes, reviewing and approving invoices, and more. The potential for economic value creation here is much higher as these agents will have access to critical corporate knowledge to do their work, often will be line of business and industry specific, contain proprietary context about their specific workflow, and tie into other existing software and agentic platforms. 4. Finally, when AI Agents act effectively as autonomous workers, this leaves the greatest room for economic value. Imagine an AI Agent that can complete an entire FDA submission process, or review and negotiate a legal contract for you, or code an entire application. These agents will be tuned to custom business processes, contain industry-specific knowledge, have access to proprietary data, often autonomously be able to use tools, and more. You'll be able to very directly measure their productivity in a business process. Ultimately, when AI Agents become near perfect over time (we still have a ways to go!), there’s almost no upper limit on their economic value. As models improve, and as Agents get more context, have proprietary data to work with, can access tools, and become more industry specific, they’ll become insanely powerful.

Whether AI is a bubble depends on the answer to three economic questions: 1. How much of human labor can it really augment? 2. To whom do the benefits accrue? 3. What are the costs to deploy it effectively? The results are increasingly impressive on question 1. From software engineers to marketers and call centers, the people who already find it essential to their work is growing. We’ll need agents to work well (to link from task to task), and multi-modal (e.g., video and image) to continue to develop, but the technological capabilities of the models are unlikely to be the limitation to value capture. Concerns about hallucinations and errors are real, but there are increasingly good technology approaches to manage those, and no shortage of high economic value applications where a 95% answer is good enough (and better than most humans) Question 2 is a little more complex. Many employees love it, but the reality is it is saving many people 20 minutes a day. That’s great to let you go home and walk the dog, but that does not aggregate up into meaningful business impact. For benefits to accrue that you can actually see in a P&L, organizations need to take a domain-based approach. Focus on an area where you can make a large and measurable difference, and change how work gets done in that area. This will be more effective than treating it as a generalized productivity tool, and is likely to require genAI working hand in hand with other forms of AI and technology interventions. Question 3 is a subject of hot debate. The costs of running the models themselves are quite low and deploying a pilot is pretty easy relative to many other forms of technology. So it has been easy to claim that this is a low cost disrupter - the PC versus the mainframe is a commonly used analogy. What this misses is that it is never just about the technology. To make this work in an enterprise requires investments in the people, the operating model, the data and the adoption and scaling approach, guided by a top-down goal - in-fact our full #rewiredbook recipe. So where do we net out? The economic value of AI is real, and can accrue to companies, not just consumers and tool providers. But the “activation energy” to make it work is real too. If you want AI in your company to unleash measurable impact, not just employee enthusiasm, you need to be prepared to treat it as systematic transformation. It’s about becoming Rewired, not just flipping a switch. #mckinseydigital #quantumblack

🌟 Unlocking Business Value Through Cost-Efficient AI: A Strategic Framework for Success 🌟 AI is revolutionizing industries, but let’s be honest—adopting it isn’t cheap. Too many enterprises rush into AI without asking the most critical question: How do we ensure our AI systems deliver maximum business value while controlling costs? That’s why I created the Cost Efficiency Ranking System, a framework designed to help businesses assess, optimize, and scale their AI solutions strategically. It’s not just about cutting costs—it’s about aligning your AI investments with your business goals. This system empowers enterprises to build smarter, scalable AI architectures that adapt to dynamic needs, eliminate inefficiencies, and generate sustained ROI. Why is this so important? 🔹 Businesses that prioritize cost efficiency move beyond experimentation and turn AI into a driver of competitive advantage. 🔹 It’s not about using the biggest or newest AI tools—it’s about using the right tools to deliver real results, whether that’s improved customer satisfaction, cost savings, or faster time-to-market. 🔹 In a world where budgets are tighter and success is measured in ROI, enterprises must approach AI with both purpose and precision. In my latest article, I break down how the Cost Efficiency Ranking System works, why it’s essential, and how businesses can use it to navigate every phase of AI adoption—from planning and design to deployment and operations. Whether you’re just starting out, or already scaling your AI, this framework can help you elevate your results while minimizing risk. ✅ If you’re aiming to turn your AI initiatives into high-performing, cost-efficient systems, this is the perfect place to start. 💬 Let’s discuss! How is your organization tackling the cost challenges of AI? Are you seeing the ROI you expected, or are inefficiencies holding you back? Drop a comment or DM me—I’d love to hear your thoughts. Let’s stop building AI systems just because we can and start building them because they work for the business. It’s time to rethink AI strategy and get serious about cost efficiency. 🚀 #AI #BusinessStrategy #EnterpriseTechnology #CostEfficiency #CloudComputing #ScaleSmart

Remember when everyone thought websites were expensive to build? Cute. Most executives are making the EXACT same miscalculations with AI economics today. What I've learned the hard way: - Hardware costs for serious AI models can devour $100K+ monthly - but the performance-to-cost curve is logarithmic, not linear - One manufacturing client spent 6X MORE on data preparation than on the actual AI implementation - The "J-curve effect" of AI economics terrifies quarterly-focused executives but creates enormous advantages for patient operators - The most counterintuitive discovery? Successful AI implementations often INCREASE costs before dramatically reducing them. At one FinTech unicorn, we initially blew our budget by 10X, only to reduce operational costs by 62% two years later while growing revenue 340%. The winners won't be those with the biggest AI budgets, but those who understand the unique economic principles governing this technology. Read the full article to discover the funding hacks, opportunity frameworks, and implementation guides that can position your business ahead of the Great AI Reshuffling. - Who else has experienced the shocking gap between projected and actual AI economics? Share your stories below! 👇 #AIEconomics #TechLeadership #FutureOfBusiness #StartupStrategy https://lnkd.in/gpEYsCtm