MadTech’s Post

Pleased to share my latest article in AI Business, "AI Depends on a Consumable Data Layer." In this piece, I highlight the key traits of data-mature organizations and explain how achieving AI effectiveness requires more than just cutting-edge technology but constant human input, oversight, intervention, and leadership. #AI #DataMaturity #DataScience #Leadership #AIInnovation #MadTech Bob Walczak Heather Macaulay

🟢 🚀 Becoming an AI-native organization at scale involves making the most of technology, data, and governance. Success follows when leaders embrace an operating model that leverages the strengths of both humans and machines; is rooted in agility, flexibility, and continuous learning; and is supported by strong data and analytics talent. Another condition of success is to invest in data quality and quantity, focusing on the data life cycle to ensure high-quality information for training the gen AI model. Building capabilities into the data architecture, such as vector databases and data pre- and post-processing pipelines, will enable the development of use cases. Talent, data, technology, governance—none of these can be an afterthought. This collection presents the top insights on gen AI from McKinsey and its AI arm, QuantumBlack, providing a detailed examination of the significant opportunities and challenges it offers for leaders looking to steer their organizations into the future. 🚀Contact me today for advice on driving trustworthy AI development https://lnkd.in/eh8e5z25 ✅ Follow me for insights into responsible AI best practices, guidance on EU AI Act compliance, and AI literacy for leaders #ArtificialIntelligence #aiThoughtLeaders

A common stumbling block for companies that can result in failed AI implementations is not rooted in the technology itself but rather organizational structure and corporate culture that impede progress. To successfully deploy AI, executives must foster a culture that enables seamless collaboration. Hence, a framework is required to help executives prepare their organization for implementing AI at scale. This framework should include embracing a data-driven decision-making culture, clearly defining roles and responsibilities of the stakeholders, building AI awareness across the organization and ensuring a solid integration and change management process. #Artificialintelligence #Data #Implementation #Bigdata #Dataculture #Innovation #Technoloty #Strategic

AI Report. C-suite leaders. MIT Technology Review and Fivetran have just released a report that "...draws on insights from a survey of 300 C-suite executives and senior technology leaders, as well as on in-depth interviews with four leading experts." "The consultancy McKinsey projects that, when fully deployed, predictive AI (including analytics and machine learning) will add between $11.0 and $17.7 trillion to annual global economic activity. Generative AI is projected to contribute an additional $2.6 to $4.4 trillion." That's more than 10% of global GDP in the bear case! Every organization that is generating any sort of data should probably consider embedding AI into their strategy for automation and improving productivity. 82% of C-suite and other senior executives surveyed would surely agree. "While generative AI offers an impressive and powerful new set of capabilities, its business value is not a given. While some powerful foundational models are open to public use, these do not serve as a differentiator for those looking to get ahead of the competition and unlock AI’s full potential. To gain those advantages, organizations must look to enhance AI models with their own data to create unique business insights and opportunities."

81% of organizations trust their AI/ML outputs despite admitting to fundamental data inefficiencies. What do you think that costs them, on average? ~$406 million — all because of business decisions based on inaccurate AI models. But let’s trace the problem back even further than that. Inaccuracies in AI and ML models are directly caused by the data they're fed. When that data is flawed—be it inaccurate, incomplete, or just low-quality—it goes right into the AI hopper, all the same. And when bad data goes in, bad data comes out. Now, imagine launching new products based on faulty predictions or setting prices based on misleading trends. You might lose revenue (say, $406 million) or, worse, trust. Because when leadership repeatedly sees AI projects failing to deliver on their promise due to bad data, it shakes their confidence in the data and the data team. This skepticism slows down (or completely shoots down) executive buy-in for genuinely beneficial data projects, setting back all the data team’s hard-earned efforts toward gaining a seat at the table. To bridge this gap, data teams need to reinforce data quality as a priority — even if that means pushing back on the executive call for AI and ML development. This means: • Investing in robust data management practices • Continually monitoring and cleaning data • Fostering a data culture that understands the value of high-quality data. We have to take one step back to take two steps forward. Only once we trust the data can we fully trust AI outputs to make decisions that are reliable, responsible, and, ultimately, profitable. #dataengineering #dataquality #genai #ai #gigo #dataaccuracy

AI is the future … no arguments there. But without the right data foundation, it’s a future that will remain out of reach for most organisations. Organisational leaders see AI as transformative. Yet, so many AI projects fail to deliver on their promise, often due to a foundational issue: Data Quality. To unlock AI’s potential, the underlying data must be trusted, actionable, and relevant. This means, organisations must ensure that every stage — collection, cleansing, governance, and integration of data — is executed with precision. Without a disciplined approach, AI models lack the reliability and accuracy needed to make strategic decisions that actually drive growth. Here are three insights I’ve learned over the years, advising on data strategy: ➡ Quality Trumps Quantity: Feeding an AI model vast amounts of poorly organised data can lead to misleading results. Clean, curated data is far more powerful and can drive much clearer insights. ➡Data Governance Is Key: Establishing strong governance frameworks upfront can prevent costly roadblocks later. Governance isn’t just about compliance; it’s about building a robust data foundation that AI can depend on. ➡Alignment Across Departments: Siloed data is an AI killer. Cross-functional alignment is essential to ensure data consistency and accessibility, especially when AI initiatives span multiple business functions. The path to AI success isn’t just about adopting the latest tools; it’s about getting the data right. I'm dedicated to helping organisations lay a solid data foundation to fuel AI innovation that delivers real, measurable outcomes. Is your organisation’s data ready for AI? #AI #DataQuality #DataStrategy #DigitalTransformation #AILeadership

🚀 Unlock the Future of Business with AI Leadership! 🌟 Are you ready to transform your organization into an AI powerhouse? Here are a few questions to ask before you get started. ↳ How can AI align with and enhance our core business objectives and strategic initiatives? ↳ Is the organization’s data AI-ready? ↳ What are employee's AI maturity and where are the skill gaps? ↳ What kind of programs should be in place to upskill employees? Read more in the MIT article. #AI #digitaltransformation #Artificialintelligence https://lnkd.in/gVBhd-Vg

🚀 𝐀𝐈 𝐢𝐬 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: 𝐀𝐫𝐞 𝐘𝐨𝐮 𝐑𝐞𝐚𝐝𝐲? Artificial Intelligence isn't just changing the game—it's creating a whole new playbook for businesses. Our latest blog explores: • How AI is transforming organizational operations • The evolving roles of data scientists and business leaders • Key skills needed to thrive in an AI-driven world • Strategies for successful AI integration Don't just adapt—lead the change. Discover how to harness AI's potential and future-proof your organization. #ArtificialIntelligence #BusinessStrategy #DataScience #FutureOfWork #TechticsAI https://lnkd.in/gcneuyHY

We can all agree that the transition towards AI-driven organizations is not just imminent; it's crucial. As this article says, it's clear that #Leadership will play a pivotal role in harnessing #ArtificialIntelligence to amplify our human capabilities while also transforming the way businesses and individuals operate and innovate. What can organizations do? An AI playbook that involves deep introspection on critical business problems, ensuring data readiness, and addressing the AI maturity level of teams is a good start. But beyond the technicalities, leading in the AI era demands a culture shift: embracing experimentation, fostering cross-functional collaboration, and championing responsible AI use. How are you embracing AI? https://lnkd.in/eMJiwxbY

Despite rising #investments in #artificialintelligence (AI) by today’s enterprises, trust in the insights delivered by AI can be a hit or a miss with the #csuite. Are executives just resisting a new, unknown, and still unproven #technology, or their hesitancy is rooted in something deeper? #executives have long resisted data analytics for higher-level decision-making, and have always preferred to rely on gut-level decision-making based on field experience to AI-assisted decisions. The business community is committed to driving #transformation through AI-powered automation. However, senior leaders and boards need to be aware of the risks associated with the technology and the best practices to proactively mitigate them, AI has the power to transform the world, but as the popular saying goes with great power comes great responsibility. Data-based decisions by AI are almost always based on probabilities (probabilistic versus deterministic). Because of this, there is always a degree of uncertainty when AI delivers a decision. There has to be an associated degree of confidence or scoring on the reliability of the results. It is for this reason most systems cannot, will not, and should not be automated. Humans need to be in the decision loop for the near future. To effectively apply #generativeai for business value, companies need to build their technical capabilities and upskill their current workforce. This requires a concerted effort by #leadership to identify the required capabilities based on the company’s prioritized use cases, which will likely extend beyond technical roles to include a talent mix across engineering, data, design, risk, product, and other business functions. #csuite #executivedirectors #culturetransformation #strategytoexecution #nonexecutivedirectors #ceos #businessgrowth #values #trust #innovation #strategyplanning #strategicleadership #strategicgrowth #jointventures #businesstransformation #globalbusiness #trustbuilding #investment #productinnovation #peopleandculture #innovationculture #partnerships https://lnkd.in/e_YZuA9j