AI Innovation Management

This paper offers a comprehensive analysis of AI-driven business model innovation (BMI), identifying six key research dimensions crucial for understanding and advancing the field. 1️⃣ Triggers: Various factors trigger AI-driven BMI, including customer demand for AI-based solutions, technological advancements, data democratization, ecosystem developments, competitive pressures, regulatory compliance, and societal trends. These triggers drive companies to adopt AI to create new value propositions and enhance business model efficiency. 2️⃣ Restraints: Several barriers hinder AI implementation in business models. These include ethical concerns (such as algorithmic bias and misuse of AI), safety and security issues, legal and regulatory challenges, employee resistance, and the opaque nature of AI (the "black box" problem). These restraints can lead to hesitation or failure in fully adopting AI-driven BMI. 3️⃣ Resources and Capabilities: Successful AI-driven BMI requires extensive resources and capabilities, including a robust data strategy, skilled digital talents, adequate system infrastructure, and sufficient financial resources. These elements are essential for collecting, processing, and leveraging data to drive AI applications and business model innovations. 4️⃣ Application of AI: Implementing AI in business models involves understanding the current model, formulating an AI strategy, and selecting appropriate AI tools and technologies. Multidisciplinary teams play a crucial role in managing AI projects, ensuring effective rollout, communication, visualization, and continuous improvement of AI initiatives. 5️⃣ Implications: AI can support, enable, innovate, or disrupt business models. It enhances existing processes, redefines operations, creates new value propositions, and can lead to industry-wide transformations. The implications of AI-driven BMI are profound, offering incremental improvements, fundamental operational changes, innovative new services, and disruptive market shifts. 6️⃣ Management and Organizational Issues: Effective management is critical for driving AI initiatives and facilitating business model changes. This includes cultivating an AI-centric organizational culture, acquiring practical AI experience, rethinking governance structures, and aligning AI initiatives with company strategy. Addressing cultural deficits, fostering agility, and democratizing AI within the organization are essential for successful AI-driven BMI. ✍🏻 Philip Jorzik, Sascha P. Klein, Dominik K. Kanbach, Sascha Kraus, AI-driven business model innovation: A systematic review and research agenda, Journal of Business Research, Volume 182, 2024, 114764, ISSN 0148-2963. DOI: 10.1016/j.jbusres.2024.114764

From Cuts to Restructuring: Corporate Innovation is Being Rewritten Innovation is shifting, and not just quietly. After months of deep reading, sharp conversations, and thoughtful reflection, I finally took the time to consolidate the patterns I kept seeing (even when they seemed obvious, because sometimes the obvious needs to be said). Below are 7 key trends I’ve observed shaping corporate innovation: ⚖️ 1- From Ambidexterity to ‘Future-Now’ Innovation – Companies are prioritizing innovations with significant long-term impact that can be implemented with tangible deliverables and generate value in the present, rather than separating them into short and long-term initiatives. 💰 2- From ROI to ROI²: Measuring Impact from Day 1 – Innovation success is now defined from the start, with metrics that clearly outline what success looks like and are monitored throughout the entire journey. ROI2 = Return on Investment in Innovation. 💪 3- From Builders to Finishers: Rethinking Governance – The focus of innovation is no longer just on idea generation but is now balanced with execution, ensuring projects are scaled and deliver real value. After all, innovation is not about what you start, but what you finish (and it is important not to forget to add new initiatives into the funnel as well). ♟️ 4- From Funnel to Tunnel: Advancing Strategic Focus – The innovation funnel is becoming more selective, prioritizing visibility, integration, and strategic decisions beyond simple go/no-go choices. Just an attention point here: the term "Innovation Tunnel" can sometimes be misinterpreted pejoratively, implying the funnel’s intake is being narrowed (and sometimes the companies do it). 📣 5- From Culture-First to Results-First Approach – Companies are reversing the innovation logic, delivering real impact first and letting culture (and communication) evolve naturally from results. 👩💻 6- From Human to Human + AI Decision-Making – AI is revolutionizing innovation, enabling real-time analysis and decision-making, increasing companies' capabilities by 10x, 100x, or even more. By next year, the number of AI agents is expected to surpass the global human population. 🏠 7- From Outsourcing to a Balanced In-House Strategy – Organizations that once heavily relied on open innovation are now reassessing what should remain external and what must be brought in-house to maintain control and strategic differentiation. That said, open innovation, when used strategically, remains highly effective. These are the trends I’ve been seeing emerge more and more clearly. If you're curious to dive deeper, I shared more insights in the attached PDF. Now, I'd love to hear your thoughts. What shifts are you noticing in your organization or industry?

💡 Are Compliance Standards Killing Innovation, or Are We Framing Them Wrong?💡 Compliance standards are often viewed as barriers to creativity, especially in fields like artificial intelligence (AI). But frameworks like ISO42001 are not obstacles as much as they are enablers. They provide the structure needed to innovate responsibly, ensuring organizations can offer accountability, trust, and scalability. For leaders implementing an Artificial Intelligence Management System (AIMS), conformance to the standard can help establish a foundation for trustworthy AI systems, reducing risks and enabling sustainable innovation that also aligns with the OECD.AI’s Principles. ➡️ How ISO42001 Drives AI Innovation 1. Clarity Creates Confidence 🔹 Challenge: Teams hesitate to deploy AI when risks like bias or privacy breaches remain unresolved. 🔹ISO42001 Solution: Establishes clear processes for risk management, documentation, and decision traceability. 🔸Impact: Developers can innovate confidently within a framework that reduces uncertainty. 2. Risk Management Enables Bold Ideas 🔹Challenge: AI development involves unpredictable outcomes and operational risks. 🔹ISO42001 Solution: Provides structured tools to identify, mitigate, and monitor risks throughout the AI lifecycle. 🔸Impact: Teams can pursue ambitious ideas with safeguards in place, balancing creativity with accountability. 3. Accountability Builds Trust 🔹Challenge: Stakeholders demand transparency and fairness in AI decision-making. 🔹ISO42001 Solution: Embeds accountability mechanisms, ensuring decisions are traceable and ethical. 🔸Impact: Encourages collaboration and risk-taking, knowing ethical considerations are part of the process. 4. Collaboration Fuels Innovation 🔹Challenge: Innovation often stalls when teams operate in silos. 🔹ISO42001 Solution: Defines clear roles and responsibilities, enabling cross-functional alignment. 🔸Impact: Teams work together more effectively, addressing risks early and accelerating progress. ➡️ AIMS as a Platform for Innovation ISO42001 creates the environment where AI innovation thrives. By integrating ethical considerations, risk management, and lifecycle monitoring, you can scale your AI solutions responsibly while fostering creativity. 🔹Example: AIMS ensures challenges like bias or transparency are proactively addressed, allowing developers to focus on building impactful AI systems. 🔸Long-term Value: Innovations are not just scalable but also aligned with societal and organizational goals. ➡️ Rethinking Compliance Governance/Management frameworks like ISO42001 are not roadblocks, they are opportunities. They establish trust, reduce uncertainty, and provide the structure you need to innovate responsibly. 🔸Key Takeaway: Success in AI isn’t defined by how quickly systems are built, but by how effectively they deliver ethical, sustainable value. A-LIGN #TheBusinessofCompliance #ComplianceAlignedtoYou ISO/IEC Artificial Intelligence (AI)

I've seen dozens of AI initiatives fail at large companies. One common theme explains why: The disconnect between what leaders THINK AI can do and what's possible. This disconnect leads to expensive mistakes: - Projects getting canceled mid-way  - Non-AI initiatives incorrectly labeled as AI - Investments in AI that fail to generate ROI (the whole point) How do you avoid making these mistakes? Teams and leaders must work together to bridge the knowledge gap. Here's how: 1/ CIOs, CTOs and executives in charge of technology and innovation must learn what it takes to build an AI-ready and capable company. As the decision-makers, they're responsible for laying the foundation for AI adoption. This includes closing their own knowledge gaps and using the right frameworks and resources to support effective AI integration. 2/ Innovation managers such as product managers, engineering managers and domain experts need a broad AI understanding to make informed investment decisions. Without it, they often risk pursing AI for the wrong problems. 3/ Managers who are in charge of implementation-level initiatives need technical AI understanding to navigate implementation initiatives effectively. This ensures projects align with business goals and follow proper development practices. 4/ Lastly, data scientists, AI engineers, and data engineers must develop strong business acumen that go beyond their technical skills. This helps them effectively translate the company's vision into reality through proper scoping, planning, and execution. AI initiatives affect every corner of your organization. As a leader, you must understand your role in driving successful implementation. For your enterprise AI projects to succeed: Close the knowledge gap between leadership and implementation BEFORE any development begins. If you skip this step, you risk more than failed projects... You risk wasting investments that could have generated real business value.

Based on many discussions with external and internal AI champions—and reflecting on my experiences with clients—I started writing down some of the recurring points that surface time and again: ✅ Start small, scale smart. AI adoption works best when focused on specific pain points—not trying to overhaul everything at once. This also enables a roadmap that shows progress and allows flexibility to adjust as AI rapidly evolves. ✅ Encourage curiosity & experimentation. AI isn’t plug-and-play; testing and learning are key to driving real value. The best way to get comfortable with AI tools is to use them and experiment. ✅ Governance matters. Set clear policies to guide responsible AI use—balancing innovation with compliance. A good AI use policy gives clear boundaries and reinforces that teams have permission to use it. ✅ Share what works (and doesn't) throughout the organization. As individuals and teams experiment with AI, their learnings will be a goldmine of knowledge. If you don't share what has been learned, the opportunity to learn and collectively troubleshoot will be missed, potentially slowing the time it takes to get greater value out of AI. ✅ AI is a tool, not a threat. Position AI as a way to enhance efficiency and improve processes, not replace jobs, to gain buy-in and ease concerns. ✅ Stay informed, stay adaptable. AI moves fast. Organizations that continuously learn and evolve will have the edge. Bringing AI into your org isn’t just about technology—it’s about people, culture, and strategy. Want to make AI work for your business? Let's talk. #AI #Leadership #Innovation

𝐌𝐢𝐝-𝟐𝟎𝟐𝟓 𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞: 𝐀𝐈 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐯𝐞𝐬 𝐟𝐫𝐨𝐦 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 According to #IBM’s “5 Trends for 2025” report, leaders are now scaling innovation and empowering teams to unlock AI’s full potential. 🔹𝐊𝐞𝐲 𝐒𝐡𝐢𝐟𝐭𝐬 𝐢𝐧 𝐀𝐈 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧  👉AI is moving from experimentation to execution ▪46% of executives say their organizations are scaling AI this year, focusing on optimizing existing processes and systems. ▪44% are using AI for innovation, driving new opportunities and business models. ▪Only 6% of organizations are still in the experimentation phase, down sharply from 30% just a year ago.  👉AI is now a core driver of business transformation ▪85% of executives believe AI is enabling business model innovation. ▪89% say AI is driving product and service innovation. 🔹𝐇𝐨𝐰 𝐋𝐞𝐚𝐝𝐞𝐫𝐬 𝐀𝐫𝐞 𝐏𝐮𝐬𝐡𝐢𝐧𝐠 𝐓𝐞𝐚𝐦𝐬 𝐅𝐨𝐫𝐰𝐚𝐫𝐝  👉Empowering people at every level ▪Democratizing decision-making so teams can act quickly and effectively. ▪Providing robust tools, training, and support for employees to succeed with AI.  👉Fostering a culture of innovation ▪Leaders are redefining leadership by delegating more decisions as AI augments roles across the organization. ▪Teams are encouraged to rethink workflows and deploy AI agents in new ways to boost performance.  👉Strategic support for teams ▪Implementing strong security and governance as AI becomes more embedded in operations. ▪Leveraging data-driven decision support for smarter, faster choices. 🔹𝐓𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐚𝐬𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧  👉AI is now a business imperative ▪68% of CEOs say AI is changing core aspects of their business. ▪61% believe competitive advantage depends on having the most advanced generative AI. ▪64% of leaders see automation’s productivity gains as essential to staying competitive.   👉Bold investment and risk-taking ▪62% of leaders invest in new technologies before fully understanding their value, determined not to fall behind. ▪The winners are balancing experimentation with strategic, incremental innovation. 🔹𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐒𝐭𝐞𝐩𝐬 𝐋𝐞𝐚𝐝𝐞𝐫𝐬 𝐀𝐫𝐞 𝐓𝐚𝐤𝐢𝐧𝐠  👉Talent and skills ▪Rethinking talent strategies—people are the most important tech investment. ▪Focusing on targeted training, upskilling, and making AI proficiency a must-have.  👉Technology and data ▪Building integrated, enterprise-wide data architectures for cross-functional collaboration. ▪Using proprietary data to unlock the full value of generative AI. The organizations that will win are those where leaders empower their people, invest in skills, and foster a culture where AI-driven innovation thrives. 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gRNGWqNQ #AI #DigitalTransformation #GenerativeAI #GenAI #Innovation  #ArtificialIntelligence #ML #ThoughtLeadership #NiteshRastogiInsights 

𝗢𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗯𝗮𝗿𝗿𝗶𝗲𝗿𝘀 𝘁𝗼 𝗮𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗶𝘁 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆. But no one is laying out an effective roadmap for AI governance So, here are the key components to build that roadmap: 📈 Strategy Alignment: Align AI initiatives with business objectives to maximize competitive advantage. 🗂️ Governance: Establish clear accountability frameworks for AI decision-making and oversight. 📈 Performance: Regular KPI reviews ensure AI delivers measurable business value. 🔍 Risk Management: Proactive risk assessment prevents regulatory, ethical, and technical failures. 🛠️ Technology and Data: Ensure adequate tech understanding and that data is used responsibly and ethically 👥 Talent: Development programs to bridge AI expertise gaps across all organizational levels. 📜 Compliance and Reporting: Maintain transparency in AI activities while adhering to relevant laws and ethical standards. 🤝 Stakeholder Engagement: Engage with employees, customers, and the public on AI-related issues to build trust. 𝗚𝘂𝗶𝗱𝗶𝗻𝗴 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: - How are AI initiatives aligned with business objectives? - Are accountability frameworks established for AI oversight? - How frequently are AI KPIs reviewed for business value? - What measures are in place for proactive risk assessment? - Is data handled responsibly and ethically in AI applications? - What programs address AI expertise gaps in your organization? - How is compliance and transparency ensured in AI reporting? - How do you engage stakeholders in AI discussions? 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: - 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗥𝗶𝘀𝗸: Navigate AI challenges confidently. - 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Achieve measurable business value. - 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗧𝗿𝘂𝘀𝘁: Build strong relationships with stakeholders. - 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻: Drive responsible AI experimentation. What do you think? ---- 🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX #AI #innovation #technology #automation

𝗔𝗜 & 𝗛𝘂𝗺𝗮𝗻 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆: 𝗦𝘁𝗿𝗶𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗖-𝗦𝘂𝗶𝘁𝗲 🤵♀️ C-suite leaders are under pressure to integrate AI for efficiency and innovation, but many are struggling to balance its implementation with preserving human creativity. According to a McKinsey report, while 70% of executives are prioritizing AI adoption, only 23% report meaningful returns due to a lack of alignment with creative human processes. 🤖 You’ve invested in AI, hoping for exponential growth, but instead, your team is overwhelmed and innovation is stalling. You’re not alone—83% of senior leaders worry that AI adoption can create cultural friction, as reported by Deloitte. Your challenge? How to enable AI to boost—not overshadow—the human creativity that made your company successful in the first place. Without balancing AI with human creativity, companies face probable obsolescence. Studies show businesses that fail to properly integrate AI lose competitive edge, reporting up to 19% slower innovation cycles. Additionally, Gallup indicates disengaged teams can cost companies up to 34% of their annual payroll. That’s an unsustainable loss for any forward-thinking organization. 🔥 The solution lies in a Human-Centered AI Strategy. AI should support your team, not replace their unique contributions. Start by identifying repetitive tasks AI can handle, allowing your team to focus on high-value creative work. Implement collaborative AI systems that feed data-driven insights into creative decision-making, rather than dictate it. 📈 Companies that effectively integrate AI as a creativity enabler see impressive results. According to the MIT Sloan Management Review, businesses that strategically combine AI with human intuition report 3.5x higher revenue growth and a 2x increase in employee satisfaction. Teams perform better when AI takes care of the routine, freeing them to innovate and think outside the box. With the right approach, AI enhances—not hinders—creativity. The return? A more engaged workforce, faster decision-making, and an uptick in innovation that drives market leadership. In fact, companies that align AI and human creativity see a 15% higher market share growth year-over-year, as shown by PwC. 👌 The data is clear: It’s time to shift your mindset. AI should work for your team, not against it. How will you ensure AI empowers your best talent to deliver extraordinary results? ***** Unlock transformative leadership insights and grow alongside other conscious leaders with The Wisdom Circle- Conscious Leaders. 🌟 Discover more here: https://lnkd.in/gKUhBKcr #personaldevelopment #motivation #mindfulness #inspiration #selfhelp #productivity

Mastering AI adoption is a superpower. But how do you manage this change effectively? Successful AI adoption is 20% about technology and 80% about people and processes. It’s NOT just about: • Buying the latest AI tools • Ignoring employee concerns • Rushing into implementation • Overlooking the need for training • Expecting instant results • Neglecting company culture • Forgetting to communicate It’s really about: • Clear and consistent communication • Addressing fears and misconceptions head-on • Involving employees in the AI adoption process • Providing comprehensive training • Starting with pilot projects • Identifying and empowering AI champions • Aligning AI with company culture and values Want to make AI work for your business? Developing a solid change management strategy is your ticket. → You will integrate AI smoothly. → You will boost employee engagement. → You will drive successful AI initiatives. Master AI adoption today. And lead your business into the future.

In the Spotlight: How does AI transform the earliest stage of innovation – ideation? That is the guiding question explored in a new article by Christian Pescher and Gerard Tellis, recently published in the Journal of Product Innovation Management (JPIM). Their paper, titled “The Role of Artificial Intelligence in the Ideation Process,” offers a comprehensive review of how AI reshapes the front end of innovation – from identifying opportunities to generating and evaluating ideas. 🔍 Key takeaways: 1. Firm culture will become an even more critical driver of radical (vs. incremental) innovation in the age of AI. As AI increasingly mediates this relationship, managers should actively foster a culture that supports innovation. 2. AI enhances the speed, efficiency, and cost-effectiveness of ideation. Managers should leverage AI tools to accelerate and scale the ideation process – and stay adaptive as the technology evolves rapidly. 3. AI improves the average creativity of generated ideas, but research is conflicting on whether it enhances the creativity of top ideas. Until conclusive evidence shows otherwise, managers should continue to invest in exceptional human talent. 4. AI performs well in idea screening but still falls short in idea selection. To avoid overlooking high-potential concepts, firms should combine AI-driven insights with human judgment. 🚀 Although research on AI in ideation is still in its early stages, a clear and fast-growing research agenda is taking shape – signaling a transformative shift ahead. cc: Minu Kumar, Gerda Gemser, Ruby Lee, Luigi M. De Luca