Technology Strategy Consulting

What's driving global innovation? Four indicators drove the largest moves on the Global Innovation Index from 2020 to 2025: 1) Business R&D 2) Knowledge outputs 3) Government effectiveness 4) Venture capital The leaderboard shuffled from 2020-2025 because Asia and the Baltics produced more innovation outputs as Europe's mid-pack lost VC, R&D intensity, and patent activity while WIPO weighted those things more heavily. A few shifts to watch: 1) Korea's climb appears likely to continue, driven by HBM. Samsung and SK Hynix together account for roughly a quarter of Korean R&D, and both are raising 2026 CapEx into AI memory. Samsung is reportedly targeting a ~50% HBM capacity increase this year. 2) AI capex is becoming a core innovation indicator. Global hyperscaler CapEx is projected at ~$830B in 2026 across the top nine cloud providers, the vast majority spent by US or Chinese firms. 3) Germany's industrial base is further contracting. VW is cutting 35,000 jobs through 2030. BASF is targeting €2.3B in annual savings by end-2026 and shifting administrative and digital functions to India and Malaysia. Four in ten German industrial firms plan job cuts in 2026. 4) MENA is on a tear. The UAE rose to #30 in GII 2025, its highest ever. Saudi Arabia (#46) is among WIPO's fastest climbers since 2019. Both are buying exposure to the AI stack and the innovation points that come with it. In 2025, the Global Innovation Index added VC deal data as a third cluster-scoring metric. As AI compute, AI R&D, and VC concentration become more central to global innovation, the index and its leaders will likely follow.

Technology Convergence Reportmby World Economic Forum The accelerating combination of technologies such as artificial intelligence (AI), quantum computing and engineering biology is transforming industries and unlocking new economic and societal value. Yet many organizations struggle to identify where and how to invest. Written in collaboration with Capgemini, the Technology Convergence Report offers leaders a strategic lens – the 3C Framework – to help them navigate the combinatorial innovation era. This framework highlights three critical phases: combination (the integration of distinct technologies), convergence (restructuring of value chains) and compounding (network effects and ecosystem transformation). Drawing on a survey of 2,000 global executives and expert insights, the report maps 23 high-potential technology pairings across eight key domains. Kudos Aiman Ezzat, Jeremy Jurgens, Cathy Li and co. #Tech #Foresight #Futures #Insights

want to know the dirty little secret about trend forecasting? while everyone's obsessing over what's "next," the real innovators are already capitalizing on what's here. i've spent weeks analyzing reports from YouTube, Meta, Spotify, and others. here's what's actually changing (and what's just recycled thinking): 3 massive shifts happening RIGHT NOW: 1. emotional depth revolution ↳ gen Z isn't asking for personalization, they're demanding real connection ↳ example: patagonia turning product repairs into community narratives 2. AI moving from behind the scenes to center stage ↳ we're shifting from AI-powered to AI-partnered ↳ brands winning: look at snapchat's AI characters giving style advice 3. hybridized experiences taking over ↳ physical spaces becoming content studios ↳ digital/physical divide? it's already disappearing bottom line: 2025's "trends" are unfolding in today's consumer behavior. the most successful brands aren't waiting for tomorrow - they're acting on the patterns hiding in plain sight. question is: what signal are you seeing today that you can act on while others are still planning for tomorrow? #FutureOfBusiness #Innovation #DigitalTransformation #MarketingStrategy #Leadership

Too many AI strategies are being built around the technology instead of the business challenges they should solve. The real value of AI comes when it is directly tied to your goals. I have arrived at seven lessons on how to align your AI strategy directly with your business goals: 1. Start with the "why," not the "what." Before discussing models or tools, ask what business problem you need to solve. It could be speeding up product development, or cutting operational costs. Let that answer be your guide. 2. Think in terms of business outcomes. Measure AI success by its impact on metrics like revenue growth or employee productivity not by technical accuracy. 3. Build a cross-functional team. AI can't live solely in the IT department. Include leaders from all relevant departments from day one to ensure the strategy serves the entire business. 4. Prioritize quick wins to build momentum. Identify a few small, high-impact projects that can deliver results quickly. This builds organizational confidence and makes people ready to take on larger initiatives. 5. Invest in data foundations. The best AI strategy will fail without clean and well-governed data. A disciplined approach to data quality is non-negotiable. 6. Focus on change management. Technology is the easy part. Prepare your people for new workflows and equip them with the skills to work alongside AI effectively. 7. Create a feedback loop. An AI strategy is not a one-time plan. Continuously gather feedback from users and analyze performance data to adapt and refine your approach. The goal is to make AI a part of how you achieve your objectives, not a separate project. #AIStrategy #BusinessGoals #DigitalTransformation #Leadership #ArtificialIntelligence

𝗞𝗲𝘆𝗻𝗼𝘁𝗲 𝗦𝗽𝗲𝗮𝗸𝗲𝗿 𝗳𝗼𝗿 𝗜𝗕𝗠 𝗘𝗠𝗘𝗔 𝗶𝗻 𝗠𝗮𝗱𝗿𝗶𝗱: 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗻𝗴 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗳𝗼𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 🤍 [Anzeige] Hola from IBM in Madrid! Yesterday I’m was speaking to an exclusive C-suite audience about why technologies like Quantum & AI must be translated – not just developed. According to the latest research from the IBM Institute for Business Value, quantum advantage could emerge as early as 2026. 𝗧𝗵𝗮𝘁’𝘀 𝗡𝗢𝗪. Many leaders think: “Quantum computing? I have more urgent problems.” And I get it. We are still: • Building resilient AI infrastructures • Securing data architectures • Debating AI sovereignty • Training organizations to use AI responsibly But here is the key question: 𝗛𝗼𝘄? Through 𝗛𝘆𝗯𝗿𝗶𝗱 𝗖𝗹𝗼𝘂𝗱 𝗯𝘆 𝗱𝗲𝘀𝗶𝗴𝗻 – giving leaders the flexibility to run AI anywhere (on-prem, cloud or edge). The infrastructure decisions made today are what make tomorrow’s quantum advantage possible. As technology becomes more powerful, governance becomes non-negotiable. & we are also witnessing a shift: From “AI that chats” to “Agentic AI that works”. From experimentation to trusted, agentic workflows embedded into real business processes. That future is not abstract anymore. It is a 2024–2025 business objective. And now Quantum too? 𝗬𝗲𝘀. Because in five years, you’ll be grateful you started today. Look closer and you’ll realize: 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗶𝘀 𝘀𝘆𝘀𝘁𝗲𝗺-𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁. From the IBM study, three realities stand out: 𝗜. 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗶𝘀 𝗮𝗻 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗴𝗮𝗺𝗲 → Quantum-ready organizations are 𝟯𝘅 𝗺𝗼𝗿𝗲 𝗹𝗶𝗸𝗲𝗹𝘆 to belong to multiple ecosystems → 𝟳𝟵% say ecosystem partners accelerate adoption → 𝟳𝟳% say ecosystem data improves outcomes No company will win quantum alone. 𝗜𝗜. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲𝘀 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 → 𝟳𝟱% see semiconductor dependence as a strategic risk → 𝟵𝟯% say technology sovereignty must be factored into 2026 strategy Quantum compute is even scarcer, more complex and geopolitically sensitive. Access = advantage. 𝗜𝗜𝗜. 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 Preparing does not mean building your own quantum computer tomorrow. It means: • Identifying high-impact use cases • Evaluating post-quantum cryptography • Building internal literacy • Securing the right partnerships — including a Hybrid Cloud architecture able to handle future data complexity • Experimenting before advantage becomes visible In this is why translation matters. And it is not only nice storytelling… It is 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗜𝗖 𝗘𝗡𝗔𝗕𝗟𝗘𝗠𝗘𝗡𝗧. Grateful to collaborate with IBM to make quantum computing not only more powerful but actionable. Thank you Patrick Bauer!! 🤍🦾 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗯𝘂𝗶𝗹𝘁 𝗯𝘆 𝘁𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗶𝗻𝘃𝗲𝗻𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴. 𝗜𝘁’𝘀 𝗯𝘂𝗶𝗹𝘁 𝗯𝘆 𝘁𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱. Now to you: Is Quantum on your 2026 agenda? IBM Partner Plus

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁𝗵) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

A #quantum journey doesn't always begin with a quantum computer.   I recently sat down with Jonathan Reichental, PhD at Forbes together with Julian van Velzen to discuss how organizations are really approaching quantum today. What we're seeing is that they tend to start in three places.   First, enterprises typically want to know where the technology genuinely stands beyond the hype or the dismissive claims (take your pick). And quantum isn't one thing. It's a wide range of technologies maturing at very different speeds. Some are ready. Many aren't. That's a nuance that many click-bait publications often gloss over, one way or the other.    Second, Post Quantum Cryptography (PQC), or the development of new encryption algorithms specifically tailored to resist quantum-empowered cyber attacks. The irony is that PQC isn't a quantum solution at all: it's how you protect today's encryption from tomorrow's quantum machines. But it's become a real entry point into quantum for many organizations. Assessing the risk builds the knowledge and urgency a broader quantum strategy will need. Banking, insurance and the public sector are already moving, because data harvested now can be decrypted later.   Third, quantum simulation or "quantum for science" as I call it. Modeling molecules and materials that are themselves quantum by nature. Paired with #AI, this is where the tangible promise sits: new or better materials, less or no corrosion, faster drug or protein discovery, etc…   So to sum up, our core belief is that quantum isn't a single technology arriving on a single date, and waiting out the uncertainty is the real mistake. The risk isn't moving too early: it's the long, costly catch-up that hits the unprepared tech leader.    Or said in another way: The quantum winners won't be the fastest. They'll be the ones who prepared earliest.   Full interview below, worth a read 👇 https://lnkd.in/eygXSY6x

Emerging Technologies of 2025: #Innovation and Societal Impact . The World Economic Forum report spotlights breakthrough innovations poised to transition from scientific discovery to real-world application, aiming to catalyze dialogue and shape technology agendas. It details ten specific technologies, ranging from structural battery composites and osmotic power systems to #AI watermarking and engineered living therapeutics, explaining their novelty, development progress, and transformative potential across various sectors like #energy , #healthcare , and urban systems. •Key Themes of Emerging Technologies: The 2025 technologies reveal exciting patterns, often representing a convergence of fields: ◦Combining Energy Systems with Advanced Materials: This includes innovations like structural battery composites, which integrate energy storage within load-bearing structures, improving functionality and efficiency in transport. ◦Using Biological Approaches to Improve Human Health: Examples are engineered living therapeutics (genetically engineered microbes producing medicines in the body) and GLP-1s for neurodegenerative disease (repurposing drugs for Alzheimer's and Parkinson's). ◦Reimagining Industrial Processes for Sustainability: This involves technologies such as green nitrogen fixation for low-carbon ammonia production and nanozymes (laboratory-produced nanomaterials with enzyme-like catalytic properties). ◦Creating New Foundations for Trust in Connected Systems: This includes collaborative sensing (distributed sensors connected to AI systems for context-aware decisions) and generative watermarking (invisible markers in AI-generated content to verify authenticity) Each technology's overview includes its strategic outlook, ecosystem readiness, and the challenges to its widespread adoption, emphasizing their capacity to address complex global challenges and foster resilient, sustainable societies.

The Biggest Risk to Your Next ERP Programme isn't Technology. It's Ownership. For SAP Partner CEOs, Managing Directors, and Heads of Transformation, one of the most expensive programme risks rarely appears in the project plan. It becomes visible only once delivery begins. Decisions slow. Workshops are repeated. Requirements continue to evolve. Delivery teams spend more time resolving uncertainty than delivering value. While these may seem like delivery issues, they quickly become commercial ones. Every delayed decision affects utilisation. Every unresolved issue puts pressure on margin. Every programme that loses momentum reduces the likelihood of follow-on work, executive advocacy, and strategic extensions. During my recent conversation with Alan Cozens on Navigating Enterprise Transformation, one point stood out. Successful ERP programmes are increasingly business-led rather than technology-led. But that only happens when business ownership is established from the very beginning. Alan highlighted the importance of clear Product Ownership, accountability, and decision-making authority throughout the programme. Without it, experienced consultants spend more time facilitating decisions than creating outcomes. Governance expands, delivery effort increases, and profitability begins to erode, even if the programme eventually reaches go-live. For leadership teams, the impact extends well beyond a single project. If customers don't experience confident, business-led transformation, they're far less likely to return with their next strategic initiative. Executive confidence weakens, reference value declines, and future opportunities become harder to secure. That raises an important question. Before taking responsibility for delivery success, are you ensuring the customer is prepared to own the transformation? The strongest SAP Partners understand that successful delivery doesn't begin with configuration or testing. It begins by helping customers establish the ownership, accountability, and leadership needed for transformation to succeed. When ownership is clear, delivery becomes more efficient, relationships become stronger, and successful programmes are far more likely to generate repeat business and long-term growth. At JPS Resourcing Limited , we support organisations when commercial momentum begins to slow. By strengthening leadership capability, market positioning, and commercial confidence, we help ensure that delivery capability translates into sustainable business growth. My thanks to Alan Cozens for sharing this perspective on Navigating Enterprise Transformation. How are you ensuring business ownership is established before delivery begins, rather than trying to recover it once the programme is underway?

I used to think strategic sourcing was just "get three quotes and pick the cheapest." Then, I realized there are dozens of sourcing approaches! ...And picking the wrong one wastes time and money. Here are 7 Sourcing approaches with their strengths and weaknesses: → 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗳𝗼𝗿 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 (𝗥𝗙𝗜) ℹ️ Market research before formal sourcing ✅ Best for: New categories, emerging tech, supplier discovery ❌ Skip if: You already know the market well 💡 Examples: AI tools, sustainability solutions, new software → 𝗙𝗶𝘅𝗲𝗱 𝗦𝗰𝗼𝗽𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗕𝗶𝗱𝗱𝗶𝗻𝗴 (𝗥𝗙𝗤) ℹ️ Price competition with clear specifications ✅ Best for: Standardized goods, commodities, repeat purchases ❌ Skip if: Quality differentiation matters 💡 Examples: Office supplies, simple raw materials, packaging → 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗔𝘂𝗰𝘁𝗶𝗼𝗻 ℹ️ Real-time online price competition ✅ Best for: High-volume commodities with 5+ qualified suppliers ❌ Skip if: Relationship or innovation is critical 💡 Examples: Freight services, MRO items, bulk materials → 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗦𝗰𝗼𝗽𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗕𝗶𝗱𝗱𝗶𝗻𝗴 (𝗥𝗙𝗣) ℹ️ Evaluates technical approach AND pricing ✅ Best for: Professional services, complex projects, implementations ❌ Skip if: Requirements are simple and standardized 💡 Examples: ERP systems, consulting projects, construction → 𝗦𝗶𝗻𝗴𝗹𝗲-𝗦𝗼𝘂𝗿𝗰𝗲 𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 ℹ️ Direct negotiation with one supplier ✅ Best for: Patented tech, specialized expertise, proven partnerships ❌ Skip if: You haven't validated it's truly single-source 💡 Examples: Proprietary software, niche equipment, critical IP → 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗔𝗴𝗿𝗲𝗲𝗺𝗲𝗻𝘁𝘀 ℹ️ Pre-negotiated terms with approved suppliers ✅ Best for: Recurring but unpredictable demand, multiple stakeholders ❌ Skip if: Prices are volatile or volumes are guaranteed 💡 Examples: Temp labor, professional services, maintenance → 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗼𝘂𝗿𝗰𝗶𝗻𝗴 ℹ️ Joint development with shared investment and risk ✅ Best for: Innovation goals, sustainability targets, competitive advantage ❌ Skip if: You need arms-length vendor management 💡 Examples: Product co-design, circular economy, R&D partnerships The biggest mistake? Defaulting to what you know just because it's familiar. Match your sourcing strategy to category complexity, market maturity, and strategic importance to maximize impact! What sourcing approach is missing from my list? Let me know in the comments 👇 _________________________ 𝗣.𝗦. If you liked this, consider subscribing to my weekly digital procurement newsletter. Every Sunday, I give 10,000+ procurement leaders the tips and tricks they need to build digital procurement capabilities to automate the sourcing approaches above! Subscribe here for free: https://lnkd.in/dS_Km7He