Kalle Heinonen

The product experience (#demo and #demoautomation and interactive demo) news you've been waiting for! Publishing for #Gartner clients tomorrow, this report makes it crystal clear that if you want to market and sell AI #agent and #agentic products, you have to show them to users and buyers. While the need to demo obviously is not news, knowing that multiple product experiences (ranging from POVs, which were most important, to free offers to sandboxes and interactive demos) comprise >50% of the most valuable ways for buyers to engage and evaluate products can help you shorten sales cycles, aid discovery, and understand and inform buying teams. Agents/agentic are poorly understood by most buyers, and as a result, their application and potential outcomes may be mysterious. This is why POVs (proofs of value) are so important, but defraying the mystery and showing the value are also key early on. #presales #b2bmarketing #b2bsales #productmarketing

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Excited about our new product announcement - MCP Server for Stack Overflow for Teams - based on popular demand from our Stack Overflow enterprise customers! AI assistants and agents often fall short in bringing your team the answers they need due to lack of grounding in up-to-date and trustworthy context. With the MCP Server for Stack Overflow for Teams—now available as a free trial for a limited time - you can access trusted knowledge in real time, get grounded responses inside workflows, and contribute feedback to your knowledge base in just a few clicks. Current Stack Overflow for Teams customers on the Enterprise plan can enable the MCP Server today. Learn how: https://lnkd.in/gi47zQhe

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AI Strategy Roadmap: Key Learnings for Scalable Success Microsoft’s research with 100+ IT and business leaders reveals that success with AI isn’t just about technology—it’s about readiness across five key drivers: 1. Business Strategy: AI must serve clear business objectives, not just tech experimentation. Leadership buy-in is essential to align teams, secure resources, and focus efforts on real, measurable business problems, not vague ambitions. 2. Technology & Data: AI’s impact depends on data quality and robust infrastructure. Treat data as a strategic asset. Continuously refine, label, and integrate data to keep models accurate and reliable. Decide strategically when to buy vs. build AI solutions, balancing speed and customisation. 3. AI Experience & Strategy: Start small—run targeted pilots, learn iteratively, and expand with data-driven improvements. Prioritize initiatives that deliver tangible value and track adoption with clear KPIs. 4. Organization & Culture: Build cross-functional teams. Foster a culture of learning and invest in training. Progressive rollouts and hands-on exposure help overcome resistance and drive broader adoption. 5. AI Governance: Embed AI usage within strong compliance, security, and ethical guidelines. Involve legal and compliance teams early, establish practical guardrails, and refine policies as your AI use evolves. Bottom line: A successful AI journey requires integrated strategy, quality data, empowered people, and ongoing governance—transforming AI from a tech trend into a business value engine. #AIStrategy #AIDriven #EnterpriseAI #DigitalTransformation #MicrosoftAI #AIGovernance #DataStrategy #AIFuture #AILeadership #AIEthics #Innovation #TechForBusiness #ResponsibleAI #AIAdoption

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Your company’s best condition for entering new markets, dramatically improving customer value propositions, or achieving better margins: a state of uncertainty. Many business leaders dislike uncertainty and radical change, preferring to play it safe with an incremental approach to progress. But no one has ever made great strides by embracing the status quo or being overly conservative. If you want to help your company leapfrog its competitors by creating new value for customers, look at the uncertainties around you. Look at the macroeconomic environment, the effect of technology changes such as the advent of Artificial Intelligence, or the changing realities of your company’s market…and be glad. These are the exact conditions you need to establish the next phase of a generational company. Uncertainty might look like a problem when you first encounter it - but look a little closer. It’s actually your greatest advantage.

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The AI landscape is moving fast, but one of the biggest shifts is happening in how we design intelligent agents. Whether you’re building predictive AI, orchestrating multiple agents, or experimenting with hybrid approaches — each paradigm has unique strengths and trade-offs. Predictive AI (Single-Agent Systems) -Works in a linear loop: Goal → Perception → Knowledge → Decision → Action. -Uses rule-based reasoning or model-driven inference to decide outcomes. Strength: Great for well-defined tasks where context is stable. Limitation: Struggles when the environment changes dynamically or requires adaptability. Multi-Agent Systems (MAS) -Starts with agent initialization and environment awareness. -Agents get roles — some coordinate like leaders, others collaborate as peers. Key processes: Knowledge sharing → Distributed planning → Task negotiation → Parallel execution → Collective evaluation. Strength: Enables scalability, resilience, and decentralized intelligence. Limitation: Coordination overhead can increase as the number of agents grows. Hybrid Agents -Combine reactive and deliberative layers. -Reactive: Handles immediate responses and low-level control. -Deliberative: Focuses on high-level planning and goal reasoning. -Adds coordination logic, adaptive execution, and learning feedback. Strength: Best of both worlds — fast reactions + thoughtful reasoning. Limitation: Designing the right balance between layers is complex. Why this matters: Single-agent systems gave us predictive AI. Multi-agent systems enable distributed intelligence. Hybrid agents promise adaptive, human-like cognition. The future of AI may not be about choosing one over the other — but about layering these paradigms to build truly intelligent ecosystems. What do you think — will hybrid agent architectures dominate, or will specialized MAS frameworks continue to scale better?

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When most people hear automation, they think of expensive robots, complex integrations, and big enterprise budgets. It feels out of reach for smaller businesses. But it doesn’t have to be. Research from MIT Sloan School of Management shows that SMEs can approach automation differently, and affordably, without losing sight of competitiveness. One way is to start with peripheral processes. These aren’t the core operations that need high reliability, but the supporting activities that often drain time and attention. Think QR codes to monitor container unloading, sensors to track equipment health, or simple smartphone apps to give real-time visibility. Small steps like these can improve efficiency by 10–15% with minimal cost. Another way is to use stand-alone solutions. These don’t require deep IT integration, which makes them easier to deploy and scale. AI chatbots, IoT sensors, or plug-and-play analytics tools can be rolled out gradually, growing with the business rather than demanding heavy upfront investment. The lesson is simple: automation costs spiral when you chase customisation, tight integration, or unnecessary reliability. Costs come down when you design for compatibility, modularity, and just-enough functionality. For SMEs, the challenge isn’t whether automation is possible, but it’s learning where to start, and being smart about how far to go. Start small, scale at your own pace, and you’ll find automation doesn’t have to break the bank. #Automation #DigitalTransformation #SMEs #Innovation

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