How to build AI-native products for your business

I’m starting to think my new headline should be: “Helping engineering leaders turn AI adoption into results they can talk about on their next earnings call.” If you want a real headline on your next call about AI adoption or business value unlocked from GenAI tools, spending months building internal ways to measure it isn’t the path. Your mission hasn’t changed: build better software faster than your competition. What’s changed is the speed of that race. With AI accelerating everything from development to delivery, your engineering org needs to be laser-focused on experimentation, adoption, and acceleration. Every engineering hour spent on internal measurement tooling is one less hour focused on using AI to stay competitive, ship faster, and create customer stickiness. Our customers are establishing org-wide baselines of developer productivity and AI impact in weeks, and using that data to focus on enablement and acceleration. They’re moving fast enough to have results worth sharing on their next call. #dx

Market research isn’t being transformed by more tools it’s being transformed by smarter systems thinking in product development. Because let’s be real: Most research teams don’t have a “data problem.” They have a workflow translation problem: ➡️ Researchers think in nuance ➡️ Stakeholders think in outcomes ➡️ Product teams think in systems And somewhere in the middle… slides, spreadsheets, and sanity get lost. 😅 The shift happening now? Product development is finally acting as the universal adapter — turning messy human processes into scalable, predictable, insight-generating infrastructure. ⚙️✨ Here’s what that actually means in practice: 🔹 Automations built around how research really happens, not how we wish it happened 🔹 Data flows that reduce rework instead of creating it 🔹 Platforms that capture tacit knowledge (the stuff that lives in people’s heads and 47 Slack threads) 🔹 Tools that connect everything rather than adding another step to complain about The funny part? When research gets productized, chaos doesn’t disappear… it just finally gets documented, optimized, and given a roadmap. 🙌📈 The future isn’t “AI versus researchers.” It’s AI + product + researchers building insight engines that actually keep up with business speed. #MarketResearch #ProductDevelopment #InsightsTech #ResearchOps #Innovation #SystemsThinking #ProductMindset #DataInsights #ResearchReimagined #FutureOfWork #MRX

Every week, there’s a new “revolutionary” AI tool. A sleeker dashboard. A smarter workflow platform. Yet, somehow… teams still drown in chaos. The truth? Businesses rarely fail because of bad technology. They fail because they’re solving the wrong pain. I’ve seen it over and over again: → Demos that dazzle. → Interfaces that impress. → Integrations that promise everything. But when the spotlight fades, users quietly go back to spreadsheets. Why? Because innovation without empathy never scales. If your product doesn’t ease real daily frustration, no amount of AI magic can save it. The real winners aren’t the ones building “smarter software.” They’re the ones who make people whisper — “Finally… this actually helps.” At DSHGSonic that’s our obsession. We don’t chase innovation for its own sake. We chase the moments where tech stops being impressive… and starts being indispensable. 🚀 The future won’t belong to those who build the most advanced tools — but to those who fix the most human frustrations. #Innovation #AI #TechForGrowth #BusinessStrategy #Founders #Owner #DigitalTransformation #Leadership #TechInnovation #DSHGSonic #CEO

𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗯𝗹𝗼𝗰𝗸𝗲𝗱 𝗯𝘆 𝗔𝗜 𝘁𝗲𝗰𝗵. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗯𝗹𝗼𝗰𝗸𝗲𝗱 𝗯𝘆 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. When I ask companies why they haven’t deployed AI yet, I usually hear one of three things: • 𝘉𝘶𝘥𝘨𝘦𝘵 • 𝘛𝘢𝘭𝘦𝘯𝘵 • 𝘊𝘶𝘭𝘵𝘶𝘳𝘦 But here’s the pattern: The teams that ship AI this quarter start small and ship something real in 30 days. Not a moonshot. Not a 12-month transformation plan. A scoped use case tied to a measurable outcome. Example: One client wanted an AI agent to overhaul support. Big vision. Instead, we started with: 𝗔𝘂𝘁𝗼-𝗱𝗿𝗮𝗳𝘁 𝗿𝗲𝗽𝗹𝘆 𝘀𝘂𝗴𝗴𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗧𝗶𝗲𝗿 𝟭 𝘁𝗶𝗰𝗸𝗲𝘁𝘀. Took 3 weeks. Cut handling time 18 percent. After that, the budget and culture conversations magically got easier. So ask your team: What is the smallest AI workflow we can deploy this quarter that actually touches customers or internal operations? If you cannot answer that, the blocker is not budget. It is clarity. If you want a quick brainstorm of high-impact, low-lift use cases, comment 𝘜𝘚𝘌 𝘊𝘈𝘚𝘌𝘚, and I’ll share examples. 🚀 #AIEngineering #AIImplementation #ProductLeadership #DigitalOps #ShipToLearn

Every product leader wants the same outcome— faster releases, smarter engineering, and stronger market alignment. Generative AI is quietly becoming the strategic lever behind all three. It’s helping enterprises cut development cycles by up to 50%, enhance code quality, and bring new products to market with unprecedented speed and precision. Yet, the true transformation isn’t in automation—it’s in how leaders re-architect their development models around AI-enabled intelligence. That’s where the next wave of competitive advantage begins. At Predikly, we’re helping forward-thinking enterprises translate GenAI’s potential into tangible engineering agility and innovation at scale. Swipe through our latest carousel to see how GenAI is redefining product development—from concept to code. #GenerativeAI #ProductDevelopment #TechLeadership #InnovationStrategy #Predikly #AIFuture Sunil Bodke | Sachin Niravane | Sunil S. Ranka | Anant Varade | Martin Fenton

If the PoC works, it will TOTALLY scale… Companies are investing heavily in AI proofs of concept that never make it to enterprise scale. At Sphere AI Solutions, we help organizations move from promising prototypes to production-ready success. This is part 2 of our six-part series on what happens when things go wrong. A Proof of Concept is not a business-ready solution. Yet many teams act as if success in a controlled environment guarantees success across the organization. Why do things fail? PoCs are often built in ideal conditions: limited users, clean data, temporary integrations, and manual effort behind the scenes. Once the PoC “wins,” teams rush to scale without addressing the fundamentals of data, process, architecture, change management, or operating model. Then reality sets in. Costs increase, timelines slip, and stakeholder confidence fades. For instance, we’ve seen teams build their own RAG solutions that perform “fast enough” with a few million documents, only to find the answers no longer reference the right information at scale. It’s like building a one-foot-tall backyard rocket that reaches 1,000 feet, then making it hundreds of times larger and expecting it to reach orbit. Scaling often requires a fundamentally different approach, not just a bigger version of the PoC. How #OrgBrain makes it better so clients don’t fail OrgBrain is designed to bridge the gap between prototype and production. Our frameworks ensure data, architecture, and operations are ready for scale before deployment. We validate the PoC, then test scalability, cost, and change impact, so that when a solution goes live, it performs reliably at enterprise scale. That is how we help organizations move from PoC promise to production performance. #AIAdoption #EnterpriseAI #UsefulAI #AIAtScale #AgenticAI #AIAgents

This mess will stay here for a while... IT services industry is facing one of its toughest transformations in decades. Traditional product engineering demand is shrinking fast - clients no longer buy capacity, but interested in outcomes. Combined with economic and business uncertainties the decision cycle increases significantly or sometime drives business away completely from classic partners in a hope that internal teams powered by AI tools can cover the competency gap. For many software service providers, this shift feels uncomfortable to the extend that they start trying to find answers through mass competitors researches and experts interviewes. Legacy delivery models, rigid contracts, and siloed teams were built for a different era - one where speed and scale mattered more than measurable business impact. Projects measured by “lines of code” or “number of sprints” are being replaced with value-based engagements tied directly to revenue growth, cost efficiency, or AI-driven innovation. But every downturn brings renewal. The next cycle of growth will belong to those who can reimagine engineering as a business value engine - embedding AI into every layer of product design, development, and decision-making. The companies that embrace this value-based, AI-empowered model won’t just survive the shift - they’ll lead the next generation of digital transformation. #BusinessTransformation #ProductEngineering #AI #DigitalInnovation #ValueCreation

Systems don’t break from speed. They break from faulty logic. We saw it coming. We just didn’t stop quick enough. Did we miss it because it was hiding in plain sight? Atlas. Comet. Chrome. Blackwell. Grace Hopper. MI300. Devin. Cognition. AutoGPT. Every layer accelerating at once. Hardware. Software. Decision-making. In the time it took you to read this, three new AI tools launched. The pace right now feels almost unmanageable. Every week, something faster. Every week, another reason to rebuild what we just finished. The dashboards glow. The people fade. The temptation is to keep running. To stay in motion so it feels like progress. But fundamentals still matter. Awareness. Judgment. Integrity under pressure. What separates good operators from great ones isn’t adoption. It’s discernment. Knowing what to integrate. What to ignore. What to protect. Tools don’t solve problems. People do. The question isn’t how fast AI can move. It’s what still deserves our attention when everything moves this fast. Execution Beats Theory. Every Time. 👋 Hi, I’m Tim. I help engineering-led startups and mid-market scaleups build performant GTM systems, unify revenue, and scale with Agentic AI. Entry Point 1 #gtm #agenticai #marketdevelopment #gotimmarket #b2bexecution #gtmarchitecture #aiexecution

Reading Tim’s post really struck a chord with me. The way he frames the current pace of AI evolution feels almost poetic yet brutally real. The tension between velocity and discernment is something I think about often. What resonates most is the reminder that real leadership in this era is not about how quickly we adopt new tools but about how intentionally we integrate them. Awareness and judgment matter more than ever when the noise keeps getting louder. For me, this reinforces a principle I hold close: technology amplifies human capacity, but it cannot replace human clarity. In a world of constant acceleration, the real competitive edge lies in focus and integrity. #AI #Leadership #Technology #Innovation #Execution #DecisionMaking #DigitalTransformation #Strategy #FutureOfWork