How AI differs from traditional software development

View profile for Hans Sandhu

Business Strategy & Revenue | Technology Operator & Investor

Most software is still built like it’s 2005 (great year btw). “Build 90% once, sell everywhere.” I’ll admit that playbook paid my bills for a long time. (It even helped me invest in venture capital while covering too many group bar tabs.) But in AI, that model keeps you stuck in endless POCs. From what I see, the teams that succeed treat the product as scaffolding to co-create with customers at scale. → Start with a core base = Faster adoption → Adapt it for each client’s specific use-case = Stickier retention → Align to the exact outcome they’re chasing = Competitors can’t copy it Fun part - it's typically worth 3-10x more in ARR :) Do you think this model scales, or does AI need something entirely different? Check out some examples of this being done precisely via TheAgentic Launchpad.

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Aalim A. Moor, II

- In Stealth Mode. AI that redefines the future — problems solved, solutions are here. More to come—stay tuned…

1mo

Love this topic! Hans Sandhu you’re spot on. This isn’t just faster adoption—TheAgentic rewrites the playbook. Smarter selling that fits your team, turning speed to value and staying sticky because you tailor to your processes. AI aligned to your outcomes builds relationships that evolve, not just transactions. “Rule-followers win on speed; leaders win on impact”. Competitors can copy tech, they can’t copy real-world alignment that fuels durable relationships. Imagine AI speaking your sales language—metrics, processes, people. That’s the future of selling, networking, and authentic relationships. Companies that follow this playbook are going to dominate their respective industries into the future…3-10x more ARR works for me! #AI #TheAgentic #FutureOfSales #SalesTech #Networking

Nalini Mohan

GTM Expert for Enterprise Software - AI, Data & Analytics, Security, Data Governance, AI & ML | Technical Product Marketer ex-IBM, ex-Tibco, ex-Redis

1mo

"But in AI, that model keeps you stuck in endless POCs." Hans Sandhu this is Truth well told! 🙏🏽

Anna Jacobi

Gathid AI Advisor | Head of Product & Strategy | AI & Infrastructure Specialist | Industry-First Execution From Moonshot to Market

1mo

AI should be microservices in a hoodie—modular, observable, with contracts, retries, and ownership. But let’s be honest: we didn’t stay in 2005… we went back there. Too many agentic stacks are just monoliths in disguise: one big orchestrator, hidden state, no kill-switches, no lineage when outputs drift. It’s like we skipped the entire microservices decade and forgot everything we learned about boundaries, schema evolution, tracing, and escalation paths. Now we’re rebranding function calls as “agents” and state machines as “autonomous workflows,” but with nondeterministic components and black-box data provenance. Which means the risks are nastier than before.

Dov Friedman

B2B SaaS Sales & GTM Leader | Agentic AI Evangelist | Featured in Forbes, CIO Review, Tech & Learning | Entrepreneur & Business Owner |

1w

Hans Sandhu your teams guidance has proven exactly this. Going straight to MVP with our agents will help us scale and sell faster and enable us to keep releasing new features and functionalities way faster then even imagined

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