Knorket.AI

How would a platform-driven model look like as it relates to future of service (solutions) delivery? Below are some characteristics and simplified view of how it 'might' look like... Its all about building an ecosystem with following: 1. Unified platform through which clients interact with providers ecosystem and consume "solutions" 2. Combination of Self-Service Apps and Services consumed increasingly in an "as-a-service" model 3. Provider involvement more "fluid" rather than "start' and "stop" "project" model of delivery. Reduced contract friction enabling faster / seamless involvement (into the Execution value-chains of clients a.k.a "plug" and "play" ) 4. Specialized software that is uniquely designed to work within the platform-model and help delivery of work using AI to automate a non-trivial % of hitherto people-driven work (e.g. a CX software for 'doing' CX work with significant automation capability but needing human in the loop) 5. Hybrid Talent (i.e. Combining employees + Flexible Freelance Talent) 6. "Mesh" together seamlessly partnership and their capabilities where needed to deliver world-class solutions 7. Speed of execution and value-delivery focused (i.e outcomes) 8. Ability to scale non-linear What are we missing? Do you agree? #workOS #executionOS #servicedelivery #digital

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I promise this is the last time this gets wheeled out, but 5 years on, the story is so real now. The only difference is I'd use now is 'integrated data platforms' https://lnkd.in/eAM3rTwE Saurabh Gupta Elena Christopher Thomas Reuner Francis Carden David Cushman #rpa #ai #dataplatforms

It was never just about the data—it’s what you do with it. 🎞️ Netflix entertains you… and it uses data to do that. 📊 Salesforce transforms your customer relations… and it uses data to do that. 🚗 Uber gets you where you need to go… and it uses data to do that. 🌐 Duolingo connects you to new cultures… and it uses data to do that. 🛍️ Amazon makes your doorstep a global marketplace… and it uses data to do that. 🍏 Apple makes creativity more intuitive… and it uses data to do that. A data strategy is critical for leverage, especially in the AI age, but it’s not the whole story. Move past data hoarding. Focus on the intention, the abstraction, and the value. Repost if this resonates.

🚀 Part 1: Building a #SystemofIntelligence for Decision Making in Post #GenAI word: The Data Layer and Single Pane of Glass This is continuation of y'day's post on emerging need for systems of intelligence. https://lnkd.in/gE6dTbxC In the vast universe of digital transformation, the creation of an effective system of intelligence(SoI) starts with its core - the Unified data layer (Note: Understanding decision needs informs the data that is required). This foundation is crucial for enabling seamless decision-making, drawing from a complex but unified web of multi-modal, multi-source data and multi-cloud and location data. 🌟 A Glimpse into the Future: The Star Trek Analogy Let's draw a parallel from Star Trek (in video attached), where an officer, facing a daunting challenge, consults the ship's computer (SoI). To drive insights / diagnose the issue, a core enabler for the SoI was to have centralized access to diverse data —from logs, visuals to personnel records etc.. This analogy beautifully encapsulates the essence of the data layer's role in enabling informed decisions and diagnosis. 🛠️ The Technical Scaffold: Crafting a Unified Data Layer To construct a data layer capable of acting as the cornerstone for a system of intelligence, its architecture must be meticulously designed to ensure it effectively consolidates and harmonizes data from diverse sources. Some principles / reqs of this layer are: a. Seamless Integration 🔄: Merge data from varied sources effortlessly, fostering a cohesive and unified data environment. b. Uniform Access 🔑: Offer a consistent, standardized interface for data access, regardless of the data's origin, ensuring a uniform query experience. c. Data Consolidation 🗂️: Centralize data into one repository, streamlining the path from data to insights. d. Modularity 🧩: Craft your data layer with flexible, interchangeable components, making it simple to update or modify data sources as necessary. e. Adaptability 🌱: Ensure your system remains agile, capable of embracing new data formats and sources with minimal adjustments. f. Comprehensive Connectivity 🌍: Utilize a broad spectrum of connectors and APIs, bridging everything from cloud services to on-prem databases for truly extensive coverage. g. Real-Time Data Access & Data Virtualization ⚡🔁: Harness streaming and data virtualization techniques for instant data utilization, eliminating delays and replication needs where use case allows h. Dynamic Data Pipelines 🚀: Build sophisticated data pipelines where applicable that automate and refine the data journey from source to destination, keeping your data layer fresh and relevant. By embracing these principles (where applicable based on use cases), the data layer becomes a powerful conduit for data, providing a holistic view across the organization's data landscape and enabling decision-makers to derive insights from a unified data hub. More details here in this article: https://lnkd.in/gzy62jzD

Great post of what a modern data product platform is about