We’ve spent the last decade-plus romanticizing automation and AI, and now the hangover’s (finally) kicking in. Most of us are avoiding CoPilot like the new Clippy, while GenAI and Agentic get tossed into PowerPoint decks like fairy dust. And meanwhile, our businesses still run on Excel macros, spaghetti code, and tribal knowledge.
Bad data is killing businesses
The truth? Enterprises that win are the ones who treat data as their strategy, not just some exhaust from the grind of their broken workflows and systems that no one trusts to base decisions on. A major study we conducted across global 2000 organizations reveals just how poor our data is right across the enterprise:
The HFS OneOffice model initially laid the groundwork in 2016 to define how businesses need to operate. We just didn’t realize back then it would take another decade via a pandemic, rampant inflation, several geopolitical conflicts, and daily economic uncertainty to create the burning platform where enterprises have to be autonomous, less border-sensitive and much more cost-conscious, with capable talent and partners to help them function in these emerging business ecosystems and across convoluted supply chains.
OneOffice inspired a new enterprise mindset in 2016
“HFS in 2016: The onset of digital and emerging automation solutions, coupled with the dire need to access meaningful data in real-time, is forcing the back and middle offices to support the customer experience needs of the front. Consequently, we’re evolving to an era where there is only “OneOffice” that matters anymore, creating the digital customer experience and an intelligent, single office to enable and support it
Today’s college graduates are simply not coming out of school willing to perform mundane routine work: operations staff proactively need to support the fast-shifting needs of the front office. So the focus needs to shift towards creating a work culture where individuals are encouraged to spend more time interpreting data, understanding the needs of the front end of the business, and ensuring the support functions keep pace with the front office.”
Fast-forward to today, and we finally have an injection of rocket fuel from Generative and Agentic AI to create intelligent agents that can sense, decide, and act with autonomy… as long as we have our data strategy right. To be a truly autonomous organization, the principles of OneOffice hold truer than ever: workflows executed in real-time between customers and employees that engage partners right across our business ecosystems.
The OneOffice Data Cycle in 2025 places data at the core of enterprise effectiveness
OneOffice is HFS Research’s enterprise model that breaks down traditional silos between front, middle, and back offices to create a single, digital, customer-centric enterprise. It’s where automation, AI, and data converge to create real-time workflows between customers, employees, and partners—without handoffs or latency. A OneOffice model creates one intelligent enterprise that operates in real-time, adapts to change, and delivers seamless customer experiences. Data is the lifeblood of OneOffice: it fuels decision-making, powers AI models, and ensures every function is aligned with customer and business outcomes.
Let’s walk through this OneOffice Data Cycle, powered by the actual technologies and companies that are making it real.
We need to understand how the data cycle works to get us ahead of our markets. Here are five steps we must take with examples from enterprise clients, service providers and technology firms:
1. Get data to win in your market.
This is where you must align your data needs to deliver on business strategy. You can’t manage what you can’t measure. This is about aligning data capture with business priorities: customer behavior, operational bottlenecks, talent churn, and cash velocity.
Examples where AI is being effectively applied:
- Unilever uses Microsoft Azure OpenAI to analyze internal and external datasets, including consumer sentiment, to adjust brand strategies in near real-time.
- Anthropic’s Claude is being embedded by data-rich enterprises to run secure, real-time analysis agents that summarize, flag anomalies, and interact with humans when decision thresholds are crossed.
- Stitch Fix leverages OpenAI GPT to extract signals from customer feedback and style preferences, improving product recommendations and inventory decisions.
- Siemens uses Braincube’s agentic AI platform to monitor industrial IoT data, detect deviations in production, and adjust machine behavior—without waiting for human commands.
- Infosys Topaz integrates GenAI with real-time analytics to help clients personalize digital banking experiences based on behavioral data.
- LTIMindtree applies autonomous agents to enable real-time tracking and predictive inventory management for retail clients, eliminating stock-outs.
- Publicis Sapient uses GenAI to analyze large-scale customer journey data, surfacing real-time behavior patterns to inform digital product strategy.
- Movate integrates agentic AI into CX analytics platforms to proactively detect churn signals and initiate retention playbooks autonomously.
- Firstsource integrates GenAI-powered document understanding to extract structured data from scanned medical records and billing forms—feeding analytics for healthcare payer and provider decision-making.
Net-net: If your data signals aren’t tied directly to how you win revenue, you’re running blind.
2. Re-think processes to get the right data
If your processes don’t capture useful data, they’re not processes—they’re liabilities. You must re-think what should be added, eliminated, or simplified across your workflows to source this critical data. Do your processes get you the data you need from your customers, employers, and partners?
Examples where AI is being effectively applied:
- Chubb Insurance applies Amazon Bedrock to re-engineer underwriting and claims documentation workflows—turning dense PDFs and call transcripts into structured, searchable insights.
- Capgemini has embedded Google Gemini into supply chain operations to automate root cause analysis and recommend corrective actions across vendor networks.
- Publicis Sapient reimagines customer experience and marketing processes using custom-built GenAI copilots that generate real-time campaign responses based on audience behavior.
- UnitedHealth Group is developing a concierge bot developed with both GenAI and agentic technologies to support medical patients dealing with the complexities (and huge inefficiencies) of the US healthcare system.
- UiPath Autopilot now combines GenAI with autonomous agents to observe process execution and propose optimizations—cutting down rework loops by over 30% in pilot enterprise clients.
- Wipro’s ai360 platform deploys agents to continuously optimize procurement and finance workflows by identifying anomalies and recommending improvements.
- NVIDIA partners with major automotive and manufacturing firms to deploy AI agents on its DGX Cloud infrastructure—allowing real-time model training, simulation, and inference at the edge.
- Cognizant’s Neuro agent-based modules continuously scan workflow telemetry to self-heal and recommend business rule updates.
- Coforge is leveraging agent-based automation to dynamically adjust airline booking flows based on real-time seat availability and fare rule compliance.
Net-net: If the process doesn’t give you valuable data, kill it or fix it. Preferably both.
3. Design workflows to scale in the cloud.
Moving legacy sludge to the cloud without rethinking the model just means faster dysfunction. We’ve seen billions of dollars wasted on botched cloud migrations in recent years because underlying data infrastructures were not addressed effectively, and bad processes became even less effective or completely dysfunctional.
Examples where AI is being effectively applied:
- Ford Motor Company deploys agentic AI agents built on OpenAI, Anthropic, and Nvidia GPUs to convert 2D sketches into 3D models and run rapid stress analyses—shortening simulation from 15 hours to 10 seconds
- ServiceNow’s GenAI Workflow Studio lets ops teams redesign service flows using natural language—automating ticket routing, approvals, and data handoffs in cloud-native apps.
- Workday AI uses GenAI to automate job description creation, compensation modeling, and hiring workflows—all in cloud-native HR suites.
- Tech Mahindra uses Google Cloud’s Gemini to enable telecom clients to deploy conversational agents across cloud-native CRM and support systems.
- FedEx is using DataRobot agents to orchestrate cloud services across route planning, weather modeling, and fleet management—autonomously shifting strategies on the fly.
- Accenture SynOps embeds agentic capabilities to autonomously orchestrate and optimize cloud-based workflows in finance and customer operations.
- Anthropic Claude is embedded in some cloud-first healthcare platforms to provide HIPAA-safe, autonomous intake triage for patient engagement.
- Publicis Sapient enables retail clients to run full-funnel autonomous campaign workflows across cloud stacks, where agentic bots optimize audience targeting and message testing.
- Uber built a near-real-time data infrastructure processing PBs of user and driver data—supporting instantaneous pricing, fraud detection, and dispatch decisions across its platform
- Birlasoft has deployed cloud-native agent frameworks that autonomously monitor SAP-based operations for global manufacturers—streamlining issue detection and resolution across procurement and logistics.
- Genpact leverages cloud-based GenAI models to power finance-as-a-service workflows, dynamically updating reporting logic and dashboard content based on real-time operational data.
Net-net: If the cloud is your warehouse, AI is your forklift driver. But you still need to design the blueprint.
4. Automate processes and data.
Automation is not your strategy. It is the necessary discipline to ensure your processes provide the data – at speed – to achieve your business outcomes. This is where we separate real automation from Franken-bots duct-taped to bad data.
Examples where AI is being effectively applied:
- Morgan Stanley has deployed OpenAI-powered advisors internally via GPT-4, enabling wealth managers to get instant access to decades of investment research—automated, summarized, and contextualized in seconds.
- SAP Joule helps automate business insights from ERP data across finance and supply chain, using GenAI to generate insights that would’ve taken analysts days.
- Cognizant’s Neuro Agent Framework is being used by Fortune 500 firms to automate backend processes across billing, collections, and claims management—learning and adapting with each data cycle.
- Sonata Software integrates GenAI into its Harmoni.AI platform to automate customer service and backend documentation in manufacturing.
- TCS Digitate’s Ingio platform uses agentic AI to resolve IT incidents automatically by sensing anomalies, identifying root causes, and executing fixes autonomously.
- Capgemini’s Perform AI framework deploys agents to dynamically rebalance workloads and scale automation across insurance and telecom.
- Microsoft uses AI builders in Power Automate at Eletrobras Furnas to detect electric grid anomalies and trigger alerts—minimizing regulatory risk.
- WNS deploys an agentic-based solution called Travel Buddy to supports it clients’ needs for corporate travel bookings without the need for constant human oversight.
Net-net: Automation that doesn’t learn is just glorified scripting. Intelligent automation enables intelligent outcomes.
5. Apply AI to data flows to anticipate at speed.
This is where the magic happens—predictive, adaptive AI running on clean, automated, cloud-native data. Welcome to the self-optimizing enterprise. AI is how we engage with our data to refine ourselves as digital organizations, where we only want a single office to operate with agility to do things faster, cheaper, and more streamlined than we ever thought possible. AI helps us predict and anticipate how to beat our competitors and delight our customers, reaching both outside and inside of our organizations to pull the data we need to make critical decisions at speed. In short, automation and AI go hand in hand… AI is what enables a well-automated set of processes to function autonomously with little need for constant human oversight and intervention.
Examples where AI is being effectively applied:
- Pfizer uses IBM Watsonx to accelerate clinical trial discovery by analyzing academic papers, medical literature, and trial results, surfacing new therapeutic targets in record time.
- JPMorgan Chase is applying GenAI to risk models—feeding real-time data from markets, news, and internal portfolios to simulate potential exposures and make split-second adjustments.
- Accenture leverages agentic AI for proactive customer churn prevention—automatically adjusting engagement models based on behavioral and transaction patterns.
- Telefonica has deployed Aisera’s agentic AI to manage internal IT and employee service requests—handling over 80% of Tier 1 tickets autonomously.
- Shell uses agent-based digital twins to simulate field operations and adapt to variables like pricing, weather, and inventory—helping them model and tweak operations in real time.
- Inflection AI’s Pi assistant is being explored for continuous learning across enterprise L&D platforms to nudge workforce behavior in real-time.
- Genpact applies AI-powered forecasting to proactively adjust finance and accounting workflows for large clients, predicting anomalies in receivables and dynamically adjusting credit risk scores across markets.
- Anthropic Claude is embedded in financial services platforms to act as a proactive co-pilot for risk monitoring, triggering alerts and recommendations autonomously.
- Coforge is piloting predictive agent-based maintenance for airport ground services using IoT sensor data and historical patterns.
- Bridgewater Associates launched a multi-agent system (“AIA Labs”) that breaks investment queries into sequential agent tasks—an early-stage agentic AI demonstration
Net-net: AI isn’t the cherry on top—it’s the engine room. And it only runs on connected, clean, automated data.
Bottom-line: GenAI is the Brain, Agentic AI is the Muscle. As long as you have good data.
Let’s be clear: GenAI generates. Agentic AI executes. Together, they transform OneOffice from a strategy into an operating model. However, if your processes can’t capture good data, you’ll automate the wrong things. If your cloud isn’t built for agility, you’ll scale chaos. And if your talent isn’t empowered with intelligent tools, they’ll become blockers, not drivers. Your employees are now your most important customers. Treat them like it—or watch your best data (and people) walk out the door…
Posted in : Agentic AI, Artificial Intelligence, Automation, Buyers' Sourcing Best Practices, Cloud Computing, Data Science, Design Thinking, GenAI, Generative Enterprise, Global Business Services, OneOffice, Sourcing Best Practises
