Category: automation

Auto Added by WPeMatico

Computer vision deployments drive retail productivity gains

Computer vision deployments are driving retail productivity gains as operators automate physical shelf tracking to protect eroding margins.

This hardware deployment directly addresses the persistent in-store execution failures currently costing the industry billions. A study authored by Coresight Research – in partnership with technology providers Simbe and RELEX Solutions – calculates the exact cost of these operational shortfalls.

Inefficiencies consume 6.4 percent of gross sales across the sector. Hardware, mass merchandise, and grocery categories will surrender $196.4 billion to these operational failures in 2026. The monetary value of these losses is jumping 21 percent over the previous year. This deficit vastly outpaces the three percent projected sales growth for the entire sector.

Nine in ten retailers report active difficulties managing their shop floors. Empty shelves and inaccurate pricing structures directly suppress operating margins. Margin erosion exceeds five percent for 89 percent of operating businesses.

Full-scale deployments of store intelligence platforms operate across 60 percent of enterprise footprints. This adoption rate represents an 18-percentage-point jump year-over-year.

Experimental pilot programmes account for a mere 18 percent of current market activity. The adoption curve skews heavily toward top-tier enterprises. 73 percent of retail companies generating over $5 billion in annual revenue maintain fully scaled deployments.

Mid-market operators lag behind, with only 42 percent of sub-$1 billion companies achieving similar deployment maturity. Treating physical stores as separate entities from digital channels degrades customer lifetime value. Capital expenditure directly targets out-of-stock tracking, automated pricing, planogram verification, and assortment planning.

Production deployments in hardware and grocery

BJ’s Wholesale Club provides a documented case study of applied shelf digitisation. The operator deployed Simbe robotics platforms to monitor inventory and price accuracy across its locations.

Management used this hardware foundation to generate digital twins of individual warehouse clubs. This application established real-time visibility systems previously absent from their physical operations.

BJ’s applied these digital models to route planning for online orders and curbside fulfillment. The engineering team recorded a 40 percent year-over-year improvement in picking efficiency through this data application. CEO Bob Eddy reported the technology enabled the company to elevate quality standards within fresh merchandise categories.

Grocery operator Albertsons applies AI to automate complex retail operations. The grocer targets $1.5 billion in productivity gains spanning three fiscal years. CEO Susan Morris explained: “We will be equipping our merchants with AI-driven insights and automated execution to optimise pricing, promotions, and assortment decisions, transforming category management and driving margin improvement.

“Our vision is the future where intelligent automation guides these decisions, freeing our people to focus on strategy and innovation.”

Flaws in deployment sequencing

Many organisations prioritise the installation of pricing software while ignoring foundational sensor infrastructure. 43 percent of surveyed technology leaders direct their capital toward pricing optimisation software.

Supplier collaboration platforms rank second in priority, attracting investment from 36 percent of operators. Only 33 percent of these organisations invest in the shelf digitisation hardware required to feed accurate data into those pricing models.

This hardware includes the sensors and cameras needed to verify physical stock availability. Store intelligence deployments require strict sequencing to function properly. Retailers must first digitise the shelf, deploy data analytics, install inventory tracking software, and finally execute pricing automation.

This inversion of the technology stack creates downstream data failures. Markdown algorithms process outdated inventory counts when physical tracking sensors are absent. Mispricing rates hit 13 percent in 2026, marking a four-point increase since 2024.

Pricing and promotional execution dominates the priority list, presenting an active difficulty for 92 percent of operators. Kim Anderson, VP of Store Operations at Schnucks Markets, states that shelf data must precede all other implementations. Without accurate physical inventory monitoring, downstream applications fail to meet their performance targets.

Out-of-stock events remain severely disruptive, with 52 percent of operators ranking inventory availability as highly demanding. Operators attempt to fix multiple problems simultaneously, with 40 percent directing capital toward three or more operational inefficiencies at once.

Labour reallocation and efficiency metrics

Lowe’s demonstrates the financial impact of automating the associate workflow through its ‘Perpetual Productivity Improvement’ initiative. Executive VP of Stores Joseph McFarland directed the deployment of workforce management tools and inventory solutions to eliminate redundant associate tasks.

The engineering rollout saved 80 non-productive labour hours per store on a weekly basis. Lowe’s advanced the initiative by deploying full shelf replenishment technologies powered by AI to track stock depletion in real-time.

Management distributed financial bonuses to the workforce based on documented productivity enhancements. The company issued $5,000 to associate store managers and varied payouts to hourly staff.

Broad industry data validates the performance metrics recorded by Lowe’s. The deployment of intelligence applications drives a 14 percent average reduction in time spent on manual store tasks. 86 percent of organisations record defined decreases in manual assignment hours.

Retailers report distinct performance disparities based on total revenue. 56 percent of operators generating over $5 billion report advanced reductions in task completion times, compared to only 36 percent of mid-market companies.

Organisations cite operational efficiency as their primary investment objective, followed closely by the unification of store data. Retailers expect these tools to generate new capital, with 40 percent of leaders seeking to establish alternative revenue streams like retail media networks.

Securing market competitiveness

Store intelligence technologies function as an interconnected ecosystem rather than standalone fixes for isolated problems. Deploying these systems without a coherent sequencing plan forces operators to build upon an unstable foundation.

Establishing real-time, shelf-level visibility proves strictly necessary before attempting to scale downstream software. Pricing automation, supplier collaboration platforms, and inventory forecasting applications require verified physical data to generate accurate outputs.

Customer behaviour responds directly to correct operational upgrades. Proper deployments increase customer lifetime value by 11 percent across the sector, while conversion rates improve for 50 percent of the operators executing physical automation frameworks.

48 percent of companies record increased enrollment in their loyalty programmes following system integration. Accurate pricing and consistent stock availability elevate online review metrics for 47 percent of surveyed operators.

Retailers compounding value through integrated, properly sequenced hardware and software capabilities possess a distinct market advantage over competitors accumulating disconnected applications.

See also: HSBC expands AI banking partnership with Google Cloud

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Computer vision deployments drive retail productivity gains appeared first on AI News.

June 18, 2026
Google Cloud generative AI automates council planning operations
Government ministries are deploying Google Cloud generative AI across municipal agencies to automate council planning operations.

Public sector administration handles vast volumes of unstructured data that delay infrastructure development. The UK central government established a target to construct 1.5 million new homes by 2029. Local planning authorities encounter administrative backlogs caused by dense paperwork, delaying these development timelines.

To address these constraints, the Ministry of Housing, Communities and Local Government (MHCLG) and the Department for Science, Innovation and Technology (DSIT) expanded two machine learning tools designed to accelerate municipal processing. Speaking at the Google Cloud Summit London, officials confirmed the nationwide deployment of the ‘Extract’ application and the progression of the ‘Augmented Planning Decisions’ (APD) prototype.

Lila Ibrahim, Chief AI Readiness Officer at Google DeepMind, said: “The UK has an opportunity to build the homes our communities need, but local councils face a mountain of paperwork. That’s why we’re co-creating a sophisticated planning tool directly with councils to solve real-world bottlenecks.

“This will help significantly cut decision times, freeing up planners to focus on the future to get Britain building faster.”

Householder applications – which include routine domestic modifications such as loft conversions or property extensions – account for nearly 70 percent of all planning applications submitted annually. Evaluating these standard submissions manually requires planning officers to spend hours cross-referencing regional policy documents, historical archives, and unstructured PDF files.

Such a repetitive evaluation process consumes administrative hours that would otherwise support major infrastructure and commercial developments. The deployment of automation targets this administrative distribution, aiming to reduce application decision timelines by 50 percent.

Core capabilities of the Google Cloud generative AI tools

Engineers at MHCLG and the government’s applied AI team, the Incubator for AI (i.AI), built the Extract tool internally using Gemini foundation models. Following trials across more than 20 local planning authorities, administrators expanded the application to every council in England.

Extract parses unstructured data locked within legacy PDF records, converting hundreds of pages of historical planning documentation into structured digital datasets within minutes. Operational data from the trial phases indicates that the tool will eliminate roughly 255 hours of manual data entry per council annually. This reduction allows local authorities to reallocate personnel to complex evaluation tasks.

Integrating large language models into public sector workflows requires enterprise-grade security environments. Local authorities process sensitive civic records, requiring strict risk management protocols to prevent data exposure.

The government hosted the Gemini models on Google Cloud to establish a protected operating environment where data sovereignty is maintained. The cloud environment features active security controls to block malicious inputs, including prompt injection attacks. This technical framework ensures that sensitive municipal data remains secure during both testing and production computing cycles.

The APD system, meanwhile, acts as an analytical assistant for municipal planning officers by automating four primary administrative tasks:
1. The system consolidates incoming documentation by pre-processing data backlogs, flagging missing information gaps, and extracting core geographical site data onto a unified user interface for officer review.
2. The software identifies relevant national and local zoning laws, assesses compliance margins, and appends precise policy citations for manual verification.
3. The application parses public consultation letters, summarising stakeholder objections or historical legal precedents.
4. The model generates initial drafts of final evaluation reports, including the technical rationale and recommended approval conditions.
Protocols dictate that human planning officers retain final decision-making authority over every application. The software does not automate final approvals or rejections independently. Staff members review every line of text generated by the machine learning models, modifying the analytical reasoning before validating the report.

To maintain regulatory accountability, the APD prototype records its internal processing steps sequentially. This mechanism establishes an auditable chain of thought, creating a verification trail for every processed application to support the officer’s final determination.

Local council planning trials and scaling timelines

The development of the APD prototype relies on a collaborative framework linking public sector administrators with engineering teams from Google Cloud, Google DeepMind, and Faculty.

The alpha version undergoes live testing within three local authorities: the London Borough of Barnet, Dorset Council, and the London Borough of Camden. Testing across these distinct regional jurisdictions provides developers with varied municipal datasets to test the software against diverse local policies.

Central planners intend to complete the alpha phase and deploy the APD tool to all 300-plus English local authorities by 2027. Google Cloud provides the elastic computing infrastructure required to manage the thousands of concurrent inferencing queries generated during daily operations.

Paul Maltby, Director of Public Services at Faculty, commented: “The English planning system is clogged up. Planning officers are forced to spend half their time reviewing applications to convert an attic, putting those for housing estates and warehouses on hold.

“Built with planning officers, our AI system will take the drudgery out of reviewing simple planning applications so they can make quick decisions. It will let planning officers focus on the major developments that matter, and crucially, let families improve their homes without months of delay and uncertainty.”

Naisha Polaine, Executive Director for Growth at Barnet Council, added: “The tool’s ability to collect relevant information, undertake a provisional assessment, and draft the foundations of a report has the potential to save significant officer time spent working on the administration of planning applications and direct this to speeding up the decision-making process for residents. In turn, this will contribute significantly to delivering our house building growth targets in the borough.”

The coordination between MHCLG, i.AI, Google DeepMind, and Faculty establishes a structured division of labour for enterprise software engineering. Public ministries define the policy guidelines and statutory boundaries, while external technical partners engineer and deploy the underlying model architectures.

The successful integration of these systems demonstrates the feasibility of hosting advanced language models within a secured public cloud infrastructure to process core administrative workloads and modernise public service delivery.

See also: EU publishes its AI content labelling playbook ahead of the AI Act’s August deadline

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Google Cloud generative AI automates council planning operations appeared first on AI News.
June 17, 2026
Visa ChatGPT integration enables AI agent retail purchasing

Visa has linked its payment infrastructure to ChatGPT, enabling AI agents to recommend retail products and execute financial transactions.

The deployment removes human intervention from the final stages of the retail funnel. Autonomous agents will now process user prompts, evaluate merchant catalogues, and complete the checkout process using Visa’s payment rails at any supporting merchant.

Previous retail AI integrations restricted automated purchasing to single-vendor environments. Retailers built proprietary chatbots confined entirely to their own inventory. Visa’s integration bypasses closed-loop architecture.

The payment giant connects the open-web reasoning capabilities of a large language model directly to a universal transaction network. Users simply command the agent to procure an item, and the model handles the vendor selection, product comparison, and financial settlement.

Enterprises should be aware that commercial transactions will increasingly execute without a human buyer ever seeing a retailer’s website, digital advertisement, or promotional email.

Restructuring retail data for AI agent buyers

Marketing departments design campaigns around human psychology, emotional triggers, and visual merchandising. AI agents operate on pure data evaluation.

When ChatGPT receives a mandate to purchase a specific product type, it parses technical specifications, aggregated sentiment scores, and pricing structures. Display ads and user interface optimisations hold zero weight in the model’s selection criteria.

Retailers will need to expose machine-readable inventory data. Search engine optimisation transitions into language model optimisation. The algorithms driving ChatGPT rely on structured data feeds, clear API documentation, and explicitly-formatted product attributes to evaluate whether an item meets the user’s parameters. Merchants failing to maintain high-quality, structured metadata will find their products invisible to the autonomous agent.

Personalisation occurs entirely on the user’s device or within the user’s secure LLM profile. The AI retains the consumer’s past preferences, sizing requirements, budget constraints, and brand affinities. Instead of the retailer attempting to guess the consumer’s needs through tracking cookies and site behaviour, the agent arrives at the digital storefront with a highly-specific procurement mandate.

Completing a transaction without human intervention requires a secure, automated handshake between the reasoning engine and the payment gateway. Visa provides the financial layer necessary to establish trust in an inherently untrusted agentic environment. Traditional checkout flows require manual data entry, CAPTCHA verification, and two-factor authentication loops. These mechanisms block autonomous agents.

Visa implements programmatic tokenisation to solve the authentication problem. The user pre-authorises the ChatGPT environment with specific spending parameters. When the LLM decides on a purchase, it generates a single-use payment token through the Visa network. The agent transmits this token via API to the merchant’s backend systems. The transaction settles exactly like a standard digital wallet payment, bypassing the visual user interface completely.

A digital storefront requiring multi-page navigation or mandatory account creation introduces failure points for the agent. Enterprises actively deploying headless commerce architectures possess an advantage. They can process the agent’s payload, confirm stock levels, and execute the payment token in milliseconds.

Enterprises track bounce rates, session durations, and cart abandonment to understand consumer behaviour. An AI agent does not browse—it queries an endpoint, extracts the necessary data, and either executes the payment or terminates the connection.

Retailers must develop new telemetry to measure agent interactions. Tracking the frequency of API queries from known LLM IP addresses replaces tracking unique human visitors. Understanding why an agent selected a competitor’s product will require analysing the structural differences in product data feeds rather than running A/B tests on website layouts.

Customer retention strategies also need adjustment. An autonomous agent evaluates the market fresh with every prompt unless explicitly instructed by the user to reorder a specific brand. Loyalty programmes must be engineered into the payment token or the user’s LLM profile. If the AI cannot automatically apply a loyalty discount during its background calculation, the merchant loses the pricing advantage intended to secure the repeat purchase.

Managing and securing the agentic AI supply chain

Prompt injection attacks could theoretically manipulate an agent into purchasing from malicious vendors or authorising inflated transactions. Visa’s network acts as the final validation layer, applying fraud detection models to the incoming token requests.

Businesses face the secondary challenge of managing automated returns and customer service queries initiated by the AI. If the delivered product fails to meet the parameters defined in the original prompt, the user can instruct the agent to reverse the transaction.

In this scenario, the AI will autonomously navigate the merchant’s return policy, initiate the refund request, and generate the necessary shipping labels. Retail customer service operations must deploy their own automated systems capable of negotiating directly with the consumer’s agent.

Visa’s ChatGPT integration confirms the enterprise transition from human-operated software interfaces to autonomous digital proxies. The customer is no longer necessarily a human navigating a web browser, but an algorithm executing a script.

See also: Aviva deploys AI to stop £230M in sophisticated insurance fraud

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Visa ChatGPT integration enables AI agent retail purchasing appeared first on AI News.

June 11, 2026
How C3 AI agents will automate predictive maintenance for Shell

Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance.

The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipment across upstream and downstream operations. Shell now intends to lean heavily into autonomous AI agents, putting them in charge of the entire maintenance lifecycle.

Going from that first warning sign all the way to a completed repair, this level of automation strips away the need for constant human oversight and makes sure the company’s resources are pointed exactly where they are needed most.

“This expanded partnership with Shell proves what’s possible when enterprise AI is fully operationalised at global scale for predictive maintenance—reducing unplanned downtime and delivering hundreds of millions of dollars in economic value,” said Stephen Ehikian, President of C3 AI.

“Shell has built mature AI predictive maintenance programs on our platform, and together we’re now pushing into agentic AI, advancing how this technology can further transform reliability, safety, efficiency, and operational performance.”

C3’s AI agents help Shell move past basic anomaly detection

In the beginning, Shell used machine learning simply to spot odd patterns in sensor data, giving engineers an early heads-up before things broke. To pull this off, the system ingests a massive amount of real-time operational technology (OT) data and mixes it with business context from ERP platforms such as SAP.

The next step introduces AI agents built for actual reasoning and independent action. While older systems stopped at pinging an engineer when things looked unusual, this next-generation framework independently investigates why an alert fired in the first place.

Once it pinpoints the root cause, the agent steps up to draft precise work orders, confirm part availability in the inventory, and generate procurement requests.

C3 AI’s platform handles the heavy lifting, providing a model-driven space to easily integrate high-frequency sensor feeds with structured financial and maintenance logs. These AI capabilities are trained to learn the normal operating baselines for specific gear, like pumps, turbines, and compressors.

The agentic layer sits on top of this foundation. Operators configure an individual agent for a given piece of equipment by defining its objectives and permitted responses. If the core machine learning models detect a deviation from normal operations, this agent activates, gathering extensive contextual data to build a complete picture of the situation. This context usually includes recent maintenance history, environmental conditions, and upstream process variables.

Using all that information, it suggests a fix backed by solid evidence. Human operators can then easily approve or override the plan. As the system proves itself over time, Shell can fully automate its responses to certain types of alerts. Connecting straight into systems like SAP is critical here, allowing the agent to work inside the exact same workflows that human planners already use.

The real impact of agentic AI for predictive maintenance

Putting agentic AI to work at this scale tackles the classic “last mile” headache in predictive maintenance. Many industrial companies can predict failures just fine, but turning those insights into fast, efficient action remains a challenge. Usually, engineers still have to manually dig through alerts, investigate the causes, and write up the work orders themselves.

Shell wants to shrink that timeline. By letting AI handle root cause analysis and work orders, the delay between a predicted failure and the actual fix drops. That directly improves equipment uptime and protects production.

Moving to a model where repairs only happen when the equipment condition actually demands it naturally saves money, simply because nobody is wasting time tinkering with perfectly fine machinery. Leaving healthy hardware alone also means it lasts much longer.

On top of the cost savings, stepping in before a catastrophe hits makes the whole operation much safer and cuts down on environmental risks, which is always top of mind in the energy sector.

“What Shell and C3 AI have built on Azure over the past several years is exactly what enterprise AI should look like—real applications, running in production, delivering measurable value at global scale,” commented Sandy Gupta, VP GISV, Software Development Companies at Microsoft.

This expanded rollout shows that we are finally talking about practical industrial AI production workflows instead of just algorithms. Rather than just the prediction itself, the real value comes from the system’s ability to act on it with barely any human oversight.

See also: Meta Business Agent drives AI-powered conversational commerce

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post How C3 AI agents will automate predictive maintenance for Shell appeared first on AI News.

June 9, 2026