Operational details lost between shifts.
Engineers spend time searching manuals and maintenance documentation.
Quality investigations and compliance documentation are time-consuming.
Production data is often manual, delayed or inconsistent.
Procurement teams manually compare supplier quotes.
Maintenance issues are often identified after failure.
Critical engineering knowledge sits with a few experienced staff.
Embedded inside everyday tools like Teams, Outlook and Excel.
Removes repetitive operational processes.
AI agents interact with systems and support operational decisions.
Rolls-Royce Intelligent Factory Programme Using AI and advanced analytics to optimise production.
Results include:
Developers spend time searching documentation and internal knowledge.
Support teams handle large volumes of repetitive customer queries.
Manual testing processes slow down release cycles.
Engineering and operational reports require manual preparation.
Important technical knowledge is often spread across systems.
Analysing user feedback and support tickets is time consuming.
Productivity inside tools like Teams, Outlook and development environments.
Removes repetitive operational and support processes.
AI agents support developers, support teams and operations.
GitHub Copilot – AI Assisted Development GitHub Copilot uses AI to help developers write code faster by suggesting code snippets and solutions as they work. Developers can generate code suggestions, automate repetitive tasks and access documentation more quickly.
Results include:
Engineers spend time preparing reports, specifications and documentation.
Large volumes of engineering data and simulations require detailed review.
Important updates can be missed across engineering teams and contractors.
Engineers must review industry standards and regulatory requirements.
Previous project documentation and insights can be difficult to locate.
Reviewing complex designs and identifying potential risks takes time.
Productivity inside tools like Outlook, Teams, Word and Excel.
Removes repetitive engineering reporting and documentation tasks.
AI agents analyse engineering data and support design decisions.
Siemens AI Engineering & Digital Twin Technology Siemens has deployed AI-driven digital twin technology to analyse engineering designs and optimise performance before physical production. AI models simulate real-world conditions and identify design improvements earlier in the engineering process.
Results include:
Teachers spend significant time preparing lesson plans and learning materials.
Staff handle large volumes of repetitive student questions.
Students and lecturers spend time reviewing large volumes of research material.
Course documentation, reporting and paperwork require manual effort.
Providing structured feedback for students takes considerable time.
Identifying students who may need additional support can be difficult.
Productivity within tools like Word, Teams, Outlook and learning platforms.
Removes repetitive education administration tasks.
AI agents support staff and students with instant access to information.
Arizona State University – AI Student Support
Arizona State University has deployed AI assistants to answer common student questions and support administrative teams. These systems help students access information about courses, deadlines and university services.
Results include: Over 50% reduction in student support enquiries handled by administrative staff Faster response times for students Improved student support experience
The Open University – Predictive AI for Student Success
The Open University uses AI analytics to identify students who may be at risk of falling behind.
Their systems analyse: learning activity assignment submissions engagement data
Impact: earlier academic intervention improved student retention better learning support AI allows education providers to support students proactively rather than reactively.
Agents spend time writing listings and marketing materials.
Property teams handle large numbers of repetitive buyer and tenant questions.
Contracts, compliance documents and property reports require manual review.
Agents spend time gathering property and market information.
Responding to enquiries, viewings and updates takes significant time.
Accurately pricing properties requires analysing multiple market factors.
Productivity inside tools like Outlook, Teams, Word and CRM systems.
Automation reduces manual operational processes.
AI agents interact with property data and client enquiries.
Zillow AI Property Platform Zillow uses AI and machine learning to analyse large volumes of property data to support pricing estimates, property recommendations and market insights. The company’s AI-powered property valuation model processes millions of data points to estimate home values and market trends.
Results include:
Manual donor communications and campaign management.
Significant time spent researching and preparing grant applications.
Volunteer schedules, availability and communication are often manual.
Reporting outcomes to donors and funders can be time-consuming.
Donor data is often fragmented across systems.
Small teams handling large volumes of administrative work.
Policies, safeguarding guidance and procedures can be difficult to locate quickly.
Embedded inside everyday tools like Teams, Outlook and Word.
Removes repetitive operational processes.
UNICEF AI for Fundraising Programme UNICEF uses AI to analyse donor behaviour and personalise fundraising campaigns.
Results include:
Significant time spent manually processing invoices and receipts.
Matching transactions across accounts is manual and time-consuming.
Preparing tax returns and compliance documents requires detailed review.
Identifying trends and anomalies across client accounts takes time.
Accounting teams handle frequent repetitive questions about tax and payroll.
Month-end and year-end reporting processes are often delayed by manual work
Productivity inside tools like Excel, Outlook and accounting software.
Removes repetitive accounting processes.
KPMG AI Audit & Tax Platform KPMG uses AI to automate audit and tax processes, allowing teams to analyse large volumes of financial data significantly faster.
Results include:
Reviewing claims documentation and evidence is manual and time-consuming.
Analysing risk profiles and pricing policies requires detailed data review.
Support teams handle large volumes of questions about policy coverage.
Identifying fraudulent claims requires continuous monitoring and analysis.
Processing policy documents, renewals and compliance paperwork takes time.
Adjusting premiums based on market changes and risk data is complex.
Productivity inside tools like Outlook, Teams and document systems.
Removes repetitive insurance processes.
Zurich AI Claims Processing Zurich Insurance has deployed AI to automate the processing of routine claims, such as property damage and travel insurance.
Results include:
Managing invitations, registrations and event updates is time-consuming.
Coordinating venues, caterers and AV teams requires manual follow-up.
Creating and adjusting complex event schedules and agendas takes time.
Monitoring costs across multiple suppliers and event components is difficult.
Analysing post-event surveys and attendee feedback requires manual review.
Ensuring all operational details are aligned before the event is complex.
Productivity inside tools like Outlook, Teams and event platforms.
Removes repetitive event administration processes.
Cvent AI Event Technology Event management platform Cvent uses AI to help planners automate attendee engagement, recommend event sessions and analyse event success.
Results include:
Tracking plant stock, growth stages and availability is difficult.
Manually checking temperature, moisture and light levels across greenhouses takes time.
Predicting which plants will be in demand for upcoming seasons is complex.
Identifying pests, diseases or nutrient deficiencies early is challenging.
Managing wholesale orders, quotes and customer communications is manual.
Ordering seeds, soil and materials requires continuous tracking.
Productivity inside tools like Outlook, Teams and Excel.
Removes repetitive nursery and operational processes.
Nature’s Source AI Environmental Monitoring Some commercial nurseries are using AI-driven sensors to monitor greenhouse conditions and automate climate control systems.
Results include:
Advisers spend significant time writing post-meeting summaries and file notes.
Regulatory documentation and suitability records require detailed reporting.
Analysts review large volumes of financial reports and market data.
Client updates, portfolio reports and email responses take time.
Identity checks, KYC and onboarding documentation are manual.
Detecting suspicious transactions and financial crime requires constant oversight.
Productivity inside Outlook, Teams, Word and Excel.
Removes repetitive operational processes.
AI agents retrieve information, monitor risk and support decision making.
JPMorgan AI-Assisted Research & Operations JPMorgan has deployed AI tools to support analysts by summarising financial research and automating document review. AI systems help analysts process large volumes of financial information significantly faster.
Results include:
Coordinating staff, farriers, vets and exercise schedules is complex and manual.
Maintaining accurate health, vaccination and veterinary records takes time.
Tracking training progress and competition results requires detailed documentation.
Managing livery client updates, invoices and enquiries is time-consuming.
Organising show entries, travel logistics and competition schedules is complex.
Tracking arena maintenance, feed orders and equipment requires manual oversight.
Productivity inside tools like Outlook, Teams and management software.\
Removes repetitive yard and administrative processes.
Equine AI Health Tracking Startups like Equisense use AI and wearable sensors to track horse movement, symmetry and workload during training.
Results include:
Lawyers spend hours reviewing case law and legal precedents.
Manual contract analysis and clause identification is time-consuming.
Preparing agreements, letters and legal documents takes significant time.
Large volumes of documents must be reviewed during transactions.
Legal teams struggle to quickly locate internal precedents.
Keeping up with legal and regulatory changes is complex.
Productivity inside Word, Outlook, Teams and legal document systems.
Removes repetitive legal documentation and case management tasks.
AI agents retrieve legal knowledge and support legal teams.
Allen & Overy – AI Legal Drafting & Review International law firm Allen & Overy has deployed AI tools to support lawyers with document drafting and legal analysis. AI systems assist legal teams by reviewing large volumes of contracts and legal documents significantly faster.
Results include:
Coordinating cast, crew, rehearsals and venues is highly complex.
Personalising marketing and communications for different audience segments is difficult.
Managing ticket sales, seating allocations and customer queries takes time.
Analysing scripts, identifying casting requirements and planning production requires significant manual work.
Tracking costs across set design, marketing and talent is challenging.
Productivity inside tools like Outlook, Teams and Word.
Removes repetitive entertainment administration processes.
Ticketmaster AI Audience Engagement Ticketmaster uses AI to analyse fan behaviour and personalise event recommendations and marketing campaigns.
Results include:
Doctors and clinicians spend hours completing patient notes and reports.
Finding the right patient information across multiple systems takes time.
Scheduling, cancellations and patient coordination create significant workload.
Healthcare teams must prioritise patients quickly and accurately.
Clinicians spend time searching clinical guidelines and evidence.
Hospitals struggle with patient flow, bed availability and discharge delays.
Productivity inside Outlook, Teams, Word and clinical systems.
Removes repetitive healthcare administration tasks
AI agents retrieve clinical information and support healthcare teams.
NHS AI Diagnostic Support Programmes The NHS has introduced AI tools to support clinicians in analysing medical images and identifying potential health risks earlier. AI-assisted diagnostic systems are being used to detect conditions such as cancer and cardiovascular disease earlier.
Results from trials have shown:
Managing stock levels for live plants with different lifespans and care requirements is complex.
Staff spend significant time answering repetitive questions about plant care and suitability.
Predicting demand for specific plants, compost and seasonal goods is difficult.
Managing multiple growers and coordinating deliveries requires manual oversight.
Creating content for seasonal promotions and gardening advice takes time.
Monitoring plant health on the shop floor to reduce wastage requires continuous attention.
Productivity inside tools like Outlook, Teams and retail systems.
Removes repetitive retail and administrative processes.
AI-Powered Plant Identification Apps Many garden centres are integrating AI tools like PlantSnap or internal knowledge bases to help staff and customers instantly identify plants and access care instructions.
Results include:
Stock levels and demand forecasting are often inconsistent.
Customer service teams handle large volumes of repetitive questions.
Predicting product demand and seasonal trends is difficult.
Teams spend time updating product descriptions and listings.
Retail marketing teams must constantly create promotions and content.
Retailers must constantly monitor competitor pricing and margins.
Productivity inside tools like Outlook, Teams, Excel and marketing platforms.
Removes repetitive retail operations and reporting tasks.
AI agents support customer engagement and retail operations.
Walmart AI Inventory & Supply Chain Optimisation Walmart uses AI systems to analyse purchasing patterns and optimise inventory management across its stores and supply chain. These AI systems help predict demand, optimise stock levels and reduce supply chain inefficiencies.
Results include:
Daily site reports and project updates take time to compile.
Drawings, specifications and RFIs are spread across systems.
Monitoring safety procedures and inspections requires manual reporting.
Coordinating contractors, materials and timelines is complex.
Construction projects frequently exceed planned budgets.
Important updates can be missed across contractors and teams.
Productivity inside tools like Outlook, Teams, Word and Excel.
Removes repetitive construction reporting and documentation tasks.
AI agents analyse project information and support construction teams.
Skanska AI Construction Planning Construction firm Skanska has used AI tools to analyse project schedules and construction data to improve planning and reduce delays. AI systems analyse project information to identify risks and optimise construction timelines.
Results include:
Consultants spend time researching industries, companies and markets.
Drafting proposals and client presentations requires significant effort.
Preparing reports and analysis documents requires manual work.
Previous project knowledge and frameworks are often difficult to find quickly.
Meeting notes and action summaries must be written manually.
Consultants spend time compiling client briefings before meetings.
Productivity inside Outlook, Teams, Word and PowerPoint.
Removes repetitive documentation and reporting processes.
AI agents retrieve knowledge and support consulting teams.
Deloitte AI Knowledge & Research Tools Professional services firm Deloitte has deployed AI systems to help consultants search internal knowledge and analyse research faster. These tools allow consultants to access previous project experience, summarise research, and generate insights more efficiently.
Results include:
Optimising delivery routes manually can be time consuming.
Operational delivery reports often require manual compilation.
Inventory updates and warehouse reporting can be inconsistent.
Customer service teams handle frequent delivery status queries.
Predicting shipment volumes and delivery demand is difficult.
Managers often lack real-time insights across logistics operations.
Productivity inside Outlook, Teams and operational reporting tools.
Removes repetitive logistics reporting and coordination tasks.
AI agents analyse logistics data and support operational teams.
DHL AI Logistics Optimisation DHL has implemented AI systems to optimise delivery routes and improve warehouse operations using predictive analytics. These tools analyse shipment data, traffic patterns and operational performance to improve delivery efficiency.
Results include:
Operational processes often rely on multiple teams and manual coordination.
Tracking operational performance across departments requires multiple reports.
Managing operational workflows and approvals can slow down processes.
Operational data is often spread across multiple systems and reports.
Planning resources and workloads requires manual analysis.
Preparing operational reports and updates requires significant time.
Productivity inside tools like Teams, Outlook, Excel and operational systems.
Removes repetitive operational processes.
AI agents analyse operational data and support decision-making.
UPS AI Operations Optimisation Global logistics company UPS uses AI systems to analyse operational data and optimise delivery routes, warehouse workflows and operational planning. AI systems analyse millions of operational data points to improve efficiency and reduce operational costs.
Results include:
Customer service teams receive large numbers of repetitive customer queries.
Customers expect quick responses across multiple support channels.
Support requests must be triaged and routed to the right teams.
Agents often spend time searching for answers across knowledge bases.
Understanding customer sentiment and recurring issues requires analysis.
Preparing support performance reports takes time.
Productivity inside tools like customer service platforms, Teams and email systems.
Removes repetitive customer support processes.
AI agents interact directly with customers and support teams.
Vodafone AI Customer Support Assistants Vodafone has implemented AI-powered customer support assistants to handle common customer enquiries and support service teams. AI systems analyse customer questions and provide automated responses or route issues to the appropriate support team.
Results include:
IT teams receive large volumes of repetitive support tickets and user requests.
Identifying and resolving technical issues quickly requires detailed investigation.
Monitoring infrastructure, applications and networks generates large volumes of data.
IT teams must search through documentation, logs and past tickets to diagnose problems.
Employees frequently ask questions about systems, access and technical issues.
IT teams must prepare reports on system performance, incidents and service levels.
Productivity inside tools like Teams, service desk platforms and documentation systems.
• Ticket summaries – faster understanding of support requests
• Technical documentation drafting – quicker knowledge updates
• User support communication – faster responses to employee queries
OUTCOME: Reduced support administration and faster issue resolution.
Removes repetitive IT service management processes.
• Ticket classification automation – faster issue routing
• Password reset and access workflows – automated support tasks
• IT reporting automation – faster system performance insights
OUTCOME: Reduced service desk workload and improved IT operational efficiency.
AI agents analyse system data and support IT teams.
• IT support agent – answer common employee technical questions
• System monitoring agent – detect anomalies in infrastructure performance
• Incident analysis agent – analyse logs and identify root causes
OUTCOME: Faster incident resolution and improved system reliability.
Microsoft AI for IT Operations (AIOps) Microsoft uses AI-driven monitoring and analytics to help IT teams detect system issues earlier and improve operational reliability. AI systems analyse logs, performance data and system alerts to identify patterns and potential failures before they impact users. Results include:
• Earlier detection of system anomalies and performance issues
• Faster incident investigation through automated log analysis
• Improved infrastructure reliability AI helps IT teams move from reactive troubleshooting to proactive system management.
Leaders often rely on multiple reports to understand overall business performance.
Developing long-term strategies requires analysing large volumes of market and operational data.
Leadership teams must review information from multiple departments before making decisions.
Tracking industry trends, competitors and market changes requires continuous research.
Preparing board reports and executive summaries takes time.
Understanding how different parts of the organisation are performing can be complex.
Productivity inside tools like Teams, PowerPoint, Word and business intelligence platforms.
Removes repetitive reporting and analysis processes.
AI agents analyse business data and support strategic planning.
Amazon AI Data-Driven Decision Making Amazon uses AI and advanced analytics across its leadership teams to analyse customer behaviour, operational performance and market trends. AI systems help leaders evaluate large volumes of data to guide strategic decisions across the organisation.
Results include:
Monthly and quarterly reports require significant manual preparation.
Finance teams spend time analysing large volumes of financial data.
Processing invoices and expense claims requires manual review.
Predicting financial performance requires complex analysis.
Finance teams must maintain detailed records for audits and regulations.
Leadership teams require fast access to financial performance insights.
Productivity inside tools like Excel, Outlook, Teams and finance systems.
Removes repetitive financial administration processes.
AI agents analyse financial data and support finance teams.
Mastercard AI Fraud Detection Systems Mastercard uses AI and machine learning to analyse billions of transactions and detect fraudulent activity in real time. AI systems monitor transaction patterns and identify anomalies that may indicate fraud.
Results include:
HR teams spend time reviewing large numbers of CVs and applications.
HR teams handle frequent questions about policies, benefits and procedures.
Preparing onboarding documents and processes requires manual coordination.
Policies, contracts and employee documentation require regular updates.
Preparing structured feedback and performance documentation takes time.
Understanding employee engagement and feedback requires data analysis.
Productivity inside tools like Outlook, Teams, Word and HR platforms.
Removes repetitive HR administration tasks.
AI agents support HR teams and employees with instant access to information.
Unilever AI Recruitment Platform Global consumer goods company Unilever uses AI-powered recruitment tools to screen candidates, analyse video interviews and identify the best applicants more efficiently. AI systems assess candidate responses and compare them with successful hiring profiles.
Results include:
Sales teams spend time identifying which prospects are most likely to convert.
Sales representatives manually update CRM systems after meetings and calls.
Gathering information about companies and decision makers takes time.
Maintaining consistent follow-up communication across prospects is difficult.
Predicting deal outcomes and revenue performance requires complex analysis.
Preparing pipeline reports and performance summaries takes time.
Productivity inside tools like Outlook, Teams, CRM platforms and sales tools.
Removes repetitive sales administration processes.
AI agents analyse sales data and support revenue teams.
Salesforce AI Sales Intelligence Salesforce has introduced AI tools to help sales teams analyse customer data, prioritise leads and automate follow-up communication. AI systems review CRM data, customer interactions and sales activity to identify the most promising opportunities.
Results include:
Marketing teams must constantly produce new content across websites, email and social media.
Planning and launching campaigns requires coordination across multiple tools and teams.
Understanding different customer audiences and behaviours requires detailed analysis.
Analysing campaign performance and marketing data takes time.
Creating personalised content for different audiences is difficult at scale.
Tracking trends, competitors and customer sentiment requires continuous research.
Productivity inside tools like marketing platforms, email tools and collaboration systems.
Removes repetitive marketing processes.
AI agents analyse marketing data and support marketing teams.
Coca-Cola AI Marketing Content & Insights Coca-Cola has adopted AI tools to help marketing teams generate campaign content, analyse customer behaviour and personalise marketing experiences. AI systems analyse customer data, market trends and campaign performance to guide marketing strategy.
Results include:
Employees spend significant time writing emails, reports and documentation.
Staff must review large volumes of documents and data.
Meeting notes and action summaries require manual effort.
Employees spend time searching for information and summarising content.
Teams must continuously produce new written material and communications.
Finding answers across internal documents and systems can take time.
AI assistants support everyday work tasks across common business tools.
AI assistants help employees work more efficiently across departments.
AI assistants help employees find and summarise information quickly.
Microsoft Copilot Productivity Gains Microsoft Copilot integrates AI directly into tools such as Outlook, Word, Excel and Teams to support everyday work tasks. Employees use Copilot to draft emails, summarise meetings, analyse spreadsheets and generate reports.
Results from early studies show: Up to 30–40% time savings on common productivity tasks AI assistants help organisations achieve immediate productivity gains while preparing for more advanced AI adoption.
Many operational workflows still rely on manual tasks and data entry.
Business data is often spread across multiple applications and platforms.
Teams spend time compiling reports and analysing spreadsheets.
Leaders struggle to access real-time insights across departments.
Approvals, document processing and manual reviews slow down operations.
Important insights are often buried in data and difficult to access quickly.
Automation connects systems and removes repetitive manual tasks.
• Approval workflow automation
• Document and form processing
• Data synchronisation between systems
• Automated notifications and task routing
OUTCOME: Reduced manual work and faster operational processes.
Analytics platforms transform operational data into actionable insights.
• Automated reporting dashboards
• Financial and operational performance analytics
• Customer and sales insights
• Real-time business intelligence
OUTCOME: Improved visibility and faster data-driven decision making.
AI enhances automation by analysing data and supporting decision making within workflows.
• Invoice and document processing
• CRM and ERP data automation
• Lead and customer workflow automation
• Operational performance monitoring
OUTCOME: More efficient processes and improved operational scalability.
Microsoft Power Platform Automation Many organisations use the Microsoft Power Platform (Power Automate, Power Apps and Power BI) to automate business processes and generate real-time insights from operational data. Examples include:
• Automated invoice processing workflows
• Procurement and approval automation
• Real-time operational dashboards
• Automated reporting across departments Results often include: 60–80% reduction in manual processing time for repetitive workflows Automation and analytics allow organisations to remove operational friction while improving visibility across the business.
Many business systems store data but do not provide meaningful insights.
Teams must analyse information before taking action.
Digital systems often lack personalised or intelligent interactions.
Large volumes of operational data are rarely fully analysed.
Insights are spread across multiple systems and reports.
Building intelligent capabilities into existing systems can be complex.
AI can be embedded directly into business applications to enhance functionality.
AI applications enhance digital experiences for customers and employees.
AI applications analyse data to support operational and strategic decisions.
Salesforce Einstein AI Platform Salesforce Einstein embeds AI capabilities directly into the Salesforce platform to support sales, marketing and service teams. The system analyses customer data, predicts opportunities and surfaces insights directly within the CRM interface.
Examples include:
Results include: Improved sales productivity and more accurate forecasting across sales teams AI applications allow organisations to move from simply analysing data to embedding intelligence directly into everyday business systems.
Teams spend time completing repetitive tasks across multiple systems.
Support teams must manually review, prioritise and route incoming requests.
Employees spend time searching for knowledge across documents and systems.
Many business processes require manual coordination between teams.
Customer or internal requests often wait for human availability.
Important tasks are slowed down by manual processing.
AI agents can complete operational tasks across business systems.
AI agents can monitor requests, categorise issues and resolve common problems.
AI agents can retrieve knowledge and provide insights to support teams.
Enterprise Service Desk AI Agents Many organisations are deploying AI agents within IT and service operations to automate ticket handling and knowledge retrieval. An AI service desk agent can:
Many business operations involve multiple systems and teams.
Staff must manually move tasks between departments or systems.
Individual automations exist but are not connected into a complete process.
Processes stall when waiting for manual input or decision making.
Important information is spread across multiple platforms.
Manual coordination makes it difficult to scale operations efficiently.
Agentic workflows coordinate multiple AI agents to complete end-to-end processes.
Each agent within the workflow specialises in a specific role. Examples may include:
Agentic workflows can adapt dynamically as new information becomes available.
AI-Driven Customer Support Workflow A typical agentic workflow for customer support might include multiple specialised AI agents working together.
Example workflow:
Customer enquiry received ➡️ Support agent analyses the request ➡️ Knowledge agent retrieves relevant documentation ➡️ Resolution agent determines the appropriate solution ➡️ Execution agent updates systems and responds to the customer
Each agent performs a specific function within the workflow while sharing information to complete the overall process.
Results include: Faster resolution times and the ability to automate complex support processes end-to-end Agentic workflows enable organisations to automate multi-step operations that previously required coordination between several teams.
Businesses often struggle to scale operations without increasing headcount.
Large portions of work involve repetitive digital tasks such as data processing or reporting.
Manual workflows slow down operations and create bottlenecks.
Manual data entry and repetitive processes increase the risk of errors.
Organisations must improve efficiency while controlling operational costs.
Businesses must handle growing volumes of transactions, enquiries and data.
Digital workers can execute operational workflows across enterprise systems.
A digital workforce allows organisations to run large volumes of processes continuously.
Digital workforces are designed to work alongside human teams.
Enterprise Digital Workforce Automation Many organisations are deploying digital workers to automate high-volume business processes across finance, operations and customer service.
Examples include: