AI use cases in business are repetitive tasks on text, documents, speech, images and numbers: summarising and drafting, pulling fields out of documents, classifying requests, answering questions from company knowledge, forecasting demand and failures, and spotting defects on camera. Below are 30 concrete tasks by department, with sourced examples and criteria for choosing where to start.
Five kinds of work AI genuinely does well
"AI can do anything" is the sentence that most often stops a company from doing anything at all. In practice, almost every use case that pays off falls into one of five kinds of work, and each implies a different technology and level of human review.
| Kind of work | What it does | Typical data | Where the human stays in the loop |
|---|---|---|---|
| Generating and summarising | Drafts, shortens, translates | Email, reports, meeting recordings | Reading before anything is sent or published |
| Extracting and classifying | Reads fields, sorts requests into categories | Invoices, forms, shared inboxes | Checking low-confidence records |
| Search and Q&A | Finds sourced answers in company documents | Procedures, manuals, contracts | Verifying the cited source |
| Forecasting and detection | Predicts demand, failures, unusual transactions | Sales, sensor, ledger data | Setting thresholds and deciding the action |
| Seeing and hearing | Recognises defects in images, topics in calls | Cameras, call recordings | Reviewing false alarms |
The first three rows can usually be delivered quickly with large language models (LLMs); the last two tend to need a model trained on your own data. If fixed rules can do the job, answer the AI versus rule-based automation question first.
Why a list is not enough: the cost of not choosing
Reading a list is easy; choosing the task that will actually move the numbers is hard, and research shows that most companies investing in AI have yet to see it reach the bottom line.
In McKinsey's "The state of AI in 2025" survey, only 39% of respondents reported any enterprise-level EBIT impact from AI, and most of those said AI accounted for less than 5% of EBIT. (McKinsey — The state of AI in 2025)
IBM's 2025 study of 2,000 CEOs found that only 25% of AI initiatives in recent years delivered the expected return on investment (IBM Institute for Business Value — CEO Study 2025). Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025 over data quality, risk controls, cost or unclear value (Gartner press release, July 2024). The lessons behind these figures are unpacked in why AI projects fail.
BCG's 2024 research found that 62% of AI value comes from core functions: operations 23%, sales and marketing 20%, R&D 13% (BCG — Where's the Value in AI?). In other words, value concentrates in applications built into operations, sales and R&D rather than in a general office assistant. For the picture in Turkey, see AI adoption in Turkey.
30 AI use cases in business, department by department
Each item links to a deeper guide. Sourced examples are public statements by third-party companies, measured in their own processes; they will not transfer to yours one-for-one.
Customer service
- 1. Reply suggestions and case summaries for agents: AI summarises history and proposes a reply. In an NBER study of 5,179 support agents, issues resolved per hour rose by 14% on average (NBER — Generative AI at Work). More in agent-assist AI.
- 2. Round-the-clock answers to routine questions: Order status, appointments and document requests answered without waiting for a person, via a customer service chatbot.
- 3. Topic and complaint analysis from calls: Recurring problems counted across calls: call centre speech analytics.
- 4. Classifying and routing incoming email: Invoices, quotes and complaints sorted and assigned to the right person: AI email classification. Drafting the reply is a separate task: AI email reply drafting.
Sales and marketing
- 5. First drafts of proposals and decks: A skeleton built from CRM notes; the salesperson still writes price and commitments: AI proposal writing.
- 6. Opportunity scoring: Past sales data ranks leads by closeness to closing: AI in sales.
- 7. Content and campaign variations: In a 2023 MIT experiment with 444 professionals, those using ChatGPT finished a professional writing task in 37% less time than the control group (Noy and Zhang, MIT working paper). More in AI in B2B marketing.
- 8. Spotting customers at risk of leaving: Early risk signals from orders and complaints: B2B churn analysis.
- 9. Price recommendations: A range suggested from demand, stock and competitor prices: AI dynamic pricing.
Finance and accounting
- 10. Reading invoices and receipts: Supplier, date, VAT and totals captured without keying: OCR invoice processing.
- 11. Matching bank lines to customer and supplier accounts: Suggestions for vaguely described transfers: bank reconciliation automation.
- 12. Flagging unusual payments and entries: Duplicates and odd amounts surfaced: anomaly detection.
- 13. Draft commentary for month-end reports: A first text explaining the causes of variances. For in-house teams see AI in finance and accounting; for firms serving clients, AI for accounting firms.
Procurement and supply
- 14. Comparing supplier quotes: Mixed-format quotes in one table, missing terms flagged: AI in procurement.
- 15. Flagging risky contract clauses: According to Vodafone, analysis in its legal department found that Microsoft 365 Copilot users saved four hours per person per week on average, which the team links to faster contract review (Microsoft customer story — Vodafone). More in AI contract review.
- 16. Demand forecasting: According to Getir, forecast accuracy improved by 10% across more than 10,000 SKUs (AWS case study — Getir). More in AI demand forecasting.
Human resources
- 17. Drafting job ads, interview questions and training material: CV screening, however, is high-risk under the EU AI Act. See AI in human resources and the EU AI Act for Turkish companies.
- 18. An internal assistant for staff questions: Leave and procedure questions answered with citations, the job of an enterprise LLM assistant.
Production, quality and maintenance
- 19. Camera-based quality inspection: Defects and wrong labels caught on the line: computer vision quality inspection.
- 20. Early warning before failures: Vibration and temperature trends reveal wear: predictive maintenance guide.
- 21. Answers for technicians from manuals and fault history: Sourced answers on the shop floor: maintenance technician assistant.
- 22. Comparing schedule scenarios: The knock-on effect of a rush order, calculated fast: AI production scheduling.
- 23. Safety breach detection: Missing helmets or vests trigger an alert: AI PPE detection.
- 24. Writing work instructions and procedures: According to Eaton, time per standard operating procedure fell from one hour to 10 minutes (Microsoft customer story — Eaton). More in AI in manufacturing examples.
Logistics and warehousing
- 25. Route planning: In 2020 UPS said its ORION system saves about 100 million miles a year (UPS press release). More in route optimisation.
- 26. Voice picking and warehouse operations: Hands-free picking: voice picking; the wider picture is in AI in logistics and warehousing.
Management, knowledge and IT
- 27. Plain-language data questions and weekly summaries for managers: Answers without waiting for a report: AI management reporting.
- 28. Searching company documents: According to OpenAI, Morgan Stanley's adviser assistant raised document access from 20% to 80% (OpenAI — Morgan Stanley). More in AI enterprise search.
- 29. Meeting transcripts and decision summaries: Speech, including Turkish, turned into text with decisions and actions listed: AI meeting notes and summaries. The underlying technology is covered in speech-to-text for business.
- 30. Cutting IT alert noise: Real incidents separated from log noise: AIOps for IT operations.
For sector views, read AI in hospital operations and AI in agriculture.
On the public and service side there are AI in local government and AI in hotels and hospitality; every topic is listed under artificial intelligence articles.
From list to project: which of the 30 first?
Not every task suits every company. Work through these steps in order:
- Measure the volume: Write down how many times a week the task repeats and how many person-hours it takes.
- Locate the data: Are the documents, records or images digital and reachable?
- Weigh the cost of a mistake: Can a wrong suggestion be undone? Drafts and summaries get a human final read; credit or hiring decisions carry far more risk. Where the human stays in the loop is covered in human–AI collaboration at work.
- Check data privacy: Where personal data is involved, look at the risks of sending company data to ChatGPT. In Turkey this means KVKK, the national personal data protection law modelled on the GDPR.
- Define success in advance: Pick one measure such as handling time, error rate or rework count; the method is in measuring AI project ROI.
To score and rank candidates department by department, use the AI use case prioritisation matrix.
The broader starting roadmap is in AI in business: where to start, and what controlled experiments really show is covered in AI productivity research.
How we do this at Digital Bridge
We do not sell off-the-shelf packages; every engagement starts with a needs analysis. With your departments we walk through this list, pick two or three candidates with the right volume and data, and set out scope, phases and cost in a written proposal.
The pilot runs on your own data, narrowly scoped. For document-heavy work we use document OCR, on the shop floor computer vision, and for questions on company knowledge an enterprise LLM assistant. Where an off-the-shelf model falls short, custom AI model training teaches it from your own examples.
Value appears only when results flow back into existing systems, so our AI integration work connects outputs to your ERP, CRM or production software through system integrations, and we compare the agreed measures before and after the pilot.
Starting without a project: AI features in Smart360
Some tasks need no software project. Our own product, Smart360, brings AI into everyday email and document work:
- SmartMail summarises messages and automatically classifies them as correspondence, invoice/payment, quote/tender, official letters, promotions and updates (task 4). It lets you ask questions of a message, offers writing assistance and translates into 30 languages. From an invoice PDF attachment it extracts the parties, dates, VAT, grand total and tax number without the file being downloaded (task 10).
- SmartFiles analyses each uploaded document without anyone asking and produces a report in four parts: Summary, Key Points, Structure and Notable Details. You can ask questions of a single document or a whole folder (the document side of task 28); more in AI document analysis.
AI use is permission-controlled: AI chat is a per-mailbox permission in SmartMail, and in SmartFiles the AI right is granted to a person or department. SmartFiles also has a monthly AI allowance, and an administrator can switch the feature off entirely.
Next step
Mark the three tasks closest to your business and note their weekly volume. Get in touch through our contact page to book a needs analysis, and together we will decide which task deserves a pilot and which can run on an existing tool.