In practice, the greatest value comes from implementations that automate repetitive tasks or genuinely speed up teams’ work in areas such as sales, customer service, finance or logistics. The key is to translate broad concepts into precise tasks that can be tested quickly and measured. Just as important is choosing projects with sensible feasibility (data, integrations) and an acceptable level of legal and reputational risk. In this section, I show how to map processes into tasks for AI and how to prioritise projects so you can see results as quickly as possible. This makes it easier to plan a pilot, and then scale the solutions too.
Mapping processes to tasks for AI
Mapping processes to tasks for AI starts with a list of the processes in the company, and then breaking them down into repetitive and measurable activities that can be automated or supported by a model.
A good starting point is to list the main areas (e.g. sales, customer service, finance, logistics) and then define where there is a high volume of work and “bottlenecks”. Next, break each process down into the types of tasks AI can perform, e.g. classification, information retrieval, content generation or predictions. The quickest wins are use cases with high volume and a clear success metric, e.g. automatic tagging of helpdesk tickets. This approach immediately shifts the conversation towards results and reduces the risk of building a tool that has no real-world application.
- List the processes (e.g. sales, customer service, finance, logistics) and indicate areas with a high volume of work.
- Break the process down into repetitive tasks: classification, information retrieval, content generation, predictions.
- Choose tasks with a clear KPI and a simple definition of “success” (e.g. accuracy of ticket tagging).
- 01List the main processesSales, customer service, finance.
- 02Break them down into activitiesMeasurable and repetitive.
- 03Define the AI task typeClassification, prediction, generation.
- 04Prioritise impactHigh volume, clear return.
Focusing on results and real-world application minimises the risk of building a tool with no business value.
Criteria for prioritising AI projects
It is worth basing the prioritisation of AI projects on assessing each idea across a matrix: business value, feasibility and risk.
Business value can be described as saving working hours or increasing revenue, while feasibility refers to data availability and the ability to integrate with systems. Risk includes legal and reputational issues, because not every idea is suitable to start with. A good example is that an internal chatbot for HR procedures is often easier and less risky than a bot advising customers on financial products. This kind of prioritisation helps you choose a project that delivers results faster and moves more smoothly from pilot to production.
- Business value: time saving (hours) or revenue growth.
- Feasibility: data availability and required integrations.
- Risk: legal and reputational.
Choosing the right type of AI for a specific problem
Choosing the right type of AI for a specific problem comes down to matching the technology to the nature of the task and the available data, rather than “sticking” LLMs everywhere you can launch a chat.
When working with text and knowledge (e.g. FAQs, contracts, instructions), the LLM + RAG approach usually works best, because it lets you answer based on company sources rather than “guessing”. By contrast, when forecasting demand or detecting anomalies, classic ML models (e.g. XGBoost, Prophet) are often the better choice, because they offer measurable accuracy and are easy to compare using metrics. A good example is sales forecasting at SKU level, which usually requires time-series data and features related to price and promotions, rather than language generation. This choice of tools reduces the risk of disappointment and makes it easier to evaluate the quality of the solution later on.
- 01Match the technology (not just LLM)Purposeful implementation, not “sticking on”
- 02Text and knowledge (FAQs, contracts)Use LLM + RAG (based on sources)
- 03Forecasting and anomalies (e.g. sales)Choose classic ML (accuracy, metrics)
The key is to choose the tool for the nature of the data and the specific problem, not language generation for its own sake.
RAG as the standard for company knowledge
RAG has become the standard in the area of company knowledge, because it allows the model to answer in line with an organisation’s documents without the need to train it from scratch.
In practice, implementing Retrieval-Augmented Generation includes an index (e.g. Pinecone, Weaviate, Azure AI Search), connected knowledge sources (e.g. Confluence, SharePoint) and an LLM (e.g. Azure OpenAI, AWS Bedrock). This means responses can include quotes and links to documents, which makes auditing easier and limits the problem of “hallucinations”. If predictability and control are the priority, RAG provides more verifiable answers than the model alone without access to company materials. This approach works particularly well where compliance with procedures and quick access to the right fragment of documentation matter.
Assessment of AI model quality
Assessing AI model quality involves selecting metrics and tests suited to the type of task so that results can be compared over time and between versions of the solution.
For classification, metrics such as F1, precision and recall are used, and for forecasts MAPE or MAE, because they directly describe the accuracy of predictions. In LLM systems with RAG, it is crucial to measure “groundedness” (the share of answers supported by sources) and “answer rate”, meaning how often the system actually answers the question. In practice, a set of 200–1000 test questions is built and the model’s results are compared with the reference base and between prompt versions. This approach makes it possible to catch quality regressions before users notice a drop in response quality.
- 01Metric matchingTo the type of task
- 02Metrics by task (Classification/Forecasts)Accuracy of predictions. Classification: F1, Prec., Recall. Forecasts: MAPE, MAE.
- 03Quality of LLM systems (RAG/Groundedness)Support from source answers
- 04Comparing over timeBenchmark tests and versions
“Comparable and accurate results prevent quality regression.”
Implementation and maintenance costs of AI
The costs of implementing and maintaining AI in a company arise primarily from licensing fees, token usage, infrastructure and the ongoing maintenance of the solution.
In generative AI, tokens are often the most expensive component, and the cost rises with the number of queries and the length of the context, which is why it usually pays to limit context and shorten prompts. In practice, teams use response caching and choose “small” models for simpler tasks so as not to burn through the budget with every interaction. A good pattern is a pre-filter (classifier) that passes only 20–30% of more difficult cases to a more expensive model, while handling the rest with rules or a cheaper version. Costs planned this way are more predictable and easier to control at scale.
Operational model and AI management
An operational model and AI management means an approach in which the solution is treated as a product rather than a one-off implementation project.
In practice, this comes down to assigning a business owner from the outset, involving the data/ML team, security and operations (MLOps/LLMOps), so that maintenance and development proceed in a predictable way. When AI runs in production, roles, responsibilities and the update cycle become key, not just “delivering the POC”. This setup also makes it easier to enforce quality standards and respond to changes in processes and documents.
AI product management should include regular quality reviews and knowledge updates, because responses and recommendations depend on the freshness of the sources and the way the system operates. A practical pattern is quarterly reviews of the bot’s response quality and updating the knowledge base after procedural changes. With a steady maintenance cycle, you reduce the risk that the tool will “age” after deployment and stop being used. This approach also supports operational control as the solution scales to additional teams.
Legal and regulatory risks when implementing AI
Legal and regulatory risks when implementing AI relate primarily to GDPR, data confidentiality and the licensing terms and rules for processing information.
If the solution involves customer data or sensitive documents, it is usually necessary to implement anonymisation or pseudonymisation, access control and a record of processing activities. Equally important are data processing agreements with the cloud provider, so that roles and responsibilities are formally regulated. In practice, the question is not whether you “can” upload data into the model, but whether it is compliant with the company’s policies and legal requirements.
In regulated industries such as finance or medicine, an EU region and “no training on your data” options (e.g. in Azure OpenAI) are often chosen to limit the risk of unwanted use of data. Content filters provide an additional layer of protection, reducing the likelihood of generating messages that do not meet requirements. Such architectural decisions are best made at the outset, because retrofitting compliance to an already working system can be more difficult organisationally. The earlier you define the rules for processing and access, the easier it is to implement AI without legal and reputational risk.
FAQ
Frequently asked questions
How do you map company processes to tasks for AI?
Start with a list of processes such as sales, customer service, finance or logistics, then break them down into repetitive and measurable tasks. Look for areas with high volumes of work and bottlenecks, because that is usually where AI delivers the fastest results.
Should every AI project be implemented at the start?
No, because prioritisation is best based on business value, feasibility and risk. It is best to choose projects with a clear KPI, access to data and an acceptable level of legal and reputational risk.
When is it better to choose RAG instead of the LLM model alone?
RAG works well when AI needs to answer based on company documents, procedures or FAQ. It provides more verifiable answers, often with citations and links to sources, rather than relying solely on the model to generate responses.
How do you assess the quality of AI models in a company?
Metrics need to be matched to the task: for classification, use metrics such as F1, precision and recall, while for forecasts use MAPE or MAE. In LLM systems with RAG, groundedness and answer rate are also important, as well as tests on a reference question set.
How do you reduce the cost of implementing and maintaining AI?
Costs rise mainly because of licences, tokens, infrastructure and maintenance, so it is worth limiting context and shortening prompts. It also helps to cache responses, use cheaper models for simple tasks and add a pre-filter that sends only more complex cases to the more expensive model.
What legal risks need to be taken into account when implementing AI?
The main ones concern GDPR, data confidentiality, licences and rules for processing information. For customer data or sensitive documents, anonymisation or pseudonymisation, access control, a record of processing activities and appropriate agreements with the cloud provider are usually needed.
