From hype to business value – building powerful and responsible AI solutions

AI promises smarter ways of working, faster decisions, and new opportunities. But the path from potential to real business value isn’t always obvious — and without careful consideration, the technology can do more harm than good.

In this article you'll learn how AI can be used as a powerful, responsible part of the business. Not to impress – but to solve real problems, for real.

Beyond ChatGPT - Build AI that uses your own data and gets the job done directly

AI is no longer news. Many people use tools like ChatGPT daily to write copy or GitHub Copilot to code faster. The next step is to move from general web tools like ChatGPT to solutions that are directly integrated with the company's own data, processes and use cases. That way you avoid copying and pasting information between systems and chat because the AI already has access to all relevant data and you don't need to spend time instructing it in detail since it is designed for a specific task. The result is tools that not only assist but, with AI's strength, can analyze, filter, recommend, find patterns and formulate responses tailored to the business.

You don't need to build your own models – the APIs already exist and are available from, for example, OpenAI and Microsoft. The real challenge is turning the technology into business value: understanding the problems, packaging them the right way and building applications where AI solves the task in an efficient, ethical and user-friendly way.

When should you use AI – and when should you not use AI?

With the hype around AI it’s easy to think you should plug AI in everywhere and that it’s automatically the best technology to solve every problem. But not everything gets better just because it’s AI. On the contrary, there are many problems that are solved more simply and more robustly with classical programming, rule engines or computational models. For example, if you need to simulate the development of an occupational pension over time or calculate shipping costs based on weight, distance and transport agreements, there are crystal-clear rules for how this should be calculated. Because AI models (specifically LLMs) predict words rather than perform deterministic calculations, they can create value in contexts where not everything is crystal-clear but requires interpretation. But if you use them for tasks that require exact answers you risk introducing errors because they are not built for that kind of calculation

AI makes a real difference in situations where data is complex, unstructured or where multiple sources need to be evaluated and interpreted together. This can involve analyzing thousands of customer comments, understanding natural language in combination with numbers, or identifying patterns that aren’t preprogrammed. In short: use AI when the problem requires interpretation, context or flexibility – not when there are already clear rules that produce clear answers

Custom AI vs standard tools – what delivers the most business value?

The major general products like Teams, Microsoft Office, Slack, Salesforce, Jira and others are already well on their way to integrating AI in a way that provides general added value for most users. Features such as text assistance, meeting summaries and automated tasks already exist or are coming soon. There is therefore no reason to build your own versions of these features. They will be covered by the standard tools

However, there are always problems and tasks that are specific to a particular business or industry. They are often too complex to be solved solely with general tools and at the same time too narrow for the vendors of general office software to prioritize building them in. Here an opportunity arises: using AI to transform your organization’s own data into solutions that strengthen competitiveness, streamline processes and create new value for both employees and customers

Examples of tailored AI solutions

The truly exciting developments happen when AI is connected to the data and processes that already exist within the organization. Then you can build solutions that not only streamline routines but also detect risks, find patterns and provide decision support in ways that general standard tools never can. Here are some possible scenarios based on data and processes most organizations already have, where AI can add something entirely new.

The Contract Assistant

Many organizations sit on hundreds of contracts in PDFs and folders that no one really has an overview of. An AI can read, interpret and monitor the contracts: warn when a notice period is about to expire, find unfair terms and compare contracts with one another

Implementation: Contracts are retrieved and indexed using traditional development. An AI model interprets the content, extracts key clauses and flags risks. The results are presented in an interface with alerts and reports

The Cost Analyst

Hidden costs often lurk in processes such as invoice handling, logistics or project routines. AI can analyze large volumes of transactions and documentation to identify systematic errors, bottlenecks or inefficient processes. Where traditional BI tools stop at numbers, AI can help explain why problems arise and suggest improvements

Implementation: Data is fetched from the ERP system and structured. The AI model is used to analyze free text and descriptions and delivers insights at the process level

The Customer Barometer

Companies often lose customers without fully understanding why until it is too late. An AI can analyze support cases, proposal comments and feedback in emails to detect early signs of dissatisfaction. When the same signals begin to appear across multiple customers the system can warn of a churn risk and provide concrete suggestions for strengthening the relationship

Implementation: Data is fetched from CRM, support and quoting systems. The AI model analyzes free text to identify churn risk. Results are presented as alerts and recommended actions in a dashboard or directly in the salesperson’s tools

The Support Crystal Ball 

Instead of merely categorizing incoming tickets, AI can foresee what will become the next big problem. By analyzing early signals in customer support, forums or social media it can warn in advance: “This product update will likely generate a wave of questions next week – prepare guides and training now”

Implementation: Tickets are retrieved from CRM or support systems. An LLM analyzes free text, identifies trends and combines this with time-series statistics to provide forecasts

Ethics in AI solutions

AI can deliver great value when used thoughtfully. Poor design, on the other hand, can have negative consequences for both employees and company culture. Therefore we need to discuss how to relate to ethics, trust and power balance when building AI solutions

• Preserve employee influence
  Swedish work culture is built on employees having significant influence over their work while also bearing substantial individual responsibility. This has proven successful and created strong trust between employees and management. AI solutions must not risk undermining this trust by centralizing decision-making power. Otherwise the consequences can be reduced motivation, a poorer work environment and lost innovative capacity. On the contrary, AI should be designed to strengthen a culture of employee responsibility and participation so the technology becomes a support rather than a control mechanism
• Never single out individuals
  When conclusions are drawn at an individual level they will always risk being perceived as surveillance, which can create distrust and feel invasive. Therefore AI solutions should be designed to analyze processes, workflows and customer relationships, never individual employees. The purpose should be to strengthen the organization as a whole by giving employees powerful tools, not to reduce employees to data points
• Efficiency culture gone too far
  Measurements can lead to improvements but can also turn efficiency into a counterproductive goal in itself. If you push too hard you risk squeezing out quality, care and learning. AI should be used to remove unnecessary friction and repetitive tasks and to raise service and quality, not to maximize every minute of the working day

The goal is solutions that free up time, create security and provide support. Then AI becomes a tool that strengthens processes and relationships without undermining trust in the workplace. A well-considered AI service can reinforce the culture of responsibility and collaboration that already exists and make it easier for people to succeed in their work

Summary - How to succeed with AI

• Don’t build custom solutions where standard products already meet the need. Focus on what is unique to your business and unlikely to be standardized
• Use AI for tasks that require interpretation, context or flexibility. Choose traditional technology when the task is rule-based and the solution is more reliable with deterministic methods
• Leverage the major providers of AI APIs that exist. They go a long way and are often cost-effective even with large data volumes or frequent usage
• Think through the consequences. Ensure the solution is ethically defensible and something you can stand behind

Would you like to explore how AI can be used in a way that strengthens your particular business? Feel free to get in touch and we’ll talk further

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