AI Adoption: Solving What Matters

Successful AI adoption starts with a clear business problem. Organisations need to understand where AI can improve a workflow, what data it requires and how its performance will be assessed.

Candy Zhang, Client Solution Consultant at Beyondsoft’s AI Innovation Center, brings experience in financial services and asset-management systems to this work. Her approach connects business priorities with technical delivery, helping teams move from potential use cases to practical implementation.

Start with the business need

Before choosing a model or platform, identify the task that needs to improve.

Are employees spending too much time searching for information? Does a process depend on repetitive document handling? Are fragmented data sources slowing decisions?

A clearly defined problem gives business and technical teams a shared starting point. It also makes success easier to measure through indicators such as processing time, output quality, error rates or the amount of manual review required.

AI should be assessed alongside other options. Some problems may be addressed more effectively through simpler automation, better data management or process changes.

Build trust into the solution

Generative AI can produce convincing answers that contain factual errors. Managing this risk requires more than selecting a capable model.

Depending on the use case, an implementation may combine:

• Retrieval from trusted sources: Give the system access to relevant, maintained information through retrieval-augmented generation (RAG).
• Clear instructions: Define the task, acceptable sources and circumstances in which the system should express uncertainty or escalate.
• Evaluation: Test outputs against representative examples and agreed quality criteria.
• Human review: Establish which outputs require checking before they inform decisions or trigger actions.

These measures can reduce risk, but they do not guarantee accuracy. Access controls, source quality and ongoing monitoring remain essential.

Focus on practical applications

Three areas provide useful starting points for assessing AI opportunities.

Knowledge management

AI can help employees retrieve, summarise and navigate information across documents and knowledge bases.

The value depends on whether users can find relevant information, check its source and access it within their existing workflow. Permissions must continue to govern which information each user can retrieve.

Document and workflow automation

AI can support tasks involving unstructured information, such as extracting details from invoices or classifying incoming requests.

A practical workflow combines AI with validation rules, exception handling and appropriate approvals. The aim is to reduce repetitive work while keeping responsibility for consequential actions clear.

Forecasting and decision support

Predictive models can help teams analyse patterns in historical data, including changes in demand or customer behaviour.

Their usefulness depends on data quality, suitable evaluation and whether the results support a real operational decision. Teams also need to recognise when changing conditions make earlier patterns less reliable.

Begin with a focused pilot

Choose a manageable use case with a clear owner, accessible data and measurable outcomes.

Before implementation, agree on:

• The current process and its performance baseline.
• The improvement the pilot should demonstrate.
• The data and systems it can access.
• Acceptable error levels and review requirements.
• The criteria for expanding, changing or stopping the pilot.

A pilot should test both technical performance and operational fit. Users need to understand the output, recognise its limitations and know how to handle exceptions.

Treat data quality as an operating responsibility

Incomplete records, outdated documents and inconsistent definitions can undermine an AI implementation.

Data preparation should therefore include ownership, access permissions, maintenance and quality checks. Business teams and technical teams need to work together to keep information useful throughout the solution’s lifecycle.

Scale what proves useful

A successful demonstration is a starting point. Broader adoption requires integration, user training, monitoring and ongoing support.

The strongest case for expansion comes from evidence: a workflow that performs better, users who can work with it effectively and controls that remain appropriate as usage grows.

Beyondsoft helps organisations connect AI opportunities with business needs, data readiness and implementation requirements.

Explore how AI could support a specific workflow in your organisation. Get in touch with Beyondsoft.