What user problemshould AI solve?
A specific task creates a clearer implementation target than a broad objective to “add AI”.
Add useful AI capabilities to the software your customers and teams already know. WDI integrates features such as conversational search, document assistance, and content generation around your existing workflows and data access requirements.
Adding AI to an existing product means introducing a defined intelligent capability into software that already has users, workflows, data and technical dependencies.
That capability might help users find information, interact conversationally, receive recommendations, summarise or interpret information, reduce repetitive actions, work with documents more efficiently, identify patterns or receive decision support.
The existing product remains the foundation. AI enhances specific experiences or operations. Some products can use existing APIs, while others may need better integrations, data access or modernisation.
AI is worth evaluating when there is a clear product problem that conventional functionality does not address as effectively.
AI may not be the right choice when the business problem is unclear, reliable information is unavailable, conventional automation already handles the task well or the operating risk outweighs the likely value.
The first decision should be where AI creates enough value to justify the additional complexity.
A specific task creates a clearer implementation target than a broad objective to “add AI”.
AI quality and feasibility depend on the information or data available to support the use case.
Existing architecture, APIs, workflows and permissions determine practical integration options.
Some product behaviour is better handled through conventional rules or software logic.
Higher-impact, uncertain or exception-based outputs may require human review or approval.
Fallback behaviour should be defined before the feature becomes part of an important workflow.
A useful AI feature is not defined by how advanced the underlying model is. It is defined by whether it improves the intended product task under realistic operating conditions.
Share your existing product, the user problem you want AI to address and any known data or integration constraints.
Discuss Your AI IntegrationIntroduce a defined AI capability into an existing product without turning the entire application into an AI-led experience.
Add suitable generation, summarisation, interpretation or conversational functionality where approved product information and evaluation requirements are clear.
Introduce assistance within a product for defined user tasks while retaining appropriate escalation and human ownership.
Add recommendations where suitable user, product or business information can support relevant suggestions.
Help users locate relevant information where conventional navigation or search does not sufficiently support the task.
Help users extract, organise, summarise or work with appropriate document information within the product.
Introduce AI assistance into selected workflow steps where interpretation adds value beyond fixed rules.
Use suitable historical information to support forecasting, classification or recommendations where the use case justifies it.
AI integration should be assessed as part of the existing product, not as an isolated technical component.
The problem AI is expected to address and why it matters
Who will use, manage or be affected by the capability
Current architecture, workflows, interfaces and dependencies
Whether the required data or business information is available and usable
What information may be processed, which sources are appropriate and what restrictions apply
APIs, system access, permissions and application constraints
How usefulness is assessed, when people review outputs and what happens when AI is uncertain
Whether direct AI integration, product modernisation or another solution best fits the requirements
WDI’s authorised AI approach begins with the business problem, users, existing systems and success measures before recommending an implementation route. The assessment may conclude that AI can be integrated directly, that selected product modernisation is required first, or that conventional software or workflow automation is more appropriate.
Generative AI integration services are relevant when the product needs to generate, summarise, interpret or assist users with information — for example conversational assistance, document summarisation, approved information access, guided content generation or information extraction. Broader AI integration can also include recommendations, predictive analysis, classification or intelligent workflow support where suitable data exists. The right approach should follow the product task; generative AI should not be selected simply because it is the most visible form of AI.
Extend an existing mobile product with suitable AI capabilities and supporting application changes.
Introduce AI-assisted functionality into browser-based products where the architecture and user journey support it.
Develop or integrate AI capabilities within custom software and enterprise applications.
Improve existing architecture or integrations when technical constraints prevent practical AI implementation.
Extend supporting web functionality required by an AI-enabled product experience.
Support application environments and delivery requirements where AI integration changes operational needs.
Add defined engineering or AI capability alongside an existing internal product team.
Add selected AI capabilities to established mobile journeys without assuming the whole application needs redesign.
Extend SaaS workflows, account experiences or product functionality with AI where it supports a defined user outcome.
Integrate AI into browser-based products that already contain established users, information and workflows.
Introduce appropriate assistance, search, recommendations or document support into existing customer-facing platforms.
Add AI capabilities to business software where integration, permissions and operating requirements are sufficiently understood.
Improve employee-facing software with suitable knowledge, document, workflow or decision-support capabilities.
Add AI where discovery, recommendations, support or information handling can improve defined marketplace interactions.
Introduce AI after the core product journey has been validated, or earlier where AI is essential to the proposition being tested.
Resolve architecture or data-access constraints when they prevent a practical AI integration path.
The engineering considerations behind an AI feature: interfaces, information flows, model interaction, evaluation and exceptions, chosen around the product task the capability is meant to support.
Connect suitable AI capabilities to existing product workflows through defined application interfaces.
01Extend application services and business logic required to support AI-enabled product features.
02Define how suitable product information moves into and out of the AI-enabled workflow.
03Define prompts, inputs, context and product interactions according to the intended task.
04Establish practical evaluation criteria around the user task and expected product behaviour.
05Design appropriate review or approval steps where AI outputs should not be used automatically.
06Define alternative product behaviour when AI results are unsuitable, incomplete or unavailable.
07Identify what should be observed after release to support ongoing evaluation and product ownership.
08Help users find relevant information within suitable product or business sources.
Provide contextual support for defined tasks without removing appropriate escalation.
Surface relevant options where suitable information and evaluation criteria exist.
Improve discovery when conventional search does not sufficiently support the user task.
Assist users with suitable extraction, summarisation or interpretation activities.
Support selected repetitive steps where interpretation provides value beyond rules-based automation.
Present useful information or analysis while retaining ownership of important decisions with people.
Adapt suitable experiences where sufficient information exists and the product objective justifies it.
The evaluation method should reflect what the feature is supposed to do. Depending on the use case, evaluation may consider relevance to the user task, completeness of the response, consistency, response quality, task completion, human-review outcomes, workflow effectiveness, product usage and operational performance.
Some outputs may be appropriate for direct user interaction. Others may require review before they influence a business process. Implementation planning should also define who owns the feature after launch, how unsuitable outputs are handled, what should be monitored and when the capability should be reassessed.
These examples describe possible product applications rather than claims of specialist WDI AI expertise in every sector.
A fraction of what we have shipped.
Use this Solution page when you already have a product or MVP and need to decide where and how AI should be integrated into it. Use WDI’s broader Artificial Intelligence Development Services page when the requirement spans AI opportunity assessment, AI product development, machine learning, generative AI, automation or a wider AI initiative. If the primary requirement is customer-support automation or an AI chatbot, use Reduce Support Load with AI. Related routes: AI Mobile App Development, AI Web Application Development, AI Software Development, Application Modernization, Cloud Applications & DevOps, Team Augmentation and Launch a New Product or MVP.
Evaluate and implement AI capabilities where intelligent processing can add practical value to products, software or business workflows.
Plan and build mobile applications that connect users, workflows, data and supporting business systems.
Develop purpose-built software platforms, enterprise applications and operational systems aligned to business requirements and workflows.
Build web applications and digital platforms that support customer, employee or operational experiences.
Research, define and design user journeys, interfaces and product experiences for web, mobile and software platforms.
Extend internal engineering capacity with defined roles, responsibilities and delivery governance.
Address broader business objectives through software, AI, integration, automation, data and digital-product solutions.
The initial conversation should establish what the existing product does today, which user or business problem you want AI to address and what constraints may affect implementation.
Useful context may include the current product and primary users, the AI capability you are considering, existing workflows and user journeys, available business information or data, current APIs and integrations, known product or architecture constraints, where human review may be required and how you would recognise a useful result.
WDI can review that context and discuss whether the likely next step involves AI opportunity assessment, technical discovery, product changes, integration planning or another approach.
Any formal discovery, design or development engagement is scoped and agreed separately.
Sometimes. Feasibility depends on the product architecture, available APIs, information, permissions and workflows. Some products can support targeted AI integration, while others may need selected modernisation first.
AI integration services focus on adding AI capabilities to an existing product or system.
Broader AI development services may include opportunity assessment, new AI products, machine learning, generative AI, automation and other AI initiatives.
Not every AI use case depends on large proprietary datasets. The information requirement depends on the task. Some use cases need suitable business content; predictive approaches may require relevant historical data.
No. Generative AI is suitable for certain information and conversational tasks. Other requirements may be better served by conventional software, rules-based automation, recommendations or machine learning.
The product team should identify what information the AI may process, which sources are appropriate, who should have access and what usage restrictions apply. Specific legal or regulatory requirements should be reviewed separately where relevant.
The appropriate controls depend on the task and impact. Product planning may need to define permissions, human review, fallback behaviour, evaluation criteria and ongoing ownership.
Yes. WDI provides team augmentation and dedicated-team models. The appropriate arrangement depends on the capability required, responsibilities, governance and ownership within the engagement.
The strongest AI feature is not necessarily the most complex one. It is the capability that improves a defined user or business task without creating unnecessary product or operational complexity.
Share your existing product, the problem you want AI to address and any known information, integration or architecture constraints. WDI can review the context and discuss the most appropriate next direction.
