Creating the conditions for scalable AI
Businesses have the ambition to move forward with their AI plans, but getting the results they want remains a challenge. Early pilots show promise, but they often fail to scale when use cases are unclear, data is fragmented, governance is missing or expectations aren’t aligned with what AI can realistically achieve.
To get meaningful results, businesses need the right foundations in place. That means preparing their data before applying AI, defining clear governance, aligning AI with business strategy and identifying use cases that support real processes and everyday work. As AI continues to evolve, tools such as agentic AI can support more complex workflows by planning, acting and interacting with systems within defined boundaries.
Turn AI ambition into measurable value with the right AI partner
At Columbus, we’re an experienced AI implementation partner that brings business process expertise, industry knowledge and deep technology experience together to help organisations apply AI in ways that create measurable value. We take time to understand your business goals, current processes, data readiness and what’s realistic to achieve. From there, we help you identify and prioritise the most valuable AI use cases. This includes assessing the business case, required data, technical feasibility, governance needs and expected impact.
We then support delivery through a dual-track approach, progressing practical AI use cases while building the foundations needed to scale. This allows organisations to start realising value early while strengthening their strategy, data, infrastructure, governance and operating model over time. As a Microsoft partner, we help organisations make use of Microsoft AI, data and automation technologies, including Copilot, Power Platform, Azure AI and Microsoft Fabric to deliver AI solutions that connect with your wider systems and processes.
Build the foundations to realise value from AI
Our approach focuses on three connected areas when helping organisations get more value from AI:
Strategy and operating model
AI needs to be part of the wider business strategy. This means defining where AI can support business priorities, how teams should work with AI and what needs to change across the organisation to support adoption. We provide guidance on assessing where AI can create the most value in your company, prioritising realistic opportunities and shaping an AI roadmap connected to business outcomes. We also help define the operating model needed to support adoption, including ownership, leadership alignment, ways of working and AI literacy across the business.
Data and infrastructure
AI depends on the quality, availability and structure of the data behind it. Together we understand your current data and technology setup, identify gaps and lay out the steps needed to build the foundations for scalable AI use cases. This includes improving data readiness, integration, infrastructure and the platforms needed to connect AI with your systems and processes, so you have AI outputs the business can trust.
Governance and security
As AI adoption grows, organisations need clear rules around ownership, access, compliance, traceability and responsible use. We support you in embedding governance and security with clear controls for how AI is accessed, managed and scaled. This helps reduce risk, improve trust and make sure the business can introduce AI initiatives safely, with the consistency and control needed to scale over time.
Applying AI to real business challenges
See below real-life examples of how we’ve worked with organisations to deliver measurable value from AI:
Speeding up technical document validation in manufacturing
An international industrial machinery manufacturer was spending significant time handling and validating technical documentation.
By using a Copilot Studio solution with Azure AI components, the company was able to generate and validate technical documents, guide users through document workflows and integrate checks with approval processes. This helped speed up approval cycles, reduce manual workload and improve audit readiness.
Reducing downtime with predictive maintenance
A global industrial equipment manufacturer was relying on reactive maintenance and manual inspections, which created unplanned downtime, costly repairs and limited visibility into machine health across sites.
By using an IoT-driven predictive maintenance setup, the company could detect anomalies and forecast failures earlier. A Power Platform interface also enabled technicians to query asset status and create maintenance work orders, helping reduce unplanned downtime, lower maintenance costs and improve asset availability.
Improving access to complex information with AI search
A national energy distributor needed to search for tenders, rulings and decisions across multiple public websites. The process was manual and slow, with the risk of employees working with incomplete or outdated information.
By building an AI agent with Copilot Studio, the company could automate the collection and search process. This significantly reduced search time, improved decision-making and made information easier for non-technical users to access.
Automating document checks in food and beverage
A global food partner was carrying out a significant amount of manual validation every week. The process was time-consuming and prone to error, with incorrect validation creating significant financial risk.
By automating the extraction and validation of data, the company was able to respond faster and escalate issues immediately. This helped reduce manual work, improve accuracy and support faster action when shipment risks were identified.
Scaling product content with AI in retail
An international retailer was manually creating thousands of product descriptions each year, with content managed through multiple spreadsheets and external consultants.
By using Power Apps, the retailer could generate product descriptions, translations and content drafts in real time. This helped reduce external consultant costs, speed up product launches and improve content consistency across channels.
Improving demand forecasting and inventory planning
An international manufacturer wanted to improve forecasting and inventory optimisation across its operations.
Using Azure Machine Learning and Microsoft Fabric, forecasting models were built using historical sales, promotions and seasonality data. Power BI then provided planning insights to support better decisions. This increased stock availability while reducing excess inventory.
