How We Work on
IT Project Delivery
IT project delivery covers the path from defining an operational challenge to deploying a working solution. AutomationCore works alongside your people, existing systems and agreed data to build a solution that fits the way you operate – from the first conversation and discovery through development, launch and ongoing improvement. The aim is not a standalone demo: production-ready solution delivery means preparing the solution for use, monitoring and measurement in day-to-day operations.
How We Deliver IT Projects
Our approach to IT project delivery covers automation, AI, data and custom-technology projects, including software project delivery from implementation through deployment and improvement. For us, each project is more than a task or a handover model where a finished system is passed to the client and the work ends. We bring the client team into the process from the start, so both teams can follow the agreed stages, review working components, share feedback and make timely decisions when needed.
-
Each stage informs the next, while testing or feedback can return the team to earlier decisions.
-
Success criteria and a baseline connect technical progress with business impact.
-
AutomationCore coordinates delivery; the client provides process knowledge, access, decisions and user feedback.
-
Results are compared with the initial baseline to identify gaps and guide improvements.
Project Discovery
and Definition
A project discovery phase turns an initial idea or business challenge into a clear, workable plan. How do we do that? Before development begins, we define the problem, scope, systems, data sources, constraints and success criteria. This helps both our team understand what needs to be built and you understand what kind of change it will bring to your team.
We start with a discovery workshop that combines business process discovery with a review of how the process works in practice. That means speaking with the people involved, reviewing the current workflow and systems, identifying risks and checking what data is available. Such conversations often reveal details that are easy to miss at the start, such as manual workarounds, approval steps or dependencies between teams.
For complex projects, an Automation & Data Audit is usually the best starting point. It gives us a broader view of your processes, data and automation opportunities. If the requirement is already clear, the audit can be shortened or incorporated directly into the project.
At the end of this stage of IT project delivery, the client has a prioritised list of opportunities, a process and data map, a recommended scope, agreed success criteria and an initial implementation plan.
Power Automate
Power Apps
Power BI
From Discovery
to Production
In IT project delivery, the software implementation process has seven connected stages. A pilot may use a shorter automation implementation cycle; a complex integration may repeat stages.
1. Discover
We begin by understanding the business problem and the process behind it. Together, we review the systems, data and risks, record the initial baseline and define preliminary KPIs, success criteria and an ROI hypothesis. The result is a prioritised set of opportunities and an initial implementation plan. This stage usually takes 1–3 weeks.
2. Connect
We set up secure access to the required systems and data, assess data quality and access rights, test the connections and agree how information will move between systems. The result is a set of working connections and an agreed access model, with a shared data layer or integration prototype where relevant. This stage usually takes 1–4 weeks.
3. Build & Train
We then build the automation logic or AI agents around the agreed process and select the model or model combination according to the task, cost, security, data location and functional requirements. Evaluation tests and guardrails are used to check expected and edge-case behaviour. This stage produces a working prototype or MVP and usually takes 2–8 weeks.
4. Integrate
The working solution is connected to the client’s workflows and systems. We define which actions can run automatically and which require human approval, then configure permissions, access controls, audit logs and error handling. The result is an end-to-end integration with a traceable history of actions. This stage usually takes 1–4 weeks and may overlap with development.
5. Deploy
Before deployment to production, the IT project delivery process includes functional, integration, security and user acceptance testing. Launch is approved by the client’s process owner together with the AutomationCore project manager or technical lead once the acceptance criteria have been met. The result is a production-ready solution with user instructions, defined responsibilities, monitoring and an agreed support plan. Deployment can take from 2 working days to 2 weeks.
6. Measure
After launch, we compare the results with the agreed baseline and review the relevant KPIs. The first review usually takes place after 2–6 weeks.
7. Improve
The first production results inform the next improvement cycle. We use system logs, client feedback during implementation, user feedback after launch and KPI results to refine the solution, address remaining gaps and plan further changes.
Working Alongside Your Team
Remote Delivery
Work remotely through shared digital channels.
Meet weekly, with more frequent contact during development and launch.
Provide a process owner, technical contact, access, data samples and timely decisions.
Join user acceptance testing and review working components.
Best suited to projects that do not require continuous observation.
Forward-Deployed Collaboration
Work remotely and on site when the project requires it.
Collaborate directly with process owners and daily users.
Observe the real process in its operating environment.
Give faster feedback on working components.
Best suited to complex projects connecting delivery with operations.
Working With
Real Systems and Data
Power Automate
Power Apps
Power BI
For real-world implementation, effective IT project delivery requires an accurate understanding of real systems and real data, including the working practices they support. Development and initial testing take place in a secure environment separated from production, using anonymised data or data provided under the agreed security model. This allows the solution to be tested against realistic operating scenarios without testing directly in the live environment.
Before launch, AutomationCore tests the solution first. Client process owners and end users then validate it through UAT against realistic operating scenarios. Their feedback is incorporated before the agreed version is released into production.
Ready for Real Use
-
Works in a real environment. The solution is tested across the agreed systems and workflows, using real operating conditions.
-
Uses real data safely. Data quality, access permissions and security controls are checked before the solution is connected to your operations. Development and testing use anonymised data.
-
Accepted by the people who use it. Client process owners and end users complete UAT before launch.
-
Controlled and traceable. Logging, monitoring and human approval are defined for higher-risk actions, including AI-supported decisions.
-
Ready to operate after launch. Documentation, responsibilities, training and support arrangements are prepared so the solution can be used and maintained in production.
-
Tested for the project’s risks. Load and performance testing, rollback and backups are added where required.
What Happens
After Launch
Measure and Improve
Results are compared with the baseline using KPIs such as time saved, process duration, manual actions, errors and adoption.
Stabilise
Following the launch stage of IT project delivery, an agreed hypercare period (usually 2–4 weeks) covers active monitoring and correction of issues.
Maintain Continuity
Baseline hypercare is included. Longer-term support, SLA arrangements and maintenance are agreed separately. Documented source code ownership arrangements can support future maintenance and development.
Ready to
Start a Project?
A great first step in IT project delivery is a conversation with us. Let’s talk about the process, systems, data and results you want to improve. From there, we can see whether an Automation & Data Audit would help define the priorities and next steps before automation implementation.
FAQ
A project usually begins with a conversation about your business’ problem, current processes, systems, data and intended result. The project discovery phase then defines the scope, participants, constraints, risks, initial baseline, success criteria and preliminary KPIs before development. To sum up, it produces a prioritised set of opportunities, a process and data map, and an initial implementation plan.
After discovery, the team confirms the priorities and scope, arranges secure access, and connects the required systems and data. The software delivery process then moves through development, integration, user testing and deployment to production. Some stages may even overlap or repeat depending on the project.
A small pilot may take several weeks, while a production-ready solution involving several systems can take several months. The exact IT project delivery timeline depends on the project scope.
Usually, clients need to appoint a process owner and a technical contact, provide the required access and some representative data samples, and make time for decisions, feedback and user acceptance testing. Throughout IT project delivery, they join the kick-off, progress meetings and reviews of working solution components, with AutomationCore working alongside the client team to keep development aligned with how the business actually works.
Remote delivery is organised through shared digital channels, a main AutomationCore contact and regular meetings, with more frequent communication during active development or launch. Meanwhile, forward-deployed collaboration adds closer work with process owners and users and may include time on site when direct observation of the operating environment is needed.
Usually not. The solution can connect with suitable existing CRM, ERP, Microsoft 365, SharePoint, email, databases, APIs and other infrastructure.
Yes. An AI proof of concept or pilot can test the main value and technical feasibility hypothesis before a wider implementation. A production-ready solution goes further: it is integrated with the required systems, accepted by users, documented, monitored and prepared for day-to-day operation
Data use and storage are defined in the project agreement and solution design. Enterprise services, access controls and security measures are selected according to the client’s requirements, including requirements relating to model training and data location.
Testing includes functional and integration checks, security and access review, internal validation and user acceptance testing. For AI-supported solutions, evaluation tests, access limits, audit logs, monitoring and human approval for higher-risk actions help control how the solution behaves before and after launch.
Agreed KPIs are compared with the initial baseline after launch. Measures may include time and costs saved, process duration, the number of manual actions and errors, adoption, service quality and other business results agreed during discovery.
The solution enters an agreed stabilisation period during which its performance and any issues identified after launch are monitored. Results are measured against the agreed KPIs, while longer-term support, maintenance and improvement cycles are arranged separately according to the solution’s requirements.