How to Get AI Approved in Your Engineering Organisation: A Practical Implementation Guide
Key Takeaways
AI excels at automating middle-stage engineering tasks (code lookups, design iterations, calculations) whilst engineers remain essential for problem definition and solution validation at the start and end of each workflow.
Getting AI approved requires three things: showing clear value through specific examples, making sure your basics are in place (quality tools, organised data, clear processes), and addressing IT security concerns with a contained system approach.
Integrated engineering platforms provide the ideal environment for AI, offering pre-approved infrastructure where tools, data, and AI capabilities work together within secure boundaries that satisfy both IT and management.
Why Engineers Need AI Tools Now
Engineers want to use AI in their daily work. More importantly, they'll soon need to. As AI capabilities improve and competitors adopt these technologies, the question isn't whether AI will transform engineering workflows but how quickly your organisation can implement it effectively.
Understanding where AI fits into engineering processes is critical for successful adoption. Every engineering problem has three distinct phases: the beginning, middle, and end. AI demonstrates its greatest value in that middle section, automating the repetitive, time-consuming tasks that don't require senior engineering judgement but consume significant resources.
The middle phase includes looking up design codes and standards, checking compliance with methods and specifications, running calculations within set parameters, and iterating on designs to meet requirements. These are precisely the kinds of rule-based, repeatable activities where AI tools excel. Rather than having experienced engineers spend hours on code lookups or design iterations, AI can handle these tasks in minutes, freeing up time for higher-value work.
However, the beginning and end phases remain firmly in human control. At the start, engineers must define the problem clearly, translate it into terms AI can understand (through effective prompting), and specify the constraints and requirements of the solution. Similarly, at the end, engineers must check and validate AI-generated solutions, verifying outputs against safety standards, checking regulatory compliance, and taking professional responsibility for the final design.
This division of responsibilities represents the future of engineering practice. Getting comfortable with AI as part of your workflow isn't just beneficial for efficiency, but rather, it's becoming essential for staying competitive in the years ahead.
How to Get AI Approved in Your Organisation
Getting AI tools approved in your engineering organisation requires more than enthusiasm about technology. You need a structured approach that addresses real concerns whilst showing tangible value.
1. Identify Specific Problems AI Can Solve
Start by pinpointing genuine gaps in your current workflows where AI could make measurable improvements. Don't lead with the technology, lead with the problem. Are design reviews taking weeks instead of days? Are engineers spending too much time on code compliance checks? Does your team repeatedly perform similar calculations manually?
Document the time cost of these bottlenecks. If three engineers each spend five hours weekly on design code lookups, that's 780 hours annually, which is nearly a half-time position devoted to tasks AI could handle. This quantification transforms vague efficiency claims into concrete business cases that management can evaluate against costs.
2. Demonstrate AI's Capability for Your Use Cases
Managers and IT teams rightfully worry about accuracy, reliability, and risk when considering AI tools. Your job is showing confidence through evidence, not just claims. Run pilot tests on non-critical projects if possible. Document examples from similar engineering firms that have successfully implemented AI for comparable tasks.
For suggestions on using AI in engineering workflows, consider starting with low-risk, high-frequency tasks. Technical report generation offers an excellent starting point, that is, the combination of structured data and standardised text makes it ideal for AI assistance whilst allowing engineers to verify outputs easily. Design code compliance checking represents another strong initial use case, where AI can flag potential issues for engineer review rather than making final decisions.
Other practical applications include preliminary design iterations within set parameters, calculation checking and error-spotting, documenting standard engineering processes, and extracting data from technical drawings or specifications. Heck, even just checking your report before it goes to your senior engineer for approval! Each of these use cases shares common characteristics: they're repetitive enough to justify automation, structured enough for AI to handle reliably, and critical enough that human oversight remains in the loop.
3. Develop an Implementation Strategy
Your AI implementation plan should address several key areas beyond the tools themselves.
Training: how will team members learn to use AI effectively? This includes both technical skills (how to prompt AI systems, interpret outputs, verify results) and professional judgement (when AI is appropriate, when human expertise is required).
Workflow integration: where does AI fit into existing processes without disrupting productive work patterns? Rather than forcing engineers to adopt entirely new workflows, identify specific steps within current processes where AI can help with existing practices.
Success metrics: what measurable outcomes will show AI's value? This might include time savings on specific tasks, reduction in errors or rework, increased design iteration capacity, or faster response times for routine engineering requests. Establish baseline measurements before implementation so you can demonstrate actual improvements.
4. Present Concrete Use Cases, Not Solutions Looking for Problems
The weakest AI proposals position the technology as inherently valuable and search for places to apply it. Strong proposals identify existing problems and show how AI provides better solutions than current approaches.
Frame your case around specific, repeatable scenarios: "Our team currently spends X hours per week manually checking design compliance with Building Code Y. AI tools can perform initial compliance scans in minutes, flagging potential issues for engineer review. This would free Z hours weekly for value-added engineering work whilst improving consistency."
The Foundation for Successful AI Implementation
Before AI can work reliably in engineering workflows, three foundational elements must be properly in place: quality tools, organised data, and clear processes.
High-Quality Tools and Well-Organised Data
The engineering software that feeds into and works alongside AI must be robust and reliable. Poor-quality tools produce unreliable inputs, and unreliable inputs guarantee AI failure. Third-party engineering software, home-grown Python scripts, and sophisticated web applications all have valid roles in modern engineering workflows. What matters is that each tool reliably does its job and produces outputs in formats that other tools (including AI) can use effectively.
The programming principle "garbage in, garbage out" applies particularly strongly to AI systems. Engineering data must meet certain quality standards: accuracy (data reflects actual conditions and requirements), proper versioning (you can identify which data version was used for specific analyses), clear documentation (information explains what data represents, its source, and limitations), and appropriate structure (data is organised logically and consistently across projects).
Achieving this requires data governance, that is, the policies and procedures ensuring data quality and consistency across teams and projects. For organisations assessing their readiness for AI, a structured framework proves helpful (see our article on this!). A good approach examines five critical layers: data governance and management as the foundation, system integration to enable data flow, AI and automation infrastructure to deploy capabilities, business-driven use cases to deliver real value, and competitive differentiation to create lasting advantages.
Well-Defined Processes
AI works most reliably when it can follow established workflows rather than inventing new approaches. This doesn't mean that every process must be fully documented and optimised before considering AI (that'd be way too unrealistic!). It does mean that common workflows should be reasonably understood and consistently applied.
When processes are clear, AI can follow them reliably. Engineers can specify "perform structural analysis following our standard workflow for steel frame buildings" with confidence that AI understands what that means. The combination of quality tools, organised data, and clear processes creates an environment where AI can work safely and effectively, transforming it from an unpredictable experiment into a reliable part of engineering practice.
Addressing IT and Management Concerns: The Engineering Platform Approach
When IT teams and managers express concerns about implementing AI into engineering workflows, these worries typically stem from legitimate questions about security, control, governance, and risk management. The solution isn't dismissing these concerns but addressing them through smart choices about where AI lives.
A engineering platform approach offers a compelling answer to many of these challenges. Rather than deploying AI freely across network drives or within sprawling SharePoint structures where data governance becomes nearly impossible, consider a single platform that brings together design tools, design data, and deliverable management in one contained system.
This approach transforms AI from a potential security and governance nightmare into a manageable, auditable part of your engineering infrastructure. IT teams can approve the platform once, establishing security protocols, authentication requirements, access controls, and data protection measures at the platform level. Every AI implementation within that engineering platform inherits these controls automatically, removing the need for repeated security reviews and reducing friction.
All engineering data lives within the platform's governed environment. AI tools can access this data within defined security boundaries, apply AI capabilities to engineering tasks, and generate outputs that remain tracked and versioned within the system. There's no need for AI to access unsecured network locations or extract information from email threads as everything operates within a controlled, auditable environment.
The engineering platform can connect sophisticated simulation tools with structured design databases in controlled, cloud-based environments. Rather than the traditional fragmented workflow (e.g. run simulation, export to Excel, manipulate data, create charts, discover errors, repeat) integrated platforms allow seamless process flow. Engineers define design parameters digitally, run simulations directly within the platform, and see results processed automatically, with complete version control and tracking throughout.
Consider technical reporting, a task that often represents a surprising bottleneck in engineering workflows. Traditional approaches involve running simulations and analyses, exporting results to Excel, manipulating data to create tables and charts, and combining numerical results with standardised text from approved templates. Every design iteration or changed parameter requires manually updating the entire report – believe it or not we have a solution for that too.
An engineering platform with AI capabilities transforms this entirely. Interactive interfaces connect engineering data directly with text, creating dynamic reports where analysis and documentation remain synchronised. When underlying data changes, reports can be updated rapidly rather than requiring complete reconstruction. AI can draft standard text sections based on current data and project parameters, allowing engineers to focus on technically demanding portions requiring professional judgement.
This model has proven successful beyond AI implementation. Progressive engineering firms have discovered that automation and integrated platforms for engineering tools and data provide ideal foundations for deploying custom engineering applications. Such platforms handle authentication, data security, version control, and integration with existing systems, whilst engineers focus on building applications that solve specific problems.
Taking Action: Your Path to AI Implementation
Getting AI approved and successfully implemented in your engineering organisation won't happen overnight, but you can take concrete steps now to move the process forward.
Start with education by developing your own understanding of AI capabilities and limitations through experimenting with publicly available tools. Use ChatGPT, Claude, or similar systems (particularly whichever is approved for use in your organisation!) to test how AI handles engineering tasks similar to your daily work. This hands-on experience will inform better proposals and help you answer questions from sceptical colleagues or managers.
Document your use cases by creating a shortlist of 3-5 specific scenarios where AI could deliver measurable value in your engineering workflows. For each use case, document the current process, estimated time investment, potential AI application, expected benefits, and remaining human oversight requirements. This documentation becomes the foundation of your business case.
Find allies who understand both the potential and practical challenges of AI implementation. These colleagues, engineering managers, or IT staff can help refine your proposal, navigate organisational politics, and provide additional credibility when presenting to decision-makers.
Propose a pilot rather than requesting organisation-wide AI deployment. Suggest a limited pilot programme on non-critical work with specific success metrics and a clear timeline (perhaps 3-6 months). Pilots reduce perceived risk whilst demonstrating value that can justify broader adoption.
The engineering profession is at an inflection point. AI tools won't replace engineers because the professional judgement, creativity, and experience required for beginning and ending engineering processes will remain distinctly human. However, AI will significantly boost engineering capabilities, handling repetitive middle-stage tasks and freeing engineers to focus on work requiring their expertise.
Organisations that implement AI thoughtfully now will develop knowledge and competitive advantages that late adopters will struggle to match. Don't wait for AI adoption to be imposed from above. Take initiative, build your case, and help shape how AI enhances engineering practice at your organisation.
