Challenge
- Engineers lose time to manual fee proposals, data entry and task estimation.
- Estimates rely on memory and experience rather than the firm's own project history.
- Siloed systems make it hard to steer on margin and staff utilisation.
An AI strategy and roadmap delivered in 10 weeks for a leading Australian engineering firm, targeting proposals, planning and margins.
3-Minute Read
From a talk at the World Summit AI to the company’s inaugural AI strategy.
“Working with Emma and her team at Ideal Shift AI has been an outstanding experience. Our collaboration began when I attended Emma’s presentation at the World Summit AI, where she provided the clearest and most insightful framework I’ve seen on strategically investing in AI. Impressed by her clarity and practical approach, we commissioned Emma and her team to develop our company’s inaugural AI strategy.”
“Throughout the process, Emma’s team conducted a comprehensive analysis of our operations, including extensive and insightful interviews with our staff. Their meticulous approach and attention to detail resulted in a tailored AI strategy covering Levels 0 to 2, perfectly aligned with our business needs and goals. We greatly appreciated the thorough and collaborative approach, making the development journey both engaging and highly effective. I wholeheartedly recommend Emma and Ideal Shift AI to any organisation seeking to harness the power of AI strategically and effectively.”
At “X”, a leading engineering and design firm in Australia, a significant share of the day was not spent on engineering. Fee proposals were written by hand. Data was re-keyed. Task durations were estimated from memory and experience rather than from what previous projects had already proven.
That costs more than time. It weighs on delivery quality, on consistency between teams, and on the morale of people who joined this profession to design, not to fill in forms.
Ideal Shift AI designed a tailored AI strategy and implementation roadmap using the MIT AI Strategy Framework, custom-fit to “X”’s technology, data and business needs. It was preceded by a thorough analysis of operations, including extensive interviews with staff on the floor.
That analysis made the size of the opportunity concrete and measurable:
In this market, quoting faster is not an internal optimisation. It is time to market: the firm that puts a well-founded proposal on the table first wins the work more often.
“X” is automating fee proposals with GenAI and Azure ML: extracting the scope from the RFQ, suggesting pricing based on more than 500 past projects, and generating a structured proposal. The firm’s experience becomes reusable instead of living in people’s heads and scattered files.
Predictive models estimate task durations and recommend the optimal team setup using past timesheet data. Estimates become traceable, and improve with every project completed.
A centralised Azure Data Lake merges siloed systems, enabling real-time reporting, scalable AI deployment and seamless automation.
Power BI dashboards track profitability and staff efficiency and surface bottlenecks in real time. Course correction happens during the project, not at the post-calculation stage.
We focus on human-centric AI: machine learning combined with the insights of the people who do the work every day. Fast MVPs build momentum, while cloud-native infrastructure ensures the solutions scale. That is why the staff interviews were never a formality — they were where most of the opportunity became visible.
By investing in AI that works for people rather than instead of people, “X” is redefining how engineering firms operate: delivering better results while empowering their teams.
Discover where AI can save time immediately, improve processes and give employees better support.