Better Futures AI Tool Boosts Engineer Productivity

Engineers’ Productivity Bottleneck

Engineers seek to innovate; however, regulations, company structures, and routine administrative tasks often hinder this process. Recent studies indicate that engineer productivity ranges from 25%–30%, with much of their time spent on low-level documentation such as assessing test plans.

Administrative work slows down product development, delaying innovations from reaching the market.

Generative AI Offers a Solution

Dublin-based startup Better Futures believes generative AI can help. Its Engineering Verified Assistant (EVA) 2.0 automates routine engineering tasks, allowing engineers to focus on higher-value work. The platform integrates advanced AI assistants into the engineering process to improve efficiency and workflow management.

The company’s goal is to free engineers from paperwork and low-level tasks, unlocking productivity and enabling innovation in engineering and manufacturing environments.

Anthony McLoughlin, founder and CEO of Better Futures, says that adoption of generative AI among engineers is low.

“The issue is that GenAI is not being adopted. The adoption is very low – sitting at around 4%,” he says.

While generic LLMs such as ChatGPT are useful, McLoughlin stresses that engineers require reliable AI models that include regulatory knowledge and control mechanisms.

“Generic LLMs do not work with engineering because they lack accuracy and control, they do not blend well into the complex ecosystem, and they don’t contain the regulation and necessary knowledge,” he says.

“This is where we come in. We believe that for AI to work for engineers we need a dedicated vertical AI platform. This is why we build EVA and we uniquely focus on paperwork automation instead of design automation like others.”

A recent MIT study found that 95% of generative AI initiatives fail because companies rush to implement the technology. Better Futures has taken a measured approach.

“We use micromodels built on our proprietary engineering knowledge layer to automate reports and checks with precision. By capturing and reusing this expertise in our unified knowledge base, we combine it with generative AI to deliver accurate, controlled, and reliable results for engineers,” he says.

Adoption Challenges in Engineering

Despite the potential, adoption of AI in engineering remains slow, particularly in regulated sectors. McLoughlin points to the need for engineers to trust the technology while maintaining oversight.

“An LLM can offer 80% accuracy, which is fine for fields like marketing, but in engineering, where you are building products like planes, 80% accuracy could be dangerous,” he says.

Better Futures addresses this with AI-powered digital assistants designed to automate routine engineering tasks, manage technical information, and guide workflows.

“These assistants automate the low-level task and put together a first draft. EVA then explains why it produced that result and where the information came from. Engineers must validate the work themselves,” he says.

“Simply put, EVA does the low end paperwork so engineers can focus on validation and innovation”

Validated knowledge is added to the system’s knowledge layer to improve accuracy over time.

Highlighting the Benefits of EVA

Better Futures has documented the impact of EVA through use cases, most notably with ABB.

“ABB makes complex products, and we are helping them by automating their relay engineering-to-order process. Companies like ABB have issues configuring a product for a customer need – it is complex knowledge fragmented across the organisation,” McLoughlin says.

Offer creation and engineering to order is today a highly manual process for engineering sales teams. EVA is starting to prove that 50 to 80% can be automated which has a phenomenal business impact potential to increase direct revenue and provide a competitive advantage. 

In the medical technology sector, EVA can automate 50% of the certification process, enabling manufacturers to bring products to market 10–20% faster.

Overcoming Setbacks in AI Accuracy

McLoughlin notes that ensuring accuracy and control is essential when using AI in engineering. EVA’s design ensures engineers remain the final validators, mitigating risks associated with errors.

Industry Recognition and Awards

Better Futures has received recognition for its work. The startup won the Distribution Solutions Award in the ABB Startup Challenge 2025, selected from over 155 applicants across 37 countries. This partnership allows collaboration with ABB for commercialisation.

Better Futures also won the Irish Times 2024 Innovation Awards, further solidifying credibility in the engineering sector.

Adoption Barriers and Market Perception

Despite growing interest in AI, McLoughlin emphasises that misinformation and hype hinder adoption.

“There is a lot of rubbish around AI that is not helping, such as AI taking jobs. That is not going to happen – our business is about augmenting AI to help humans and engineers, not replace them,” he says.

A survey by Bain & Company showed that nearly two-thirds of aerospace professionals rejected at least one AI use-case, demonstrating the cautious approach in regulated industries.

Other companies using EVA include Lifecycle Engineering (UK) and Olikop Aerospace (Germany).

Future Plans for Better Futures

Better Futures recently raised €500,000 in pre-seed funding to accelerate EVA’s deployment, backed by an angel investor.

“The mission is bigger than me, bigger than the company. Our company is driven by this mission, and I think we offer a generational opportunity for engineers and society to build a better physical world,” McLoughlin says.

“We tried to fix this problem 15 years ago and couldn’t because the technology was not there. Now we have the technology to drive engineers forward, freeing up their time.”

McLoughlin cites research indicating that even a 1% productivity increase across 20 million engineers globally equates to the engineering effort of the Apollo programme, highlighting the potential of scaling generative AI in engineering workflows.

“In the next five years, we hope to reach that 1% productivity globally—but the focus has to be adoption,” he says.

“If you are an Irish or UK manufacturer serious about productivity, we can help you find use cases and get our AI up and running in weeks.”