Preparing for an AI future – are you ready?

Artificial intelligence applications are already impacting our industries, heightening the need for companies to start preparing now for a future with AI to avoid being left behind.

Politecnica, a 52-year-old Italian engineering and architecture consulting firm serving local and international clients, has been exploring AI applications while simultaneously preparing for the European Union’s new AI legislation – the Artificial Intelligence Act.

We asked Politecnica Partner Engineer – Head of International Business Development Enea Sermasi about how the company is approaching AI, including any learnings it can share to help guide consultancies with their preparations for using and integrating AI.


What do you consider AI’s impact will be on engineering consultancies?

Positive impacts:

  • Low-level repetitive tasks will be tackled by AI, leaving more time for high-level decisions.
  • Faster analysis of big data, and more accurate calculations (for example, more accurate and reliable environmental and social impact scenarios).
  • Greater comprehensive analysis of problems (from minor AI input to complete human evaluations).

Overall positive impact: Engineering consultancies will benefit from AI tools in the short to medium term. Consultancies that use AI will replace those that do not.

Negative impacts:

  • Lazy approaches will be enhanced by easy AI-driven responses; lack of commitment.
  • AI is learning from existing feedback on all topics. No genuine creativity can be expected from AI driven outputs (to date).
  • Abuse: using AI for unnecessary tasks, creating addiction and lack of proactiveness (for example, laziness).

Overall negative impact: Average level of firms’ creativity will decrease. Consultancies that do not abuse AI (use AI for specific tasks rather than all tasks) and keep their staff “creative” will benefit.

At Politecnica, we are monitoring AI applications, including studies on the impacts of AI in our industry in Italy. CRESME, a non-profit association undertaking industry research in the construction sector, recently released the following table comparing company outcomes following the use of AI:


Before AI

After AI


Average project duration (months)




Total costs (million €)




Workplace accidents per year 




Resource utilisation (%)




Customer satisfaction (%)





This table encourages the use of AI. We also expect consultancies to experience similar trends to the first two items in the table (projects completed faster and more competitive fees in the short to medium term).

What are the challenges and opportunities of AI for engineering consultancies?


  • Bridging the generational gap (junior versus senior professionals): young professionals tackle AI more easily than their older colleagues. Senior professionals are at risk of being cut off.
  • Lifelong learning: AI requires re-skilling of personnel and fighting resistance to change (especially by senior staff).
  • Protecting consultancies’ added value: the human factor in the decision-making process, human understanding of the ultimate goals of the consultancy’s mission, and human creativity.


  • Enhancing productivity. Repetitive low-value tasks completed more quickly.
  • Enhancing benefits from larger database analysis. Greater accuracy in forecasts.
  • Reducing the knowledge transfer challenge between generations of professionals. Young professionals will become more quickly acquainted with senior knowledge, and seniors will be able to boost their potential until the end of their career.

How should consulting firms be facing into AI – how are you preparing your workforce?

  • Bust the myths (AI is a machine, not a human being). Understand the real potential of AI.
  • Survey how AI is currently being used, informally and formally. How many staff are already using AI (possibly even without telling others)?
  • Identify the firm’s “AI enthusiasts” and appoint one to drive the firm into AI. Appoint others to represent other departments (for example, business development, administration, technical sectors, etc).
  • Ascertain how many of the firm’s IT staff are already engaged in AI, and those who have minimum AI knowledge.
  • Check the firm’s processes within each department: list the main working processes to identify low level tasks, most time-consuming tasks, most repetitive tasks.
  • Create an AI evaluation group to select potential applications for the firm’s processes.
  • Disseminate firm’s intentions to exploit AI potential.
  • Launch an internal competition to gather ideas on AI use and test AI commitment of all staff.
  • Plan the firm’s regulation on the use of AI in the short to medium term.
  • Subscribe to a generative AI tool available on the market (for example, we choose to test ChatGPT) to test PRO-version (a paid version - contains more features than its free counterpart).

You used a company specialising in big data services and generative AI to investigate how AI could automate processes and analyse corporate data. What did you find? 

Investigation outcome:

  • Identified the departments that could benefit from AI use.
  • Confirmed the company’s project database is the most important database to access.
  • Firms specialising in AI take three to six months to make customised AI tools for the company.
  • Budget for AI customisation may range from US$100,000 to US$150,000 for a single AI tool.

Make or buy dilemma:

  • AI industrial developers can issue new revisions of an existing AI platform or new AI tools within six months. During this period, the AI industry might launch new AI solutions that would make the AI customisation already obsolete by the time it is used.

Dilemma: should consultancies survey the AI market to find something suitable for their needs, or should they create their own AI tools?

How can engineering consultancies prepare to use and integrate AI? Can you share some practical examples? 

Engineering consultancies derive the greatest benefits of AI use from their project databases. Comprehensive access by AI tools to this database allows companies to:

  • Base design analysis on previous projects with great accuracy.
  • Support high-level decisions with large data sets.
  • Ensure young professionals are quickly acquainted with the company’s past knowledge through fast and efficient access to the company’s portfolio, experiences and lessons learned.
  • Ensure senior professionals' benefit from historical data. They can enhance their experience by interacting with AI tools in a practical and quick manner (voice commands or other user-friendly interfaces).
  • Improve the quality and quantity of client proposals by retrieving technical and economic data with greater accuracy and speed.
  • Automate many repetitive tasks in the firms’ administrative departments, increasing staff productivity.

Additional benefits of AI tools come from their capacity to analyse and process external data:

  • Environmental and economic data.
  • Renderings and visualisations.
  • Technical specifications.

How is Politecnica preparing for the EU’s new AI legislation – the Artificial Intelligence Act? 

The AI Act is a landmark law that aims to protect fundamental rights, democracy, the rule of law and environmental sustainability from high-risk AI while boosting innovation. Currently, the AI Act doesn’t contain much reference to the practical issues that Politecnica is evaluating.

The main topics of the AI Act are:

  • Banned applications (for example, biometrics, social scoring algorithms, manipulations).
  • Identification of applications for law enforcement.
  • AI topics considered at high risk, such as log-ins, transparency, human oversight and accountability.
  • Topics about copyright, privacy protection, fakes.
  • How to regulate environments for SMEs and start-ups to develop AI tools.

Topics with the greatest impact for Politecnica in the short term are:

  • How to manage intellectual property.
  • How to manage the protection of privacy.
  • How to ensure that all AI outcomes have human responsibility.

At Politecnica, we have appointed a firm with specialised legal knowledge to draft an internal company policy on AI usage in accordance with our internal ethics and EU directives while we await further outcomes of the national legislation. This need recently became essential because many ethical questions are arising from the free and unregulated use of AI. Work to define these policies is ongoing.

Why are we increasingly suspicious of AI ethics?

How many of you have ever wondered:

  • “Is this written by AI?” as if the AI outcomes were always unethical (for example, using AI to do school homework).
  • Is there a way (for example, software) to determine whether a project proposal was written by AI or a human?
  • Would we be disappointed if an AI-written proposal was better than one written by a human?
  • Do we care more about the goal or the means to reach it?
  • Do we consider AI shortcuts to be cheating?

As with all innovative tools, the answers rest in the balance between use and abuse. AI evolves much faster than any previous technology and abusive practices occur much earlier. Therefore, we are both fascinated and suspicious as we approach AI.

The EU is promptly intervening to address AI abuses and testing new software to detect the origin of texts and proposals. However, it's apparent that no regulatory limit can really mitigate AI abuse. The only way to keep AI under reasonable control is by continuing to use some of our traditional, old human intelligence.

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