Artificial intelligence is only as useful as the information organisations can confidently put behind it. As AI becomes increasingly embedded into business processes, building reliable, structured and traceable data foundations becomes an essential part of turning technological potential into measurable value.
We spoke with Ingrid Egle, Data Engineer at FI Group by EPSA Brazil, about the role of data engineering behind AI and how the EngenIA project is helping build a more reliable foundation for analytics, productivity measurement and future AI solutions.
I work as a Data Engineer at FI Group, with a focus on data engineering and artificial intelligence. A key part of my role is organising the data we have across the company and transforming it into reliable information that can be used across the organisation, not only by our Technology and Innovation teams.
Today, we have large volumes of documents and information coming from different sources. Bringing that information together and making it accessible and reliable is an important part of creating a stronger foundation for analysis, decision-making and, ultimately, AI.
Having an intelligent solution is not enough. You also need the right structure behind it and need to ensure that the information it relies on is trustworthy.
AI can support us in automating time-consuming processes, analysing information and answering specific questions, but it is not 100% reliable on its own. Its outputs still need to be validated.
This is why having consistent, reliable and traceable data is so important. It gives analytics and AI solutions a stronger foundation and increases confidence in the evidence they generate.
EngenIA is a project designed to centralise, structure and govern the data generated through our consultancy activities.
We are creating a governed data pipeline that brings information from different sources into a unified environment, where it can be processed, standardised and made available for analysis.
The objective is to ensure that the information we use is consistent, traceable and reliable, while creating a scalable foundation that can support analytics and AI solutions as our needs evolve.
One of the ways we structure this is through what we call a Bronze, Silver and Gold data pipeline.
The Bronze layer receives the information in its original form. In the Silver layer, that information is processed, cleaned and organised. The Gold layer then contains data that is ready to be used for reporting, analysis and decision-making.
We also work with standardised metadata so that we can understand where information came from, how it has been processed and when it was changed. This gives us greater traceability and means that, if something needs to be reviewed, we can understand what happened throughout the data lifecycle.
One of our goals is to understand how technology actually affects consultancy activities. By bringing the data together, we can measure the time spent on different activities and compare productivity with and without technological solutions.
Through BI and dashboards, we can then analyse indicators such as productivity and efficiency, identify potential bottlenecks and better understand where technology is creating an impact.
The important point is that these analyses are based on consistent and auditable information, so they can provide reliable evidence to support decision-making.
Governance is built into the way we structure the project. We want to ensure traceability, data quality and security throughout the entire process, from ingestion and processing to storage and analysis.
The project also aligns the lifecycle of data and technological solutions with ISO/IEC 42001 guidelines and FI Group’s AI Policy, helping us maintain responsibility, security and transparency across the data ecosystem.
AI should support people, not simply replace them. It can help automate processes that take a significant amount of time and make it easier to work with information, but human validation remains essential.
AI is not 100% reliable, so the information it generates needs to be reviewed. From my perspective as a Data Engineer, part of that responsibility is making sure that the information feeding our AI solutions is concrete and trustworthy, rather than allowing unreliable information or hallucinations to become part of the process.
Being a woman working in technology carries a sense of responsibility for me, particularly in a field that has traditionally been very male-dominated.
Being able to contribute not only through data engineering, but also to FI Group’s wider innovation and artificial intelligence journey, is very important to me.
This interview is part of FI AI: Innovating with Integrity, our ongoing exploration of how artificial intelligence can create value while being used responsibly. Bringing together expert perspectives on data, technology, security, people and transformation, the series looks at how we are approaching AI across FI Group by EPSA while keeping quality, transparency and human oversight at the centre.
Technology is often presented as a force that makes life easier. It automates processes, removes repetitive tasks, improves access to services and creates possibilities that would have seemed out of reach only a few years ago. Yet one question is still too often absent from the earliest stages of innovation:
An application can work exactly as specified and still be difficult to use for someone with low vision. A speech-recognition system can perform well in testing but struggle with particular accents. An automated recruitment tool can improve efficiency while reproducing historical patterns that disadvantage some candidates. A sophisticated device can still require movements, visual abilities or digital skills that many intended users do not have.
None of these outcomes requires a deliberate intention to exclude. Exclusion can emerge from the assumptions built into research, product requirements, training data and testing. This matters as artificial intelligence, automation and digital services move into increasingly sensitive areas of daily life, including employment, education, healthcare, banking, mobility, commerce and access to public services.
The World Health Organization estimates that 1.3 billion people, or around 16% of the global population, experience significant disability. Demographic change is widening the issue further: by 2030, one in six people worldwide will be aged 60 or over. These are not exceptional needs affecting a small niche. They describe a substantial and growing share of the people who use products and services.
Inclusive innovation responds to that reality. It is not about adding accessibility features at the end of development simply to satisfy a checklist. It means considering different abilities, contexts and ways of interacting with technology while the problem is still being defined and while design choices can still change.
This approach supports social inclusion and regulatory readiness, but its business value is equally important. Products that work for more people can reach wider markets, reveal unmet needs and perform more reliably in the conditions in which they will actually be used.
When a company discovers an accessibility problem after development is complete, correcting it may require extensive change. Teams may need to redesign interfaces, reorganise information, alter the technical architecture, modify physical controls or collect new data. The problem did not suddenly appear at launch; it was simply detected late.
This is why inclusive design begins with different questions:
These questions affect the direction of research and development. Many products exclude people because they were designed around an overly narrow idea of a ‘normal’ user. Once that imagined user becomes the standard, every difference is treated as an exception to be accommodated later. Inclusive innovation challenges that sequence. It treats human variation as a design condition from the beginning.
Product teams often create a representative persona and design around it. Personas can help teams focus, but they become risky when one profile is used as a proxy for everyone. People have different visual, auditory, motor and cognitive abilities. They use different devices, speak different languages and live in different cultural and economic contexts. Some have used digital tools for decades; others may be accessing a particular service for the first time.
Ability is not fixed throughout a person’s life. A limitation may be permanent, temporary or situational. Someone may be unable to hear a video in a noisy place, struggle to read a screen in direct sunlight or need to operate a device with one hand while completing another task. A person recovering from an injury may encounter the same interface barrier as someone with a long-term motor disability.
This helps explain why features developed for accessibility often benefit a much larger population. Captions are useful to Deaf and hard-of-hearing users, but also to people watching a video without sound. High-contrast displays improve access for people with low vision and remain useful in bright environments. Voice interfaces, predictive text and flexible input methods can help users across a wide range of situations.
The World Wide Web Consortium identifies this broader value in its business case for digital accessibility. Designing for varied ways of interacting can encourage innovation, improve user experience and extend market reach. Accessibility, viewed in this way, is not a final compliance test. It is a method for making products more flexible and resilient.
Artificial intelligence adds another layer to the issue. In a conventional product, an inaccessible design decision may create a visible barrier. In a data-driven system, exclusion can be harder to identify because it is embedded in patterns, classifications and probabilities.
AI systems learn from historical data, labelled examples and past decisions. If those inputs do not adequately represent the population on which the system will be used, performance can vary substantially between groups. This does not mean that every difference in model performance is automatically discriminatory. It does mean that representativeness, data quality and evaluation choices have material consequences.
The US National Institute of Standards and Technology has documented demographic differences in the performance of facial recognition algorithms. Its evaluations show that error rates can vary across demographic groups and that the composition and quality of image datasets influence measured performance. The same underlying concern extends well beyond facial recognition.
Consider a system developed primarily with data from one country and then deployed in another market. A language model may have limited exposure to certain languages, dialects or forms of expression. A health tool may be trained on a patient population that does not reflect the people who will eventually use it. A decision-support model may reproduce historical decisions made in a context where unequal treatment already existed.
Technology does not operate outside society, and data does not arrive without context. UNESCO warns that AI systems can embed or amplify bias and contribute to discrimination, inequality and digital exclusion when these risks are not addressed throughout the system lifecycle. The practical question for developers is therefore more specific than whether a model performs well overall: for whom does it perform well, and for whom does it perform worse?
A model can achieve a strong overall accuracy score while producing significantly different outcomes for particular groups. A single average may conceal exactly the information a company needs to understand before deployment.
Evaluation should therefore examine who is represented in the data, whether performance differs between groups, whether smaller populations have enough examples for meaningful testing and how labels were assigned. Teams also need to understand what happens when a model fails and whether users have a clear route to challenge or correct an outcome.
The consequences of error matter as much as the error rate. An inaccurate entertainment recommendation may have limited impact. A mistake in a system used to assess credit, screen job candidates, support a diagnosis or verify identity can affect access to essential opportunities and services. In those settings, inclusive development is closely connected to technology risk management, governance and product quality.
Data is not the only source of blind spots. The people who choose the research question, define success and test the product also shape what a technology can see.
No team can represent every possible user experience. Even so, teams that bring together different disciplines, backgrounds and lived experiences are more likely to question assumptions that might otherwise go unnoticed. This is especially important in R&D, where early decisions determine which problem will be studied, which hypotheses will be tested, who will participate in trials, which features will receive priority and which measures will define success.
Diversity in innovation should therefore be understood as more than a human-resources indicator. It can strengthen an organisation’s ability to identify needs. An engineer may see a technical limitation. A designer may notice a barrier in the user journey. A clinician may recognise that a solution does not fit real working conditions. A person who uses assistive technology may identify a problem that other testers never encounter. A commercial team may explain why a technically impressive feature is unlikely to be adopted.
These perspectives do not automatically produce a good product. They improve the quality of the questions, provided the organisation creates a process in which different views can influence decisions rather than simply being present in the room.
Inclusive innovation also changes the role of users in development. Teams cannot reliably infer every need from inside an office. People with disabilities, older adults and other groups that have historically been underrepresented in design processes should not appear only at the end to validate a nearly finished solution.
Their experience can inform initial research, problem definition, prototyping and testing. Co-creation and continuous user research help teams find barriers while the product remains relatively easy to change. The World Health Organization recommends involving people with disabilities and their representative organisations in decisions about the policies and services that affect them. The same principle is directly relevant to technology development.
Participation must also be meaningful. Inviting one person to a final review does not make a process inclusive. Organisations need to recruit a relevant range of users, compensate their contribution appropriately, provide accessible ways to participate and show how their feedback affected the product. When users are involved early, accessibility can become a source of insight. When they are consulted only after launch, it is more likely to become a corrective exercise.
The global demographic shift makes this work increasingly urgent. The World Health Organization projects that the population aged 60 and over will reach 1.4 billion in 2030 and 2.1 billion by 2050. Financial services, digital health, mobility, e-commerce, connected devices and communication tools will serve populations with a wider range of abilities and expectations.
Innovation for ageing populations does not necessarily require separate products labelled for older people. It may require existing products to become easier to understand and operate: reducing unnecessary steps, offering several ways to complete a task, avoiding interfaces that depend on precise gestures, allowing users to enlarge text, improving contrast and designing secure authentication that does not become an access barrier.
Language also matters. Clear instructions support users of every age, while oversimplified or patronising content can undermine trust and autonomy. An inclusive product recognises different needs without treating older users as incapable. This distinction affects adoption. A technically accessible product may still fail if the experience makes its intended users feel that it was designed for someone else.
Regulation is increasing the pressure on companies to act. Since June 2025, the European Accessibility Act has applied common accessibility requirements across the European Union to specified products and services, including computers, smartphones, banking and payment services, e-commerce, electronic communications and certain transport-related services.
Compliance is an immediate reason to review products, but it is a limited starting point for innovation. If an organisation considers accessibility only when a legal obligation appears, the project will tend to aim for the minimum requirement. If accessibility is treated as a product criterion, the business asks a more useful question: how can more people use this product successfully?
That question can reveal underserved markets, inspire functions that improve the mainstream experience and reduce the risk of expensive redesign. The W3C’s business case links accessibility with innovation, market reach, brand value and improved user experience, as well as reduced legal exposure. The strongest business case will vary by organisation, but the underlying logic is consistent. A product that excludes fewer people has more opportunities to create value.
Inclusion is easier to manage when it is part of the original specification. Adjusting contrast or navigation during early design may be straightforward. Reworking an established design system, architecture or physical product after deployment may be costly and slow.
Testing with diverse users requires time and budget. So does discovering after launch that part of the market cannot use the service, that customer-support demand has increased or that a product must be redesigned to meet a regulatory requirement. Early investment should be assessed against those downstream costs, not against a fictional process in which accessibility requires no work at all.
The same applies to AI. Reviewing data coverage, testing subgroup performance and establishing monitoring before deployment require resources. Correcting a model after it has been embedded in business decisions is usually more complex. Organisations may need to gather new data, retrain models, revise workflows, notify affected users and reassess previous outputs.
A final audit remains useful, but it cannot carry the entire responsibility for inclusion. Teams need checkpoints throughout research, design, development, procurement, testing, deployment and monitoring. Each checkpoint should have a clear owner, relevant evidence and authority to change the product when a material risk appears.
Organisations do not need to solve every dimension of inclusion at once before they can improve. They can begin by incorporating five questions into R&D and innovation reviews:
These questions do not replace technical standards, accessibility assessments or formal AI governance. They provide a practical way to change the conversations that shape innovation. They also create a record of assumptions that teams can revisit as the product, market and user population evolve.
For inclusion to influence product decisions, it needs to be built into the way innovation projects are governed. Organisations can start by defining accessibility and inclusion criteria in the original project brief, rather than treating them as a late-stage review point. Those criteria should be connected to the users, markets and operating environments the solution is expected to serve.
Practical measures include mapping user groups and usage contexts during discovery, involving a relevant range of users in research and testing, and checking prototypes across different devices, input methods, assistive technologies and real-world conditions. For AI and automated systems, teams should review whether the data adequately represent the intended population, evaluate performance across relevant groups and define what happens when the system produces an incorrect or contested outcome.
Inclusive innovation also requires clear ownership. Accessibility, data representativeness and user participation should be reviewed at defined checkpoints across research, design, development, procurement, testing, deployment and monitoring. Teams should document the assumptions they are making, the evidence used to support those assumptions and the product changes made in response to user feedback. This creates a practical record that can be revisited as the technology, market and user base evolve.
Accessibility has often been treated as a specialist review at the end of the process. A product was designed, developed and then checked to determine whether it was accessible. Inclusive innovation reverses that order. It considers human diversity while the problem is still being defined, reviews data before a model begins influencing decisions and involves intended users before the team assumes it understands how the technology will be used.
No product will respond in the same way to every possible need, and no single design can remove every barrier. That complexity is not a reason to postpone inclusion. It is a reason to treat it as a continuing part of research, development and product management.
For organisations, the opportunity extends beyond compliance.
The central question is therefore larger than whether a product meets an accessibility standard. Organisations should also ask how many innovation opportunities they are losing because they continue to design around a limited group of users.
At FI GROUP by EPSA, we understand innovation as the process of turning knowledge and technology into practical impact. That impact will remain limited if some of the people who should benefit from a solution cannot use it. Incorporating inclusion, accessibility and diverse perspectives from the first stages of R&D expands the range of problems a company can solve and the number of people its innovation can serve.
Organisations can begin by reviewing one current innovation project against the five questions above. The answers will show whether inclusion is already influencing decisions or whether it appears only at the point of final validation. That distinction can determine whether accessibility becomes a late correction or a source of better product decisions from the outset.
AI innovation hubs are becoming critical to global R&D strategy. Locations such as King’s Cross, the San Francisco Bay Area, Paris, Toronto, Montreal and Singapore offer more than access to talent. They bring together research institutions, public funding, tax incentives, investors, infrastructure and regulatory environments that can directly influence the cost, speed and commercial success of AI development.
For businesses investing in AI, location is no longer a purely operational decision. It is a strategic R&D choice that can shape access to funding, partnerships, specialist expertise and long-term innovation capacity.
AI innovation is often discussed as though it happens entirely in the cloud. In practice, much of it still depends on place.
The strongest AI ecosystems tend to emerge where talent, research, capital, infrastructure and public policy are concentrated. This is why locations such as King’s Cross, the San Francisco Bay Area, Paris, Toronto, Montreal and Singapore matter to businesses planning AI-led R&D. They are not simply office locations. They are operating environments.
For companies investing in AI, the decision on where to build, test and scale can influence access to people, funding, partners, tax relief and regulatory certainty. The technology matters, but so does the ecosystem surrounding it.
AI R&D is resource-intensive. It requires specialist engineers, data scientists, domain experts, compute infrastructure, research links, commercial partners and patient capital. These inputs are rarely distributed evenly across a country. They concentrate.
Clusters form because proximity reduces friction. Universities train talent and generate research. Start-ups turn ideas into products. Larger companies provide scale, procurement routes and career pathways. Investors bring capital and market discipline. Public bodies provide grants, infrastructure and incentives. When these groups are close enough to interact regularly, collaboration becomes easier.
This is particularly important in AI because many projects sit between disciplines. A healthcare AI company may need machine learning researchers, clinicians, regulatory specialists, data governance support and commercial partners. A robotics company may need AI engineers, hardware expertise, manufacturing partners and testing environments. A financial services AI business may need technical talent, compliance expertise and access to enterprise clients.
Clusters help companies assemble these capabilities faster.
There is also a talent effect. Skilled workers are more likely to move to places where there are multiple employers, strong research institutions and credible career options. Companies benefit from deeper labour markets. Employees benefit from choice. This creates a reinforcing cycle: talent attracts companies, companies attract capital, and capital attracts more talent.
Venture capital adds another layer. According to OECD analysis, AI firms accounted for 61% of global venture capital investment in 2025, worth USD 258.7 billion. That capital is highly concentrated. Businesses outside the main AI hubs can still raise funding, but clusters improve access to investors who understand the technology, the commercial risks and the route to scale.
Knowledge spillovers are harder to measure, but equally important. They occur through conferences, shared labs, alumni networks, founder communities, investor meetings, university partnerships and informal contact. In a fast-moving field, proximity to the right conversations can be strategically valuable.
King’s Cross has become one of Europe’s most visible examples of an urban AI and science cluster.
Its strength comes from the wider Knowledge Quarter, which covers King’s Cross, St Pancras, Euston and Bloomsbury. Within a small area sit the British Library, the Alan Turing Institute, the Francis Crick Institute, UCL, Google DeepMind and a growing group of technology and life sciences companies.
This mix is important. King’s Cross is not only a technology district. It connects AI with biomedical research, data science, public institutions, transport infrastructure, cultural organisations and commercial space. That gives it a broader innovation base than a pure software cluster.
Google’s Platform 37 development adds further weight. The building is expected to bring Google and Google DeepMind teams together in King’s Cross, alongside the AI Exchange, a public-facing space designed to improve understanding of AI. This matters because anchor organisations shape ecosystems. They attract talent, suppliers, investors and adjacent firms.
The UK Government’s 2024 AI Sector Study estimated that the UK had 5,862 AI companies, £23.9 billion in AI-related revenue and more than 86,000 AI-related jobs. London, the South East and the East of England accounted for around three quarters of registered office locations for UK AI firms. King’s Cross sits inside that concentration.
The district also benefits from its life sciences base. UCL analysis has suggested that the Euston and King’s Cross innovation district already generates more than £8 billion in annual GVA and could add up to £3.5 billion more by 2035 with the right planning, transport and investment conditions. The Francis Crick Institute’s partnerships to expand commercial lab space in the area show how physical infrastructure is being built around scientific demand.
This is the point businesses should recognise. King’s Cross has grown because several conditions overlap: research density, corporate commitment, transport links, public institutions, commercial space and access to finance. It is not a single-factor success story.
King’s Cross is part of a wider global pattern. AI hubs are developing in different ways, shaped by their talent base, policy environment and funding model.
The Bay Area remains the commercial benchmark for AI. It combines world-class universities, large technology companies, venture capital, experienced founders and a deep technical workforce. Its advantage is scale. Companies can access capital, talent, infrastructure providers and customers in one market.
The cost base is high, and competition for talent is intense, but for frontier AI companies the Bay Area remains difficult to ignore.
Toronto and the wider Ontario ecosystem offer a different model. The Vector Institute, the University of Toronto and the Toronto-Waterloo corridor have helped build one of North America’s strongest AI talent bases. Ontario also benefits from Canada’s SR&ED tax incentive programme and provincial support such as the Ontario Innovation Tax Credit.
For companies looking for North American AI capability outside the Bay Area, Ontario offers strong research depth and a structured incentive environment.
Montreal’s AI position is closely linked to Mila, the Quebec AI Institute, and its academic network across Université de Montréal, McGill, Polytechnique Montréal and HEC Montréal. The city is especially strong in deep learning research and talent development.
Quebec has also strengthened its innovation support through the CRIC tax credit for R&D, innovation and pre-commercialisation activity. That gives Montreal a clear proposition: research strength, public support and a lower-cost environment than many US hubs.
Paris has become a stronger European AI contender through a mix of state strategy, academic depth and start-up infrastructure. Station F has increased its AI activity, while France 2030 has backed nine AI clusters with €360 million to strengthen research, training and innovation.
France also has one of Europe’s most established R&D tax incentive systems through the Crédit d’Impôt Recherche. For companies assessing European AI locations, Paris combines market access, talent and public support.
Singapore’s strength lies in coordination. Its government has treated AI as part of a broader national innovation strategy, linking talent, regulation, digital infrastructure and regional market access. The National AI Strategy and Enterprise Innovation Scheme give businesses a clear policy framework.
Singapore is especially relevant for companies looking at Asia-Pacific expansion. It offers connectivity, a pro-business environment and a growing base of AI partnerships across professional services, finance, healthcare and advanced industries.
AI hubs do not grow through market forces alone. Public policy often shapes whether a cluster becomes durable.
Governments use incentives for a practical reason: R&D creates benefits that extend beyond the company funding it. New knowledge, skilled workers and technical capability can spread through supply chains and wider industries. Without support, businesses may underinvest.
R&D tax incentives help reduce the cost of eligible activity. In the UK, the merged RDEC scheme applies from 1 April 2024 at a 20% credit rate, while Enhanced R&D Intensive Support provides additional support for qualifying loss-making R&D-intensive SMEs. In France, the Research Tax Credit supports qualifying R&D expenditure. Canada’s SR&ED programme remains a major federal incentive, often layered with provincial schemes. Singapore’s Enterprise Innovation Scheme offers enhanced deductions or allowances for qualifying innovation activity.
These incentives affect the real cost of experimentation. For AI businesses, this can include work on model development, data processing methods, software architecture, automation, testing, technical uncertainty and integration challenges, provided the activity meets the relevant scheme rules.
Grants play a different role. They can help companies start projects that would otherwise be delayed or reduced in scope. UK programmes such as BridgeAI support AI adoption and development in priority sectors. EU AI Factories are designed to improve access to compute, data and expertise. France 2030 is using public investment to strengthen national AI capability. Quebec’s support for Mila shows how direct funding can reinforce research institutions and talent pipelines.
Public-private partnerships are also central. Innovation hubs need convenors: institutions that bring companies, researchers, funders, standards bodies and public agencies together. The Alan Turing Institute, Digital Catapult, Innovate UK, the Francis Crick Institute, Station F and Singapore’s EDB all play different versions of this role.
For businesses, the lesson is clear. Incentives should not be treated as an afterthought once a location has been chosen. They are part of the location decision.
AI location strategy should start with the work being undertaken. A company developing frontier models has different needs from a manufacturer applying AI to production processes, or a healthcare company building regulated decision-support tools.
Five factors deserve close attention.
AI hubs are becoming strategic engines of R&D because they bring together the conditions that help companies move from technical possibility to commercial use. Talent, capital, infrastructure, research institutions and incentives all matter. Their value increases when they work together.
King’s Cross shows how this can happen in a dense urban district. Its advantage is not only the presence of major AI companies. It is the combination of AI, life sciences, public research, transport, universities and commercial investment in a small geography.
Other hubs offer different strengths. The Bay Area has unmatched capital depth. Ontario and Montreal offer research strength and structured public support. Paris benefits from national AI investment and a strong R&D tax regime. Singapore offers regional access and coordinated policy.
For businesses investing in AI, the location decision should be treated as part of the R&D strategy. The right hub can improve access to talent, shorten collaboration cycles, reduce project costs and open routes to funding. The wrong location can make the same project slower, more expensive and harder to scale.
AI may be digital, but innovation still has an address.
Artificial intelligence has moved beyond experimentation. It is now a core driver of competitiveness, efficiency and strategic differentiation across industries. As adoption accelerates, however, a parallel force is reshaping its development: regulation.
Governments worldwide are no longer taking a passive approach to AI innovation. They are actively defining the conditions under which it must evolve. The European Union’s AI Act, alongside emerging frameworks in the United States, Asia and Latin America, signals a structural shift. AI is no longer simply a technological opportunity. It is now a regulated domain.
This creates a fundamental question for organisations: does regulation slow innovation, or does it redefine how innovation happens?
The answer is increasingly clear. Regulation is not a constraint on innovation. It is a filter. It determines which innovations are viable, fundable and scalable.
For businesses, the question is no longer whether to comply. It is how to use compliance to unlock investment, accelerate adoption and build long-term competitive advantage.
The first wave of AI adoption was defined by experimentation. Companies tested models, explored use cases and prioritised speed over structure. Governance, explainability and risk management were often secondary considerations.
That phase is over.
We are now entering the era of trusted AI, where systems must demonstrate not only performance, but also accountability, transparency and control. This shift is being driven by regulators, but also by market expectations. Clients, investors and partners increasingly require assurance around how AI systems operate, use data and make decisions.
In practice, organisations must redesign their AI roadmaps around three core pillars:
As internal governance frameworks mature, companies are formalising processes that include risk classification, human oversight and traceability. This aligns with emerging standards such as ISO/IEC 42001, where AI systems are managed through structured, auditable processes across their lifecycle.
The implication is direct. AI is no longer a standalone capability. It is an integrated, regulated system that must be designed for trust from day one.
One of the most underestimated effects of AI regulation is its direct impact on funding.
Public funding programmes, particularly in Europe, are increasingly aligned with regulatory compliance. Funding is no longer awarded solely on the basis of technological ambition or innovation potential. It is increasingly linked to a project’s ability to demonstrate ethical use, security and responsible governance.
This is especially visible in the European ecosystem, where the AI Act sets a clear framework for acceptable practices. Projects that incorporate risk management, data governance and human oversight are better positioned to access funding instruments, including R&D incentives and strategic innovation programmes.
At the same time, governments are prioritising investment in sovereign and strategic technologies. AI sits at the centre of this agenda, but only when it aligns with broader objectives: resilience, security and control over critical data and infrastructure.
For companies, this creates a structural shift:
Organisations that treat compliance as a checkbox will fall behind. Those that integrate it into their innovation strategy will be better positioned to access capital that others cannot.
A second major shift is taking place in how AI systems are designed.
Historically, compliance was reactive. Systems were developed first, and regulatory requirements were addressed later, often at significant cost. Today, this approach is no longer viable.
AI systems are increasingly being built to be compliant by design. This means that requirements relating to data protection, cybersecurity, traceability and governance are integrated from the earliest stages of development.
This shift has several consequences:
For industrial AI projects, the impact is direct. Digital transformation programmes must now include governance layers, documentation standards and risk controls as core components, rather than add-ons.
Companies that adopt this approach achieve two key advantages. First, they reduce the risk of costly redesigns or compliance failures. Second, they accelerate adoption by building trust with stakeholders from the outset.
A common pattern is emerging in successful projects: when governance and compliance are embedded early, organisations move faster in later stages. They face fewer barriers in deployment, scaling and market acceptance.
While regulation is becoming universal, its implementation varies significantly across regions. These differences have direct implications for companies operating internationally.
Europe leads with a structured and comprehensive approach. The AI Act introduces a risk-based framework that defines clear obligations, particularly for high-risk systems. The European model prioritises trust, safety and fundamental rights. While this creates higher compliance requirements, it also establishes a strong foundation for scalable and trusted AI.
The United States adopts a more flexible, market-driven approach. Regulation exists, but it is less centralised. Innovation tends to lead, with governance frameworks evolving in response. This creates a more dynamic ecosystem, but also greater uncertainty over the long term.
Asia, particularly China, combines strong state involvement with rapid technological deployment. Regulation is present, but often aligned with national strategic objectives. The focus is on scaling AI capabilities quickly while maintaining control over key sectors.
Latin America is at an earlier stage. Adoption is accelerating, but regulatory frameworks are still developing. This creates opportunities for rapid implementation, but also risks linked to future alignment with global standards.
For businesses, the implication is clear: AI strategies must be designed for regulatory diversity. Solutions that are compliant in one region may not scale globally without adaptation.
Despite the visibility of regulation, many organisations are still underestimating its business impact.
The first misconception relates to cost. Compliance is often seen as an expense that reduces margins. In reality, its absence can be far more expensive. Retrofitting compliance into existing systems, facing regulatory penalties or rebuilding non-compliant solutions can significantly increase costs.
The second blind spot is scalability. AI systems that are not designed with regulatory requirements in mind face major barriers when expanding into new markets. What works locally may not be transferable internationally.
The third and most critical risk is obsolescence. AI solutions developed today may become non-compliant tomorrow if they do not anticipate regulatory evolution. This can create stranded investments and limit long-term value creation.
Organisations that fail to address these challenges early face a compounding disadvantage. They risk losing access to funding, facing operational constraints and struggling to scale.
By contrast, companies that integrate governance frameworks, including structured AI management systems, mitigate these risks. They create a foundation where innovation can evolve within clear, sustainable boundaries.
AI regulation marks a turning point in how innovation is conceived, developed and scaled.
It is not simply a set of constraints imposed on technology. It is a framework that defines what sustainable, scalable and investable innovation looks like.
For businesses, the implication is strategic. Compliance is no longer a legal requirement operating in isolation. It is a core lever for funding, differentiation and growth.
Organisations that embed governance, transparency and accountability into their AI systems will not only reduce risk. They will position themselves to capture new opportunities, access strategic funding and build trust in increasingly competitive markets.
The shift is already underway.
The question is not whether regulation will shape AI innovation. It is whether companies are ready to use it to their advantage.
This milestone highlights the company’s unwavering commitment to leveraging Artificial Intelligence in a secure, ethical, and responsible way, fully aligned with the highest international governance standards. Achieving the ISO/IEC 42001 certification, the world’s first global standard for Artificial Intelligence Management Systems (AIMS), reinforces our leadership in AI governance and compliance.
ISO/IEC 42001 establishes a comprehensive framework fr managing AI systems throughout their entire lifecycle, from design and development to deployment, monitoring and retirement. It places strong emphasis on risk management, transparency, human oversight, and continuos improvement. FI Group’s certification demonstrates that responsible AI i not treated as an isolate initiative but as an integral part of its global governance, technology and compliance model.
«ISO/IEC 42001 certification reflects our conviction that Artificial Iteligence must be developed and used with integrity, accountability and trust at its core.»
Iñigo Paniagua, Corporate Chief Tech & Digital Officer
As AI becomes increasingly embedded in business operations, governance and trust are essential. ISO/IEC 42001 certification provides independent assurance that FI Group:
This achievement reinforces FI Group’s AI vision: Innovating with Integrity and its corporate commitment to deploying innovation responsibly, transparently and in line with evolving regulatory expectations, including the EU Artificial Intelligence Act and emerging AI assurance frameworks.
The certification follows a rigorous, independent audit assessing the design, implementation and effectiveness of FI Group’s AI Management System. Achieving this milestone required cross-functional collaboration across technology, security, legal, compliance, quality and business teams.
Key elements of FI Group’s AI governance framework include:
Rather than treating certification as a one-off exercise, FI Group has operationalised ISO/IEC 42001 through everyday processes, controls and people.
Albert Martín, AI Governance and Product Owner
For FI Group’s clients, ISO/IEC 42001 certification provides assurance that the internal use of AI supporting client services and operations is governed by a robust, transparent and internationally recognised management framework.
While AI at FI Group is currently used primarily to enhance internal processes, decision-making, efficiency and service quality, the certification ensures that any AI-enabled support functions influencing client outcomes are managed with:
This governance approach reduces operational risk, strengthens service reliability and reinforces client confidence in how FI Group applies advanced technologies behind the scenes. From a market perspective, ISO/IEC 42001 positions FI Group as an organisation that adopts AI with maturity and responsibility, anticipating regulatory expectations and demonstrating leadership in ethical and compliant innovation, even when AI is not a client-facing product.
Responsible AI governance strengthens trust not only in what we deliver to clients,
but in how we operate as an organisation.
ISO/IEC 42001 certification marks an important step, but it’s not the finish line. FI Group remains committed to the continuous improvement of its AI Management System, applying the PDCA (Plan–Do–Check–Act) cycle to evolve alongside technological advances and regulatory developments.
By aligning its AI governance with global standards today, FI Group is strengthening its readiness for tomorrow’s regulatory landscape while continuing to unlock the value of Artificial Intelligence in a responsible and sustainable way.
This certification confirms that innovation and strong governance can and must progress together.
Artificial intelligence has never been more integrated into daily life and business world. Since the launch of ChatGPT in 2023, the number of companies adopting AI has jumped by 20%, reaching 78% of all organisations questioned in the «State of AI: Global Survey» by McKinsey & Company.
Clearly, AI is no longer just an optional tool; it’s becoming an essential strategic asset for companies aiming to stay competitive and innovative. But as businesses invest efforts into AI, they also face a subtle but significant risk: cognitive debt. According to recent MIT research (2025), overreliance on AI can erode essential human skills like creativity and critical thinking.
So how can companies harness the power of AI without compromising their greatest resource: their people?
AI is often associated with boosting operational efficiency, but its potential goes far beyond that. It can significantly accelerate innovation across every stage of the R&D process, from generating ideas and prototyping to testing and launching new products.
Traditionally, innovation was slow, costly, and limited by human resources and physical experimentation. Now, AI lets companies rapidly run thousands of simulations and test numerous hypotheses at once, tasks that used to take months or years can now be completed in days or even hours.
For instance, pharmaceutical giants like AstraZeneca have partnered with AI startups such as BenevolentAI to quickly screen millions of chemical compounds. This collaboration has dramatically shortened the drug discovery timeline, bringing new treatments to market much faster. AI’s ability to analyse huge datasets and detect meaningful patterns also improves decision-making, ensuring companies invest their R&D budgets wisely.
According to Gartner (2024), companies that integrate AI-driven analytics into their research can more accurately predict market trends, customer preferences, and technological feasibility. Brands like Unilever and Procter & Gamble use AI analytics to better anticipate consumer trends, aligning their innovation strategies closely with market demands.
Despite its undeniable advantages, AI also presents hidden risks.
A recent MIT study, «Your Brain on ChatGPT» (Kosmyna et al., 2025), highlighted that excessive reliance on AI can reduce brain activity linked to creativity, critical thinking, and memory. Participants who regularly depended on AI exhibited weaker cognitive engagement and poorer recall compared to those who completed tasks without AI. This phenomenon, known as «cognitive debt», occurs when individuals consistently outsource mental tasks to technology, gradually weakening their own cognitive abilities. Though AI streamlines workflows, excessive reliance can unintentionally undermine the very human qualities that drive sustained innovation.
To fully realize AI’s benefits while avoiding cognitive debt, businesses should consider these best practices:
By implementing these strategies, businesses can leverage AI effectively while preserving and enhancing their human capital.
AI presents tremendous opportunities for accelerating innovation, increasing efficiency, and creating lasting competitive advantages. Yet, as businesses embrace AI, maintaining awareness of cognitive risks remains crucial. The smartest path forward involves strategically balancing AI’s powerful capabilities with the irreplaceable creativity and ingenuity of human talent.
Companies that master this balance will lead the future, strategically integrating AI to unlock sustained innovation and growth.
While it may sound futuristic, it has become an integral part of our daily lives, whether in health, leisure, or work.
Smartwatches were the gateway to a broader ecosystem of wearable devices designed to act as constant companions to our bodies. These include health-monitoring bracelets, smart rings, and fitness trackers, all aimed at tracking vital signs in real time. Users can monitor their cardiovascular health during workouts and daily activities with ease.
Many wearables also analyse sleep patterns, offering insights into sleep quality by measuring duration, sleep stages (light, deep, and REM), and disturbances. This data helps users make informed adjustments to improve rest. Additionally, blood pressure monitoring features support the management of hypertension and overall heart health.
Equipped with accelerometers and gyroscopes, these devices accurately track physical activity—counting steps, estimating calories burned, and monitoring various types of exercise. Built-in GPS functionality allows users to map routes and track distances during outdoor activities such as running, cycling, or hiking.
Modern wearables also include stress management tools, such as guided breathing exercises and mindfulness reminders. Hydration and movement alerts further encourage healthy daily habits.
As public awareness of health and wellness grows, wearable technology has evolved into a vital tool for personal health management. These devices empower users to set and achieve fitness goals, gain real-time health insights, and adopt healthier lifestyles.
With ongoing advancements, wearable technology will continue to shape the future, becoming more secure, efficient, and accessible for all.
Key Takeaways
In recent years, the intersection of technology, medicine, and healthcare has created an environment ripe for innovations that transform how we manage our health and interact with medical services. Over time, the healthcare sector has increasingly absorbed technological advances and applied them within the field – and this relationship has opened the way for a powerful strategy known as cross-industry collaboration, where different sectors come together to share knowledge, experience, and technology, resulting in two distinct strands: HealthTech and MedTech.
In many cases, an innovative solution developed in one industry can serve as an effective remedy for challenges faced in another sector. This is the essence of the cross-industry approach: a strategic method that encourages the exploration of hybrid solutions by fostering collaboration between companies from diverse fields.
By leveraging unique insights and technologies from various industries, this approach aims to catalyse innovation, accelerate growth, and uncover a wide range of tailored solutions. These collaborations can create mutually beneficial opportunities, driving value for all parties involved while pushing the boundaries of what’s possible in their respective domains.
According to the World Health Organisation (WHO), healthcare can be defined as «the application of organised knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to address health problems and enhance the quality of lives.”
Even with the terms HealthTech and MedTech often used synonymously, they serve distinct purposes in the healthcare ecosystem. HealthTech solutions are more concerned with leveraging technology to augment the overall healthcare experience for patients, including innovations that enhance telehealth platforms, mobile health applications, and data analysis tools that allow patients to monitor their health in real time, thereby empowering them to take an active role in their health journey.
MedTech solutions, on the other hand, are focused on advancements in medical treatment and diagnostic processes. This encompasses improvements in diagnostic efficiency and accuracy, as well as the design and development of state-of-the-art medical devices, surgical tools, and patient monitoring systems. MedTech innovations particularly cater to healthcare professionals by providing them with the tools necessary for effective patient diagnosis and treatment.
A simplified distinction can thus be established: MedTech focuses on the development and application of technologies aimed at managing healthcare and enhancing diagnostic capabilities, while HealthTech prioritises the creation of tools and systems that enhance the patient experience and support consumer engagement in their own health management.
With these differences established, we can now cite some examples illustrating the impact of various technologies in the health sector that have affected – and revolutionised – medical practices and the general day-to-day lives of patients around the world:
Neurotechnology has existed in the medical realm for some time, yet continues to progress in astonishing ways. It includes both implantable and external devices, covering all elements designed to comprehend brain functions. With the aid of these technologies, we can visualise the workings of the human brain and control, repair, or enhance its operations.
Neurotechnology components can include computers, electrodes, and other devices that interpret electrical impulses. At this moment in time, neurotechnology is utilised for various processes such as:
Telemedicine has grown rapidly in recent years, and many health systems now use it. It benefits both patients and healthcare workers: for patients, telemedicine offers convenience, making it easier to access care, save money, avoid travel expenses – and the risk of missing work for in-person visits. For healthcare professionals, it lowers costs and limits their exposure to illness, while also allowing them to see more patients with greater flexibility.
Wearable technology, commonly referred to as wearable tech, encompasses a range of devices designed to track various health metrics, such as monitoring the heart rate in real-time, analysing sleep patterns to assess sleep quality, measuring blood pressure, and even tracking physical activity levels such as the number of steps taken and calories burned. On top of that, many models incorporate features like GPS tracking for outdoor activities, stress management tools, and reminders for hydration and movement. As health awareness grows, these devices have morphed from mere novelties into essential tools for many, aiding users in achieving their fitness goals and maintaining a healthy lifestyle.
These advanced technologies support surgeons in performing minimally invasive procedures with remarkable precision and agility. They not only simplify straightforward surgical procedures, but also enable the performance of more intricate operations, thus enhancing the overall effectiveness and outcomes of medical interventions. By providing a steady hand and clear visual field, they permit surgeons to navigate complex anatomical structures with confidence and skill.
As these technologies continue to evolve, collaboration across industries will become ever more crucial. This synergy will not only drive innovation but also create a more efficient, personalised, and patient-centric healthcare ecosystem. The success of this collaborative approach can lead to a future where healthcare is more accessible, effective, and tailored to individual needs, promoting lasting well-being for all.
In a dynamic and competitive world, access to information is crucial for business success. Companies that want to stand out need to go beyond simply collecting data by turning them into actionable insights that drive the growth and profitability of their business. It is in this context that business intelligence (BI) becomes an essential tool.
Business intelligence is a set of processes, technologies and tools that transform raw data into strategic information to help companies make assertive decisions.
By collecting, organising, analysing and visualising data from various sources, BI provides managers with a complete and comprehensive view of their organisation’s performance, allowing them to identify opportunities, optimise processes, reduce costs, increase efficiency and make smarter decisions to achieve strategic objectives. This article provides an introduction to BI, but it can only cover the tip of the iceberg.
Traditional business intelligence emerged in the 1960s as a system for sharing information within organisations. In the 1980s it developed alongside computer models to aid decision-making and transform data into information. Modern BI solutions prioritise governed data on reliable platforms, the autonomy of business users, and fast access to information.
More and more people generate and/or create data every day, making the latter ever more diverse and unstructured.
In this sense, and once we know what business intelligence is, a careful use of the BI approach can help any organisation gain a competitive advantage by reducing the time and effort needed to acquire, integrate, distribute, analyse and respond to new data.
The better a company’s data processing is, the more it will benefit from BI. In fact, the leaders in data processing exert enormous pressure on all competitors who fail to recognise the potential of their data in good time. Late adopters are forced to accelerate their analytical ambitions to keep up with competitors and new market entrants.
BI is thus right at the core of all data-driven companies, making it the epicentre of their transformation. The implementation of new BI tools is aimed at boosting an organisation’s impact and rendering it more efficient. But with the right BI technology, you can also gain a number of additional benefits.
These include:
In the past, business intelligence tools were mainly used by data analysts and IT users. But nowadays, BI platforms make business intelligence available to everyone, from executives to operations teams.
To this end, FI Group has launched a new digital space that integrates several applications in a single platform. This way our clients can benefit from greater transparency about the services they develop with us.
For the future, it is vital that the people and companies around us adapt and use new digital resources to reach their full potential, converging on innovative and exciting ideas.
FI Connect is a HUB of digital applications created by the FI Group to transform, automate and optimise our customer relationships.
This suite of applications enables FI Group to offer a more structured consultancy support, making our clients’ lives easier and letting them focus on their R&D projects by ensuring better communication and greater efficiency in our delivery of their R&D claims.
The versatility of Business Intelligence allows it to be applied across various sectors and areas of activity within a company. Some examples of how BI can be used to generate value in different sectors include:
The provision of reliable information for making strategic decisions, to optimise processes, reduce costs and increase competitiveness makes BI a crucial differentiator for companies wishing to stand out in an increasingly competitive and dynamic market.
Investing in a business intelligence solution is an investment in your company’s future.
Share this article on business intelligence with your network of contacts. Explore FI Group’s article archive to find more related and relevant content.
FI Group weighs the benefits and challenges of business automation
Automation driven by technological advances such as artificial intelligence, robotics and machine learning is rapidly changing the landscape of work. Repetitive and manual tasks previously performed by humans are being automated, freeing up time and resources for more creative, strategic, and complex activities.
Automation is guaranteed to boost the productivity, efficiency and safety, along with reducing costs and human error. It is essential to recognise, however, that this new reality also entails some adversities including rising unemployment, social inequality, and a need to retrain the workforce. To reflect on this need to adapt and develop new skills is essential if a business is to thrive in an increasingly automated world.
Business automation is the current use of computerised systems, software applications, devices and other technological tools to perform processes automatically or semi-automatically, entirely without or with minimal human intervention. It can de facto be applied to various levels and areas of the company, from operational to managerial processes.
As the technological advances and automation tools themselves are increasingly becoming accessible to people unable to program them, business automation is found ever more frequently in all company routines. Especially so in the routines of companies aiming for continued competitiveness by banking on innovation.
By the strategic implementation of innovative technologies, business automation can offer a range of benefits that optimise the performance and open up a range of growth potentials.
Automation saves time, reduces effort, and the incidence of manual errors. Automated processes guarantee consistent results and a high quality, with tasks carried out identically and without human error. And if there should be errors nonetheless, they can all be corrected by changing the underlying process.
By eliminating redundant tasks, automation can optimise the use of resources, significantly reducing the company’s operating costs, along with the waste of materials, time and energy.
Wherever manual tasks are «tedious» or very demanding, automation enables staff to turn to more pleasurable and creative activities, boosting their satisfaction. And by dedicating themselves to activities that please them more, they end up having more time to focus on better customer service.
In an increasingly competitive market, automation puts a company at the forefront of innovation, providing a crucial strategic advantage for success. By automating specific processes, you will be able to respond to market changes and customer needs with the flexibility that entails. Freeing up resources will meanwhile also help you make the most of new market opportunities.
When weighing up the benefits and challenges of business automation, it is imperative that business automation not be viewed in an exclusive manner, as there are various automation technologies that organisations can apply.
Business process automation (BPA) is a strategy pursued to optimise a company’s processes by implementing software and technologies. As a rule, BPA involves creating automatically sequenced activities that respond to process flows and not just individual tasks. Various software programs are available to automate the management and provision of financial reports, HR processes, marketing activities, the commercial management, and even workflows, to name but a few.
Robotic process automation (RPA) uses software «bots» to imitate human responses at the user interface, automating recurring and rule-based tasks. This in turn permits problems to be solved without interrupting human workflows or the need for human monitoring and supervision, unlike other automation methods.
RPA supports companies in activities such as processing requests, sending notifications, updating profiles, making complex calculations, monitoring already automated tasks, and many others. Call centres, data migration, help desks and credit applications are only four examples where RPA can be useful.
Finally, intelligent process automation (IPA) is a technology resulting from the convergence of robotic process automation (RPA) and various AI technologies in the automation of business processes. IPA aims to take automation to a higher level of complexity, increasing agility across the board. Some examples of IPA include new, intelligent CRM systems that eliminate manual tasks, inventory control with the ability to automate an organisation’s entire value chain, or quality management.
According to a report by the McKinsey Global Institute, automation could eliminate 15 % of all global working hours by 2030, leaving around 400 million people unemployed.
Among the countries this report takes a closer look at, it is estimated that workers in Japan will be the most affected by this development. But the story is similar in the United States, where 23 % of all working hours could be lost to services and automation processes, taking millions of jobs along with them. [Jobs threatened by automation | source: Statista]
Medium-term automation could lead to the loss of 39 million jobs in the US by 2030, while rapid automation could make 73 million people lose theirs. But to offset the potential job losses, around 20 million of these newly unemployed could transfer to similar jobs where they perform slightly different tasks.
Even so, a significant proportion would need to be fully retrained in the US and many other developed countries. According to McKinsey, a third of the US workforce may have to be retrained by 2030, as well as almost half the Japanese workforce.
Rapid automation could also cost China and India 236 and 120 million jobs, respectively. The worst-case scenario in Japan would lose 30 million jobs. Mexico could have 18 million workers made redundant by then, and Germany 17 million.
The jobs most at risk from automation tend to be physical and predictable, such as fast food workers or machine operators. The safest jobs are generally the less predictable ones, including managers, engineers, scientists, teachers, and plumbers. [Automation could eliminate 73 million jobs in the US by 2030 | source: Statista]
The concept of digital twins has emerged as a powerful tool across various industries in recent years, revolutionizing the way organizations design, operate, and manage complex systems.
From astronomy to smart cities, digital twins are reshaping the landscape of innovation, and driving efficiency, productivity, and sustainability.
A digital twin is a virtual replica or simulation of a physical asset, process, or system that enables its real-time monitoring, analysis, and optimization.
The object under study is equipped with various sensors linked to vital areas of its functionality. These sensors generate data on several aspects of the physical object’s performance. The data are then sent to a processing system and applied to the digital copy.
Once populated with the data, the digital copy can be used to run simulations, investigate performance issues and develop possible improvements, all with the aim of generating valuable information.
Digital twins are divided into 4 levels, bottom-up, depending on the integration level of data and parameters:
Digital vehicle twins allow engineers to analyse how different factors such as aerodynamics, fuel efficiency, and safety features impact the overall performance. By simulating various driving conditions and scenarios, engineers can identify potential issues, refine designs, and improve the reliability and safety of vehicles.
The twins also enable predictive maintenance and condition monitoring of vehicles, enabling fleet operators to anticipate maintenance needs, minimize downtime, and optimize asset utilization.
Telecommunication companies use digital network twins to create virtual replicas of their infrastructures, including towers, antennas, switches, and cables. These digital twins simulate network behaviour, traffic patterns, and performance metrics, enabling operators to identify bottlenecks, predict capacity requirements, and optimize resource allocation.
By integrating real-time data from network elements, sensors, and customer interactions, digital network twins provide operators with actionable insights into network health, enabling proactive maintenance, fault prediction, and service restoration.
In the construction industry, digital twins are known as Building Information Models (BIM). BIMs are a digital representation of a building or infrastructure project that mirror the geometry, spatial relationships, and other relevant data.
Digital twins of construction projects enable architects, engineers, and contractors to collaborate more effectively, visualize designs in 3D, and identify potential conflicts or errors before the start of construction. By simulating construction processes and sequencing activities, BIMs help to optimize project schedules, reduce costs, and improve project efficiency overall.
Digital health twins are virtual representations of individual patients. They enable clinicians to tailor treatment plans and interventions to the patient’s unique medical history, genetic makeup, and lifestyle factors.
By analysing data from wearable devices, electronic health records (EHRs), and medical imaging, clinicians can identify trends, detect early warning signs, and intervene proactively to prevent adverse health outcomes.
Pharmaceutical companies can leverage digital twins to simulate drug interactions, predict drug efficacy, and identify patient subpopulations for targeted therapies, leading to more efficient drug discovery and development processes.
Smart cities: optimizing urban systems with citywide twins
In smart cities, digital twins are known as citywide twins. By modelling transportation networks, energy grids, water systems, and other critical infrastructure, citywide twins help to identify inefficiencies, anticipate future needs, and develop strategies for sustainable growth. They also support resilience to and preparedness for disasters by modelling the impact of natural catastrophes, pandemics, and other crises. In addition to which they also facilitate citizen engagement and participatory planning by providing interactive platforms for residents to explore urban data, provide feedback, and contribute to the development of their communities. By fostering transparency and collaboration, citywide twins empower citizens to play an active role in shaping the future of their cities.
Digital twins of telescopes allow astronomers to test different configurations, calibrate instruments, and optimize their performance before conducting actual observations. In addition, virtual observatories can integrate data from multiple telescopes and sensors, enabling astronomers to correlate observations and detect hidden patterns in the vastness of space.
The development of new technologies has changed our perceptions and how we grow businesses of late, bringing a range of new concepts and routines to our daily lives. Web 3.0 and metaverses, for example, are two emerging technologies that are expected to revolutionize the way we do business in the years to come, and continually scrutinized and evaluated at this point in time.
Web 3.0, which may also be known as the semantic web, is a concept developed for the next generation of the world wide web, using artificial intelligence (IA) and learning algorithms as well as blockchain technologies to understand data in their sharing, and facilitate the search for information and its storage in a decentralized computer network, based on the context.
Blockchain is a method of storing information – i.e. a database – that is shared among a network of computers and duplicates and distributes transactions and information, making it difficult or impossible for the system to be manipulated and hacked.
Compared to the current internet network – referred to as Web 2.0 – a decentralized web would offer greater security and privacy, along with more data ownership, as it allows users to manage and control their personal information, rather than relying on the architecture of a central server and its relationship with the client, as is the case today.
“Metaverse” is a term referring to virtual worlds that allow online social interaction using digital avatars, embracing virtual reality (VR) or augmented reality (AR) technologies to create an immersive experience. Within the space created, aspects of the physical world are simulated and reinforced through resources such as social media and digital currencies, as well as avatars, events, online activity centres, etc., elements that vary from one platform to the next.
In the last two decades, the emergence and proliferation of games promoting popular metaverses, such as Minecraft and Second Life, have engendered attempts to create ever more platforms aiming to integrate virtual and physical spaces in metaverse interactions.
According to Citi, metaverse businesses are expected to contribute between $ 8 and $13 trillion to the global economy by 2030, with an estimated five billion users. But what is more important to start with is to consider the users likely to go for these new technologies, and what they are looking for.
In the most popular metaverses such as Roblox and Minecraft, people in general and generation Z (born between 1997 and 2010) in particular are increasingly spending their money on virtual items and accessories, many of them exclusive to the respective metaverse. Which indicates that, apart from creating virtual versions of existing physical products, there are also development potentials for unique virtual products and experiences.
Marketing can play a key role in this, too. In 2022, major brands like Disney and Nike announced strategies or projects that embraced metaverses as a new means of engaging with customers, of broadening the understanding and study of online consumer behaviour, and enabling even more personalized and precisely focused experiences tailored to the interests and needs of each target audience.
Web 3.0 could likewise enhance customer relationships. Trust-building between businesses and their customers is eased given the transparency ensured by the «immutability» of data stored in blockchain technologies, infusing the latter with greater confidence in the information’s authenticity. Better legal compliance is another benefit, with immutable transaction records that are transparent to all parties helping businesses meet governance requirements.
As Web 3.0 is designed to be decentralized, applications are unlikely to require expensive servers and data centres, and can be run on computer networks provided by end users, eliminating the need for third-party service providers. Another cost-saving benefit is the potentially easier supply chain monitoring, enabling possible issues to be identified with greater agility and better time management.
Web 3.0 and metaverses are highly networked in their focus on sharing content and experiences online, and both based on advanced technologies such as the AI employed in their development and blockchains, a concept undergoing constant evaluation as an integral element of Web 3.0, set to power metaverse services.
Although adjustments may be necessary, the future potential of these two technologies is huge and highly promising, offering countless opportunities for innovation in a new digital wave able to change the way we do business in all kinds of ways. Potential challenges do exist – such as the incorporation of Web 3.0 in the metaverse, potentially leading to a virtual world that is fully integrated with the internet, or the availability of resources that support these new tools – but they can be overcome with time, promising a new era of access and change.
2023 will go down as the breakthrough year for ChatGPT due to the intuitive capabilities and wide scope of applications of the tool.
But where did it all start?
Created in 2015, OpenAI has notable founders such as Elon Musk and Sam Altman with the aim of integrating artificial intelligence into society for the benefit of humanity. Looking back on the last seven months, you can definitely say their work has impacted society through their creation of ChatGPT.
In a nutshell, ChatGPT is an AI application that has been ‘trained’ to understand natural language and conversation. Pulling data from the internet and presenting its findings in easy to understand responses. Furthermore, it can be used as a translator, create content, summarise text and process / write code.
Ironically, you can find out about the R&D behind ChatGPT by simply asking it. The tool identifies five components that make up its development:
However, it can be difficult to put all this development into perspective and understand the gravity of its intelligence. The English language is comprised of one million words. In comparison, ChatGPT is made up of ten billion words, including fifteen languages (incl. English, French, Russian, Chinese, Arabic and Urdu), and sixteen programming languages (incl. Python, Java, JavaScript, C++ and, HTML/CSS).
If you wanted to dive deeper into the development of the tool, tokenization is the breaking down of the sequence of text into smaller units, appropriately called tokens. These help ChatGPT’s natural language processing (NLP) for further development.
As previously mentioned, OpenAI reached their goal of impacting society through their work. The applications for ChatGPT are vast, content creation, consultancy, and customer support to name a few.
A popular term being used more and more within industries is, ‘ChatGPT won’t take people’s jobs, people that use it will’. Which to an extent is correct. If utilised correctly by competent professionals with in-depth knowledge of their profession, then ChatGPT can allow for streamlined workflows and increased productivity.
Despite having ten billion words, mistakes can still be made. Users who copy and paste its responses risk not only incorporating the errors the tool might have created but also limiting themselves by hindering personal development in their respective careers.
FI Group has made the decision to move away from ChatGPT. At a glance this decision might undermine what was said previously about streamlining workflows, however, we have opted to use a different tool. We are launching MarIA which uses AzureGPT functions. Similar to ChatGPT, but can ensure that conversations and data shared with it remain in the Azure environment which provides an additional level of security.
On the topic of ChatGPT, FI Group France Scientific Director Charlie Grosman said:
‘while it generates useful and relevant information, in a short period of time, it is not a technology that employees should trust at an exact level».
What does MarIA mean for our clients?
FI Group requires a lot of sensitive information from our clients such as confidential R&D project details and employee salary, if this information is shared with ChatGPT it is then stored in OpenAI’s database and outside of our control. MarIA ensures that any private information is kept safe in our own database and cannot be accessed by anyone outside of FI Group.
Moreover, MarIA will also limit how much FI Group employees can use its functions. Ensuring that work is being done by real people and aided by the tool, when necessary, rather than the other way round. Meaning, clients can be assured they are getting genuine expert client opinions.
Articifial Intelligence is a technology that belongs to the field of computer science and aims to create systems and algorithms that run in a dynamic environment, based on the collection and processing of data. These computer programs must be able to simulate human intelligence. The main objective of AI is to create intelligent machines that can help solve complex problems in many fields.
There are several categories of Articifial Intelligence (AI), which can be classified according to their capability and level of sophistication. Here are some of the most common categories of Artificial Intelligence :
To get as close as possible to human behaviour, Articifial Intelligence needs a lot of data, as well as a processing and learning capacity. To achieve this, three components are needed:
To enable computers to learn from data, Articifial Intelligence relies on Machine Learning models (a method that aims to teach machines to learn from data and improve with experience). There are 3 learning methods used:
It is important to note that AI is a constantly evolving field of research, and that definitions and distinctions between different types of Artificial Intelligence may change.
The main goal of AI is to create intelligent machines that can help solve complex problems in many fields, such as medicine, engineering, finance, security, social sciences, gaming, etc. Articifial Intelligence is seen as a key technology for the future and has the potential to transform the way we live and work.
These examples are just a small portion of the application areas for AI, and new uses are regularly discovered as the technology continues to advance. AI can therefore be used to improve efficiency, accuracy, safety, and quality in many different areas.
One of the most popular supervised AI at the moment is Chat GPT (Chat Generative Pre-trained Transformer). This conversational tool aims to help its users solve problems, answer questions, and provide information in various domains. It therefore generates text from input data (questions, queries, etc.). It is based on Natural Language Processing technology (NLP) and uses unsupervised learning. It has been trained on a very large corpus of text to learn how to generate consistent and relevant answers based on user queries. This is possible because it has access to huge amounts of text, from various sources.
As a pure player in the R&D ecosystem, FI Group France has created a Scientific Department back in 2019. They lead research in Artificial Intelligence and NLP mostly. This department is composed of seven Researchers (including two Industrial PhD CIFRE). One PhD student is conducting a thesis on data extraction and the construction of algorithms to evolve their grouping by theme and subject. The second PhD student is doing a thesis on unsupervised learning on data flows. She is developing methods capable of clustering data continuously.
The objective of this department is to allow the realization of a regular scientific and technological watch to propose new approaches, and thus to support R&D Financing Consultants in their daily missions.
These projects are possible thanks to the development and experimentation of techniques in Machine Learning and Automatic Language Processing. These processes facilitate the search for information in a large volume of data. A third research topic concerns the acquisition of new knowledge and the involvement of collaborators, via Gamification processes and serious games.
One of the projects supported by FI Group is called NASA. This AI makes it possible to search for scientific articles based on various concepts.
For each query, the articles published between 2019 and 2021 (about 13 million) are used to represent this knowledge in the form of a graph of concepts. It is then possible to display 10 scientific articles published for each concept.
The first prototype of NASA «First STEP» (Scientific Taxonomy Exploration Prototype) was launched in March 2022. The second and the third were respectively put online in September 2022 and February 2023. This «Third STEP» proposes improvements in performance, quality and user experience.
Case studies have argued that 50% of employees could be replaced by machines because of the big technological advancements in AI in the era of Digital Transformation, however, we see this as a complement instead as a negative human replacement.
We see computer software’s and AI as an addition to our activities, working alongside people who are part of the chain of vision. People can improvise, be reactive and think critically, showcasing the advantage of people in unexpected situations. There is a clear link between the collaborative work that can be done by AI and people, rather than a separation.
Knowing how to be an efficient and productive team is always the main aim of any successful partnership. As discussed, both people and AI have different strengths and weaknesses. By delegating tasks based on these strengths and weaknesses the digital transformation and partnership between people and AI can become more efficient and impactful.
It is becoming harder and harder to talk about Consultancy without mentioning digitisation. The digitisation of consultant tasks can be eased by Robotic Process Automation (RPA) software’s. These systems are designed to emulate human’s actions when interacting with digital systems, such as recognising and extracting relevant data.
This automation undoubtedly contributes to the streamlining of the consulting process. It can gather information immediately, transfer documents instantly, create news queries in a few minutes, solve the incidences quickly and in the stages of verifying or making changes to procedures.
So, the digital transformation allows for flexibility within automation systems, which develops ease and efficiently, making the consultants project writing time shorter. This is also a benefit for the client as it allows for quicker turn around time on R&D reports.
Focusing on the improvement of the value add of our services has always been one of our greatest priorities. Innovation and technology are core pillars of our business, we are always looking for ways to improve our internal procedures through digital advancements that are being made, to provide the best and most efficient results for our clients.
We offer a free audit to highlight and show our clients areas in their R&D process that can be improved by our team and service.
To improve this process, we have created an application that have improved the line of communication between FI Group and clients, and allows 24h access to updated claim information, improving our traditional services through new digital tools.
Introducing FI Connect. A new digital space that integrates several applications on a single platform. Benefiting our customers by providing greater transparency and showcasing the digital transformation that FI Group is currently undergoing and that will drive productivity for clients and consultants alike.
Find out if the service is available in your country to book a demo: