Resource Regions: Western Europe

Generative AI has wowed consumers and individuals across the globe with its ability to find information and author high-quality content. For enterprises, the use cases are still being explored and defined. In this blog, we will explore a potential ‘killer app’ for generative AI: The Virtual Mentor as a new way to do learning and onboarding.

In today’s organizations, the vast majority of mentoring is done by speaking to experienced colleagues, looking for answers in the public internet or in company-specific intranets, trawling through various PDF guides and presentations or maybe e-learning courses or classroom sessions. The problem is that there is no easy way of finding the information employees need using existing technologies and approaches.

Current e-learning and onboarding solutions struggle with multiple challenges. Firstly, the content is costly and time-consuming to produce. Secondly, it quickly becomes outdated and is generally static, once produced. Thirdly, the one-size-fits-all standard approach to learning and onboarding doesn’t quite meet the needs of the individual, who already knows all about A but would like to deep-dive into B.

We believe that generative AI will be a game changer in solving these problems, because the system themselves – for the first time in world history – can generate the needed learning content. Future virtual mentors will meet many of today’s unserved learning and onboarding needs and employee would be able to interact digitally, remotely or in the office, intensively or in drip-feed style, and the learning content would be created on the fly determined largely by the nature of the interaction and the learner queries.

AI-Powered Virtual Mentor vs. Previous Learning Approaches

First of all, let’s define generative AI. We define generative AI as a branch of computer science that involves unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images and code.

Secondly, let’s define what an AI-powered virtual mentor is. We envision the AI-powered mentor is have the following characteristics:

• Always available. Like Microsoft’s failed personal digital assistant Clippy (remember the animated talking paperclip?), a virtual mentor will be an omni-available resource to the learner.
• Creates content itself. If fed enough material, a generative AI-powered virtual mentor will be able to create the relevant teaching material itself by synthesizing existing content.
• Conversational. Just like a real-life, human mentor, the AI-powered virtual mentor interacts via conversation. The human mentor converses verbally, while the virtual mentor works best via written conversation (although verbal user experience is on its way, as well).
• Adaptive. A virtual mentor goes far beyond what is known today as ‘adaptive learning’, I.e., an e-learning experience with some variation in the course depending on the individual learner. A virtual mentor can freestyle and go where the learner would like to go within a general topic area.

An employee would be able to ask a wide variety of general questions to the virtual mentor, such as:

• What is the pricing structure for product X?
• Do we have representation in Peru?
• What are the key new features in the version YY.YYY of product Z?
• What is the expense management policy for a client meeting?
• Who in my company works with [expertise area]?

Let’s compare what it is like to work with a generative AI-powered virtual mentor compared to traditional e-learning as well as classroom training:

Why Do We Need Virtual Mentors When We Already Have ChatGPT and Similar Generative AI Platforms?

ChatGPT is of limited use in an enterprise context for one simple reason: Employees using the platform are likely to reveal sensitive company information. This is why most organizations have banned the use of ChatGPT among employees.

Just imagine an employee at a healthcare provider uploaded the raw transcript of an internal meeting regarding the cancer treatment of patient XX and asking for an abbreviated minute of meeting. Such an upload to a public internet system would constitute a major violation of the privacy of patient XX.

Virtual mentors, on the other hand, would leverage the public internet-based Large Learning Models but would not feed any inquiries from employees back to the public internet. Such ChatGPT replicas in confined corporate setting will be the first wave of generative AI virtual mentors that we are going to see on the market.

This will, in other words, be general purpose virtual mentors based upon public internet information. These can be adopted by organizations of any size and are ready to use immediately.

A subsequent wave of virtual mentors will be based on curated content specific to a functional area or an industry or similar. Such specialized content virtual mentors will be sold by vendors that are in charge of curating content and maintaining the AI solution.

A virtual mentor in the area of accounting could be offered by learning content provider or alternatively to an accounting solution provider. Some specialized virtual mentors could be provided as free add-ons to commercial software subscriptions.

Finally, we will see a wave of organization-specific virtual mentors that will act as experts in one organization. In this case, the organization itself would be in charge – possibly aided by a services provider – of feeding the system with learning material.

A product manufacturer would input all manuals, product FAQs, marketing material, customer service interactions, HR policies, internal communication, public pricing information, everything on the intranet and company internet sites, training materials, etc. That solution could be very helpful in onboarding new employees and help answering inquiries for existing employees. However, it would take time and resources to implement and require a certain company size in order to benefit.

The figure below shows the different levels of data feeding into a virtual mentor. The interaction between the virtual mentor and the employee will be chat-based to begin with. However, in the medium term, interaction could also be done through verbal communication, games, metaverses, augmented reality, etc.

Evidence of Generative AI Replacing Existing Digital Learning and Coaching Solutions

Chegg, an established American education technology (EdTech) company known for textbook rentals, online tutoring, and a variety of student services, was among the entities to feel the competition from generative AI. Their initial projection regarding generative AI tools, such as ChatGPT, was that these technologies would take a longer period to truly influence the market.

However, the release and subsequent popularity of GPT-4 among students, credited to its swift response time, efficiency, and affordability, led to a sales slowdown and a dramatic Chegg stock price decline of 48% in early May 2023.

As response to these trends, Chegg entered into a partnership with OpenAI in April 2023, leading to the development of CheggMate. This tool, which is still in its development phase, intends to amalgamate GPT-4’s generative AI capabilities with Chegg’s existing question database.

The goal for CheggMate is to enhance user experience by better aligning user queries with the most suitable resources.

Other EdTech vendors, including Duolingo, have unveiled new AI-driven features. Specifically, Duolingo introduced a role-play chat where users can learn a language by conversing with an AI. After these interactions, they receive feedback and suggestions to enhance their language-learning journey.

We have also witnessed the first examples of generative AI approaches in mentoring. CoachHub is a leading vendor of digital coaching solutions recently unveiled AIMY, a virtual AI-powered career coach rooted in OpenAI’s ChatGPT. AIMY is designed to let users try personalized coaching sessions without any human interactions and without the costs associated with traditional coaching. It emulates human to human coaching, is still in beta phase, and not yet able to manage too complex discussions.

Challenges to Overcome for Virtual Mentor Solutions

Adopting virtual mentor solutions for learning, onboarding, and coaching purposes is not without challenges. Here are a few key obstacles that organizations might encounter when introducing these new AI-driven solutions:

• Data privacy and security concerns. The first cases of data breaches related to the use of generative AI solutions by employees have already emerged, such as Samsung’s discovery of staff uploading a variety of sensitive information to ChatGPT. Future virtual mentor solutions will not feedback data to public generative AI systems, such as ChatGPT.

As shown in the figure above, virtual mentors will use a combination of user data, curated company data, curated industry or functionally specific data as well as publicly available data as training material. Such approaches will limit the risk of data breaches significantly.

However, adoption will require significant attention to security-related aspects, such as ensuring robust encryption, compliance with data protection regulations, etc.

• Implementation complexity and skills gap. Introducing virtual mentor solutions on top of existing data is likely to require specialist AI training skills, which might not be in possession of many organizations. In terms of the overview figure above, the company-specific layer presents the biggest challenges. This is because training material is limited (compared to the vast number of resources available on the public internet) and because training material must be curated, updated, deleted (in case of obsolete material), etc.
• Risk of hallucinations. AI-driven virtual mentors can produce “hallucinations” or inaccurate answers. In a mentoring context, this can lead to confusion or misguidance and ultimately a rejection of the mentor system as unreliable by the employees. The risk of hallucinations by the virtual mentor means that organizations will have to dedicate resources to quality assurance, ticketing system for incorrect or inappropriate answers, etc.

Implications for HCM and Payroll Vendors

Generative AI will have a major impact on the field of Human Capital Management solutions. There has been a significant initial focus on the impact of generative AI on recruiting, candidate marketing, and employee performance.

However, learning and onboarding will also see massive change as a result of generative AI.

A market for curation of Large Learning Models for various industries and functional areas will appear. This could open new revenue streams for the providers with strong existing domain knowledge.

As displayed on the table above, different learning delivery methods will have different sweet spots. Classroom-based learning and traditional e-learning formats will not disappear.

What will happen, however, is that a lot of the more general learning and onboarding tasks will transition to generative AI-based learning formats. Initially, the formats will evolve around chat-based interfaces, but over time other user experiences and communication formats will emerge.

Generative AI is an opportunity for vendors of learning and onboarding solutions. However, they will need to react fast in terms of evolving existing solutions and building in generative AI features and aspects.

Existing learning and onboarding vendors will come under pressure from new providers of virtual mentors and other related generative AI-based solutions. Generative AI is a twin edged sword for HCM vendors, a blessing for those who are willing revisit their existing offerings, but a curse for those that fail to respond.

Is Generative AI possible without the cloud? This question lingers as we delve into the world of AI innovation and explore the potential of generative AI models.

Let’s try to agree on the pivotal role that cloud platforms play in unleashing the power of generative AI as they provide a pathway to rapid development, scalability, and help to unlock the full potential of what some call a groundbreaking technology.

So, do we think generative AI truly flourishes without the aid of cloud platforms? Are they really a match made in technological heaven?

The cloud serves as a catalyst for rapid development and scalability in the realm of generative AI. Imagine the obstacles faced by both startups and established vendors burdened with the need for costly infrastructure investments.

High-performance computing resources such as GPUs and TPUs become accessible without substantial upfront investments. This liberates organizations to focus on what truly matters: developing innovative generative AI solutions, free from almost any infrastructure concerns.

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Beyond this, though, one of the most important benefits of cloud platforms for generative AI is the way they provide managed access to pre-trained foundation models and APIs. These resources act as a springboard, propelling developers forward without the need to start from scratch.

Pre-trained models capture the knowledge and expertise of generative AI experts, saving significant time and computational resources. By leveraging these models, developers can advance their projects, focusing on fine-tuning and customization rather than spending countless hours on training models.

Of course, enterprises can build and host their own foundational models themselves if they so wish, but this is a very expensive, complicated and time-consuming process that requires large teams of rare specialist talent. Cloud providers offer APIs that abstract the complexities of generative model architectures, thus simplifying the integration of generative AI capabilities into already existing and newly built applications. This democratizes access to generative AI, allowing developers to use its power without too deep expertise in model development.

Building generative AI models usually requires comprehensive and efficient development environments. Cloud providers offer a wide range of frameworks, development libraries, and collaboration tools tailored specifically to generative AI. These tools simplify the development, training, and evaluation of generative models, supporting developers and data scientists in bringing their ideas to life. By partnering with cloud providers, companies building developer tools and platforms ensure seamless integration with cloud-based infrastructure and services.

Yet, as much as we want to believe this is a romantic relationship, this is in fact a marriage of convenience aka business, so both sides need to think how this partnership will work for them.

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What AI-Model Providers Should Do

Prioritize Knowledge Transfer

To fully utilize generative AI, it is crucial to invest in knowledge transfer and training programs. Collaborate with cloud providers to develop training materials, workshops, and resources that enhance the understanding and skills of employees. Empowering individuals within organizations to leverage generative AI technologies effectively will maximize the potential of this field.

Foster Continuous Learning and Research

Leverage the support provided by cloud providers for research and development. Engage in research collaborations, attend conferences, and utilize cloud resources for experimentation and innovation. Staying up to date with the latest advancements in generative AI is vital for building new solutions.

Plan for Strong Data Management

Strong data governance practices in place are a must to ensure compliance, data privacy, and responsible use of data. While it makes a lot of sense to leverage cloud platforms’ data management and governance tools to maintain data quality, data lineage, and appropriate access controls throughout the generative AI lifecycle, AI providers must never assume that cloud providers’ tools are enough.

What Cloud Providers Should Do

Invest in Hardware/Chips R&D

Enhance hardware and chip capabilities specifically tailored for generative AI tasks. Explore specialized hardware accelerators, optimize GPU and TPU architectures, or even develop new chips designed to accelerate generative AI computations. By staying at the forefront of hardware advancements, cloud providers can offer superior performance and cost-efficiency.

Develop Industry-Specific or Use-Case Specific AI Frameworks

Differentiate by developing industry-specific or use-case specific AI frameworks that cater to the unique needs of various domains. Offer pre-trained models, domain-specific data management tools, and integration with industry-specific applications. By providing specialized AI frameworks, cloud providers can enable businesses to leverage generative AI effectively and drive sector-specific innovation.

Support Model Deployment and Lifecycle Management

Cloud platform providers must develop comprehensive tools for model deployment, monitoring, and lifecycle management in support of generative AI governance. This includes intuitive interfaces for deploying models, robust monitoring for issue resolution, and higher-level tools for responsible AI delivery. Simplifying processes enhances user experience for developers and data scientists.

Together, both sides should absolutely focus on building ecosystems and on fostering collaboration models that encourage the participation of various stakeholders in the generative AI space. Cloud providers need to create open platforms and APIs, allowing seamless integration with innovative tools, services, and solutions to provide customers with a broader range of generative AI capabilities. AI creators can leverage open platforms and APIs to integrate tools and services developed by complementary companies in the generative AI space, fostering a thriving marketplace of offerings.

And please, remember, a marriage of convenience can only work in situations where both partners enter the marriage with clear expectations and mutually beneficial goals. This can be too much for real family life but should be exactly what’s needed for commercial success.

Introduction

Software project failures are a harsh reality in the world of technology. Despite the best intentions and efforts, projects can unravel due to various reasons, such as poor estimation and planning, inadequate requirements gathering, scope creep, and unrealistic timelines. These failures not only result in financial losses but also tarnish a company’s reputation and erode stakeholder trust. Addressing project failures requires a proactive approach, emphasizing communication, risk management, continuous evaluation and especially realistic estimation and planning. Embracing these lessons can lead to improved project outcomes and foster a culture of learning and growth in the software development industry.

In the dynamic world of software development, accurate cost estimation is crucial to ensure project success. Organizations rely on dependable software cost estimation practices to manage budgets, meet deadlines, and deliver quality products. To address this need, a new Software Cost Estimation Certification has emerged, complemented by the Cost Estimation Body of Knowledge for Software (CEBoK-S). In this blog, we will delve into the significance of this certification and the CEBoK-S, shedding light on how they empower professionals to excel in the field of software cost estimation.

The New Software Cost Estimation Certification (SCEC)

The new Software Cost Estimation Certification is a comprehensive program designed to equip professionals with the latest tools, methodologies, and best practices for accurately estimating software project costs. Offered by the International Cost Estimation and Analysis Association (ICEAA), its special interest group ICEAA-Software, this certification reflects the industry’s evolving demands and ensures that participants stay up to date with the latest trends.

Key Components:

1. Advanced Estimation Techniques: The certification program covers a wide array of advanced estimation techniques, from traditional methods like function point analysis and COCOMO to modern approaches like agile estimation and parametric modelling. By learning these techniques, professionals gain the flexibility to adapt their approach to diverse project requirements.
2. Risk Assessment and Mitigation: Effective cost estimation involves identifying potential risks and uncertainties that can impact the project’s outcome. The certification equips participants with the skills to assess and mitigate risks, allowing for better planning and resource allocation.
3. Industry Case Studies: Real-world case studies are an integral part of the certification program. These case studies provide valuable insights into how cost estimation principles are applied in various scenarios, offering participants a practical understanding of the challenges they may encounter.

The CEBoK-S – Cost Estimation Body of Knowledge for Software

The CEBoK-S is a comprehensive guide that provides a structured framework for software cost estimation. Developed by industry experts, this body of knowledge encompasses a wide range of topics, from fundamental concepts to advanced practices, creating a solid foundation for professionals in the field.

Key Features:

1. Detailed Framework: The CEBoK-S offers a detailed framework that covers all aspects of software cost estimation. It defines the key processes, activities, and inputs required for accurate estimation, guiding professionals through the entire estimation lifecycle.
2. Best Practices and Standards: In an ever-changing industry, adhering to best practices and standards is crucial. The CEBoK-S outlines established industry standards, ensuring consistency and reliability in cost estimation practices across projects and organizations.
3. Continuous Updates: Software development is continually evolving, and the CEBoK-S keeps pace with these changes. It undergoes regular updates to reflect the latest advancements and emerging trends in the field, making it a reliable and relevant resource for professionals.

Impact on the Software Industry

The combination of the new Software Cost Estimation Certification and the CEBoK-S has revolutionized the software industry’s approach to cost estimation. Certified professionals armed with the knowledge from the CEBoK-S are better equipped to address the challenges posed by modern software projects, leading to improved project outcomes and client satisfaction.

1. Enhanced Project Planning: The comprehensive knowledge gained from the certification and the CEBoK-S enables professionals to create accurate and realistic project plans. This, in turn, leads to better resource allocation, reduced budget overruns, and timely project deliveries.
2. Quality and Consistency: Employing standardized cost estimation practices ensures consistency in project management across different teams and organizations. This leads to higher-quality software development, as well as improved collaboration and communication among stakeholders.
3. Improved Stakeholder Trust: Clients and stakeholders place their trust in organizations that employ certified professionals and follow industry standards. The certification acts as a testament to an organization’s commitment to excellence and professionalism.
4. Higher success rates of software development projects, resulting in fewer cost and schedule overruns. This potentially saves companies huge amounts of money and reputation damage.

Conclusion

In conclusion, the new Software Cost Estimation Certification and the CEBoK-S are instrumental in equipping professionals with the knowledge and skills required to excel in software cost estimation. By combining advanced estimation techniques with a structured body of knowledge, these resources elevate the industry’s cost estimation practices to new heights. As organizations continue to embrace these certifications, we can expect to see more successful projects, satisfied clients, and a stronger, more reliable software industry overall.

IDC Metri is proud to announce that its Software Cost Estimation Center of Excellence now has two Software Cost Estimation Certified professionals: Frank Vogelezang and Harold van Heeringen. More information can be found here: https://www.idc.com/eu/idcmetri/it-intelligence

Generative AI is a fascinating topic and has emerged as a powerful technology that pushes the boundaries of what computation can accomplish.

It has the potential to transform the realms of art and creativity, but also revolutionise industry processes.

There are a myriad use cases of generative AI across industries. We can see that different industries are adopting the technology to achieve specific business outcomes or address common challenges every organisation faces.

With its ability to generate content autonomously and simulate human-like outputs, generative AI has found applications in all industries. In fields as diverse as marketing, customer experience, citizen engagement, as well as industry-specific processes, such as supply chain management automation in manufacturing, for instance.

We would like to start diving into the use cases that are commonly used by several industries.

One of the first use cases to be adopted by organisations are conversational applications. They can range from virtual assistants and chatbots to language translation to personalised recommendations.

Another use case spanning across industries is in marketing applications, which can be widely adopted, depending on the sensitivity of the customer/citizen/patient data and the industry appetite for online marketing. For example, social media automation, customer support via chatbots and personalised marketing campaigns can be used to enhance the visibility of the organisation while being more efficient in their marketing investments.

A third use case cutting across industries is knowledge management applications. This use case can be seen in organisations being applied in identifying existing knowledge, knowledge summarisation, and in language translation and geographic contextualisation.

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However, industries adopt technologies based on their specific needs, goals, and customer demands. Unique processes, regulations, and market dynamics require tailored technologies, and it will be no different with generative AI.

Diverse industry requirements, resource constraints, competition, and technological maturity stages drive varying technology adoption across organisations. Now we’d like to explore how several industries are approaching generative AI and the technology adoption patterns of each industry:

Finance

In the ever-evolving landscape of the financial services industry, the emergence of generative AI technologies, led by Open AI’s ChatGPT, has garnered significant attention from CIOs.

While some express concerns regarding privacy and ethics, and others grapple with understanding the full potential, there is a growing sense of urgency driven by the fear of missing out (FOMO). Contrary to sceptics’ concerns, the industry has demonstrated a shift in focus towards augmenting the capabilities of financial services professionals, rather than seeking to replace them.

By harnessing the power of large language models, financial institutions aim to centralise knowledge, empowering agents and professionals with essential information to enhance customer experiences and optimise operational efficiency.

An excellent example of this progressive trajectory is Sedgwick, a prominent global provider of third-party claims administration services. It has successfully integrated the Open API version of ChatGPT, named “Sidekick,” into its sophisticated claims system, exemplifying Sedgwick’s commitment to elevating its claim-handling process and delivering unparalleled customer service experiences.

Another notable application gaining traction involves leveraging generative AI to enhance conversational interfaces. By revolutionising conversational capabilities, generative AI enables more human-like responses and facilitates complex interactions. Helvetia, a pioneering force in the insurance services realm, has embarked on a bold endeavour by launching a direct customer contact service utilising OpenAI’s ChatGPT.

This experimental initiative aims to provide seamless access to various financial products, showcasing the vast potential of generative AI in transforming customer interactions.

Energy (Utilities and Oil & Gas)

According to a recent IDC Survey ― Future Enterprise Resiliency & Spending Survey Wave 2, March 2023 (FERS) ―  the utilities industry globally ranks second highest in terms of investments in generative AI technologies for 2023 (40% of respondents), surpassing the global cross-industry average of 24%.

This highlights the enormous potential for innovation, the amplification of human work, and reinvention of work processes in utility companies. The automation of certain tasks and AI-assisted transformation are expected outcomes.

While the utilities industry is still in the exploratory phase of identifying fruitful use cases, generative AI holds significant promise in areas such as content generation for sales and marketing code-generation applications. To improve productivity and employee experience, conversational applications for customer service and CX improvements, and knowledge management, which is especially crucial given the challenge of an aging workforce in the utilities sector.

On the other hand, oil and gas organisations appear to be adopting a more conservative position.

The FERS survey reveals that only 18% of oil and gas companies worldwide are willing to invest in generative AI technologies in 2023.

However, 82% are actively conducting initial assessments to identify potential use cases. These assessments include evaluating the use of generative AI for multi-scenario authentic simulations and predictive capabilities in asset operations, generating subsurface images using fewer seismic data scans in the upstream part of the business, and generating human-like text to provide responses to domain-specific questions for business leaders.

Manufacturing

The early months of 2023 witnessed a surge of interest in generative AI and a renewed focus on AI in general.

While manufacturing organisations have not been early adopters of generative AI, they are gradually recognising the technology’s potential for leveraging vast research resources to create diverse content, including text, video, images, and virtual environments.

Among the respondents to the IDC 2023 Manufacturing Survey, 27% are already investing in generative AI technologies, and an additional 38% are engaged in basic exploration. Knowledge marketing and marketing applications are areas where organisations see short-term benefits, likely due to the availability of user-friendly technology that is easily accessible, such as ChatGPT.

Moreover, manufacturers believe that generative AI can have a significant medium-term impact on various aspects of their operations, such as production planning, quality control, AI-driven maintenance, code generation for programmable logic controllers, product development, design (including modelling, testing, and product life-cycle management), and sales (including client data analysis and content management).

However, there are ongoing challenges in maximising the value of AI/ML in manufacturing organisations. Many organisations still lack the necessary tools to address issues related to data availability and quality. IDC observes that internal capabilities and training in leveraging AI-powered technology and analytical tools are often lacking.

Read blog: Gen AI in an Industrial Environment — Recommendations for Early Adopters

Government

Generative AI tools such as ChatGPT, Bard, Dall-E 2, Vall-E, Stable Diffusion, and others have rapidly transitioned from arcane terms known only to AI experts to subjects of popular discussion in newspapers and TV talk shows within a matter of months.

OpenAI’s launch of ChatGPT in late 2022 sparked a wave of curiosity and speculation among the public, private companies, and public administrations. Initially, policymakers exercised caution, but senior civil servants quickly developed an interest in generative AI. Consequently, some jurisdictions have begun issuing guidelines.

The United Arab Emirates government, for example, has released guidelines encouraging the use of generative AI and providing ideas for potential use cases.

The Portuguese government has announced the “Practical Guide to Access to Justice,” which utilises the ChatGPT platform to help citizens obtain legal information in layman’s terms.

In another intriguing instance, a member of the Italian parliament used generative AI to write a speech, surprising fellow senators by disclosing its computer-generated nature at the end of the debate.

In the long term, generative AI has the potential to improve citizen experiences, amplify the competencies and capacity of civil servants, who often face overwhelming amounts of documents and cases, and aid administrations struggling to hire new talent.

At present, however, no major government entities in Europe, the Middle East, and Africa (EMEA) have implemented generative AI at scale. Nevertheless, numerous ideas, pilots, and prototypes are under development to understand the potential benefits in terms of citizen and employee experiences, increased operational efficiency, enhanced trust and compliance, environmental sustainability, and the governance and technical challenges that need to be addressed.

Healthcare

European healthcare organisations are increasingly recognising the benefits of generative AI in empowering and engaging patients and clinicians.

The most promising area of investment lies in knowledge management applications that enable a more efficient and effective flow of information among healthcare professionals, ultimately leading to better patient care.

For instance, generative AI can be employed to create or integrate more accurate patient histories and identify disease patterns, significantly enhancing the ability to make accurate diagnoses and develop effective treatment plans.

However, effective implementation of generative AI in healthcare faces limitations related to both data and models. Generative AI models require extensive training on large volumes of high-quality data.

Healthcare data quality varies widely, and its availability can be restricted due to privacy and ethical concerns. Additionally, generative AI models have limitations in terms of reproducibility due to their probabilistic nature and complex architecture. This undermines the reliability and trustworthiness of the models, especially when used to support clinical decision-making.

Read blog: Generative AI in Healthcare: Benefits and Risks

Retail

The retail industry is moving faster than the human pace can keep up with. Evolving customer expectations and needs, fierce competition, and the quest for enhanced process efficiency ― among others ― are all factors driving retailers to rush into experimenting with emerging technologies.

In fact, in 2022 newspapers were crowded with titles of bold retailers and brands landing in the metaverse while, in 2023, the focus has already shifted to generative AI. However, while the metaverse initiatives of retailers have already cooled down in favour of new forms of (spatial) computing, generative AI technologies (such as ChatGPT and Dall-E) and solutions powered by LLMs or text-to-image models could have a major transformational business impact across the retail value chain.

IDC data shows that 40% of retailers are in the initial exploration phase of the technology, while 21% are actively investing in the implementation of generative AI tools for the year ahead. We can already see some relevant applications in the areas of product development, merchandising, supply chain, marketing, and customer experience.

Organisations such as Coca-Cola, Mattel, and Carrefour are piloting generative AI applications ― even though still on a limited scale and predominantly with a test-and-learn approach.

According to IDC findings, 50% of retailers are expecting to prioritise generative AI uses cases for marketing in the next 18 months. In particular, generative AI could have a tremendous impact on the automation and personalisation of resource-intensive and time-consuming ecommerce processes such as product page descriptions, images/videos, and marketing copies.

For example, the Chinese ecommerce giant JD.com announced the imminent release of its own retail-specific ChatGPT solution which aims to improve online retailers’ rankings of product listings on SERP, generate product descriptions that are tailored to a shopper’s preferences, and optimise online product images and video generation processes.

Overall, as shown by the IDC data cited above, the most promising and imminent area of investment for generative AI in the retail sector is marketing and, more specifically, digital marketing.

Even if, in the near future, the technology could raise important questions in terms of proprietary data sharing and customer data privacy, without a doubt the use of generative AI for text and image generation could greatly enhance and streamline the ecommerce shopping experience, leading to higher profitability of retailers’ online channels.

Architecture, Engineering, and Construction

The built environment sector has long been considered behind the curve when it comes to productivity and the adoption of digital technology. But emerging technologies, including generative AI, are accelerating innovation across the sector and aligning it with other industries.

According to an IDC Survey (Future Enterprise Resiliency & Spending Survey Wave 2, IDC, March 2023), 25% of resource and construction companies are investing in generative AI technologies this year, just above the industry average.

The potential of generative spans across the building life cycle. When planning and designing a building, drawings and BIM models typically take weeks or months to produce. Generative AI has the potential to generate building designs in an afternoon based on pre-defined criteria such as building codes, site conditions, and sustainability standards.

The construction process is also ripe for innovation: studies find that the need to correct errors during projects accounts for between 5% and 12% of costs. Here, generative AI can create optimised construction schedules and augment supply chain and material planning.

The opportunities extend to a building’s operation through to its demolition and recycling.

As with all industries, these opportunities must be balanced with potential risks. For AEC companies, there are specific physical safety risks associated with using generative AI for the automation of building designs and compliance checks. The correct safeguards and checks will need to be put in place as these technologies are piloted and rolled out.

Generative AI models also require extensive training on large high-quality data sets: the industry’s legacy of digital immaturity and data fragmentation will affect, but not stall, the rate of innovation.

Moving Forward

In conclusion, as the field of generative AI continues to evolve rapidly, it is paramount to cultivate strategies that enable us to navigate through the noise and discern between hype and reality.

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By gaining a clear understanding of the true potential and limitations of this technology, we can effectively harness its power. The wide-ranging applications of generative AI across various industries have the potential to reshape the way organisations manage their businesses and increase efficiency and productivity.

However, amid the excitement and buzz, it is vital to approach the subject with a discerning eye. Adopting an approach based on use cases, which reveals tangible evidence based on real-world results, becomes an imperative for tech vendors and end-user organisations alike.

Drawing upon practical applications and real-world experiences provides invaluable context, allowing us to differentiate between exaggerated claims and genuine achievements. By prioritising the examination of use cases and seeking concrete results, we deepen our understanding of the true potential and limitations of generative AI.

Another angle of the discerning strategy when it comes to generative AI is to rely on subject experts and look for insights that are connected to the industry in question, as experienced professionals in the field are the best source of reliable and up-to-date information. Moreover, this article was written by several humans, embedded by human intelligence with the help of computers, not generative AI.

Contributing analysts: Adriana Allocato, Davide Palanza, Gaia Gallotti, Jan Burian, Louisa Barker, Massimiliano Claps and Sofia Poggi

If you want to know more about generative AI visit our website, or for more in-depth industry insight click here.