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
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