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ChatGPT: Top 3 industries that can benefit now

ChatGPT: Top 3 industries that can benefit now

ChatGPT has taken the world by storm. The large language model (LLM) reached 100 million active users just two months after its launch, setting a record for the fastest user growth in a web platform.

When encountering ChatGPT and other LLMs for the first time, many users react with awe, excitement, fear, and curiosity. Its potential impact on the digital ecosystem, particularly in the realm of digital content, information technology, and written communication is profound.

LLMs enable many innovative breakthroughs, especially with the application of the natural language processing transformer paradigm to large training sets customized to the enterprise. Because of this potential, CIOs across all industries will be considering the adoption of ChatGPT and other conversational text-to-speech and research-oriented LLMs.

The opportunity for ChatGPT adoption, especially as soon as OpenAI makes it available as a full-fledged application programming interface (API) endpoint, will enable powerful workflows and amplify its already impressive capabilities.

[ Also read ChatGPT: 3 ways it will impact IT support. ]

While not all industries will be able to implement the technology and see immediate results, CIOs in the following three industries have both the most to gain and the agility to do so quickly.


Researching and developing new medicines has become increasingly time-consuming. On average, it takes at least 10 years for new drugs to reach the market and six to seven years to conduct clinical trials.

ChatGPT has the potential to accelerate that time to market because it can infer data from multiple parallel treatment plans. It can then quickly create insights and correlations from those vast amounts of data points to enable a better understanding of clinical trial outcomes.

It could also transform customer management. An estimated 66 percent of all U.S. adults – more than 131 million people – use prescription drugs. However, the FDA receives more than 100,000 reports of medication errors each year from pharmacies, hospitals, and patient homes. ChatGPT can improve that margin of error. Instead of being connected to an agent when getting a prescription refilled, ChatGPT or a similar technology – with a transformer text-to-speech engine and differentially private training data behind it – can message customers to correctly fill their prescriptions.

Wealth management

Staffing shortages have handicapped multiple industries, including finance. A recent Charles Schwab survey of financial advisers found recruiting and retaining talent is the top strategic priority for the first time since 2006.

ChatGPT can be used as a conversational agent with enterprise-specific data sets to message and advise customers. By providing efficient, high-level financial education, it could dramatically extend the bandwidth of customer representatives, or even eliminate the need for the role. (However, regulatory boundaries will dictate the extent of these use cases.)

ChatGPT won’t eliminate the roles of those who have access to sensitive financial information or who perform transactions on behalf of the customer. It lacks the necessary background context – for example, the customer’s account balances or the historical trends of that individual’s financial history – to make accurate insights. But in the short term, enterprise incarnations of transactional, conversational, or LLMs leveraging RLHF (reinforced learning from human feedback) can take more prominent roles, with implications to replace jobs currently performed exclusively by humans.


Rising commodity prices and supply chain shortages have impacted the automotive industry for several years. CIOs can leverage ChatGPT as a research tool to track industry trends, scan the market for certain parts, and analyze competing supply chain processes to ensure they’re using the right procedures day-to-day to mitigate profit losses.

Enterprise leaders can also utilize ChatGPT to analyze large data sets of parts and procedures. This can improve the creation and maintenance of things like manuals, part catalogs, and operational procedures to unlock new efficiencies. It could also be used to build large knowledge capitals for new part providers, who often lack the documentation advantage of incumbent providers.

ChatGPT’s wildfire popularity has made it the poster child for LLMs and other language processors. But we’re only scratching the surface of what the technology behind ChatGPT can achieve. We know it stands to make a lasting impact on society in a multitude of ways, and it’s that unknown potential that creates buzz and excitement.

Whether it be an AI-specific workflow, AI as a service, or an applied LLM, the next breakthrough is undoubtedly just around the corner. IT leaders – particularly in these three industries – will need to be creative, adaptable, and open to the numerous benefits it can bring.

The original article can be found at


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Vikas Krishan

Chief Digital Business Officer and Head of the EMEA region

Vikas (Vik) Krishan serves as the Chief Digital Business Officer and Head of the EMEA region for Altimetrik. He is responsible for leading and growing the company’s presence across new and existing client relationships within the region.

Vik is a seasoned executive and brings over 25 years of global experience in Financial Services, Digital, Management Consulting, Pre- and Post-deal services and large/ strategic transformational programmes, gained in a variety of senior global leadership roles at firms such as Globant, HCL, Wipro, Logica and EDS and started his career within Investment Banking. He has developed significant cross industry experience across a wide variety of verticals, with a particular focus on working with and advising the C-Suite of Financial Institutions, Private Equity firms and FinTech’s on strategy and growth, operational excellence, performance improvement and digital adoption.

He has served as the engagement lead on multiple global transactions to enable the orchestration of business, technology, and operational change to drive growth and client retention.

Vik, who is based in London, serves as a trustee for the Burma Star Memorial Fund, is a keen photographer and an avid sportsman.

Megan Farrell Herrmanns

Chief Digital Officer, US Central

Megan is a senior business executive with a passion for empowering customers to reach their highest potential. She has depth and breadth of experience working across large enterprise and commercial customers, and across technical and industry domains. With a track record of driving measurable results, she develops trusted relationships with client executives to drive organizational growth, unlock business value, and internalize the use of digital business as a differentiator.

At Altimetrik, Megan is responsible for expanding client relationships and developing new business opportunities in the US Central region. Her focus is on digital business and utilizing her experience to create high growth opportunities for clients. Moreover, she leads the company’s efforts in cultivating and enhancing our partnership with Salesforce, strategically positioning our business to capitalize on new business opportunities.

Prior to Altimetrik, Megan spent 10 years leading Customer Success at Salesforce, helping customers maximize the value of their investments across their technology stack. Prior to Salesforce, Megan spent over 15 years with Accenture, leading large transformational projects for enterprise customers.

Megan earned a Bachelor of Science in Mechanical Engineering from Marquette University. Beyond work, Megan enjoys playing sand volleyball, traveling, watching her kids soccer games, and is actively involved in a philanthropy (Advisory Council for Cradles to Crayons).

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes