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How to Integrate ChatGPT into Your Business by Industry?

The integration of ChatGPT in business applications has brought in extraordinary advantages besides just improving the basic business functionalities, giving better customer experiences, and driving efficiencies over time. It could be applied to all other fields of organizations where there exist a need and potentialities by using the power of Artificial Intelligence (AI) and Generative AI (Gen AI).

 

In this article, we expand on how to better incorporate ChatGPT into various sectors, supply examples of its function by industry, and explain the advantages so that you can get the most out of this tool.

1. Healthcare Industry

The following a few best strategies of ChatGPT integration in healthcare systems.

• Patient Support

ChatGPT can be a virtual assistant for both on your website and patient portal. It can be used to respond to specific user questions regarding symptoms, treatments, and medications. ChatGPT integration with EHR systems helps companies process data and provide personalized recommendations based on patient information.

• Quick Appointment scheduling

Artificial Intelligence (AI) based ChatGPT integration in healthcare assists in organizing appointments, confirmations, and reminders. Integrating LLM models with internal scheduling software makes communications between healthcare providers and patients seamless.

• Mental Health Help

The incorporation of ChatGPT into mental health applications offers users preliminary assistance and guidance. So, the integration of ChatGPT with healthcare apps facilitates the process of establishing a communication bridge between patients with appropriate professionals for additional healthcare support.

Key Benefits of Integrating ChatGPT with Healthcare Mobile Apps

• 24/7 Support: ChatGPT provides 24/7 support to the patient, thus improving the access of information and services in any time zone.
• Less paper work: It automates routine procedures like appointments and many frequently asked questions, thus free ups employees for more complex tasks.
• Patient Experience Improved: Gives immediate and personalized answers that enhance the experience and interaction level of a patient.

2. Retail

The retail sector is one of the most promising sectors that could benefit from ChatGPT integration in its internal software applications.

Top GPT integration strategies in retail.

• Customer support

ChatGPT integration on your e-commerce website or end-user applications helps you manage requests about product details, the status of an order, return policies, refund statuses, etc. ChatGPT can be connected with CRM for more personalized support.

• Personalized shopping experiences

Using ChatGPT, businesses can keep track of customer preferences, browsing history, and needs. It helps them provide personalized product recommendations and improve their experiences.

• Virtual Shopping Assistant

ChatGPT integration in AI shopping assistants can aid in assisting customers on your website, comparing products, and making purchases.

Benefits of integrating ChatGPT in Retail Systems

• Boost Sales: ChatGPT integration in retail helps retailers provide personalized recommendations and ensures effective handling of queries that would increase sales and conversions.
• Cost-effectiveness: Since there is a reduced need for large pools of customer service personnel, routine interactions are reduced with chatbots.
• Higher Customer Engagement: It offers a much more interactive and responsive shopping experience and boosts customer engagements.

3. Financial Services

The following are the top three ChatGPT Integration Strategies for the Finance Sector.

• Customer Support

ChatGPT can automate time-consuming repetitive admin tasks, such as customer communications and transaction monitoring and management. With its integration into banking systems, financial organizations can boost their employee productivity.

• Fraud Detection and Security

Through the integration of AI ChatGPT, companies can track irregular patterns in customers’ behavior and identify suspicious transactions.

• Financial Advisory

Introduce ChatGPT in financial advisory websites to provide simple financial advice and counseling to customers through the chat service, as per their queries and monetary goals.

Top Benefits of integrating LLM Models into Finance Software

• Process Efficiency: Automates daily customer communications and transactions so that employees’ time is utilized for complex work.
• High Security: It helps to detect fraudulent transactions and eventually brings about much better security.
• Superior Customer Service: It helps companies provide instant and accurate responses to financial questions. Thus, GPT is augmenting the customer satisfaction level.

4. Education

ChatGPT Incorporation Methods into Education Systems

• Student Support

Incorporate ChatGPT into educational applications to provide prompt responses to students about their inquiries about courses, administrative procedures, and also research and tutorial support.

• Administrative Tasks

Use ChatGPT for streamlining routine administrative tasks, such as queries related to admissions and scheduling, to minimize the workload for the educational workers.

• Interactive Learning

Deploy ChatGPT in educational apps to improving students’ engagement, especially through quizzes and personalized feedback.

Benefits delivered to educational institutions with ChatGPT integration

• ChatGPT integration will help educational institutions provide instant access to the entire information and provide 24/7 support to the students, thus improving their learning experiences.
• It also streamlines administrative processes, helping staff to undertake some more strategic work and boost productivity.
• LLM Models supports the students even after class hours by offering them help without time constraints. It enhances their learning experiences and skills.

5. Travel and Hospitality

The following are the best ideas to integrate ChatGPT in travel apps.

• Booking Support

With the integration of ChatGPT with travel booking apps, it would speed up the process of booking flights, hotels, and rental cars. It would be able to also address all enquiries including questions on availability, prices, and change in details of the booking.

• Customer Service

Using ChatGPT, you can even answer questions that consumers have concerning their travel, changes in journeys, cancellations, even what to see locally.

• Local Recommendations

Use ChatGPT in travel apps to provide locals with personal suggestions on dining places, attractions, and activities that best fit the preferences of the users.

Top benefits of ChatGPT Integration into travel apps.

• Enhanced Customer Experience: By deploying ChatGPT in travel apps, businesses can provide seamless booking assistance and personalized recommendations to their users. Thus, it improves overall travel experiences.
• Operational Efficiency: By automating end-to-end procedures, AI ChatGPT reduces the need for manual intervention and improves operational efficiencies.
• Increased Revenue: Facilitates upselling and cross-selling of additional services and experiences, potentially boosting revenue.

6. Real Estate

ChatGPT Integration Strategies

•        Property Inquiries 

Usage of ChatGPT on your website helps customers inquire about their property listings, availability, and prices.

•        Lead generation

Apply ChatGPT when you engage with buyers and sellers to qualify them and schedule a viewing for the property. This will harmonize lead management with the systems in the CRM.

•        Market Insights

Use ChatGPT to let clients have an opportunity of market trends, properties, and any other type of investment-related possibility for their questions.

Benefits Of ChatGPT Integration in Real Estate Apps

 

• Lead Management Efficiency: Integration of ChatGPT in real estate mobile apps will help companies better manage the process of communicating with potential clients and qualifies leads in the conversion process.
• Better Discovery of Properties: Helps clients find properties that come within their scope of interest, therefore enhancing their experience.
• Cost-Saving: It reduces the need for detailed tracking and time-consuming follow-through that saves the operational costs.

 

Conclusion

ChatGPT would integrate into the business and would provide a transformative benefit across all sectors. Implementation of ChatGPT in areas to meet industry needs of healthcare, retail, finance services, education, travel, real estate, human resource, or automotive, maximizes experiences for customers and streamlining operations for greater efficiency. Strategic integration with continuous iteration and aligned to your business objectives will result in ChatGPT as a catalyst in further success towards your strategic objectives.

 

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By Kristýna Janovská and Pavel Surynek

Imagine if all of our cars could drive themselves – autonomous driving is becoming possible, but to what extent? To get a vehicle somewhere by itself may not seem so tricky if the route is clear and well defined, but what if there are more cars, each trying to get to a different place? And what if we add pedestrians, animals and other unaccounted for elements? This problem has recently been increasingly studied, and already used in scenarios such as warehouse logistics, where a group of robots move boxes in a warehouse, each with its own goal, but all moving while making sure not to collide and making their routes – paths – as short as possible. But how to formalize such a problem? The answer is MAPF – multi-agent path finding [Silver, 2005].

Multi-agent path finding describes a problem where we have a group of agents – robots, vehicles or even people – who are each trying to get from their starting positions to their goal positions all at once without ever colliding (being in the same position at the same time).

Typically, this problem has been solved on graphs. Graphs are structures that are able to simplify an environment using its focal points and interconnections between them. These points are called vertices and can represent, for example, coordinates. They are connected by edges, which connect neighbouring vertices and represent distances between them.

If however we are trying to solve a real-life scenario, we strive to get as close to simulating reality as possible. Therefore, discrete representation (using a finite number of vertices) may not suffice. But how to search an environment that is continuous, that is, one where there is basically an infinite amount of vertices connected by edges of infinitely small sizes?

This is where something called sampling-based algorithms comes into play. Algorithms such as RRT* [Karaman and Frazzoli, 2011], which we used in our work, randomly select (sample) coordinates in our coordinate space and use them as vertices. The more points that are sampled, the more accurate the representation of the environment is. These vertices are connected to that of their nearest neighbours which minimizes the length of the path from the starting point to the newly sampled point. The path is a sequence of vertices, measured as a sum of the lengths of edges between them.

Figure 1: Two examples of paths connecting starting positions (blue) and goal positions (green) of three agents. Once an obstacle is present, agents plan smooth curved paths around it, successfully avoiding both the obstacle and each other.

We can get a close to optimal path this way, though there is still one problem. Paths created this way are still somewhat bumpy, as the transition between different segments of a path is sharp. If a vehicle was to take this path, it would probably have to turn itself at once when it reaches the end of a segment, as some robotic vacuum cleaners do when moving around. This slows the vehicle or a robot down significantly. A way we can solve this is to take these paths and smooth them, so that the transitions are no longer sharp, but smooth curves. This way, robots or vehicles moving on them can smoothly travel without ever stopping or slowing down significantly when in need of a turn.

Our paper [Janovská and Surynek, 2024] proposed a method for multi-agent path finding in continuous environments, where agents move on sets of smooth paths without colliding. Our algorithm is inspired by the Conflict Based Search (CBS) [Sharon et al., 2014]. Our extension into a continuous space called Continuous-Environment Conflict-Based Search (CE-CBS) works on two levels:

Figure 2: Comparison of paths found with discrete CBS algorithm on a 2D grid (left) and CE-CBS paths in a continuous version of the same environment. Three agents move from blue starting points to green goal points. These experiments are performed in the Robotic Agents Laboratory at Faculty of Information Technology of the Czech Technical University in Prague.

Firstly, each agent searches for a path individually. This is done with the RRT* algorithm as mentioned above. The resulting path is then smoothed using B-spline curves, polynomial piecewise curves applied to vertices of the path. This removes sharp turns and makes the path easier to traverse for a physical agent.

Individual paths are then sent to the higher level of the algorithm, in which paths are compared and conflicts are found. Conflict arises if two agents (which are represented as rigid circular bodies) overlap at any given time. If so, constraints are created to forbid one of the agents from passing through the conflicting space at a time interval during which it was previously present in that space. Both options which constrain one of the agents are tried – a tree of possible constraint settings and their solutions is constructed and expanded upon with each conflict found. When a new constraint is added, this information passes to all agents it concerns and their paths are re-planned so that they avoid the constrained time and space. Then the paths are checked again for validity, and this repeats until a conflict-free solution, which aims to be as short as possible is found.

This way, agents can effectively move without losing speed while turning and without colliding with each other. Although there are environments such as narrow hallways where slowing down or even stopping may be necessary for agents to safely pass, CE-CBS finds solutions in most environments.

This research is supported by the Czech Science Foundation, 22-31346S.

You can read our paper here.

References

• Janovská, K. and Surynek, P. (2024). Multi-agent Path Finding in Continuous Environment, CoRR.
• Sharon, G., Stern, R., Felner, A., and Sturtevant, N. R. (2014). Conflict-based search for optimal multi-agent pathfinding, Artificial Intelligence.
• Karaman, S. and Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning, CoRR.
• Piegl, L. and Tiller, W. (1996). The NURBS Book, Springer-Verlag, New York, USA, second edition.
• Silver, D. (2005). Cooperative pathfinding, Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference, Marina del Rey, California, USA.