Trending December 2023 # 7 Use Cases Of Chatgpt In Marketing For 2023 # Suggested January 2024 # Top 12 Popular

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The share of artificial intelligence in the marketing industry is rapidly increasing (see Figure 1). However, the use of relatively new tools, such as generative AI and, in particular, ChatGPT in marketing is not widely known.

Figure 1. Market value of artificial intelligence (AI) in marketing worldwide from 2023 to 2028

In this article, we will explain 7 use cases of ChatGPT to help digital marketers have an effective marketing strategy.

1- Content creation

Content creation, text generation in specific, using ChatGPT can be a powerful tool for marketing. These AI-generated texts can be used for a variety of purposes other than generating ideas, such as:

Contents generated by ChatGPT can be integrated with other marketing strategies and channels like:

Creating various contents for digital marketing campaigns

Preparing posts for social media platforms

Generating personalized, attractive and persuasive emails for email marketing.

For more on the use cases of generative AI in copywriting, check our comprehensive article.

2- Personalized customer experience

ChatGPT with its natural language processing (NLP) can generate personalized content for your customers based on their preferences, past behavior, and demographics. This can help you create targeted content that resonates with your audience, which can lead to higher engagement and conversion rates.

3- Audience research

Audience research involves gathering data and insights about your target audience to better understand their interests, preferences, behaviors, and needs. This information can help you create more effective marketing strategies, including content creation, ad targeting, and product development.

ChatGPT can be used to analyze customer data such as:

Search queries

Social media interactions

Past purchases to identify patterns and trends in customer behavior.

By analyzing this data, ChatGPT can help you identify your target audience’s preferences, interests, and pain points, which can inform your marketing messaging, content, and product development.

4- SEO optimization

ChatGPT can be a valuable tool for SEO in marketing. SEO, or search engine optimization, involves optimizing your website and content to rank higher in search engine results pages (SERPs) for relevant keywords and phrases. Here are some ways that ChatGPT can help with SEO:

Generate attractive topic ideas for content marketing

Make keyword research

Find the right and attractive titles

Group search intent

Create content structure

Generate meta descriptions

Figure 3. ChatGPT SEO-friendly title suggestions for contents in B2B marketing

5- Writing product descriptions

Product descriptions are a crucial part of marketing, as they provide potential customers with information about the features, benefits, and value of a product. ChatGPT can help create compelling and informative product descriptions that resonate with your target audience.

6- Chatbot for customer support

ChatGPT can be integrated into a chatbot to provide instant and personalized customer support. Chatbots can help customers with frequently asked questions, provide technical support, and even troubleshoot issues. Chatbots in marketing can help:

Improve customer satisfaction

Reduce response times

Decrease the workload of customer service representatives.

7- Creating customer surveys

Surveys are an effective way to gather feedback and insights from customers, which can help marketers improve their products, services, and marketing strategies. Here are some ways that ChatGPT can help with creating customer surveys:

Question generation

Organizing survey structure

Making surveys multilingual with its translation ability

Survey analysis

If you have questions or need help in finding vendors, please contact:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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How To Use Chatgpt For Coding In 2023

By now, I am sure everyone must be familiar with ChatGPT and how to use it in their daily life. But what if I say that what you are using is just the surface of ChatGPT, there are many more layers left to explore? Recently Artificial Intelligence (AI) has revolutionized various fields, including programming. Developers and coders can now use powerful language models like ChatGPT to improve their programming skills. ChatGPT is a pre-trained language model that uses natural language processing to understand and respond to human language. It can be used for various purposes, including generating text, answering questions, and even coding. If you’re a developer or a coder looking to improve your programming skills, this guide is for you. I will show you how to use ChatGPT for coding and take your skills to the next level.

Natural language processing is used by the language model ChatGPT to comprehend and respond to human discourse. It is pre-trained on enormous amounts of text data. ChatGPT has several uses, including text generation, question-and-answer functionality, and even coding. With ChatGPT, you can conduct engaging conversations, produce text that sounds like human speech, and even write programs. In conclusion, ChatGPT is a powerful tool that can help you create intelligent chatbots, create unique and engaging content, and do a variety of other language-related tasks.

ChatGPT can be used for a variety of tasks, such as writing code, answering any type of complex questions, and even generating code. For programmers and developers, ChatGPT is a valuable and productive tool since it uses natural language processing to understand and react to human language. With ChatGPT, the efficiency, productivity, and quality of your code can all be considerably raised. Before utilizing ChatGPT to code, you must first install it, set up the programming environment, train the model, and generate the code.

To use ChatGPT for coding, you need toset up the programming environment, train the model, and generate the code. Here are the steps to follow:

On your web browser search for ChatGPT.

Go to its official website.

Verify your phone number if asked.

Select the features that you want to use.

Now tap on the open in playground button.

Here type in the code or write the context of the code you want to generate.

Example: You can ask questions such as:

“Can you show me an example of how to implement a binary search algorithm in Python?”

“What could be causing a ‘TypeError: ‘int’ object is not iterable’ error in my Python code?”

“Can you suggest a way to optimize this piece of code that calculates the sum of all even numbers in a list?”

Yes. You can use ChatGPT for all types of programming languages. You can use it in different ways such as generating code based on your input, describing errors in code, optimize your code, etc.

ChatGPT can solve any problem when it comes to coding. However, the accuracy of ChatGPT’s responses can vary depending on several factors such as the complexity of the task, the quality of the input provided, and the limitations of the training data.

It doesn’t matter if you are a beginner, ChatGPT will help you through every step. Because it supports various programming languages and use cases, ChatGPT is appropriate for both newbies and experienced programmers. You can start from the basis and let ChatGPT know that you are a newbie, that way ChatGPT will generate the output data accordingly.

ChatGPT is no doubt the most powerful and rising tool in the world of AI. From producing intelligent chatbots to generating original content ChatGPT is capable of carrying out some of the most complex task and other language related stuff. ChatGPT shoes its significance and effectiveness in the field of coding as well. Trust me when it comes to coding, ChatGPT has a lot of calibre. Even if you are a rookie, ChatGPT will guide you through every step of coding language and will also solve any complex error in your code. So why wait, try out ChatGPT for coding right now and take your programming skills to the next level.

3 Use Cases & Benefits Of Digital Twins In Healthcare For 2023

It’s been reported that 66% of healthcare executives expect increasing investment in digital twins over the next three years. This is because digital twins improve healthcare organization performance, discover areas for improvements, provide customization and personalization of medicine and diagnosis, and enable the development of new medicines and devices.

In this article, we explore digital twin benefits, use cases and challenges in healthcare.

How is digital twin technology used in healthcare?

A digital twin is a digital replica of the tools, people, processes, and systems that businesses employ. In healthcare systems, digital twins are utilized to build digital representations of healthcare data, such as hospital environments, lab results, human physiology, etc. through computer models. To construct virtual twins, data that covers the individual, population traits, and environment are used.

Digital twin of a healthcare facility

Digital twin technology can be used to generate a virtual twin of a hospital to review operational strategies, capacities, staffing, and care models to identify areas of improvement, predict future challenges, and optimize organizational strategies. Therefore, digital twins of hospitals can be used for generating facility replicas, and in turn this enables:

Resource optimization: Leveraging historical and real-time data of hospital operations and surrounding environment (e.g. COVID-19 cases, car crashes, etc.) to create digital twins enables hospital management to detect bed shortages, optimize staff schedules, and help operate rooms. Such information increases the efficiency of resources and optimized the hospital’s and staff’s performance, while decreasing costs. For example, a review study has shown that utilizing digital twins to manage the smooth coordination of several processes enabled a hospital to reduce the time in treatment of stroke patients by.

Risk management: Digital twins provide a safe environment to test the changes in system performance (staff numbers, operation room vacancies, device maintenance, etc.) which enables implementing data-driven strategic decisions in a complex and sensitive environment.

Digital twin of the human body

Digital twins are also applied for modeling organs and single cells or an individual’s genetic makeup, physiological characteristics, and lifestyle habits to create personalized medicine and treatment plans. These replicas of the human body’s internal systems improve medical care and patient treatment by:

Digital twin in healthcare can improve the design, development, testing, and monitoring of new drugs and medical devices. For example:

Drugs: Digital twins of drugs and chemical substances enable scientists to modify or redesign drugs considering particle size and composition characteristics to improve delivery efficiency.

Devices: A digital twins of a medical device enables developers to test the characteristics or uses of a device, make alterations in design or materials, and test the success or failure of the modifications in a virtual environment before manufacturing. This significantly reduces the costs of failures, and enhances the performance and safety of the final product.

Figure 2: Digital twin for medication

What are the digital twin challenges in healthcare?

Some of the challenges that face digital twin implementation in healthcare include:

Limited adoption

Digital twin technology is not widely adopted in the clinical routine. Healthcare units (e.g., hospitals and labs) should increase the impact of technology on digital simulations, vital clinical processes, and overall improvement of medical care.

On the other hand, even though healthcare system uses digital twins increase, it is argued that it will remain expensive and not accessible for everyone. Digital twin technology will become a benefit reserved for people with higher financial capabilities, which would generate inequality in healthcare system.

Data quality

Artificial intelligence system in digital twins learn from the available biomedical data but as the data is gathered through private companies, the data quality might turn out bad. Consequently, the analysis and representation of such data becomes problematic. That eventually affects the models negatively, which also affects the reliability of the models in the diagnosis and treatment processes.

Check our article on data-centric AI to learn more about how you can improve the quality of your data in AI systems.

Data privacy

The applications of digital twins require gathering more and more individual level data by healthcare organizations and insurance companies. Over time, these health organizations grasp a detailed portrait of a biological, genetic, physical, and lifestyle related information of a person. Such personalized data might be in use benefitting the company’s interest instead of the individuals. One example would be that insurance company might leverage the data to increase precise distinctions significant to personal identity.

Feel free to explore data security best practices.

The Best Amazon Fire 7 Cases Of 2023

Whether you’re looking for style, versatility, or child-proofing your device, there are several options out there when it comes to choosing the right case for your Amazon Fire 7 2023. We’ve rounded up the best options for you.

MoKo Honey Comb Case for Fire 7

MoKo’s Honey Comb Series silicone case offers lightweight and impact-resistant protection for your Fire 7. The case fits cozily around the device and has openings for convenient access to the camera, control buttons, and ports.

The case does not include a front cover, so you may want to buy a separate screen protector if you’re particularly concerned about your screen safety.

The case is designed to fit the Fire 7 2023 version only and comes with a lifetime warranty.

Here’s a child-proof case from Amazon, so you can be sure it will fit the Fire 7 tablet seamlessly. As you can expect from a child’s case, it is durable and offers protection against drops and bumps; it’s also textured on the outside for additional grip.

The case allows easy access to ports, buttons and cameras. The Amazon Fire Kid-Proof Case comes in 3 colours (blue, pink and yellow) in the UK, though US shoppers also have the additional colour options of pink and black

Caseable is a favourite of ours. The brand offers stunning artwork for a wide range of phones and tablets – and you can also custom design your case if you prefer.

The cases are designed specifically for the Fire 7 (2023 version). The front flap can be folded and transformed into a viewing stand and when closed puts the tablet to sleep (it also wakes up the tablet on opening).

There are also cutouts for access to camera and speakers and a microfiber interior keeps the screen from unwanted scratches.

While the Amazon Fire 7 may not have a particularly alluring exterior, compared to the rose golds and glass casings we see these days for tablets and phones, you may still want to show off its natural splendor with a no-nonsense clear case.

This TPU case from MoKo is soft, flexible and promises to keep your Fire 7 safe from drops, scratches and water. It has raised edges to keep the screen from scuffs. The MoKo clear case comes with a lifetime warranty.

Bears vs Babies Case with Screen Protector

If your tablet needs considerable child-proofing, Bear vs Babies’ case offers a range of impressive safeguarding features that you may like.

The case is built of a heavy-duty and environment-friendly EVA foam which absorbs shock and prevents your device screen from shattering. If that’s not enough, the case even comes with a tempered glass screen protector. Your child may also appreciate the convenient soft-grip handle integrated into the case.

The back of the case features additional pull-out stands to prop up your device up. You can also use these stands with the handle to prop the device up at a lower angle. Designed exclusively for the Fire 7 2023 tablet, the case is washable and comes with a lifetime warranty.

It’s not available in the US, though there is a similar case on Amazon for $14.66 (plus $2.99 shipping) – it doesn’t include a screen protector, but the handle is fashioned like cat ears, which your child may appreciate even more.

MoKo Case for Fire 7

Designed specifically for the Fire 7 2023 tablet, MoKo offers stylish and lightweight protection for your case in an assortment of vibrant colours and graphics. The 3 sectioned front cover folds into a stand for hands-free viewing.

The case is made of synthetic leather and uses a microfiber interior to protect your screen from dust. The case also conveniently puts your device to sleep on closing. The translucent back cover is also fingerprint proof.

Heavy users will be pleased to know MoKo offers a lifetime warranty.

Navitech Black Case

For those who prefer to keep their valuables safely stowed in one place, Navitech’s tablet case doubles as a wallet. This affordable yet stylish two-in-one case is made of premium synthetic leather and secures your device in place using elastic straps. Users may also find the stylus holder useful.

The only drawback to this case is you would need to remove your device to access the rear-facing camera–but if you primarily use your tablet for web and content browsing, this may not be an issue.

Amazon Fire 7 Case

Amazon’s own case is another excellent option for those looking for sleek and simple protection for their Fire 7 tablet. The diagonally-folding front cover allows hands-free viewing in both landscape and portrait orientation. The case also puts your device to sleep on closing and stayed closed using a magnetic fastener.

This fabric-bound case is available in five different single-tone colours: Black, Indigo Blue, Purple, Red, and Yellow. The case is compatible with the Fire 7 2023 only.

Top 13 Use Cases / Applications Of Ai In Manufacturing In 2023

The industrial manufacturing industry is the top adopter of artificial intelligence, with 93 percent of leaders stating their organizations are at least moderately using AI.

Manufacturers are frequently facing different challenges such as unexpected machinery failure or defective product delivery. Leveraging AI and machine learning, manufacturers can improve operational efficiency, launch new products, customize product designs, and plan future financial actions to progress on their digital transformation.

Why is AI important in the manufacturing industry?

Implementing AI in manufacturing facilities is getting popular among manufacturers. According to Capgemini’s research, more than half of the European manufacturers (51%) are implementing AI solutions, with Japan (30%) and the US (28%) following in second and third.

The same study also reveals that the most popular AI use cases in manufacturing are improving:

maintenance (29% of manufacturing AI use cases)

quality (27%)

This popularity is driven by the fact that manufacturing data is a good fit for AI/machine learning. Manufacturing is full of analytical data which is easier for machines to analyze. Hundreds of variables impact the production process and while these are very hard to analyze for humans, machine learning models can easily predict the impact of individual variables in such complex situations. In other industries involving language or emotions, machines are still operating at below human capabilities, slowing down their adoption.

The COVID-19 pandemic also increased the interest of manufacturers in AI applications. As seen on Google Trends graph below, the panic due to lockdowns may have forced manufacturers to shift their focus to artificial intelligence.

What are the common AI use cases in manufacturing? 1. Predictive maintenance

Manufacturers leverage AI technology to identify potential downtime and accidents by analyzing sensor data. AI systems help manufacturers forecast when or if functional equipment will fail so its maintenance and repair can be scheduled before the failure occurs. Thanks to AI-powered predictive maintenance, manufacturers can improve efficiency while reducing the cost of machine failure.

2. Generative design

Generative design uses machine learning algorithms to mimic an engineer’s approach to design. Designers or engineers enter parameters of design (such as materials, size, weight, strength, manufacturing methods, and cost constraints) into generative design software and the software provides all the possible outcomes that can be created with those parameters. With this method, manufacturers quickly generate thousands of design options for one product.

3. Price forecasting of raw material

The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI-powered software like can predict materials prices more accurately than humans and it learns from its mistakes.

4. Robotics

Industrial robots, also referred to as manufacturing robots, automate repetitive tasks, prevent or reduce human error to a negligible rate, and shift human workers’ focus to more productive areas of the operation. Applications of robots in plants vary. Applications include assembly, welding, painting, product inspection, picking and placing, die casting, drilling, glass making, and grinding.

Industrial robots have been in manufacturing plants since the late 1970s. With the addition of artificial intelligence, an industrial robot can monitor its own accuracy and performance, and train itself to get better. Some manufacturing robots are equipped with machine vision that helps the robot achieve precise mobility in complex and random environments.

Cobots are another robotics application that uses machine vision to work safely alongside human workers to complete a task that cannot be fully automated. Feel free to learn more about cobots with our comprehensive guide.

5. Edge analytics

Edge analytics provides fast and decentralized insights from data sets collected from sensors on machines. Manufacturers collect and analyze data on edge to reduce time to insight. Edge analytics has three use cases in manufacturing:

Improving production quality and yield

Detecting early signs of deteriorating performance and risk of failure

Tracking worker health and safety by using wearables

To learn more about analytics in manufacturing, feel free to read our in-depth article about the top 10 manufacturing analytics use cases.

6. Quality assurance

Quality assurance is the maintenance of a desired level of quality in a service or product. Assembly lines are data-driven, interconnected, and autonomous networks. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible. When an end-product is of lower quality than expected, AI systems trigger an alert to users so that they can react to make adjustments.

You can also check the lists of data annotation and AI/ML tools and services to find the option that best suits your project needs:

7. Inventory management

Machine learning solutions can promote inventory planning activities as they are good at dealing with demand forecasting and supply planning. AI-powered demand forecasting tools provide more accurate results than traditional demand forecasting methods (ARIMA, exponential smoothing, etc) engineers use in manufacturing facilities. These tools enable businesses to manage inventory levels better so that cash-in-stock and out-of-stock scenarios are less likely to happen.

8. Process optimization

AI-powered software can help organizations optimize processes to achieve sustainable production levels. Manufacturers can prefer AI-powered process mining tools to identify and eliminate bottlenecks in the organization’s processes. For instance, timely and accurate delivery to a customer is the ultimate goal in the manufacturing industry. However, if the company has several factories in different regions, building a consistent delivery system is difficult.

By using a process mining tool, manufacturers can compare the performance of different regions down to individual process steps, including duration, cost, and the person performing the step. These insights help streamline processes and identify bottlenecks so that manufacturers can take action.

For example, a manufacturer that employed a process mining tool in their procure-to-pay processes decreased deviations and maverick buying worth to $60,000.

9. AI-Powered digital twin use cases

A digital twin is a virtual representation of a real-world product or asset. By combining AI techniques with digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance. There are typically 4 applications of digital twins in manufacturing:

10. Product development

Manufacturers can use digital twins before a product’s physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.

11. Design customization

Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.

12. Shop floor performance improvement

A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.

13. Logistics optimization

Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process.

What are the benefits of AI in manufacturing? Safety

Manufacturing is one of the highest-risk industrial sectors to be working in with more than 3,000 major injuries and nine fatalities occurring each year. The involvement of robots in high-risk jobs can help manufacturers reduce unwanted accidents.

Cost Reduction

AI technologies can reduce the operation costs of manufacturers due to several applications:

Leveraging AI technologies can enhance organizations’ analytics capability so that they can use their resources more efficiently, make better forecasts, and reduce inventory costs. Thanks to better analytics capabilities, companies can also switch to predictive maintenance leading to eliminating downtime costs and reducing maintenance costs.

This one is obvious but manufacturers don’t need to pay monthly salaries to robots. However, robots require CAPEX which needs to be weighed against the recurring cost of labor.

Faster decision making

Thanks to IoT sensors, manufacturers can collect large volumes of data and switch to real-time analytics. This allows manufacturers to reach insights sooner so that they can make operational, real-time data-driven decisions.

24/7 production in dark factories

Factories without any human labor are called dark factories since light may not be necessary for robots to function. This is a relatively new concept with only a few experimental 100% dark factories currently operating. However, dark factories will increase over time with the application of AI and other automation technologies since they have the potential to unleash significant savings, end workplace accidents and expand their production capacity.

Read more on AI applications in different industries:

If you still have questions on how AI revolutionizing the manufacturing industry, don’t hesitate to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Top 12 Use Cases Of Rpa In Procurement Process In 2023

Chief Procurement Officers (CPOs) are pessimistic: 66% of them surveyed in 2023 believe that supply chain volatility will persist in 2023.

A remedy is a more robust procurement process that keeps the business ahead of the market and geopolitical dynamics. For instance, paying vendors on time ensures timely delivery of goods which sustains the manufacturing cycle. This at least ensures that whatever supply chain issues the company is facing, it’s not procurement-related.

Robotic process automation (RPA) can assist the procurement department in managing their procurement tasks better. In this article, we will explain the top 12 use cases of RPA in procurement.

1. Input identification

RPA’s first procurement application is input identification. RPA can retrieve each product’s input list from the bill of materials (BOM) and store it in a hub.

2. Contract management

Robotic process automation can automate contract management. Use cases would include:

Drafting B2B contracts by automatically extracting the vendors’ info and putting it on the draft

Sending notification to the procurement teams whenever a contract is reaching the expiry date

Archiving each contract in each vendor’s dedicated database

Using OCR and NLP to review contracts and ensure the SLA terms comply against company policy

3. Purchase request & purchase order submission

Purchase requests and purchase orders are submitted to inform the company’s decision-makers and the vendor of the type and quantity of the needed items.

4. Category management

Different departments are in charge of purchasing their own materials. RPA, intelligent automation, and ML tools can identify and assign each product’s category with the correct procurement department and tag them. RPA also reminds procurement staff to approve delivery notices or reschedule production in case of delayed shipments.

5. Purchase request approval

RPA in procurement is useful because it can automatically approve routine purchase requests by referring to business rule engines. For example, machine learning algorithm would identify the reorders for commonly used items. The data can then be structured. RPA bots will then place reorders. So as long as the orders meet the procurement strategy, orders for current needs can be approved without human involvement.

And if the order is an exception and needs human intelligence for assessment, the request can be forwarded to the procurement manager for final approval.

6. Automated re-ordering

RPA in procurement can monitor the inventory levels on the dashboard and automatically create purchase orders for the reordering products. One of the benefits of automated re-orders is a consistent manufacturing process because the vital intermediary goods will always be in-time for the production cycle.

Automated re-orders would help bypass the need for a human to keep monitoring the inventory levels and fill out purchase orders electronically or otherwise. This ensures that the future needs of the company are tended to.

7. Inventory management

Robotic process automation (RPA) and IoT integration enables digital monitoring of inventory levels. This feature allows RPA to create automated reports and inventory audits.

Some businesses, such as restaurants, need to not overload their inventory of perishable produce. So it’s important to have a real-time Especially for businesses that rely on fresh inventory levels, such as restaurants that overload on perishable stuff, it’s important to have a real-time report of what exactly you have right now.

Automating inventory management also means products that stay in the warehouse longer can be recognized and purchased less, enabling smart procurement.

8. Three-way matching

Another use case of RPA in procurement is automated three-way matching. The RPA bots can automatically compare purchase requests, with the supplier invoices, and the delivery receipt to confirm that the ordered products are those which should’ve been ordered. Three-way matching also ensures that the goods have been delivered.

9. Automated payments

RPA bots can be scheduled to make automated payments after schedule triggers. On-time payments improve supplier relationship management and uphold the business reputation. Moreover, finance APIs allow ROA bots to make payments to the correct vendor in the right amount. That’s because they would exchange the information between the suppliers’ list and the AP automation solution. This reduces the workload of the procurement teams and makes correct, timely payments.

10. Supplier onboarding

Same as with employee onboarding, companies can leverage RPA to automate parts of their supplier onboarding. For instance, RPA bots can extract vital information from the suppliers’ websites (such as their references, prices, etc.) and put it in a report.

Moreover, RPA in procurement also means that bots can asses the suppliers through rule-based decisions. For instance, if a company wants to hire an event planner with experience in the pharmaceutical industry, and there are no case studies of that on the vendor’s website or attached to their profile, they can be ranked lowest.

These preliminary assessments can be time-consuming. By having robots completing these tasks, the employees can spend their time on higher value work.

11. Price negotiation

After receiving a vendor quote, companies can use RPA bots to automatically negotiate prices through a rules-based framework. So when it comes to approving/rejecting/negotiating a quote, intelligent automation-enabled RPA bots can compare the quoted price against the established threshold. Then following the conditional result (i.e., “if price is X% higher than Y, do Z”) the bots can send their rebuttal.

12. Digitized records For more on RPA

To learn more about RPA and its use cases, read:

Download our RPA whitepaper for in-depth look into the topic:

And if you’re ready to invest, we have a data-driven list of RPA vendors prepared.

We can help you in select the best RPA vendor according your needs:

He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.

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