As organizations embrace the digital trends, concerns around trust, model transparency, and data security are becoming a boardroom priority. Today, a majority of organizations are deploying AI, but few have embedded governance frameworks or integrated AI governance into their development lifecycles. That’s where AI TriSM comes in. AI TriSM or AI Trust, Risk, and Security Management is a unified approach to mitigating risks and cyberthreats related to generative AI like large language models (LLMs). The framework is designed to ensure that AI systems are safe and compliant, and aligned with ethical and business goals.

When evaluating AI, modern enterprises need guardrails that address bias, regulatory obligations, and emerging cyber threats. This post throws light on how AI TriSM plays a central role in strengthening enterprise AI. It also shares best practices organizations can adopt to build trustworthy, secure, and future-ready AI ecosystems.

What Is AI TriSM and Why Does It Matter?

As AI moves from pilot projects to enterprise-level deployments, a new set of risks is haunting organizations. AI TriSM (Trust, Risk, and Security Management) allows enterprises to govern their AI and cloud systems holistically, ensuring they are reliable, compliant, and aligned with the organizational values.

Gartner confirms that organizations that incorporate AI TriSM into AI model operations see a 50% improvement in adoption rates due to the model’s accuracy.

AI TriSM helps organizations overcome various challenges related to AI implementation.

Mitigates real-world risk scenarios

AI models often create unintended results like hallucinations that generate inaccurate output. For instance, between 2016 and 2021, AI systems in the Dutch taxation authority incorrectly flagged several families as committing welfare fraud.

Such issues can have serious consequences, putting people at deep financial risk and hardship. Here, AI TriSM offers structured governance to mitigate risks by enforcing strict data-handling policies. It also enforces transparency requirements and continuous monitoring of AI behavior.

Thus, organizations can spot bias, control model outputs, and secure sensitive data before it causes damage.

Aligns enterprise AI initiatives with the evolving regulatory requirements

In the ever-evolving AI regulatory landscape, organizations must ensure that AI is used transparently, responsibly, and ethically. Moreover, AI technologies should address privacy, bias, and accountability.

AI models are vulnerable to being misused by cyber criminals. These malicious actors often victimize AI to automate and optimize malware attacks, data breaches, and phishing scams. In 2024, 65% of financial organizations globally experienced ransomware attacks (up from 55% in 2022). Much of this is attributed to the growing adoption of advanced technologies.

AI TriSM aligns enterprise AI initiatives with evolving regulatory requirements and embeds security-by-design to counter cyber threats. It combines governance, continuous compliance checks, and strong security controls to ensure that organizations innovate safely without exposing sensitive information.

Improves efficiency and automation

AI TriSM allows businesses to use models safely by creating a secure foundation for AI models. It leverages measures like data encryption and multi-factor authentication to allow the production of accurate outcomes from these models.

It offers a secure platform for AI, allowing companies to focus on using these models to drive growth and boost efficiency.

For instance, AI TriSM offers an automated method to analyze customer data. Hence, businesses can identify trends and opportunities to improve their products and services and create better customer experiences.

The 4 Pillars of AI TriSM in Enterprise AI 

AI TRiSM rests on 4 interrelated pillars that work together to reduce risk, build trust, and reinforce security in AI systems. 

1. Explainability and Model Monitoring

Explainability is central to building trust and demystifying AI. Enterprises must trace how inputs translate into decisions. Methods like feature importance analysis, continuous monitoring, and tools that humanise AI can help make model behavior clearer to non-technical stakeholders.

For instance, an online AI Humanizer can humanise AI-generated content to better resonate with the audience. Further, the methods mentioned above are key to detecting biases, unfair predictions, erratic behavior, and hallucinations.

2. ModelOps

Model Operations or ModelOps advises both automated and manual performance and reliability management for AI. It recommends diligent version control and systematic testing over models to track changes and issues during development. Besides, regular retraining keeps the model up-to-date with fresh data, thereby preserving relevance and accuracy.

3. AI AppSec (Application Security)

AI applications face a host of threats that need a unique security approach, popularly known as AppSec. For instance, cyber criminals often manipulate input data to undermine model training, resulting in unwanted bias and flawed predictions.

AI AppSec protects against these threats by enforcing encryption of data at rest and in transit. It implements access controls around the AI systems and hardens development pipelines to mitigate risks from adversarial attacks and data tampering.

It also encourages enterprises to explore advanced solutions like quantum security products for AI infrastructure to protect sensitive data and prepare for cryptographic risks emerging from the post-quantum world.

4. Privacy

AI systems handle sensitive data. Hence, there are ethical and legal implications that enterprises must address. It is critical to inform users and obtain their consent regarding the collection of personal data necessary for the system.

Hence, organizations must adopt privacy-enhancing techniques such as tokenization, data anonymization, or noise injection to ensure that the data collection is consent-driven.

The 4 pillars discussed above build a closed-loop ecosystem that ensures AI outcomes are transparent, traceable, cybersecure, and privacy-respecting. The strategic adoption of AI TriSM rests on these pillars, helping enterprises prepare for the upcoming regulatory and cybersecurity demands.

Best Practices for Implementing AI TriSM

Implementing AI TriSM is primarily about building an enterprise-wide culture of governance and security. Besides investing in advanced tools, this approach is about making AI systems more trustworthy and resilient.

Establish Cross-Functional Governance Teams

AI risk management cannot live in silos. Create a steering group including IT, data scientists, legal, compliance, and business leaders to define policies, approve model deployments, and respond quickly to risks.

Map AI Systems to Enterprise Risk Frameworks

Treat every AI initiative like critical infrastructure. Maintain an inventory of models, document their intended use, risk exposure, and potential impact, and assign ownership for monitoring and remediation.

Adopt AI Assurance and Validation Tools

Use automated testing to identify bias, adversarial vulnerabilities, or model drift before deployment. Incorporate stress tests and simulated attack scenarios to confirm that systems hold up under pressure.

Enforce Transparency and Explainability

Encourage teams to document data sources, decision logic, and model limitations. Publish internal explainability reports so auditors, regulators, and leadership can clearly understand how outputs are generated.

Evaluate Vendors and Third-Party Integrations

Run security and compliance assessments on every external model, dataset, or API. A weak link in a partner system can compromise your entire AI environment.

Offer Ongoing Staff Training

Educate employees about AI ethics, data handling protocols, and incident reporting. Well-informed teams are less likely to introduce errors — and quicker to flag suspicious behavior.

Constantly Monitor and Update Models

Deploy real-time monitoring to track performance, detect anomalies, and log every decision. Update models regularly to align with new regulations, threat landscapes, and business priorities.

Summing Up

As the real-world cases of AI continue to grow in the enterprise world, trust and security will be crucial. AI TriSM offers organizations a structured path to govern risk, protect data, and ensure transparency. All this while not slowing innovation.

By working on strong governance, robust security practices, and continuous monitoring, enterprises can stay compliant. Deploy real-time monitoring to track performance, detect anomalies, and log every decision. Update models regularly to align with new regulations, threat landscapes, and business priorities. They can also build resilience against the emerging sophisticated cybersecurity threats. Use the information shared in this post to safeguard your AI investments and gain a competitive edge.

Manual data entry and receiving invoices in paper format stand as the most challenging business processes for employees. Companies that still utilize these two processes are susceptible to poor data management, high processing costs, and inefficient labor use. 

Fortunately, Optical Character Recognition (OCR) can be used to solve these time-consuming business challenges. It enables you to reproduce hard copies digitally with up to 99% accuracy for more effortless data transfer and processing.  

In this article, we’ll show you what Optical Character Recognition is and how it can help you be more efficient in your business. 

What is Optical Character Recognition? 

Optical Character Recognition is a technology that recognizes text within a digital image and converts this image into a machine-readable format. This technique typically involves using an algorithm to recognize characters within that image. OCR has been used in the past to convert paper documents into digital formats and enter data, but it has also been adapted for use in cognitive computing, text-to-speech extraction, data mining, as well as on websites and other digital media. OCR can be used to convert scanned documents, faxes, or other printed material into editable text files for processing by word processors and other applications.

So, how does OCR work? The Optical Character Recognition system works by running an image through an OCR program, which can be a specialized printer scanner or your phone equipped with an optical scanner or camera. Firstly, the original image is scanned and converted into a bitmap. Secondly, the AI-powered software recognizes color, text characters, handwriting styles, and languages by using pattern-matching techniques. Afterward, it extracts the text from the image and finally converts it into editable text on a computer screen or into a digital document file.

How is Optical Character Recognition Used in Businesses? 

Optical character recognition is used in a handful of things, and business is one of them. Below are some of the ways that you can use OCR in handling your tasks. 

1. Inputting Business Documents 

Optical Character Recognition is helpful for businesses that are opting to shift from manual to digital data entry. Mountains of physical records in storage will take a lot of time to input digitally. Using a scanner with OCR helps you sort out your records while scanning since you can pre-program your scanner to file documents containing specific words into different folders.  

You can also use OCR for recording manually filled forms digitally. Since OCR makes converting and editing PDF possible, you can scan copies of your physical forms and edit them on your computer for encoding new transactions. 

2. Transferring Calling Card Information 

Using Optical Character Recognition also helps quickly enter contact information from calling cards. Say you are in a business convention, and all your newly-found associates and prospective investors or customers hand you their business cards. Keeping it in your drawer won’t guarantee its longevity, and it will take quite a while to manually enter all that information into your database. With OCR, you can simply scan the business cards you’ve gathered and input keywords (such as Name, Company, Position, Address, and Contact Information) to automatically register and synchronize the information with your automated information database. 

3. Automatic Computation with AI 

Combing Artificial Intelligence with OCR can provide you with even more accuracy.

Using simple scanning software with no OCR and AI capabilities for scanning receipts can lead to miscalculations. For example, your scanner mistakenly captured $100 instead of $1000. Normally, it would take human review to correct this error by calculating the entries from the scanned document manually. With AI-powered OCR, you don’t have to worry about misreadings since AI automatically calculates scanned computation breakdowns to check if it has received the correct information. 

4. Inventory Tracking

You can also use OCR to utilize your merchandise’s serial numbers more efficiently. 

Extracting the serial numbers on your merchandise from a warehouse or retail store gives you a better idea of your stock count. You can do this by equipping bar scanners with compatible OCR software that has been configured to your database. 

Why Should You Use Optical Character Recognition?

Optical Character Recognition can improve your business in many ways, and below are just some of them: 

It’s Faster and Requires Minimal Effort

You save more time using OCR. According to Ratatype, an average person can type more or less 41 words per minute when copying from a source. This means that encoding a 1000-word document will take almost half an hour of typing which could have been used in other business matters. Scanning a physical document for character recognition requires minimal effort and equipment, it can be as easy as point and click. All you need is your phone’s camera with a scanner application or your printer’s scanning function, and you’re good to go. Additionally, software powered with OCR technology is the best suitable solution when it comes to scanning business documents and processing data. 

It Saves You Money 

Having your employees encode physical documents loses not only time but also money. As explained before, you can use extra staff on other tasks as soon as the encoding is finished. When you use OCR for this purpose, task delegation becomes more efficient and productive. 

Furthermore, using OCR also paves the way for a fully digital business workplace. Having a paperless business saves you money as much as it does with the environment. You don’t have to buy as many reams of paper and ink anymore. You can also use the space otherwise, for instance, used as a storage room to expand your workplace. 

Although many people assume that OCR technology may go for a hefty price, the investment is undoubtedly worth it in the long run.  Especially if you invest in an all-in-one automation platform that incorporates OCR technology together with additional advanced intelligent automation tools, then, your investment is worth it, for sure. 

It Gives You a Better Information Database 

Using an OCR system enables you to use your own centralized and automated business intelligence. Think of it as having Google but specialized for your business.  With improved data access, searchability, and usability, you can search for a name and it will show the complete information about that person, such as their contact number, email, and address. You can also find updating and deleting information much easier with a database. Securely dispatching printed documents containing sensitive information requires shredding, while digital information requires a few clicks and bin cleaning.  

Likewise, updating information requires a few clicks and revisions, whereas physical copies must be retouched and sometimes countersigned. Having an automated and centralized database will also improve your customer service. Some customers understandably need quick replies, and searching among piled-up paper trails can be pretty tricky, especially in high-stress situations. 

It’s More Secure With Some Measures

Digitizing physical documents ensures that they’ll be free from damages that they would otherwise get from time and elements. Financial expert Daniel Kurt wrote that natural disasters pose significant risks to businesses regarding physical assets such as loss of equipment, buildings, and physical documents. 

OCR technology also boasts a 99% information accuracy on documents encoded. This leads to lesser human errors, which would be easily overlooked otherwise.  Wrong information entry is particularly problematic in data-sensitive companies such as legal firms and financial institutions. 

Of course, going digital also has its own risks, such as hacking and data corruption. You can easily counter these by employing a data privacy officer and investing in highly reliable encryption and anti-virus software, or on a platform with built-in privacy and encryption features, the ability to set access rights, and so forth.

It Improves Customer Satisfaction 

In addition, OCR helps you improve the customer retention rate. Customers like it when you tend to their needs quickly and efficiently. Imagine searching for piled-up folders while you’re tending to an irritated customer. Surely the levels of stress will burn you out faster than you know it. 

With OCR, it takes a few simple clicks to verify your customer’s questions. Do they need a copy of their receipt from five years ago? Don’t worry, a quick word search of their surname after scanning a document will give you quick results. 

Using Advanced Technology for Further Business Efficiency 

The use of advanced OCR and AI technologies is increasing the business efficiency of every company. In general, technology really does make it easier for businesses to flourish. A good grasp of cognitive automation technologies and the proper automation software will give you the competitive edge needed to keep things afloat and smooth-fairing. That said, Optical Character Recognition is an excellent way to optimize your business for a more seamless operation. 

So go ahead and digitally reinvent your business. Our low-code automation software can help you make the right decisions to increase efficiency. Learn more about how Comidor Document Analyser Models use OCR technology to assist businesses in scanning documents such as invoices and creating digital copies. Talk to us.

The influence of Artificial Intelligence in the modern world is undeniable – and the software development industry is no different in this regard. But how exactly has AI influenced software development and what can be expected in the future? Without further ado, here’s everything you need to know about how Artificial Intelligence improves software development. 

#1 Developers Have New Roles 

Perhaps the most obvious way AI has been influencing software development is reflected in the role developers have in the entire process of developing a software or application. While AI still can’t write code on its own entirely without any help from developers, there are still ways in which AI makes the work of developers easier. For instance, many tasks can be automated with the help of AI-powered tools. By automating some tasks, developers can focus on other responsibilities or even develop new skills to become better at their jobs. 

In most cases, it is possible to automate smaller, simpler tasks so that developers can focus on more complex tasks. As time goes on and AI solutions become more advanced, developers will be doing fewer tasks manually and advancing their own skills to come up with more innovative solutions for different problems. Of course, this will also mean that developers will have to adapt to working with AI, so they will need to understand how the said AI functions. 

#2 Development Itself Is Expanding 

While developers’ roles in the development process are changing, the development of programs and applications itself is expanding rapidly thanks to AI. Both the speed at which development can be completed and the scale at which it can be performed are vastly different than they used to be. Thanks to the partial automation of certain tasks, there is more time for developers to focus on specific parts of development that can now be performed faster. At the same time, AI helps developers to take on projects that are more complex and bigger in scale. 

For example, machine learning and deep learning technologies can be used to shorten the time needed to test software. Many tests can be run automatically, and more scenarios can be tested thanks to AI technologies. Moreover, AI is critical for testing because manual testing often involves a higher risk of human error. Overall, AI can help streamline processes and reduce waste while handling repetitive tasks much faster than developers. Thus, development as a process is gradually transforming into a new version of itself.

#3 Decision-Making Gets to a New Level 

Another aspect of software development that is changing thanks to AI is decision-making. Thanks to AI solutions, decision-making has become more strategic and doesn’t require as much human intervention as it used to. AI tools can handle extremely large amounts of data to make predictions and guide decisions. Moreover, as more data is collected throughout the development process, past predictions can be adjusted to make even more accurate ones.

For example, developers might want to find out the specific needs of the target audience for an app they are developing. AI-powered tools can collect and analyze more data (e.g. from forum discussions among potential customers) which will give the developer a more complete picture of what the audience is looking for.

#4 Improved Error Management 

In addition to strategic decision-making, AI can help improve error management. As explained above, AI solutions don’t only make predictions at a specific time – they can adjust the said predictions based on new data. This means that errors can be identified faster, sometimes before they even happen. In most cases, error management takes a lot of time which is why software development processes often stagnate during this period. However, with the help of relevant AI solutions, this problem can be eliminated or reduced to an extent.

The best part is that AI can identify errors both during the development stage of the application development lifecycle and later on which is especially important for software as a service (SaaS) as well as cloud-based platform-as-a-service solutions. Because such services are usually running round the clock and are utilized by users constantly, downtime can be detrimental. Every minute counts, but with the help of AI, errors and issues can be found and corrected automatically much faster. It is both efficient and affordable for developers.

#5 Get Real-Time Feedback 

One major advantage of using AI in software development is that it can provide developers with real-time feedback. Such feedback is crucial for developers to continue improving the software even once it has been released and is already in use. For example, many video conferencing programs collect real-time feedback from users to further improve UX and UI. As a result, the way users use and interact with a specific application or program can change in a good way thanks to constant feedback. 

Machine learning technology can be particularly helpful in this sense. Algorithms can be programmed to track the way users act in specific situations when using a program or app. By collecting this data, developers can then fix bugs, correct errors, and so on without losing much time or waiting for users to complain and leave. In addition to that, AI can be used to personalize user experiences by showing relevant content to users based on the collected data about their activities. 

#6 Make Precise Estimations 

Last but not least, AI can help developers make more precise estimations. As mentioned earlier, decision-making has become more strategic thanks to the implementation of AI during the development process. But in addition to that, AI solutions are dramatically changing the way estimations are made in terms of both costs and timelines. It is a common problem in the software development industry where deadlines are usually too close and costs are often underestimated. Luckily, AI tools can help plan out the development process in a more realistic way which is beneficial both for developers and senior executives. 

In most cases, to make such estimations, AI-powered programs need to analyze past projects to understand what the outcomes were like in the past. This way, new projects can be planned out more accurately. Everything from budgeting to scheduling to role allocation can be organized with the help of AI estimations. Of course, even AI can’t account for all the unpredictable situations, but such intelligent automation technologies can still take into account quite a few factors that ultimately shape the progress of a specific type of project. This way, software development companies can satisfy clients, keep developers motivated, and meet deadlines. 

Conclusion 

So, what’s the bottom line? Everyone involved in software development should realize the impact AI has already had on the industry and should utilize its advantages to the fullest (e.g. decision-making, error management, and making estimations among other things). Consider the points in this article to help you better understand the influence of AI on software development and what developers can get out of it. 

Author bio
Tiffany Porter has been working as a writer at Rated by Students reviewing a variety of writing services websites. She is a professional writing expert on such topics as digital marketing, blogging, and design. She also likes to read and provide consultation for creating expert academic materials.

Image recognition is the process of identifying and classifying objects, patterns, and textures in images. Image recognition use cases are found in different fields like healthcare, marketing, transportation, and e-commerce. It can be used to identify objects in images to categorize them for future use. For example, it can be used to classify the type of flower that is in the picture or identify an apple from a banana. It also has many applications outside of image classification such as detecting faces in pictures or recognizing text on a page.

Image recognition is one of the most important technologies that are being developed today because it will help us solve many problems we face, such as improving healthcare by diagnosing diseases like cancer with greater accuracy or detecting fraud by analyzing images of banknotes.

Now usually, image content recognition is confused with machine vision. You must know that image recognition simply identifies content on an image, whereas a machine vision system refers to event detection, image reconstruction, and object tracking.

Why has image recognition become so important? 

Here we have discussed some of the reasons why image recognition is considered to be important and common use cases of image recognition that we see in our daily lives:

1. Image recognition helps you catch catfish accounts 

One of the most important use cases of image recognition is that it helps you unravel fake accounts on social media. You must know that the trend of fake accounts has increased over the past decade. Today people make fake accounts for online scams, the damaging reputation of famous people, or spreading fake news. Here you should know that image recognition techniques can help you avoid being prey to digital scams. You can simply search by image and find out if someone is stealing your images and using them on another account. So the first most important reason behind the popularity of image recognition techniques is that it helps you catch catfish accounts.  

2. Image recognition is being used in facial recognition and other security systems. 

Image recognition is also considered important because it is one of the most important components in the security industry. Today it is being used in all kinds of security systems. The most common example of image recognition can be seen in the facial recognition system of your mobile. Facial recognition in mobiles is not only used to identify your face for unlocking your device; today, it is also being used for marketing. Image recognition algorithms can help marketers get information about a person’s identity, gender, and mood. There are many more use cases of image recognition in the marketing world, so don’t underestimate it. 

3. Image recognition is used in Reverse Image Search for different purposes 

You might have heard of the online reverse image search. Reverse picture search is a method that can make a search by image for free. With modern reverse image search utilities, you can search by an image and find out relevant details about it. Image finder uses artificial intelligence software and image recognition techniques to identify images’ contents and compare them with billions of images indexed on the web. The image recognition algorithms help find out similar images, the origin of the image in question, information about the owner of the image, websites using the same image, image plagiarism, and all other relevant information. In the past reverse image search was only used to find similar images on the web. But today, you can use it for dozens of different purposes. 

4. Government agencies are using image recognition  

You would be surprised to know that image recognition is also being used by government agencies. These agencies search images to collect information about people. Today police and other secret agencies are generally using image recognition technology to recognize people in videos or images.  

5. Image recognition also plays an important role in the healthcare industry 

Today, image recognition is also important because it helps you in the healthcare industry. Here you should know that image recognition is widely being used across the globe for detecting brain tumors, cancer, and even broken images. Image recognition techniques and algorithms are helping out doctors and scientists in the medical treatment of their patients. Nowadays,  image recognition is also being used to help visually impaired people. Also, new inventions are being made every now and then with the use of image recognition. High-tech walking sticks for blind people are one of the most important examples in this regard. 

6. Image recognition is also empowering the eCommerce industry 

Today image recognition is also being used in the e-commerce industry. The visual search market has drastically increased in the past. This is major because today customers are more inclined to make a search by product images instead of using text. 

To sum up

If you still have reservations about the importance of image recognition, we suggest you try these image recognition use cases yourself. You can enjoy tons of benefits from using image recognition in more ways than just identifying pictures. Many people are just beginning to realize its potential. Now, it can be used to identify not just photos but also voice recordings, text messages, and various other sources of information.

The uncompromising competition of the business world forces 83% of companies to rely on the power of Artificial Intelligence solutions. Artificial Intelligence (AI) includes not only the automation of many recurring processes but goes further – it influences decision making. 

By eliminating human errors and analyzing vast amounts of various data quickly and constantly, AI equips businesses with a full range of information and provides structured solutions to arising issues. 

Let’s find out more about AI-driven decisions and 5 robust applications of Artificial Intelligence in decision making practice. 

What are Artificial Intelligence decisions? 

Businesses tend to choose solutions empowered with big data, Artificial Intelligence, or Machine Learning more and more often. 

Such solutions can aggregate data from various areas of the company’s operations, such as finance, accounting, customer service, and more.  

Professionals use this data to save costs, build growth strategies, streamline inner business processes, and enhance decision making initiatives. Combined with the flexibility of cloud computing, AI facilitates management, problem-solving, and strategic development.  

So, when is it worth implementing technologies based on Artificial Intelligence? The answer is simple: when you want specific processes in the company to be faster and more efficient and let qualified employees engage in more creative tasks. 

To leverage decision making with the help of AI and ML, you need to understand how it works in a simple manner:   

1. Configure a required set of tools for data collection, synchronization, transformation, and analysis. 
2. Tailor a rule or framework for data processing. 
3. Receive an output, which you can use to decide on a particular case or solve an existing challenge.  

You can follow the system’s suggestions, or you can use the output within a decision making framework. Be it SPADE, Eisenhower matrix, integrative thinking, BRIDGeS framework, or any other, the process is empowered by data, which allows you to make more precise and valuable decisions. 

In this way, AI in decision making applies to anything from minor improvements in routine processes to complex problem-solving. 

5 applications of AI in decision making 

Here is a list of practical examples that can help you quickly take your business to the next level involving AI: 

1. Decisions in business operations  

Machine Learning algorithms come to the rescue in areas built on a constant flow of heterogeneous data, whether it is several financial reports, payrolls, procurement, the analysis of employee productivity, or predicting further churn rates.  

In short, AI takes over routine administrative tasks and changes the whole way of working. It gives employees and executives more space for making faster and relevant decisions. 

Also, the capabilities of AI go further and can interact with clearly figured data requiring a single set of indicators and ephemeral parameters that have not yet been formalized.  

According to Forbes, 95% of businesses still need to process the unstructured data somehow. This approach is appreciated in many operational processes.  

Let’s view human resources as an example of decisions in business operations. In HR, the entry, categorization, evaluation of employees’ and applicants’ data is essential yet monotonous. The first stages of recruitment are usually challenging: defining a position needed for a department, figuring out all the candidate criteria and areas to cover, sourcing, selecting the first CVs, and so on. Here AI comes in hand. 

AI-empowered solutions help to facilitate the recruitment process, source better candidates, analyzes their interviews. Eventually, an HR team can make a robust decision of hiring a suitable candidate. 

Overall, AI, in terms of inner business processes, is able to leverage business intelligence and make a company data-driven in many aspects, including decision making. 

2. Complex problem-solving

The potential of AI in decision making is robust, but you can solve multilayer and complex problems, too. For this, you must remember that AI solutions depend on the data you have and the step-by-step process orchestration.  

As an illustration, a company considers launching a new product and targeting it in a new market. Pretty complex, right? To walk from a concept to the first marketing campaign, a company needs to take dozens of decisions, prioritize, optimize, investigate, forecast, and experiment.

Artificial Intelligence here gathers tons of different data and conducts an interdisciplinary study. Eventually, there’s a way to leverage anything from product development stages to digital marketing approaches of product promotion. 

Also, it’s a way to optimize various types of predictions and risk management. For example, you can predict and optimize pricing with the help of AI tools.

3. Strategic changes 

AI allows better planning of production, managing all restrictions, reducing shortcomings in operations, and improving manufacturing. 

It also helps to anticipate and adequately plan product customization, enhance postponement processes, and maintain efficiency with high levels of customer satisfaction. Besides, go-to-market and marketing strategies must be flexible due to the current competitive environment and market dynamics. AI can make those changes in strategy quicker and less harmful. 

Throughout the production chain, countless risks can impact the continuity of operations. Continuous improvement is a key aspect of AI software systems: having the capability to learn from the environment and prepare a new solution to the same problem is one of the primary advantages that AI offers to us.  

At the same time, you need to understand that the implementation of any advanced system must-have steps and a clear plan, and AI is no exception.  

4. Customer-related decisions 

AI can be valuable for customer service management, personalized customer communication, evaluation of customer behavior, predicting consumer trends and patterns.  

Today’s speech recognition equipment helps, in a significant way, to improve the customer experience. These systems provide customers with information about the status of their shipments, as well as establish conversations with them to manage unforeseen events, changes in last-minute deliveries, or incident management and feedback from them. 

Artificial intelligence enables automatic recognition and profiling of potential customers. For example, new customers can be identified and characterized based on predetermined profiles. Based on the analysis of this data, it is possible to predict the behavior of new customers and ways of attracting them. Also, advertisers use neuromarketing to influence the thinking and behavior of consumers.  

This can do wonders for your marketing department as it helps them understand the best ways to connect with your potential clients.   

For example, if you’re a SaaS company, it may give you insight into the fact that hosting educational webinars is a great way to attract and retain customers as opposed to social media advertising based on how the customers react.  

Using AI, you can decide on how to improve customer experience. All in all, Artificial Intelligence allows you to understand customers better and decide on which tactics to try. 

5. Performance assessment 

Firstly, it relates to people’s performance evaluation and afterward decisions. The employee performance review process is moving from every 6 or 12 months to ongoing. Despite this, the integrity of the employee evaluation process can be undermined by human error and potential biases. AI is capable of minimizing human errors and making employee performance data more transparent. 

AI can also recommend online courses, training, and development programs to employees based on their performance history.  Many People Management software vendors have added Artificial Intelligence capabilities to their performance monitoring software. Another point of performance assessment relates to marketing. With AI solutions, you can precisely evaluate which tactics work, which don’t. And then decide how to adjust them, what approaches to experiment with. 

Ultimately, assessing the performance of some aspects in business is a way to understand the entire company performance, its potential to grow, and which decisions should be made to leverage that. 

Sum up 

Artificial Intelligence adds to decision making a lot. It makes the process clearer, faster, and more data-driven. 

Empowered with AI, you can make small decisions on the go, solve complex problems, initiate strategic changes, evaluate risks, and assess your entire business performance. 

About author
Dmytro Zaichenko is a Digital Marketing Specialist at Railsware, a product studio helping people build great products. Apart from writing and networking, he’s a huge NBA fan.  

For businesses facing exponential growth, automation is the ideal solution for meeting increased demands. Fortunately, there are countless software tools available that can lead to the inevitable automation of entire business processes.  

These tools incorporate state-of-the-art technologies such as Robotic Process Automation, Machine Learning, and Artificial Intelligence. Collectively, they lead to the Hyperautomation of businesses. Separately, they achieve specific automation goals.   

So, what exactly is the difference between Robotic Process Automation vs. Machine Learning? And what about Artificial Intelligence vs. Machine Learning? These tools may all come together to achieve Hyperautomation, but what truly makes them unique? We have the answer to all of these questions and more. Read on to learn more about RPA vs. Machine Learning vs. AI.  

What is Robotic Process Automation? 

Ever wished there was a software that could handle all those repetitive tasks on your to-do list? Well, there is! Robotic process automation (RPA) is an automation technology that implements, and manages robots. These robots can complete routine tasks without needing human touch. Anyone in your business can use RPA software to deploy robots that will mimic human actions throughout a business process.  

What is Machine Learning? 

Machine Learning (ML) is all about data and algorithms. Instead of spending time inputting rules for Machine Learning, this technology uses real-time data to predict the next step in the process.  

Using the data available, Machine Learning will create a model of the typical workflow pattern and begin to improve the algorithm based on common relationships and historical data. In a sense, Machine Learning understands how humans act and mimics that method.  

What is Artificial Intelligence? 

Artificial intelligence (AI) is a widely-known technological area. AI is a set of technologies used to help machines understand how humans think. Once these machines understand the human mindset, they are able to complete tasks in place of humans. This technology is widely used in automation software as a way to check routine tasks off a to-do list without needing to spend time in your day doing them yourself.  

Now that we understand what exactly these tools are, we’ll take a look at the similarities and differences between Robotic Process Automation vs. machine learning vs. artificial intelligence.  

RPA vs. Machine Learning vs. AI 

Though these tools sound highly similar, there are key differences between RPA vs. Machine Learning vs. AI that make investing in all three worthwhile.  

Similarities 

To start, let’s discuss the obvious: how they’re similar. All three of these software tools are used to mimic human action in order to complete routine tasks and speed up business processes, especially for small businesses with limited resources.

Combining these tools together can assist a business in achieving intelligent process automation. This automation allows businesses to make progress toward a total digital transformation, in which they rely heavily on technology to complete tasks, finish projects, and keep customers happy.  

Additionally, Machine Learning and AI are closely intertwined. In fact, Machine Learning is actually a subset of AI. Machine Learning uses AI’s process to understand the relationships between tasks and learn on its own how to mimic those tasks.  

Differences 

Though each of these tools is an essential part of automating repetitive tasks, they each serve their own function. The differences between RPA vs. Machine Learning vs. AI are: 

• Rule-based vs. data-based. Both machine learning and AI are driven by data. RPA, on the other hand, requires rules input by humans to function correctly.  
• Programming. Since RPA needs rules to function, it requires human programming to start completing tasks. AI and machine learning will use data and algorithms to understand how to function and, therefore, do not need much human interaction.  
• Process improvement. If your RPA software is not functioning in a way that works for your team, you’ll need to manually change the rules and allow your software time to adjust. With AI and machine learning, the algorithms will automatically update as new data is discovered to better meet your business needs.   

Each tool is able to complete different tasks using its own methods. That’s why it’s essential to integrate all three into your business if your goal is to achieve Hyperautomation.  

Integrate Agile Hyperautomation with Comidor 

The question is not whether you should choose between Robotic Process Automation vs. Machine Learning vs. Artificial Intelligence. Instead, it’s how quickly you can integrate these systems into your business and start automating tasks.  

With Comidor’s array of Hyperautomation tools, you can have your routine tasks completed in a very short time! Comidor offers Business Process Management (BPM) software that, when combined with workflow automation and RPA, ML, and AI, can ensure that your business has all the necessary tools to achieve agile digital transformation.   

Request a demo today to learn more about how Comidor can help you achieve the Hyperautomation your growing business needs to thrive!