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February, 2022

The Hybrid MultiCloud is Here: What Can You Build?

Alex Thompson Digital Engineering February 28, 2022
Hybrid Multicloud

Hybrid Multicloud

You can’t turn around without coming into contact with the term “hybrid” today. Hybrid models are everywhere, ranging from vehicles to work environments. The word “hybrid” refers to something made by combining two elements or a mixture. If something is composed of mixed parts, it’s referred to as a hybrid. 

For example, a hybrid vehicle is a mixture of a car that runs off gasoline and electricity, while a hybrid employee works both at home and in the office. Leave it to technology to take hybrids to the next level, introducing the Hybrid Multicloud.

Another Level of Cloud Engineering

In the current world of technology, it can be challenging to maintain the constantly thrown around terms and ever-evolving. Lately, you’ve likely heard of the hybrid MultiCloud, but what does it mean for you and your company?

If you’ve been wondering what the hybrid MultiCloud can do for you, you might want to begin by breaking down the term. We’ve already discussed what hybrid means, which is something that is a mixture of other things, or heterogeneous if you will. The term “MultiCloud” refers to more than one cloud computing system.

Therefore, combining the terms “hybrid” and “MultiCloud” means you’re looking at an IT infrastructure that uses a mixture of private, public, or on-premises cloud technology from multiple providers. So, you can see where the term hybrid MultiCloud comes from. 

This approach makes complete sense for numerous organizations, but it’s up to you to determine if that pertains to your company or business. However, the hybrid MultiCloud allows you to benefit from and maintain the systems you’ve spent much of your time building. You’ll want to scale those cloud services wherever it makes sense to do so and pair them with the current practices you have in place for reducing your overall costs.

Utilizing the Hybrid MultiCloud Approach

If you’ve been in business for quite some time, you probably know that it’s impossible to rely on only one single technology or system to resolve issues on every project. Technology is all about troubleshooting and figuring out what will work for you and what will not.

 

In general, it’s unlikely that your business will move every piece of data that you’ve ever created to cloud technology; it’s ill-advised. So, in reality, you’re always technically working on a hybrid model in one way or another. Moving every computing system your development team has ever built to the cloud could potentially do more harm than good. Still, companies of all sizes are finding flexibility within the cloud infrastructure that can power the IT of a business forward.

  

When you use the hybrid MultiCloud approach in your company, you can easily tailor the approaches that will enhance the way your organization runs. The MultiCloud allows businesses to break things down piece by piece, focusing on specific components, tasks, and projects that need your current attention.

  

An abundance of your existing platforms is rich in history. Instead of dismantling that history, why not integrate them with new cloud technology capabilities and techniques whenever it makes sense. It would help if you always focused on utilizing the available technology for the current task at hand. 

Understanding the Components Behind the MultiCloud

If you’re running a large enterprise, the mainframe will be a vital component of your hybrid MultiCloud structure. Your mainframe is likely what drives your workload. 

Because mainframes house so much data, it only makes sense that a massive percentage of your transactions take place (at least partially) on the mainframe, no matter what those might look like. Without question, you’ve got to take this essential part of your business operations and consider it thoroughly when implementing the hybrid MultiCloud. 

The Benefits of the Hybrid MultiCloud

There are plenty of benefits of MultiCloud. Knowing the benefits can help you understand how it might make your business easier to manage.

Finding the Best MultiCloud Providers

A hybrid cloud model results in administrators understanding the best technology to assign to each task. Multiple options offer a higher level of agility, meaning you can match your business with the technology that best aligns with different parts of your business.

Agility

It’s not easy to plan a MultiCloud strategy, especially when most companies looking to move to cloud technology are struggling with outdated legacy systems. Don’t bother stressing yourself out by refusing to hire a competent provider who will help you move to a hybrid MultiCloud platform. You’ll find complete agility with your workload and switching between systems from the proper implementation.

Security

The hybrid MultiCloud offers robust security. Cloud service providers are responsible for their security, so your company will benefit from those security systems in place through the protection of your data. However, it’s still essential for your team to assess your security level over time and make changes if necessary.

Drive Innovation Through Automation

The hybrid MultiCloud encourages companies to automate and adequately distribute workloads among teams, integrating development operations and leaving room for innovation. Automation ensures orchestration across multiple cloud environments. It expands deployment capabilities and connects applications with workload processes to deliver your business services quickly.

The Challenges that Come with Change

It’s undeniable that challenges come with integrating any kind and implementing MultiCloud technology is no exception. The goal behind utilizing MultiCloud is to bring control and order to your business without interrupting business transactions. It’s all about building resilience. 

Data protection and security will add to the challenges that change, as it will be a huge part of working within the hybrid MultiCloud. In reality, your clients and customers don’t care so much about what type of technology you’re using, but they do care that their data will remain protected in the event of an attempted breach.

When you manage systems on a MultiCloud platform, it can be challenging to deal with separate configuration and security protocols and development techniques. The requirements when utilizing different cloud providers means understanding the deployment techniques of each and monitoring them in different ways. 

Building your management solutions is crucial. Not only are you working with separate cloud providers, but you are also employing different technologies within those providers. Knowing how to access and execute these systems requires a lot of internal learning and change. Planning can be the difference between failure and success.

Planning execution is nothing new in the world of business and technology. To build something great and truly harness the capabilities of the hybrid MultiCloud, you have to understand data modernization and cloud engineering as a whole. There isn’t a single developer or development team in history that started from the top to build and execute new technologies.

Success with Modern Technology

Modern technology can be frustrating for many business owners. However, embracing it is necessary to move forward successfully without falling behind the competition. Integrating your old technology with the new is the premise of successfully adopting hybrid MultiCloud technology. Without question, change will deliver copious amounts of value to your company.

MLOps and the Future of Machine Learning

Alex Thompson Data and AI February 24, 2022
ML Ops

While the compound term ML Ops can sound daunting and confusing at first, especially when things in the tech industry are forever changing, it makes complete sense when it’s broken down into terms we already understand and have become accustomed to using. Simply put, MLOps is the combination of “machine learning” and “DevOps” or development operations. 

MLOps aims to maintain machine learning models within the production industry through reliability and efficiency. The goal of ML Ops is to harness discipline and development in machine learning, which is more than necessary in this aspect of technology and data. So, now you might be asking yourself, but what exactly is MLOps? Let’s break it down a little further.

What is MLOps?

Human beings are creating a large amount of data by the second. While this is fantastic for data analysts as a whole, acquiring huge data amounts and breaking it down to help fuel the way businesses operate are two completely different concepts. It’s all about scaling our machine learning systems and operations to the needs of our businesses. This scaling is the purpose of MLOps.

MLOps encourages communication and collaboration between data scientists, automating the deployment of machine learning in more extensive operations. ML Ops aligns models with the needs of your business and is becoming an independent way to manage machine learning systems that applies to the complete ML life cycle.

MLOps covers the following phases:

Ml Ops

When the MLOps cycle finishes, it restarts again in a constant reassessing and retraining data. Without insight, MLOps seems completely aligned with DevOps, but the two approaches are quite different in reality. 

For example, MLOps is a bit more experimental than DevOps. ML encompasses continuous integration, continuous deployment, and continuous testing. MLOps seeks to keep rolling out models and predicted algorithms without losing precious time while focusing on retraining for optimal predictions and outcomes. MLOps works well within many companies to manage models, experiments, data sets, and software containers. The power of machine learning is great, and through correctly applying MLOps, we can begin to harness it.

The Clear Benefits of Implementing MLOps

There are many benefits of implementing MLOps, as if it’s done correctly, it can control more components than your typical DevOps model. Ignoring MLOps is a huge mistake for any company. It can be frustrating at first, as more roadblocks than clear paths will pop up during the beginning of the implementation process. 

However, the perks of adopting MLOps are undeniable, and they include increasing productivity and building reliable and trusted data models. There’s no question that companies correctly leveraging MLOps are genuinely making an impact in their business and industries.

Communication

Data science and operation teams can come together under the MLOps model, like the frequent friction between them lessens. Through MLOps systems, you can establish flexible data pipelines that will enhance your current development operations systems in place.

Automated Workflows

One of the most significant factors that drive machine learning is the desire to create efficient but automated workflows. Automated, streamlined changes are crucial as shifts in data occur, preventing lags and development hold-ups. MLOps will measure the model’s performance while operating, consistently monitoring behaviour and operation processes.

Outcomes

Explainable AI helps outcomes make sense and lets us know when your machine learning application might be wrong. Not only does this fuel business growth, but it enables you to serve your customer base more efficiently.

Compliance

As machine learning guidelines grow increasingly strict, MLOps can alter models to comply with new guidelines through reproduction processes. As the rules evolve, your models can still play by them without being completely dismantled and restructured.

Feedback

MLOps offer clear feedback when it doesn’t seem possible. ML analytics can often seem completely undecipherable, slowing down training or leading to complete system failure. MLOps can detect the blips that happen in ML technology and understand why that blip occurred, providing you with the information you need to keep it from happening again.

Bias Reduction

Bias reduction is an essential component of machine learning, as bias is rampant without operation management in place. MLOps can guard against certain biases during development, creating systems that avoid extreme rigidity in their reporting. By doing this, MLOps provide reliability and trustworthiness to your company and the machine learning systems you utilize. It’s all about having a better understanding.

Understanding MLOps

In general, MLOps aren’t understood by many, but their implementation has a strong impact across industries, assisting machine learning in growing into a respected aspect of software development. MLOps fuel the future in creating practical machine learning that requires less human intervention. 

If you’re wondering how to integrate MLOps into your current operation, you’re not alone. The software that engages machine learning is growing with no end in sight. Without operations to hold that software responsible, it’s impossible to respect the provided results as the risk for error is too significant. 

MLOps will motivate your teams and suggest collaboration on projects, primarily within the workflow between data and development teams. It’s time to embrace effective machine learning and optimize the lifespan and performance of your models. When it comes to developing MLOps, you’ll want to implement the following steps:

ML Ops

MLOps brings teams together while automating, auditing, and managing model interpretability. MLOps aren’t exactly easy to employ, but they’re well worth the time spent.

The MLOps Results

Companies on a global level can share the results they’re seeing with MLOps, which allows for a broader working knowledge regarding machine learning in an open-source environment. Various fields, including healthcare, public transportation, engineering, manufacturing, and safety, have begun MLOps integration. 

In the long run, a well-adapted MLOps strategy can lead to more productive, accurate, and trusted models. It’s impossible to succeed when you’re operating out of a siloed model mess, even when the processes are automated. Effective machine learning is the best path to take.

Keep Your Loyal Customers by Enhancing the Customer Journey Digitally

Alex Thompson Digital Consulting, Modernization February 22, 2022
customer journey mapping

Customer Journey Mapping

There’s no doubt that a seamless customer experience creates a loyal and repeat customer using customer journey mapping. Digital transformations can completely alter your client journey and overall experience. 

Most businesses today are going through a digital transformation on some level, and it’s all with the purpose of refining how your customers can interact with your business. However, successful digital transformations aren’t easy to pull off without factual data and a knowledgeable development team.

Understanding the Customer Journey 

Before you begin your transformation expedition, it’s essential that you truly understand what the customer journey encompasses to stop losing customers to start-ups. In short, the stages of every customer journey, which differ according to persona and goals, follow the same general formula.

customer journey map

Mapping Your Customer Journey

When it comes time to begin mapping the customer journey, you’ll want to consider the specific stages (listed above) that your customer persona will go through. Next, brainstorm for your customer journey mapping session, and consider the following:

  • Define the profile of your consumer persona and define their goals
  • Set a clear objective for your map
  • List the customer touchpoints
  • Write out the elements that you’d like your map to showcase
  • Understand the resources you’ll need to solve customer pain points
  • Put yourself in the shoes of your potential customer
  • Make any changes necessary

Digital Transformation Improves the Customer Journey

Once you’ve established your customer persona and mapped out the journey, they’ll take with your brand. It’s time to put your plans into action. A company that has appropriately transformed digitally will have a much easier time at this stage than those that have not. Here’s a look at how to pull it off.

Transform Digitally While Continuing Operations

It’s impossible for you to successfully transform your systems while bringing operations to a screeching halt, even if your current systems don’t support an optimal customer journey. Your technology has evolved over the years, layered and stacked in stark comparison to cloud-based technology’s organized, sufficient container infrastructure.

Perceive the Customer Journey with a Modern Mindset

The customer journey map is still necessary, but it’s crucial to understand that, technically, the customer journey doesn’t end directly after the purchase. In most cases, post-purchase customer activities include sharing their opinion of your product and brand long after purchasing. 

It’s so essential to improve customer experience through digital transformation for this very reason. Word of mouth and online reviews carry a lot of weight regarding future potential clients’ decisions. 

Know Your Outcome

You can’t pull off a digital transformation without knowing precisely what you want from the change. If you’re unsure of what you want from your outcomes, you can generally focus on these aspects of enhancing your customers’ journey. The outcomes are a well-weighed balance of business, user and technology goals.

Security

Security comes before everything else. When you secure business and consumer data, you secure brand reputation. Your clients need to know that they can trust you, and adequate security measures are a massive part of digital modernization.

Speed

Speed is crucial to your digital transformation and your customer journey. You need to deliver content and your products and services at high speed with a very low risk, which is where security comes into play.

Analytics

Know your analytics and learn how to understand and analyze your data in real-time. Analytics help you know how your business is running and which marketing tactics are working and which are not. When you have instant access to such information, you can make the necessary changes right away.

Optimize IT Functions

Your digital transformation has to support your IT functions from end to end. However, it’s essential to keep your costs from getting too high. The cool part of optimizing your IT framework means you can look closely at which pieces of your business are working and which need integration.

Working with partners to digitally transform on an open scale can significantly help guide successful results. Your employees also need to be willing to change, probably continuously, as digital transformations occur. Optimizing functions throughout, and bringing in a third party to help, will make this process easier.

Stepping Back to Move Forward

Mapping the customer journey and transforming your company to serve the modern customer experience requires stepping back and looking at what isn’t working. Set your priorities and know your audience.

It’s crucial to review current processes to enhance your customer journey. If they do not, they should be reformed or eliminated. By moving forward on the right track, you’ll prevent losing your customers to start-ups that are already technologically advanced enough to provide outstanding customer service. 

The end game will always be the experience your customer has. Your customers drive your business, and the knowledge they have regarding your products and services has the potential to make or break you in the long run. Why not do everything you can to keep them coming back? It is the story of discovering and fulfilling the known needs better and giving the unmet needs life.

Improving Automated Software Testing with Explainable AI

Alex Thompson Data and AI February 17, 2022
Software Testing Using Artifical Intelligence

Software Testing With Artificial Intelligence AI

Testing in software is essential to ensure that systems are running as they should while producing the desired results. Since systems and applications that use machine learning are notoriously challenging to test, there is plenty of room to fix automated software testing with Artificial Intelligence (AI).

Applications that utilize machine learning and AI are typically black boxes, meaning even those that created them have difficulty explaining data results and behavior. Also, given the layers of algorithms present, there is no way to determine if the results they supply are correct. Explainable artificial intelligence may just be the answer needed to any impending AI black box software test.

What is Software Testing?

The definition of software testing is the process of verifying and evaluating that a software application performs the way it’s supposed to perform. However, when we introduce artificial intelligence into automated applications, it can become tough to understand if the given results are correct.

An excellent test management plan can prevent bugs, improve performance and reduce development costs. When machines are learning on their own and algorithms are constantly changing, test results can look completely different than testers might expect. The good news is that it’s entirely possible to test AI and ML applications with explainable AI.

A Look Into Explainable AI

Explainable AI is essentially a type of artificial intelligence that provides system results that humans can understand. Explainable AI contrasts sharply with black box AI systems, where even the designer cannot explain why the AI came to a specific decision. Explainable AI can help us make sense of outcomes, and therefore, test to see if the software in question is making the right decisions.

Development teams worldwide that implement AI should be well-versed in explainable AI. Most companies are all about testing their software, and explainable AI is the way to go in that respect. Here are just a few benefits of explainable AI.

Model Accuracy

Explainable AI determines the accuracy of working AI and ML systems. Accuracy is crucial in any field, primarily medical, as the results our ML applications are giving us have to be correct.

Fairness

Many business owners (and consumers) across the board worry about fairness regarding artificial intelligence. Explainable AI can level the playing field by helping to produce results that make sense. As new AI regulations take hold, righteousness is one of the main talking points.

Transparency

Along with fairness comes transparency. Every business using artificial intelligence needs to have complete transparency. Clarity for consumers and fellow business owners is vital to maintaining a good reputation and providing consumers with correct answers. Explainable AI helps maintain transparency by explaining something we might not understand, providing accurate, solvable data.

Outcomes

Explainable AI helps outcomes make sense and lets us know when your machine learning application might be wrong. Not only does this fuel business growth, but it enables you to serve your customer base more efficiently.

The Importance of Testing Software

Regardless of industry, testing software is essential. First and foremost, IT testing saves money, and you can utilize communications between teams to catch problems before systems go live when you know how your applications perform.

Testing also improves consumer relationships by investing in your software. Knowing your software works before a launch solves potential frustrations, and explainable AI is a massive piece to that puzzle.

Testing software improves overall security, and it should go without saying that security issues with AI and ML applications can grow out of control when data outcomes aren’t understood. Failing software sacrifices both user experience and important personal information. 

Finally, testing your software improves the quality of your product. There is no way to tell if a product is good until it’s tested, and if you’re utilizing layers of artificial intelligence, you need explainable AI to help test software.

Explainable AI: The Beginning

Regarding how deeply we could go into the world of AI and explainable AI, we still, as a society, have only dipped our toes in the water. There is so much that artificial intelligence can do for us, but when we don’t understand the answers, it remains irrelevant and even dangerous.

Explainable AI is perfect for automated, artificially-run operating systems. Not only are you making life easier for your employees and teams by automating tedious tasks, but you’re also ensuring that you get accurate data results from your AI by adding explainable AI.

This fascinating and productive technology has the potential to add complete, understandable accuracy to current AI applications. Though explainable AI is in the semi-early stages, companies need to better understand their automated systems. Results for internal and external data require accuracy, and explainable artificial intelligence is the answer.

Effects of Application Modernization on Digitization

Alex Thompson Digital Engineering February 10, 2022
application modernization

Application Modernization

Nothing in modern times has tested the agility of businesses quite like the events of the last two years. Business applications that work well are essential to survival, and day by day, customers continue to drive the demand for such a change.

Because digitization can be a daunting and time-consuming process, even significant, established companies are operating on outdated legacy systems. Not only does this affect the customer experience overall, but it also hinders progression.

Application modernization is essential for efficiency. The time to invest in technology is now, as legacy modernization is unavoidable if you want to remain relevant.

Meeting Industry Needs

Regardless of the industry in which your business resides, whether technical or retail, your digital operations are integral to the way you do business. Adopting new technology is part of building a company with an online presence and serves its customers well.

Moving to online platforms and services enables you to make the most of the available digital technologies. The technology you choose to modernize your applications is entirely up to you but should work with your revolutionized, digitized business model.

Companies typically go about application modernization by utilizing big data, machine learning, artificial intelligence, and moving application systems to the cloud. So, how does meeting industry needs through application modernization affect digitization? First, we should briefly discuss the purpose of digitalization as a whole.

The Overall Purpose of Digitization

Without question, digitization changes a business model, becoming necessary over the past two years. It can create avenues for new revenue while increasing the quality of your company data. Digitization is all about working smarter, and even though the road to revitalizing your company through digitization is a long one, it’s always well worth it in the end.

The new technologies that come with digitalization make it possible to offer more value to your customer base. You’ll successfully be able to manage distribution, promotions, and building awareness for your brand. You become more profitable, efficient, and productive when you modernize applications

What is Application Modernization?

Application modernization is the process of updating your legacy applications to cloud app environments using technology stacks. When your company modernizes its legacy apps, you will meet user expectations as they evolve continually. By implementing the application modernization process, you can take advantage of the tech available to you.

Many companies hire a professional team to assist in application modernization, as it involves the re-development of ideas and software. Much of application modernization occurs in the cloud, and you’ll find that repurposing, consolidating, and reprogramming your legacy applications or software code to better align with your business values and customer requirements.

It’s not about ridding yourself of the legacy systems that have served you well over the years. Instead, app modernization brings them into the now. It makes them readily changeable for the future, integrating new functionality into existing apps and bringing around the possibility of building new ones.

Consumer-Driven Digitization

Without question, the main driving force behind digitization is consumerism. Consumers lead companies to improve the customer experience because your product, service, and brand reputation are at risk without it.

One of the best ways to enhance the journey of your current customer, whether your brand implements a traditional sales funnel or the marketing flywheel, is through digitization. Bring your brand forward and create a seamless experience by updating existing business applications and making new ones.

You cannot move into the future of the business while relying on your original legacy systems. The need for app modernization services is rising, and companies globally are refusing to fall behind competitors and lose customers based on a poor experience that proper digitization can fix

Application Modernization Fuels Digitization

While consumers absolutely encourage companies to embrace digitization, application modernization does the same. Without the apparent application modernization trends, the need for businesses to become more efficient to remain competitive would be nonexistent. Relevancy and ease of doing business on both the end of the consumer and then company are paramount to success.

For many businesses, the need to modernize business applications across the board jumpstarts the digitization process. Even when digitization isn’t embraced as a whole, application updates force a small step into the future. However, shortcuts regarding digitization are never recommended, but it’s clear, even with minor updates, to see the distinct connection between application modernization and digitization.

Modernization Trends

Business owners need to understand the trends that come along with application modernization to identify better what needs to happen to succeed in digitization. The COVID-19 pandemic has placed a ton of pressure on enterprises concerning the need to digitize internally and externally.

If you’re looking to move your company toward digitalization, these are the current trends that you’ll want to follow:

Lifting and Shifting

The lift and shift is the least intensive approach to digitalization, but it definitely isn’t the best way to get every benefit available from what the cloud offers. The lift and shift method means moving legacy systems without applying significant changes to the underlying code and architecture. While the lift and shift can be considered an integral part of the application modernization process, it’s not optimal.

Refactoring or Restructuring

You’ll often hear these phrases used interchangeably, along with rewriting. With refactoring, you’ll adopt restructuring the base code of your legacy applications. Your development team, or the company you’ve chosen for your app modernization services, will utilize microservices to boost the benefits native to the cloud infrastructure. It’s all about getting the best out of the cloud.

Replatforming

Replatforming is a middle ground between restructuring and the lift and shift method of application modernization. Replatforming does not ask for major code changes while embracing modern updates that allow the legacy system or application to access the benefits of the cloud platform.

There are plenty of technologies available, most of which are crucial, that assist in moving along the application modernization process. These tech advances are part of the way application modernization trends, and they include:

  • Containers for operational portability, efficiency, and scalability
  • Cloud computing or the process of migrating legacy applications to a cloud environment
  • Microservices, to decouple components into smaller pieces so you can update them independently
  • Automation for the correct management of security teams and operations by development during scaling
  • Orchestration to coordinate the automation of these varying operational tasks

Hiring a development team is crucial for modernizing applications, and you’ll see immediate effects on how your business moves toward digitization. The two are undeniably related.

The Benefits of Application Modernization

Application modernization has many benefits when executed correctly, typically by a professional development team. It’s essential to have all the working parts in place, and an expert can make sure this happens.

application modernization

Application Modernization’s Positive Effect on Digitization

It’s estimated that most tech applications have a life of around five years of relevancy. Therefore, modernizing your applications and the digitization of your company, as a result, is inevitable. Outdated technology dramatically affects how a company works, both internally and externally.

Application modernization is cost-effective, and it cuts down costs related to internal technology while improving company flexibility, consumer experience, and internal collaboration. In short, application modernization is the fuel that keeps company digitization up and running.

Understanding How to Identify and Manage Big Risks with AI

Alex Thompson Data and AI February 10, 2022
Artificial Intelligence AI

There is no question that the future of artificial intelligence and AI technology is bright. However, many organizations are just beginning to mitigate the potential risks of AI and outline a solid framework to deal with those risks.

Artificial intelligence brings about the opportunity for ethical operation issues, and it’s not overlooked that companies could potentially create a bias through the use of AI. For example, both the EU and FTC have enforced regulations regarding artificial intelligence and the inequities that may result from utilizing it.

Before we discuss the risks that come with artificial intelligence, it’s crucial to grasp what it is and what it can do for your business. When you have the correct information, you can prepare risk management accordingly.

What is AI?

If you find yourself wondering what artificial intelligence encompasses, you are not alone. There are many aspects of AI that we use today, both in our professional and personal lives. Every time you ask Alexa a question or tell her to play music or your favorite podcast, you’re engaging with artificial intelligence.

Of course, Alexa doesn’t encompass everything artificial intelligence can do, but it’s a fine example of how we use it regularly. Also, consider when you log onto a company website and ask their chatbot a question. Chatbots are fueled by AI and are a stellar example of how artificial intelligence can take business operations to the next level.

So, the answer to what’s artificial intelligence is simply this:

Artificial intelligence combines science and potent, human, and computer-powered databases that enable problem-solving.

AI technology works in all aspects of our lives, and it definitely makes things easier. However, it’s easy to see where this might become an issue for businesses, primarily significant corporations, that have access to better AI technology and thus have the option to use it unfairly, hence the ever-evolving regulations.

The Risks of Artificial Intelligence

It can be challenging to determine the aspects of AI you want to use for your company and best mitigate the risks within the territory. To control the risk factor, you first have to know them.

Unauthorized Introduction

As companies digitize and switch from old legacy systems to cloud-native applications, there is the potential to introduce artificial intelligence without your development, security, or AI team knowing. Understanding the potential for your employees to, advertently or inadvertently, use unauthorized SaaS applications at work means you can minimize that risk.

Biased Decision-Making

One of the biggest risks of companies regularly implementing AI is the introduction of a decision-making bias into significant platforms and algorithms. AI systems learn on a specific data system, that being the one in which they were initially trained. If that set of data reflects biases or assumptions, AI can then influence system decision-making.

Lack of Transparency

Most companies utilize AI systems to make better business decisions automatically, whether that be from an internal or customer service standpoint. However, the algorithms that come with AI implementation can often become so complex that those responsible for their creation cannot explain it.

AI specialists refer to this phenomenon as the “black box.” Unfortunately, transparency is crucial to good business, and AI can sometimes make that impossible, such as an automatic rejection for a bank loan that should have a stamp of approval.

Legal Responsibility

The issue of legal responsibility concerning AI is a risk for businesses because the topic itself contains many blurred lines. Machine learning can easily encourage a poorly designed AI system to refine itself, making it near impossible to assign legal responsibility if and when things go away.

Protecting Personal Privacy

Regardless of the industry of your business, your customers rely on you to protect the personal information they give you. There are endless amounts of structured and unstructured data that AI systems can manipulate, and when data breaches inevitably occur, your reputation is at stake. Top-of-the-line security measures using artificial intelligence are essential.

Managing Artificial Intelligence Risks

Now that you know the major risks that come with artificial intelligence, you can begin to figure out how to control them when it comes to your company and operations. Perfectly honing your risk management expectations and implementing security measures company-wide can help, but it’s not always possible to have complete control over our AI systems.

The use and growth of AI tools are unavoidable. While the risks are substantial, it will remain near impossible to manage those risks unless we take on the responsibility of learning more about AI systems.

artificial intelligence AI

Adopting Frameworks to Manage Risks

There is no denying that your company has to adopt and enforce a solid framework for managing AI risks. The more you focus on managing risks, the more successful your long-term AI investments will be, creating value without unwanted material erosion.

Prioritizing the management of artificial intelligence risks on an individual level is part of a greater movement to understand what AI can do for us and how we can control it to make it better. Familiarity with AI is truly a group effort. The more we can pinpoint how it will evolve in active use, the easier it will be to dodge the more considerable risks associated with long-term AI use for business.

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