We are living in a world where technologies like machine learning (ML), deep learning, artificial intelligence, and others are becoming the driving force behind technological innovations across industries. Did you know that the AI implementation rate among enterprises in the year 2019 was around 37 percent? This is an astounding 270 percent increase in AI integrations in just four years!

The integration of AI and other innovative technologies has increased ever since the COVID-19 virus struck the planet as more and more businesses found a home in advanced conversational tools and AI technology to promote remote work and ensure consumer demands are met flawlessly.


What are the challenges of AI integration?

However, AI integration isn’t as straightforward as we made it sound. In this post, we will share the potential and existing challenges of AI adoption and integration. Not just that, we will discuss several tips and tricks businesses can use to ensure a successful AI integration. Here are the major challenges of AI implementation that companies should consider when drafting an AI adoption strategy.


Leveraging quality data set

AI systems need data to function. However, not just any data would work. Since companies use AI solutions to improve their business operations, it is important that the quality of data remains high. Most AI solutions leverage quality data sets. Therefore, companies need to determine which data sets to use before even planning AI implementation. This is quite a challenge as different types of data flow across different departments in any organization, making it hard to determine which data set to sample. The only way to overcome the challenge is by getting in touch with Artificial Intelligence experts. They will show the correct approach to sampling the data and making the technological transition work.



Infrastructure replacement

Artificial Intelligence implementation finds another major obstacle in replacing outdated infrastructure. Many of the businesses willing to jump onto the AI and ML bandwagon will have to do away with the traditional legacy systems. This is important as most modern-day AI systems will require a high level of computational capacity and speed. In other words, businesses that need AI-driven systems will require substantial investment in uprooting the existing (dated) infrastructure and replacing it with high-end systems. Business owners should be ready to make a transition beyond the digital frontier if they want to adopt and leverage AI technology. The idea is to build a robust environment and flexible infrastructure that support AI solutions.



Reliance on complex algorithms

Another challenge worth mentioning in the list is the algorithms that drive AI and ML solutions. Now, these algorithms are quite complex to comprehend. Nevertheless, most business intelligence operations are driven by these complex algorithms and their performance heavily relies on how well the AI algorithm functions. Here’s what the enterprises need to do — businesses that are looking forward to implementing AI solutions should understand what they need to make the AI-based solutions work and whether or not the algorithms will help transform their outcomes. Even when they have created a reliable ML or AI model with the appropriate algorithms, they will have to invest heavily in building considerable manpower to ensure continuous training of the models, which can become a major financial challenge.



Integration into existing processes

Many business leaders believe they can easily integrate AI into existing systems, operations, and processes. This is far from the truth. It is a massive challenge to integrate AI into any existing business system.

First, any AI integration isn’t a plug-and-play affair. It demands extensive analysis of the existing system and structure to figure out how loopholes and how AI can fix them. Not just that, the analysis needs to determine if the existing system needs a complete uprooting. Secondly, AI integration cannot be accomplished without professional help. Organizations will have to approach reputable AI and ML service providers that can develop the required solution and supervise the progress, from conception to deployment.



Security and storage concerns

We hardly discussed the data security aspect of AI integration. AI applications require a huge amount of data to function. They require more and more data to understand the system better and churn better productivity and efficiency. In other words, a significant amount of data is allocated to learning and making intelligent decisions. But where will all this data go? Data storage becomes a huge challenge for businesses as AI solutions keep evolving and will keep gathering more and more data. Not just that, data storage issues might also lead to data security issues. Most AI experts suggest building an appropriate data management environment before implementing AI. This helps ensure superior data security and provides a solid foundation for future AI and Ml integrations.



How can businesses successfully adopt AI – A checklist

AI integration is a major undertaking that requires in-depth knowledge and time to ensure precision and sustainability. Here are some of the tips businesses can use to adopt artificial intelligence as a growing force in any department.


Get acquainted with AI

Business owners should take time to learn everything they can about how artificial intelligence, machine learning, and related technologies work. They should prioritize their business by figuring out ways these technologies can help generate better results and value. The wealth of online information and resources will be enough to familiarize themselves with the fundamentals of AI and ML.


List problems you want to solve

One should not expect AI integration to solve everything. If that could be a possibility, most cash-rich businesses would lay off all their employees and replace them with AI solutions. Organizations need to shortlist the operations that are causing productivity and efficiency issues. They need to begin the integration process by exploring different ideas.


Assess AI adoption-related finances

Artificial intelligence and machine learning integration can get expensive. This is why experts recommend hiring professionals that can help determine the finances related to the integration. Business leaders should focus on the value the investment brings to their enterprises — prioritizing near-term visibility and financial value.


Assess capacity for AI adoption

What the management believes AI integration can bring to the table can be in stark contrast with what the organization is actually capable of. In other words, business owners should have clarity over what the organization is capable of and whether or not a full-blown AI implementation is worth the effort.


Prepare a prototype

Always begin with a pilot project to learn from the experience. This helps identify the issues that were not discussed from the organizational or tech standpoint. However, AI integration should only start once the pilot project shows positive results. The idea is to start small with the project goals in mind, assess the results, and only then take action on the full-fledged AI implementation.


Create a taskforce

AI implementation will require data cleaning since all kinds of data run throughout different departments of an organization. This is a necessity to avoid a “garbage in, garbage out” situation. Business leaders should create a task force that understands how important it is to obtain high-quality data from different legacy systems. The task force should be handed the responsibility of integrating the different data sets together and sorting out inconsistencies.


Low-cost low-risk projects

Start small by sampling a small portion of the data instead of going all in with the AI implementation. Don’t take on too much work too soon. Instead, rely on an incremental approach to AI integration that helps prove value and collect feedback. Use the feedback to expand the integration accordingly.


Automate daily tasks

If an enterprise has certain business operations that are repetitive or redundant, it should consider automating them. However, AI integration shouldn’t be introduced as a replacement for the current workforce. Instead, it should be introduced as a solution to improve the efficiency of daily tasks.


Scale up

Keep collecting and analyzing the information to develop precise plans for scaling up the AI integration. However, this process might require organizations to team up with technology specialists and consultation with owners of businesses that have already accomplished successful AI implementation.


Final thoughts!

Artificial Intelligence has way more to give than we can imagine. However, businesses will have to think beyond conventional methods and focus on familiarizing themselves with AI. Indeed, the challenges of AI adoption are well documented.

But, overcoming these obstacles isn’t rocket science. A proper AI strategy and step-by-step planning will certainly simplify the process of AI implementation.

To put it in simple words, organizations should learn how AI works and ways it can help them improve productivity and overall work efficiency. And if that isn’t motivating enough to take action, remember, your competition will use AI and ML to improve their products and services.

It’s time to push and use AI as a major competitive advantage. Did you find this guide helpful? Let us know in the comments. Also, don’t forget to check out other informative posts in the blog!

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