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3 Greatest Challenges of AI Adoption (and How to Overcome Them)

is not whether industries will use AI technologies for their day-to-day tasks. Instead, it is the question of when they are going to implement them. Per McKinsey, 70% of companies will implement at least one of the AI technologies by 2030. Thus, one of the hottest topics among enterprise leaders today is how to adapt to these changes as fast as possible.

Let us raise the curtain on what challenges you might face at the outset of AI adoption for your business. And, of course, how to overcome them.

Tech stack for AI: How to make magic happen

In essence, the mantra could explain every AI-based technology: AI requires quality data, data needs the cloud, and the cloud needs a network. Meanwhile, it is easier said than done, as reality dictates a long list of technologies and methodologies to adopt. Moreover, as per the Juniper survey, most tech leaders define creating and managing the data sets as their most significant pain point.

So, it is vital to get the feeling of the face of your enemy first. Here is a high-level overview of AI-based application’s architecture, concluding a set of services and tools:

Data integration

ata integration — the better data you have (the cleaner and more structured it is), the smoother your further journey will be. Data integration stands as the prerequisite to AI-based solutions at scale. Since you might need data from various sources like enterprise information systems, sensor networks, or any domain-related data (e.g., social media, weather, or satellite), you need tools for integration.

Data persistence

ata persistence — you might need to work with any structured or unstructured data that you can imagine — video, audio, text, telemetry, images, census data, network topologies. But there is no “one size fits all” solution for these kinds of data. Thus, you might store your data in multiple database technologies (e.g., graph databases, NoSQL, relational, distributed file systems, blobs, key-value stores, etc.)

Platform services

latform services — there are lots of them. Again, depending on your domain, you might need platforms for data encryption, ETL, pipeline management, data privacy, cybersecurity, authentication, queuing, etc.

Analytics processing

nalytics processing — next, you might need analytics services to “digest” all the data you gathered, taking into account volumes and velocity.

Machine learning (ML) services

achine learning (ML) services — these services let data scientists develop and deploy ML models. Apart from Python, R, and Scala, ML libraries like TensorFlow, Amazon Machine Learning, PyTorch, Caffe, and AzureML enter the picture.

ata visualization tools — any AI architecture needs dashboards representing data insights to make effective decisions. You might already be using tools like Power BI, Tableau, Oracle BI., etc.

Developer tools and UI frameworks

eveloper tools and UI frameworks — developers and data scientists need the development frameworks and user interface (UI) development tools like React, Angular frameworks, and IDEs like Visual Studio or R Studio to get their hands dirty.

We do recommend attitude towards AI tech stack implementation as an investment for your company. But, of course, any technology implementation depends on the people behind it. Thus, after choosing the appropriate tech stack, you might face the following challenge: hiring the talents to tackle this task.

Filling the AI skills gap

Per Juniper research, 41% of respondents are worried about the training of the current employees to operate the AI systems. Simultaneously, 32% are concentrated on recruiting the already trained talents to cope with it. You can compare these figures to 58% of leaders concerned about developing AI models and data sets at their companies. Thus, the AI skills gap becomes another area of concern. Let’s eliminate those anxieties.

Pilots of the new technology instead of the overnight implementation

Let your employees get acquainted with the latest technology and realize how it helps their work and its impact. Of course, implementing new technology is always challenging, with nothing to say about the times of pandemics. Moreover, it’s crucial to make the cultural foundation that implies digital upskilling for employees.

Use metrics to reflect your current state

Metrics depicting the changes that new technology brings is equal to the feedback that employee can get. Thus, you will see how your team’s productivity is changing as well as the overall efficiency of the company.

Be prepared for the long game

Does AI stand as a solution for a single particular problem, or is it the tectonic shift for your enterprise? First, try to explain that idea to your employees, giving a gist of how things will change. Then, consider implementing a series of training for a smooth journey for those who are new to this technology. Sure, not everyone at your company should know how to deploy ML models to get the key deliverables, but they should know what is going on behind the curtains.

AI governance

AI governance: Putting the finishing touches

AI governance means monitoring and evaluating algorithms for ROI, risk, bias, and effectiveness. However, while being hugely interested in AI adoption, companies are reluctant to build their AI governance strategy. As per the Juniper survey, only 7% of respondents have already identified a company-wide leader responsible for AI governance.

But AI technologies imply a new approach towards compliance policies and other regulations. For example, you should consider the questions of privacy and transparency, as governments are introducing AI-related regulations worldwide.

Simultaneously, the quality and maintainability of your data are crucial for AI. For example, decisions drawn on data gathered and stored for some time could not be valid further because of the data model decay. Thus, someone should constantly maintain and update your data. Therefore, it is better to find out who it might be at the outset of your AI journey.

Also, data scientists and application developers’ approaches and skills may differ, leading to miscommunication in the team. That means that you should have a plan including all your stakeholders’ concerns and review it periodically.

It doesn’t matter whether a chatbot, recommendation engine, image, or video analysis tool stands at the core of your solution. However, the decisions you make with these tools will impact your team and clients. It is crucial to tailor them for your needs.

Bottom line

Mastering the new tech stack, filling the AI skills gap, and AI governance might be challenging. We recommend you get ready for a long game, try pilots of your projects before the final run, and set the metrics to measure the progress of AI adoption over time.

Simultaneously, it would help if you prepared the ground for monitoring and evaluating algorithms that impact your business daily. It is high time to climb this mountain towards AI-powered decisions, step by step, with balanced but courageous actions.



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