Business
Top 5 AI Implementation Challenges and How to Overcome Them
Even though AI adoption rates have skyrocketed in recent years, the proportion of companies using smart algorithms for analytics and business process automation still fluctuates between 50-60%.
One of the reasons for a reluctance to embrace artificial intelligence includes numerous AI development and implementation challenges, such as a failure to scale AI proof of concepts (PoCs) across other use cases, a lack of quality training data, and IT personnel shortages.
Additionally, your moonshot initiative might not yield the expected results due to unclear objectives and flawed project management practices, including bias, which can hinder AI algorithms producing accurate results in 85% of the cases.
We discussed the most common AI implementation challenges with Serhii Pospielov, AI Practice Lead at Exadel.
Read on to explore some of his suggestions on how your company could adopt AI in a risk-free manner and see a speedy return on your investments.
Top 5 AI Implementation Challenges and Expert Tips to Avoid Them
A little foreword: the decision to implement AI in your organization should be preceded by a thorough audit of your business processes and an IT infrastructure assessment.
In particular, you should pay attention to data interoperability — i.e., where your organization’s data resides and whether it can be sourced, aggregated in one place, and analyzed at scale.
In addition, it helps if you clearly understand what goals your company is capable of achieving by augmenting certain tasks and workflows with AI capabilities — enhancing or fully automating processes.
Let’s discuss key AI implementation challenges and how to avoid or, at the very least, mitigate problems that they may pose.
Challenge #1: Ensuring Data Quality and Availability
Data is the fuel that keeps the wheels of AI turning
Whether you opt for supervised machine learning algorithms that feed off structured data or deep learning networks that independently parse large volumes of unstructured information (like images, PDF documents, and social media posts) you need lots of data to train algorithms capable of solving your business problems.
However, in heavily regulated industries like healthcare, biotechnology, and financial services, such data can be difficult to obtain due to various regulations, such as HIPAA, GLBA, and CPRA. Publicly available datasets might contain incomplete or biased data, which is bound to affect algorithm accuracy.
Solution
To overcome this challenge, your company should devise a comprehensive data management strategy before implementing AI solutions. This strategy may cover cleaning and organizing data, storing it in a format that is easily accessible, and setting up data governance policies to ensure that data is consistently collected and managed. And if you don’t have data scientists on your IT team, enlist the help of a reliable company providing data consulting services.
Challenge #2: Lack of Skilled Talent
Skilled talent
The development and implementation of AI solutions require a team of experts, including data scientists, machine learning engineers, and software developers. However, organizations struggle to recruit competent AI talent, with the global shortage of data scientists expected to reach 250,000 professionals by 2025.
Solution
Companies must train and upskill their existing employees and ensure knowledge transfer if they work with third parties. Additionally, you could tap into IT staff augmentation services to cover your immediate AI implementation needs while reducing hiring costs.
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Challenge #3: Integration with Existing Systems
Data is the fuel that keeps the wheels of AI turning
The average company uses 40-60 SaaS apps according to a 2021 survey by Productiv. Add custom software solutions, including systems built with legacy tech stacks, and you’ll get 400 separate data sources that a modern organization needs to fulfill basic data analytics on, as well as business intelligence objectives.
Aggregating data from all these applications or infusing them with AI can be challenging since IT systems often use different technologies and architecture patterns.
Solution
The first step to integrating IT solutions for AI implementation includes replatforming older apps, breaking monoliths into microservices, and connecting systems via APIs and other middleware. When integrating AI algorithms into enterprise software, you should start small, selecting systems that require minimal changes. These may include data platforms like Salesforce, robotic process automation tools, or cloud services with readily available AI modules.
Challenge #4: Ethical Considerations
The 2021 AI Ethics in Action survey from IBM revealed an astonishing fact about the moral side of AI implementation. While two-thirds of executives consider ethics an integral component of their AI strategy, only 25% of companies have operationalized it.
When discussing ethical considerations common for AI projects, we typically refer to:
The Trade-Off Between White-Box and Black-Box AI
More advanced algorithms often fail to explain how they arrive at their conclusions. At the same time, explainable AI systems lack cognitive capabilities and seldom allow companies to tap into predictive and prescriptive analytics.
AI Bias
The phenomenon occurs when algorithms, having been trained on poor-quality or inconsistent data, deliver erroneous results. Some widespread forms of AI bias include discrimination against different gender, age, and racial groups.
Resistance to Change
An employee who’s been using Excel spreadsheets for a quarter of a century might have difficulty mastering AI-based software and, struggling to compete with intelligent algorithms, could eventually face a layoff.
Solution
To address the ethical challenges surrounding AI implementation, your company should design AI systems with explainability in mind. For this, consider using interpretable models, such as decision trees or rule-based systems, or implementing tools that enable users to understand the decision-making process. Also, collaborate with regulators and external experts to validate that your AI solutions meet regulatory requirements.
Before training AI algorithms, it’s important to select data that is diverse and representative of the population and use cases you’re targeting. Next, turn to bias detection and mitigation techniques, such as using rich training data, auditing the data and AI models for bias, and involving multiple stakeholders in the development and testing process.
Finally, educate your workforce on AI usage, so that they see how such algorithms can assist and improve their work, rather than replace it! Bear in mind it takes people time to overcome resistance to change and mastery of working with new tools. While the growing AI adoption might reduce the workforce in certain areas or industries, in the long run, artificial intelligence could create 3% more jobs than it will kill — simply because we need qualified human specialists to oversee and adjust algorithm performance!
Challenge #5: High Cost of AI Development and Implementation
How much does it cost to create and deploy an AI solution?
According to Serhii Pospielov, there’s no definitive answer to that question, as AI development and implementation expenses could vary from tens of thousands to millions of dollars, depending on the complexity of the solution and use case coverage.
What is known, though, is that the worldwide spending on AI-based systems could top $300 billion in just three years. On the other hand, the number of companies that see a sizable return on their AI investments stands low at just 11%.
Solution
One way to ensure a stable and fast payback on AI investments is to carefully consider the costs of developing and implementing an AI solution and weigh them against the expected benefits.
And here’s what Exadel can help you with. We’ve launched a three-day artificial intelligence PoC program to assist businesses in estimating AI implementation costs, selecting the right tech stack, calculating potential ROI, and more!
By creating an artificial intelligence proof of concept, you can also design a roadmap for your project early on and adopt an iterative approach to AI implementation while sticking to your bigger plan. Thinking beyond isolated PoCs will help you avoid AI scalability issues, too.
Take-Home Message
While AI implementation challenges are significant, they are not insurmountable.
Most companies successfully navigate the complexities of developing and deploying AI solutions by investing in data quality, skilled talent, application integration, and ethical AI initiatives.
And the emergence of novel artificial intelligence technologies, such as repurposable foundation AI models, lowers the barrier to artificial intelligence adoption, even for smaller companies.
That being said, you should not embark on your AI journey alone, especially if your internal IT team lacks the expertise and skills to implement artificial intelligence in a risk-free way. Instead, contact Exadel to build AI solutions that perform well, make your employees more productive, and improve your bottom line!