We use AI for everything from text-generating tools to self-driving cars. It is also useful for online learning, but companies may run into unexpected problems when integrating this advanced technology into their operations.
A recent global survey by McKinsey showed that 50% of respondents reported using AI in at least one business function. This number is expected to keep growing, with AI doubling revenues generated by organizations.
Despite its potential, the technology also brings development and implementation challenges. Although it has made significant progress lately, there are still 6 major AI challenges that need to be addressed. In this blog post, we dive into each one and show how to overcome them.
Understanding AI Challenges
To succeed with AI, leaders must invest time, resources and patience in transforming the company. Since the technology is advancing rapidly, it presents both benefits and difficulties. Let’s take a closer look at the top 6 challenges of AI and how to deal with them.
Technical Challenges of AI
— Data quality and availability
AI systems rely on training with relevant data, but companies often struggle to provide the right quality or quantity of it. This can lead to biased or discriminatory results. The saying that “an AI system is only as good as its data” comes with inherent issues. Gathering and refining inputs is a challenge, as AI needs numerous high-quality materials to recognize patterns and perform as expected.
In the past, most data was structured and textual, but with the advent of omnichannel customer experiences and the Internet of Things, unstructured data dominates. The problem is that many AI systems are designed to work with supervised data sets.
— Algorithm selection and optimization
The prevalence of AI stories with bias begs the question of how bias comes about, given the technology’s own lack of awareness and bad intentions, doesn’t it? This comes from improperly collected data — that’s the crux of the problem. When the source itself is biased, the system becomes discriminatory.
AI algorithms are developed based on the data they receive. If a large portion of the examples in the learning process comes from a particular group, the algorithm may develop a bias against other groups. This algorithmic prejudice is a major challenge for AI, especially in systems that control critical infrastructure such as transportation or healthcare, as it can potentially cause harm.
— Integration with existing systems
Statistics indicate that the successful integration of AI can lead to greater efficiency and productivity. According to a study, implementing AI in businesses across industries resulted in a 20-30% increase in productivity. Therefore, it’s critical for companies to integrate AI for long-term growth and sustainability.
Adopting the technology to your organization isn’t a walk in the park, like adding a browser extension. It involves setting up multiple interfaces and elements to meet the needs of your business requirements.
Painless implementation of AI and related software requires skilled solution providers with extensive experience to make the transition as smooth as possible. Data storage, input, and infrastructure are critical to tackle this AI challenge and ensuring compatibility with all AI requirements while maintaining the operation of existing systems. After the transition, proper staff training is essential to ensure the team can work effectively with the new system.
Non-Technical Challenges of AI
— Ethical considerations
Privacy is a major ethical AI challenge when it comes to AI. Currently, no government regulations protect your privacy rights with this technology. When you use an AI system to perform a task, it may unknowingly collect information about you because it operates on your behalf.
The threat of cyberattacks is on the rise, largely because companies are increasingly moving online in response to COVID-19. These onsets make it important to take appropriate action against such threats. Unethical practices related to AI can also halt a company‘s progress and result in scrutiny. It’s important to comply with data-related laws and adhere to ethical guidelines.
Statistics highlight the importance of data safety and cybersecurity, showing that 68% of customers are concerned about their privacy when it comes to AI applications. Furthermore, cyberattacks increased by 600% during the pandemic, underscoring the urgency of implementing robust security measures.
— Regulatory compliance
The data that algorithms collect from users is highly sensitive, and faulty algorithms or inadequate data governance systems in AI applications can lead to incorrect predictions and consequent financial losses for the company. Moreover, such problems can result in legal violations and entangle the organization in a web of AI challenges.
The crux of deep and machine learning models is the availability of data and resources for training. While we have access to vast amounts of data, it’s important to know that it is generated by millions of users around the world, making it vulnerable to potential misuse.
— User adoption
End users often don’t get involved in the development process, which can lead to low user adoption. Data products can be overly complicated, inconsistent with business processes or misused.
One of the best examples of user adoption is ChatGPT. It saw record-breaking user growth, reaching 1 million users just five days after launch, making it the fastest-growing platform in history. By January 2023, the software had an impressive 100 million users. Furthermore, ChatGPT stands out for its high engagement: 1.6 billion visits were recorded in March 2023, underlining its immense popularity among users.
6 Steps to Beat Up Artificial Intelligence Challenges
Step 1: Define Clear Objectives and Requirements
By articulating clear goals, data scientists and machine learning engineers can apply the scientific method to test different approaches, evaluate the validity of their hypotheses and determine the feasibility of the desired outcome.
For instance, Amazon introduced features such as “Frequently Bought Together” recommendations, which increased the average size of the shopping cart and order amount. This technique, known as upselling and cross-selling, increased average sales per customer, boosting Amazon’s e-commerce revenue per quarter. According to McKinsey, AI-powered recommendations contribute to up to 35% of Amazon’s revenue and 75% of the content consumed on Netflix.
Step 2: Identify and Evaluate Data Sources
An accurate data set serves as a representative sample of the population. With about 97% of people owning mobile devices and about 85% having a smartphone, location data is a valuable resource for accurately capturing population activity, especially when combined with other data sources.
When adopting predictive systems, companies need to ensure that data-savvy teams are in charge of the implementation. Unfortunately, not every organization has data engineers or scientists on staff to assess data quality for machine learning applications. When evaluating data, companies should consider four important characteristics:
- Data source and its authenticity. Can the data provider guarantee its legitimacy? Can the data set be segmented for analysis?
- Data accuracy and qualification. Does the dataset include tags and metadata for effective analysis?
- Sufficient size of the data set. It should truly represent the desired population and customer base.
- Data’s recency. How relevant is the data? How frequently is it updated?
Real-time data is vital to avoid training AI on outdated information. 32% of sales and marketing executives who adopted AI during the Covid-19 pandemic failed with their models because they relied on pre-pandemic data.
By prioritizing high-value sets of data and addressing these considerations, organizations can overcome many challenges of artificial intelligence to unlock valuable insights and make informed decisions that drive success in a rapidly evolving landscape.
Step 3: Select and Train Appropriate Algorithms
Eliminating such AI challenges as bias is tricky, but there are some ways to tackle it. Don’t throw away labeled data representing different groups just because they don’t match other examples. Instead, you can combine multiple algorithms trained on smaller data sets and use them as training data for the larger ensemble model. This approach results in canceling errors and biases of the individual algorithms and providing more accurate predictions.
To solve the problems of artificial intelligence in terms of data quality, you should evaluate different algorithms based on their performance. Consider factors such as accuracy, speed, scalability and interpretability. For example, if you’re building a chatbot, you might explore natural language processing algorithms such as LSTM (Long Short-Term Memory), which are known for their ability to understand and generate human-like texts.
Once you’ve chosen the best-suited algorithms, it’s time to roll up your sleeves and train them. Think of it as working on a masterpiece and refining it until you achieve the desired performance.
Step 4: Integrate with Existing Systems
Developing a clear strategy for integrating AI is like setting the course for a successful journey. It’s the critical first step that lays the foundation for seamless integration. Your strategy should outline the business goals, identify key players and define the necessary resources. Imagine creating a roadmap with milestones and metrics to measure progress.
To create an effective AI solutions development and integration strategy, it’s important to consider your organization’s long-term vision. Why do you want to bring artificial intelligence into your business? For example, did you know that 45% of companies use AI to cut costs? However, AI offers more than just cost reduction—it can improve the customer experience, provide valuable insights and automate internal processes.
AI integration requires a skilled workforce capable of understanding, developing, and managing AI systems. That’s why it’s important to invest in training and education. You want your employees, whether tech-savvy or not, to be able to collaborate effectively with AI and unlock its full potential.
Step 5: Address Ethical and Regulatory Considerations
As AI decisions increasingly affect people’s lives, enterprises have a responsibility to adopt the technology ethically.
Ethical artificial intelligence follows clear guidelines and values such as individual rights, privacy, non-discrimination, and non-manipulation. To deal with AI-related risks, companies need to establish principles, measure them and embed them into their daily activities.
Establish an ethics committee with diverse members to manage regulatory AI challenges. Design algorithms with expert input, situational knowledge and awareness of historical biases. Commit human authorization processes in critical areas to prevent malicious actors from compromising them.
Procedural checks can benefit from AI by running algorithms alongside human decision-making processes, comparing outcomes, and explaining machine-based decisions. MIT’s Initiative on Combatting Systemic Racism is an example of the use of computational tools to promote racial equality in various fields.
Step 6: Engage Users and Ensure the Adoption
Ensuring user adoption is a critical aspect of overcoming AI implementation challenges. It’s not enough to simply develop an AI system and expect users to embrace it. To improve user adoption, companies should focus on creating a user-friendly experience and providing adequate training and support. This includes offering clear guidance and documentation, as well as ongoing technical assistance.
An effective approach to improving user adoption is to involve users in the design and development process. This can ensure that the AI system meets the needs and preferences of the target users. User involvement can also help identify potential pain points and areas for improvement before the system is deployed.
Find a Development Team Who Will Tackle AI Challenges
Finding a development team that can effectively develop and integrate artificial intelligence software is like finding a diamond in the rough. The process requires a team that has both technical expertise and hands-on experience in creating AI development solutions.
Before you hire tech specialists, ask them to submit a portfolio of similar cases and look at feedback from their clients. Also, make sure you share the same values as the team and are on the same page when it comes to AI development and workflows.
Our expert AI developers are ready to take on any challenge and turn your AI dreams into reality. With a proven track record of successful cases, we’ve helped many companies harness the power of AI to drive innovation and growth. Whether you need to build intelligent chatbots, implement advanced data analytics or develop innovative machine-learning algorithms, we have the skills and know-how to deliver exceptional results.
Although AI app development is part of IT, businesses must learn how it works to overcome implementation and development challenges. The roadmap for AI incorporation can be tricky, but a step-by-step strategy can ease the process and minimize risks.
Despite the challenges of implementing AI, it’s essential to overcome them to reap the benefits and participate in the future of machine learning. For example, AI can help improve customer service, streamline processes and reduce costs. Contact us to build next-generation AI software development solutions to achieve your business goals!