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Top 7 AI Challenges & Solutions in 2024

Top 7 AI Challenges & Solutions in 2024Top 7 AI Challenges & Solutions in 2024

Based on our discussions with businesses, it has become clear that most AI projects fail to achieve the projected benefits.

In this article, we outlined the top challenges of AI development and implementation, as well as recommendations to overcome those challenges to help business leaders increase the chances of success in their projects.

Challenges while developing AI

This section highlights some challenges developers face while building AI/ML models.

1. Improper data preparation

Data is one of the most crucial elements of the AI/ML building process. The better the data, the more accurate the AI model will be. Developers can face the following challenges while preparing the dataset:

  • Determining the right dataset can be challenging since the developer needs to consider the scope of the project, the matching dataset, and the sourcing method. 
  • Legal and ethical considerations can also be challenging while collecting data for your AI/ML project. Consider relevant policies and country-specific regulations before collecting or using data. Click here to read more about data collection challenges and how to overcome them.
  • The more complicated the AI/ML model, the larger the dataset may need to be. Collecting large-scale datasets can be expensive. 

Recommendation: Review your data and leverage data collection methods

Improving data quality is critical. If you don’t have sufficient data for machine learning, these are the potential solutions:

You can also check our data-driven list of data collection/harvesting services to find the best option for your project.

2. Training issues

Model training issues are another reason why AI projects fail. Training is important because it tells the AI/ML model what to do and how to do it, making it one of the most crucial stages in the whole process. 

Recommendation: Use the latest ML approaches and AI training best practices to avoid over/underfitting

2.1. AI under/overfitting

A model is considered under-fitted if the model is not trained enough. Overfitting can occur when the model is trained so much so that it memorizes the data rather than learning from it. 

Recommendations: Avoid Under/overfitting

These issues can be avoided in the following ways:

  • Expanding the dataset
  • Stopping the training at the optimum level (Check the link below to learn more about the optimum level)
  • Leveraging data augmentation
  • Simplifying the model

3. Avoiding bias

These issues can significantly impact the performance of the AI model. A machine learning model is considered biased when it provides accurate results for one set of data and inaccurate for another. For instance, in 2015, Amazon’s AI-enabled hiring system was found to be biased against women and favored men for certain jobs. 

Recommendations: Reduce AI bias

You can also consider the following ways to reduce AI bias:

  • Fathom the algorithm and data
  • Establish a debiasing strategy
  • Improve human-driven processes as you identify biases in training data
  • Decide on use cases where automated decision-making should be preferred
  • Follow a multidisciplinary approach
  • Diversify your organization
  • Adopt a data-centric approach to minimize bias

Learn more about AI bias and how it can be avoided.

Challenges while operationalizing AI

This section includes some challenges of implementing AI in your business and how to overcome them.

4. Lack of business integration

Integrating new machine learning models into your business applications and systems can be a complicated process, and without such integration, models do not deliver any value.

Recommendations: Plan for operationalization

Business managers can consider the following factors prior to implementing AI-enabled solutions:

  • The compatibility of the business’s infrastructure
  • Method of data storage in the business
  • Data input/transfer process
  • Data security & protection
  • The level of digital dexterity in the business

If the company can not afford the costs connected to implementing the AI mode, it can consider AI-as-a-Service.

5. Lack of business alignment

Without goals, business and technology can not be aligned. We recommend setting clear goals in advance to promote alignment.

Recommendation: Identify project goals in advance

According to Gartner, by 2024, 50% of AI investments will be quantified and linked to specific key performance indicators to measure return on investment.

Any investment decision that is not tied to KPIs and does not have a clear ROI shouldn’t be made. It looks like Gartner’s clients lost sight of that during the hype days of AI, but we expect this basic business sense to come back and businesses to focus on results rather than new technologies.

Some may argue that benefits are hard to measure and ROI calculations take time. In our experience, if the benefits are clear, businesses can move fast quickly. It doesn’t matter if a project has 300% or 500% ROI; both of those are amazing returns, and businesses should focus on projects with clear ROI rather than scrutinizing the ROI of barely impactful projects. 

Recommendation: Inform business leaders about AI use cases

Identifying business cases for AI applications requires managers to deeply understand current AI technologies, their limitations, and the processes of their divisions. This facilitates business and tech alignment. To learn more, check out our comprehensive article on process mining. As with any nascent field, the lack of AI knowledge in management hinders adoption in most cases.

Recommendation: Talk to your peers before purchase, and if you can’t, limit the investment

Another malaise for emerging technologies is hyper optimism, leading teams to work without clear ROI tracking towards unrealistic goals. By extension, AI can also suffer from this as managers can fall into hyper-optimism after reading unrealistically optimistic studies promoted by vendors and feeling that they are falling behind the curve.

6. The difficulty of accessing the talent

The AI talent war has been excessively reported. In the current market, there is a gap between the demand and supply of AI talent. One of the reasons for this imbalance is the spread of AI-enabled solutions to almost every department in the business. From sales to marketing, everyone is using AI-enabled tools. This requires hiring more AI talent, which can be expensive.

This leads most non-FAMGA (Facebook, Apple, Microsoft, Go; you, Amazon) companies to work with smaller, second-tier teams and suffer from longer development cycles as a result.

Recommendation: Rely on no code AI and an improved current employee experience

Enabling citizen data science can reduce the workload for data science teams, enabling companies to focus their hiring efforts.

Companies can improve the employee experience, which will also help with retention and hiring, and opt for reskilling and upskilling their current workforce you can:

7. The difficulty in assessing vendors

While tech procurement is challenging in any emerging field, AI is particularly vulnerable because:

  • A large number of non-AI companies engage in AI washing
  • Some AI companies exaggerate their achievements, 
  • And businesses do not know exactly how they will ultimately use AI effectively in their business.

It is hard for managers to identify the areas they need to work with AI vendors and identify leading vendors. On this front, we have worked to help corporations.

Recommendation: Clarify where and why you need AI in your business

The following article explains in what areas of your business AI can be implemented:

Check out this comprehensive article to learn about 100+ AI use cases & applications.

You can also check out our data-driven lists of AI platforms, consultants, and companies to find the option that best suits your business needs.

Further Reading

If you need help finding a vendor or have any questions, feel free to contact us:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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