Challenges of Adopting AI in Businesses
Over the past decade, the discussion surrounding Artificial Intelligence has made waves and garnered more attention. Businesses are working towards adopting AI to harness its potential, but it comes with its challenges.
AI is now a hot topic of discussion in the business world, with big guns like Google, Netflix, Amazon, etc, benefitting largely from AI solutions and machine learning algorithms. Not just large businesses but small and medium based businesses too.
In fact, by 2025, the global AI market is expected to be almost$126 billion, now that’s huge.
There has been pressure on businesses to adopt AI solutions to get ahead. With a plethora of articles proving why it’s important to integrate AI in business practices. Because AI has proved beneficial to the successful running of businesses.
An Accenture report revealed that AI can increase business productivity by 40% and boost profitability by 38%.
However, we can’t be blind to the challenges adopting AI has posed for businesses. These challenges make the idea of the successful integration of AI seem far fetched or even unattainable.
An Alegion survey reported that nearly 8 out of 10 enterprise organizations currently engaged in AI and ML projects have stalled.
The same study also revealed that 81% of the respondents admit the process of training AI with data is more difficult than they expected.
This has shown that the expectations for businesses adopting AI might be different from reality.
Below are the top 7 challenges businesses face in the journey of AI implementation.
I bet you saw that one coming since AI feeds heavily on data.
However, there’s a lot that can go wrong with the required data for AI. Factors like the volume of data, collection of data, labeling of data, and accuracy of data come to play.
Because, for successful AI solutions, both the quality and quantity of data matters. AI needs vast amounts of data for optimum performance, and a refined dataset to arrive at accurate predictions.
According to a 2019 report by O’Reilly, the issue of data was the second-highest percentage in ranking on obstacles in AI adoption.
AI models can only perform to the standard of the data provided, they can’t go beyond what they have been fed.
There are different data challenges that businesses face, let’s begin with the volume of data.
The amount of data required by AI to make intelligent decisions is beyond comprehension.
Undoubtedly, businesses now generate more data compared to before, but the question arises, do businesses have enough data to feed AI?
Businesses don’t have enough data to satisfy AI, especially when there are limitations in data collection due to privacy and security concerns.
The same Allegion report revealed that 51% of the respondents said they didn’t have enough data.
This challenges the data infrastructure of most businesses. Businesses now need to generate more data than usual
To fix this, companies should ask: Is their present volume of data enough for the AI model? How can they generate more data?
Businesses need to know their current data acquisition and ways to acquire more data to match their AI model requirements.
Businesses can acquire more data through the use of external data sources like Knoemawhich provides 100 million time-series datasets. Also, the use of carefully created synthetic data is helpful.
Evaluating the current volume of data a business generates in comparison to what AI needs would open doors for data expansion ideas.
There are quite a number of issues that come with the collection of data.
Issues like inaccurate answers, insufficient representatives, biased views, loopholes, and ambiguity in data are major factors that affect AI’s decisions.
For example, theAI bias controversythat has sparked a grave concern.
Gartner predicted that 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, the teams managing them, etc.
