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.
1. Data Challenges
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.
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 Knoema which 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.
Collection of Data
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, the AI bias controversy that 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.
There has been an outcry of AI being biased against women, people of color, etc. However, AI is not a conscious being and can’t form opinions, it only acts based on the data available.
Therefore, this is the fault of humans, because data is provided by people, and people can be biased and stereotypical.
This usually occurs due to the mode of data collection, data collected can’t represent everyone.
This limits the wealth of data AI has at its disposal, leading to inaccurate decisions.
ML models require error-free datasets to provide accurate predictions for successful AI solutions.
Businesses have to employ efficient techniques and processes for collecting data.
Labeling of Data
AI relies on ML’s supervised learning to arrive at conclusions. Therefore, data needs to be labeled, categorized, and correct to use AI models.
AI’s data requirements make it difficult to efficiently label data, 96% of enterprises (insidebigdatadotcom) have run into problems with data labeling required to train AI.
The use of web-based data labeling tools can be employed. For example, the Computer Vision Annotation Tool (CVAT), which helps in annotating images and videos.
2. Transparency Challenges
In simplest terms, how does AI work? It arrives at conclusions and makes predictions with the data provided through the help of ML’s algorithms.
Sounds simple right? Well, that’s not all.
For complicated AI decisions, corporations will begin to experience the black box problem, this is where the picture gets blurry.
The black box model is not clear on how it arrived at a certain conclusion, this leads to distrust and doubts about AI’s accuracy.
Because of the validity of the prediction or current suggestion is questioned.
The rationale behind AI’s decisions needs to be transparent in order to build trust with businesses.
- That’s why they need for explainable AI continues to grow as this makes adopting AI challenging for businesses
and has to be given more attention.
Although, the LIME (local interpretable model-agnostic explanations) approach has been helpful towards solving this problem.
3. Workforce Reception Challenges
The non-technical workforce can find AI integration intimidating since its usage requires advanced training.
So seamless usage and normalcy of AI in the workplace is a difficult goal to achieve.
AI’s adoption can pose a state of confusion amongst employees. Questions like what is the need for AI? How to use this technology? Which of their responsibilities is the AI going to take over? arises.
Despite numerous insights on how AI is not the enemy and not here to replace people, the role of AI remains misunderstood.
The instant a business adopts AI, employees feel threatened and incompetent.
Employees begin to feel a sudden pressure to prove their relevance. They will feel like they are in constant competition with a machine, this negatively affects the workplace vibes.
Educating employees on what AI adoption means for the business and them overall, will help in preventing false assumptions or unrest amongst staff.
4. Expertise Scarcity Challenges
Expertise scarcity is a major challenge in adopting AI for businesses. Also, it’s hard to hire the right people since most adopters don’t know the technicality that involves AI.
According to Deloitte’s global study of AI early adopters, 68 percent report a moderate-to-extreme AI skills gap.
AI is a growing and evolving technology, keeping up with its complexities and needs is a major problem for aspiring adopters.
The scarcity of AI’s skill set is one that hinders a successful business adoption of AI solutions.
A survey by Gartner revealed the biggest challenge in AI adoption to be a lack of skills
According to Deloitte, by 2024, the US is projected to face a shortage of 250,000 data scientists, based on current supply and demand.
A prerequisite of a successful AI adoption is the use of Data Scientists.
However, hiring one is a challenge, except a business decides to outsource its AI projects.
Also, businesses can use AI platforms with no requirement for a data scientist, else they will need to carefully and cautiously invest in a data scientist.
One of the solutions to this problem is education, educating the technical team will pave the opportunity to have citizen data scientists.
Businesses have to prioritize educating themselves of this technological industry if at all they desire a successful AI adoption.
5. Expectations vs Reality Challenges
There’s a lot of hype about the possibilities AI poses for businesses. When business owners consume the vast information out there containing the promises of AI, their expectations go beyond reality.
They forget that AI is a journey, not a destination. This makes businesses ignorant about the challenges that come with adopting AI.
The confusion then sets in on what AI solutions their business actually needs, it’s important to know that AI is still growing and it’s not here to do everything for your business.
Unfortunately, many businesses jump into the bandwagon of adopting AI with no blueprint on what they need AI for.
Also, how prepared are they to implement AI in their activities?
An AI business strategy should include which AI possibilities align with its current business goals, and preparing the business to adopt AI.
Factors like the current capacity and expertise of business technology and data infrastructure are paramount to successfully house AI models.
If this part of a business is weak and lacks the necessary efficiency, their reality will not match their expectations.
6. Business Use Case Challenges
Prioritizing the area of AI application in the business is one of the common challenges whilst adopting AI.
AI solutions are vast, however, businesses find it hard to prioritize or select the most important problem to get started with and see ROI.
A survey by Gartner revealed that AI was mostly used either to boost the customer experience or to fight fraud.
In the bid to play it safe and experiment, businesses limit AI to a small part of the business that brings very little impact to the business revenue. This leads to the inability to see the ROI of AI in business.
A report by RELX revealed that 30% of the respondents cite an unproven return on investment (ROI) in AI adoption.
Because adopting the solutions of AI and Machine Learning is a serious investment, and one with great expectations of a high level of ROI.
According to IDC, the top AI use cases based on the 2019 market share were automated customer service agents, sales process automation, and automated threat intelligence and prevention systems.
7. Budget Constraints Challenges
Not all businesses have the resources to invest in AI models.
According to a report by Harvard Business Review, 40% of executives say an obstacle to AI initiatives is that technologies and expertise are too expensive.
The same RELX report also disclosed that 50% of companies that have not yet adopted AI cite budget constraints as the primary reason.
Small business enterprises can tap into free and paid simple AI solutions. Large businesses that want to create tailor-made solutions to fit their business use cases,
But for businesses looking to create tailor-made solutions to fit their business use cases, they are bound to experience budget constraints
One of the solutions to managing AI budget issues is to outsource AI projects than carrying it out in the house.
Also, enterprise software vendors and cloud providers provide ready to go AI services to curb Infrastructural costs.
Adopting AI is challenging for businesses but definitely worth the effort because AI is here to stay.
These challenges will cease to become obstacles as AI becomes normalized and prioritized over time.
AI promises and possibilities can be exciting and distracting altogether. So don’t get too excited that you don’t create a clearly defined path to accomplish those solutions.
Before investing time and money in AI, it’s important to make your business ready in every possible way to work with AI.
Preparing your business for the change and disruption AI is about to bring is crucial.
We are habitual beings, breaking employees out of their work routines to adopt AI is a challenge, hence the need for a planned strategy.
Having a deep and healthy understanding of what AI means for your business is a good sign of your readiness to adopt AI.
Finally, applying AI in the core parts of your business will help to track, and measure the ROI of AI implementation to give you a clear picture of AI contributions to your business.