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Challenges of Adopting AI in Businesses

challege adopt AI

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. 

  1. 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. 

Conclusion

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.

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AI and Privacy Line: AI as a Helper and as a Danger

AI and privacy

As AI becomes increasingly adopted in more industries, its users attempt to achieve the delicate balance of making efficient use of its utility while striving to protect the privacy of its customers. A common best practice of AI is to be transparent about its use and how it reaches certain outcomes. However, there is a good and bad side to this transparency. Here is what you should know about the pros and cons of AI transparency, and possible solutions to achieve this difficult balance.

The Benefits

AI increases efficiency, leverages innovation, and streamlines processes. Being transparent about how it works and how it calculates results can lead to several societal and business advantages, including the following:

Increased Justice

The number of uses of AI has continued to expand over the last several years. AI has even extended into the justice system, with AI doing everything from fighting traffic tickets to being considered as a fairer outcome than a jury.

When companies are transparent about their use of AI, they can increase users’ access to justice. People can see how AI gathers key information and reaches certain outcomes. They can have access to greater technology and more information than they would typically have access to without the use of AI.

Avoidance of Discrimination

One of the original drawbacks of AI was the possibility of discriminatory outcomes when the AI was used to detect patterns and make assumptions about users based on the data it gathers.

However, AI has become much more sophisticated today and has even been used to detect discrimination. AI can ensure that all users’ information is included or that their voice is heard. In this regard, AI can be a great equalizer.

Instilled Trust

When AI users are upfront about their use of AI and explain this use to their client base, they are more likely to instill trust. People need to know how companies reach certain results, and being transparent can help bridge the gap between businesses and their customers.

Customers are willing to embrace AI. 62% of consumers surveyed in Salesforce’s State of the Connected Consumer reported that they were open to AI that improved their experiences, and businesses are willing to meet this demand.

72% of executives say that they try to gain customer trust and confidence in their product or service by being transparent about their use of AI, according to a recent Accenture survey. Companies that are able to be transparent about their use of AI and the security measures they have put in place to protect users’ data may be able to benefit from this increased transparency.

More Informed Decision Making

When people know that they are interacting with an AI system instead of being tricked into believing it is a human, they can often adapt their own behavior to get the information they need.

For example, people may use keywords in a chat box instead of completed sentences. Users may have a better understanding of the benefits and limitations of these systems and make a conscious decision to interact with the AI system.

The Drawbacks

AI

While transparency can bring about some of the positive outcomes discussed above, it also has several drawbacks, including the following:

Lack of Privacy

A significant argument against AI and its transparency is the potential lack of privacy. AI often gathers big data and uses a unique algorithm to assign a value to this data.

However, to obtain results, AI often tracks every online activity, (you can get free background checks), AI tracks keystrokes, search, and use of the business’ website. Some of this information may also be sold to third parties.

Additionally, AI is often used to track people’s online behavior, from which they may be able to discern critical information about a person, including his or her:

  • Race or ethnicity
  • Political beliefs
  • Religious affiliations
  • Gender
  • Sexual orientation
  • Health conditions

Even when people choose not to give anyone online this sensitive information, they may still experience its loss due to AI capabilities.

Additionally, AI may track publicly available information. However, when there is not a human to check the accuracy of this information, one person’s information may be confused with another’s.

Hacked Explanations

When companies publish their explanations of AI, hackers may use this information to manipulate the system. For example, hackers may be able to make slight changes to the code or input to achieve an inaccurate outcome.

In this way, hackers use a company’s own transparency against it.

When hackers understand the reasoning behind AI, they may be able to influence the algorithm. This type of technology is not typically encouraged to detect fraud. Therefore, the system may be easier to manipulate when stakeholders do not put additional safeguards in place.

Intellectual Property Theft

Another potential problem that may arise when a company is transparent about its use of AI is the possibility that its proprietary trade secrets or intellectual property are stolen by these hackers. These individuals may be able to look at a company’s explanations and recreate the proprietary algorithm, to the detriment of the business.

Vulnerability to Attacks

With so much information readily available online, 78 million Americans say they are concerned about cybersecurity. When companies spell out how they use AI, this may make it easier for hackers to access consumers’ information or create a data breach which can lead to identity theft, such as the notorious Equifax data breach that compromised 148 million Americans’ private records.

Susceptibility to Regulation

Disclosures about AI may bring about additional risks, such as more stringent regulation. When AI is confusing and inaccessible, regulators may not understand it or be able to regulate it. However, when businesses are transparent about the role of AI, this may bring about a more significant regulatory framework about AI and how it can be used. In this manner, innovators may be punished for their innovation.

Easier Target for Litigation

When businesses are clear about how they are protecting consumers’ data in the interest of being transparent, they may unwittingly make themselves more vulnerable to legal claims by consumers who allege that their information was not used properly. Clever lawyers can carefully review AI transparency information and then develop creative legal theories about the business’ use of AI.

They may focus on what the business did not do to protect a consumer’s privacy, for example. They may then use this information to allege the business was negligent in its actions or omissions.

Additionally, many AI systems operate from a simpler model. Companies that are transparent about their algorithms may use less sophisticated algorithms that may omit certain information or cause errors in certain situations.

Experienced lawyers may be able to identify additional problems that the AI causes to substantiate their legal claims against the business.

The Truth About AI and Privacy

DATA

Anyone who has seen a Terminator movie – or basically any apocalyptic movie – knows that even technology that was developed only for the noblest of reasons can potentially be weaponized or used as something that ultimately damages society.

Due to the potential for harm, many laws have already been passed that require certain companies to be transparent about their use of AI. For example, financial service companies are required to disclose major factors they use in determining a person’s creditworthiness and why they make an adverse action in a lending decision.

Lawmakers are actively proposing and considering additional laws.

If passed, these laws may establish new obligations that businesses must adhere to regarding how they collect information, how they use AI, and whether they will first need to express consent from a consumer.

In 2019, an executive order was signed into law that directs federal agencies to devote resources to the development and maintenance of AI and calls for guidelines and standards that would allow federal agencies to regulate AI technology in a way that would protect the privacy and national security.

Even if a business is not yet required to be transparent about its use of AI, the time may soon come when it does not have a choice in the matter. In response to this likely outcome, some businesses are being proactive and establishing internal review boards that test the AI and identify ethical issues surrounding it.

They may also collaborate with their legal department and developers to create solutions to problems they identify. By carefully assessing their potential risk and establishing solutions to problems before disclosure becomes mandatory, businesses may be better situated to avoid the risks associated with AI transparency.

Image Credit: cottonbro; Pexels

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