Artificial Intelligence Opportunities & Challenges in Businesses | by Robert Adixon | Towards Data Science

● Looking for opportunities and challenges that businesses face with implementing AI?
● Confused about whether to start AI based development or not in your business?
● How is AI imapacting your business growth and profit?
Here, in this blog, you will find answers to all such questions. Let’s start!
Artificial Intelligence is a technology for which even a layman is curious about! The reason lies in the strong tendency that it has to disrupt every aspect of life. Artificial intelligence seems to radiate enthusiasm and skepticism collectively. Obviously, in different measures!
Before that let’s look at some stats and facts of AI:
● AI technology can increase business productivity by up to 40 percent (Accenture)
● The number of AI startups since 2000 has magnified to 14 times. (Forbes)
● AI will automate 16% of American jobs (Forrester)
● 15 percent of enterprises are using AI, and 31 percent of them say that it is their agenda for the next 12 months (Adobe)
It is quite true that AI is no more confined to innovation labs only. It is being praised for its amazing potential to transform businesses. However, businesses need to tackle certain challenges before they can find out the real potential of this technology.
1. Computing is not that Advanced
Machine Learning and deep learning techniques that seem most beneficial require a series of calculations to made very quickly ( in microseconds or nanoseconds or slower than that! ).
It clearly indicates that these AI techniques utilize a lot of processing power.
AI has been in the expert discussion for a long time. And always it came out that there is not enough power to implement these AI techniques.
Cloud computing and massively parallel processing systems have created hope to implementation of these techniques for short term, but as data volumes go up, and deep learning moves towards automated creation of increasingly complex algorithms, cloud computing would not help!
2. Fewer people support
AI implementation does not have enough use cases in the market. And without it, no organization would be interested to invest money in AI-based projects. It clearly means that there have been comparatively few organizations interested in putting money into the development of AI-based products.
Moreover, there are not enough people who can make other businesses understand the vision of machine powered progress in the world. In simple words, we can say that there are not enough people who know how to operate machines which think and learn by themselves.
For a remedy to this problem, a mild cure is a citizen data scientist. But this is also not a permanent or real solution. Another alternative is a shift towards offering platforms and tools that permit AI driven work “as a service”. Rather than starting everything from scratch, organizations are able to take ready-made solutions and plug in their own data.
3. Creating Trust
The problem with AI is that it is like a black box for people. People don’t feel comfortable when they don’t understand how the decision was made. For instance, banks use simple algorithms that are based on linear maths and it is easy to explain the algorithm and how they reached from input to output.
Hence, somewhere AI has not been able to create trust among people. And the only solution that seems to this problem is to let people see that this technology really works. However, the reality is somewhat different. And it shows that there is a lot of opportunities to make things better by having predictions that are more accurate.
This raises problems of government overstep. Suppose, a part of the regulation tells that citizens may have the right to ask an explanation for decisions that are made about them with the help of Artificial Intelligence.
4. One Track Minds
A big problem that should be taken into account is that most of the AI implementations are highly specialized. We also call specialized AI as “Applied AI”. And it is built just to perform a single task and keep learning to become better and better at it.
The process that it follows is to look at the inputs given and results produced. It looks at the best result produced and notes down those input values. Generalized AI is different and can jump to any task similar to a human being. However, it is yet to come in the future.
It simply means that AIs need to be trained just to make sure that their solutions do not cause other issues. Specifically, all those areas that are beyond those which they designed to consider.
5. Provability
Organizations working on AI-based products cannot demonstrate clearly about their vision and what they have achieved with the help of AI techniques. People are doubtful about this technology that how it takes decisions and whether all its decisions are perfect or not!
Moreover, such kind of confusion has surrounded the minds of people. And ultimately, a probability which is the mathematical uncertainty behind AI predictions still remain as an unclear region for organizations.
They cannot prove that the AI system’s decision-making process is fine. And its only remedy can lie in making AI explainable, provable and transparent. Organizations should implement explainable AI.
6. Data Privacy and security
Most of the AI applications are based on massive volumes of data to learn and make intelligent decisions. Machine learning systems depend on the data which is often sensitive and personal in nature.
These systems learn from the data and improve themselves. Due to this systematic learning, these ML systems can become prone to data breach and identity theft. European Union has implemented the General Data Protection Regulation (GDPR) that makes sure the complete protection of personal data.
This step is taken after taking a look at increasing awareness in customers regarding an increasing number of machine-made decisions. Moreover, there is a unique method known as Federated learning that is aimed to disrupt the AI paradigm.
This Federated learning will encourage data scientists to create AI without affecting users’ data security and confidentiality.
7. Algorithm bias
A big problem with AI systems is that their level of goodness or badness depends on the much data they are trained on. Bad data is often associated with, ethnic, communal, gender or racial biases.
Proprietary algorithms are used to find out things like who granted bail, whose loan is sanctioned etc. If in case, a bias hidden in the algorithms which take crucial decisions goes unrecognized, it could lead to unethical and unfair results.
In future, such biases will be more highlighted as many AI systems will continue to be trained to utilize bad data. Hence, the urgent need in front of organizations working on AI is to train these systems with unbiased data and create algorithms that can be easily explained.
8. Data Scarcity
It is the fact that organizations have access to more data in the present time than ever before. However, datasets that are applicable to AI applications to learn are really rare. However, the most powerful AI machines are those that are trained on supervised learning.
This kind of training requires labeled data. Labeled data is organized to make it understandable for machines to learn. One more thing about labeled data is that it has a limit. In future automated creation of increasingly difficult algorithms will only worsen the problem.
Still, there is a ray of hope. As time is passing, organizations are investing in design methodologies and focusing on how to create AI models learn despite the scarcity of labeled data.
Here is a youtube video that you can check in order to get a quick introduction to several risks of artificial intelligence in business. This YouTube video explains ten possible risks of artificial intelligence:
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Yes, there are risks and challenges that are associated with AI implementation in Business. But, just like the two different faces of a coin, AI also has several opportunities for businesses. Due to opportunities associated with AI, many businesses hire dedicated Indian developers to have their own AI based apps. Let’s have a look at them one by one.
It is the dream of every small business to maximize its marketing budgets and focus on high achieving marketing strategies. Moreover, every business wants to learn about which marketing activities deliver the highest return on investment.
But it takes a lot of time to monitor and analyze data across all the media channels. Here, the role of AI marketing solutions comes in!
AI enabled platforms such as Acquisio can easily help in managing marketing operations across various channels like Google Adwords, Facebook, and Bing.
This machine learning enabled layer analyzes live campaign data with the help of sentiment analysis algorithms and suggests a distribution of marketing activities which results in the best results.
It automates regular bids and monitors overall marketing spend so that business owners can reduce the time spent on tracking marketing campaigns and pay attention to other important areas.
It is always crucial to keep track of what your competitors are doing. Unfortunately, most of the business owners are not able to review the competition due to busy schedules. Here, the role of AI comes in.
There are various competitive analysis tools like Crayon. They track competitors with the help of different channels like websites, social media, and apps. Moreover, they provide business owners with a close look into any changes in competitors’ marketing planning like price changes, subtle message modifications, and PR activities.
It is not a big surprise that small business owners are willing to take advantage of large amounts of online as well as offline information to make informed, data-driven decisions that will make their business grow.
The most interesting thing about AI-powered tools for business is that they can be fitted in every data producing workflow and provide close insights that are quite applicable as well as actionable.
AI business tools like Monkey Learn integrate and analyze data across various channels and achieve timesaving analytics and reporting like sentiment analysis in Google Sheets, CSV, etc.
Automated chat systems permit small businesses to scale their customer service efforts and free up resources needed for more difficult customer interactions.
AI customer service solutions like DigitalGenius or ChattyPeople suggest or automate answers to incoming customer questions, classify help tickets and direct inquiries or messages to the appropriate department.
When you use AI in customer support, a big reduction in average handling time is seen. Moreover, it enhances the overall responsiveness of your customer service team.
How would you feel if you find a way to take your CRM to the next level and to get valuable insights that can help managing interactions with present and prospective clients!
CRM platforms that are embedded with AI functionality can do real-time data analysis in order to provide predictions as well as recommendations based on your company’s unique business processes and customer data.
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Conclusion
Hence, we found that AI’s time may have finally come, but more progress is required. And the adoption of AI is uneven across various companies and sectors.
Moreover, we looked at all the opportunities and challenges that businesses face while implementing AI. Now, I hope you are clear on whether you should go for Artificial Intelligence (AI) development in your company.
In case you wish to create AI-based applications, you can contact a reputed web development company that turns your dream come true. There are numerous firms that offer AI-based software to make customer service sector more efficient.