Insights from 10,000 analysts, IT specialists and executives – all about AI. If you will not be aware of the topic of artificial intelligence and wish to grasp what’s what from the experience of real firms, make sure you read this text!
Artificial intelligence has already shown that it could possibly do many useful things and simplify the work of an organization, especially in areas like AI in marketing. But it has barriers which have to this point prevented AI from taking on the world and firms from introducing it into processes. We analyze these barriers with our WGG Agency and let you know what to do.
The Main Problem: Generative AI Needs a Diet
Get a solution to any request, invent a law and analyze the market: all this is just not enough for AI to work for business.
After the boom in artificial intelligence, researchers realized that it is just not enough to take a position in the technical capabilities of AI in the hope that it should change and optimize work. Verified data has develop into more essential. Companies wish to instill in AI the value of fact-checking: after that, every little thing will change.
“Companies are adopting AI so quickly that data reliability is becoming increasingly beneficial. To instill this value in AI, it is advisable instill it in the data that feeds it. Imagine that artificial intelligence has a eating regimen: it could possibly eat fast food, or perhaps it could possibly eat proven products. Simply put, AI will give a business real profit only when it’s fueled by accurate data. Our analytics show the urgent need for reliable information now greater than ever.” – Wendy Batchelder, Chief Data Officer at Salesforce
But it’s not nearly data reliability: there are other issues which can be holding firms back. We speak about them below.
6 More Reasons Why AI is Difficult to Implement
Companies’ IT infrastructure is just not ready for AI
Company databases and their technical structure will not be yet ready for artificial intelligence. There are still few tools inside the infrastructure that may easily be synchronized with AI: just because AI is a latest thing, and when the infrastructure was created, it didn’t have the task of working with artificial intelligence.
There isn’t any unified data system
If you continue to have all of your information stored in dozens of tables, documents and applications, there may be reason to take into consideration some kind of unified platform or well-thought-out storage system.
Without an organized data system, AI is not going to produce results.
Data inaccuracy
Artificial intelligence is proscribed to company and open-source data, so it could not provide the full picture or use unreliable information.
The sales and repair departments are least confident in the accuracy of the data, and the analytics departments are the most confident.
Ethical issues
Firstly, AI doesn’t at all times make decisions based on the value of human life, although sometimes it could possibly be set as a condition.
Secondly, AI works based on data from the Internet, and it’s full of unethical stereotypes. For example, when trying to find “doctor,” men usually tend to appear, “teacher” is a girl, “woman” is a housewife, etc.
It seems that AI is biased upfront because it really works based on data from the Internet, and it incorporates stereotypes and biases. This is known as AI bias.
No system data collection and data strategy
41% of leaders say their data strategy is barely partially aligned with their goals or in no way. This means there isn’t a coherent analytics of user and market data. And without this, it’s difficult to implement AI: it simply can have nothing to investigate.
Only 32% of executives and analysts measure and study the value of data monetization.
Security Threats
78% of analysts, executives and IT leaders say they’ve difficulty achieving business goals resulting from data problems, including data security.
Firstly, precedents are already emerging where AI illegally analyzes book materials, for instance. Although the authors didn’t give consent to this.
Secondly, there isn’t a clarity: what’s going to occur to the data loaded into the AI. It is unclear whether they’ll develop into part of AI knowledge or not. And there could also be confidential details about each users and the company.
This leads to a conflict: you possibly can implement AI and achieve goals with its help, but this threatens the security of the company and users.
How to Implement AI and Solve the Problems Above: 4 Tips
Tip №1: Invest in proven AI information to get reliable conclusions at the output
79% of analysts and executives plan to take a position in data visualization and AI, 75% in training and development of artificial intelligence using verified data.
To receive verified information for loading into AI, spend money on analytics: outsourced or inhouse.
Tip №2: Change your approach to information management to scale back data gravity
We already wrote above that and not using a unified data system it’s difficult to implement AI. Therefore, managers organize information in order that it is less complicated to make use of, not only store.
For example, 85% of analysts and IT managers manage data to manage and validate the quality of information. If this is just not done, the AI will begin to eat low-quality data and produce incorrect results.
It seems that AI is an incentive to bring order to how an organization organizes databases and the way it uses them.
More mature firms (those where data is managed systematically and measured at every stage) usually tend to see the advantages of AI in democratizing access to data, for instance.
Data gravity occurs when information inside an organization is scattered across different systems or in places where it’s difficult to export, mix, and analyze.
To combat gravity, executives and analysts are managing data using different approaches and increasingly counting on hybrid or on-premise solutions.
Therefore, 75% of analysts and IT firms have already launched the migration of data warehouses and started to transfer databases to latest platforms.
Tip №3: Look for brand spanking new platforms and business solutions for data storage and evaluation to implement AI
96% of executives and analysts say AI and powerful databases speed up decision making.
The predominant criteria for brand spanking new platforms and databases are cloud storage, AI capabilities, speed and ease of hosting latest data, easy usability for users and compatibility with the current technical stack.
Tip №4: Look for processes where AI will likely be useful, slightly than implementing it simply to implement it
With the hype of news about AI, you possibly can go crazy and connect it to all processes in a row in order to not miss the opportunities of the latest era. And this could be a mistake – not all processes need AI, it doesn’t produce results all over the place and it should not simplify work all over the place.
Look at the company’s work soberly and analyze processes to search out points of application of AI before implementing it.
The same thing, but 5 times shorter
Conclusions from the study. This is what prevents the adequate implementation of AI in an organization’s work:
- IT infrastructure is just not ready for AI. Data is difficult to investigate and upload to AI, and for those who do every little thing manually, you’ll waste so much of time.
- There isn’t any single data system. When different departments work on five platforms directly, and nobody really knows where to search out some information – in Google Doc, Miro or telegram.
- The data is inaccurate , unverified, or non-existent. This is what firms with low data maturity are called: when data is just not collected and analyzed at every stage
- Ethics. Artificial intelligence is biased since it uses information from the Internet. And there are stereotypes and unverified data.
- The business has goals , there may be a desire to implement AI, but there isn’t a organized strategy for data collection and analytics. Or there may be nothing in any respect. As a result, AI simply has nothing to investigate.
- Safety. Firstly, it is just not yet clear whether it’s legal to make use of all the information that AI provides. Secondly, it’s unclear: what’s going to occur to the data that you just upload to AI for processing.
And tips about the right way to overcome the problems above and introduce AI into the work of the company:
- Invest in reliable data and analytics, either outsourced or inhouse, in order that AI produces correct output results.
- Change the approach to data management and reduce its gravity. Use hybrid data storage solutions to make it easier to export, store and use.
- Look for platforms and business solutions that will likely be easy to hook up with and synchronize with AI.
- To search for processes where AI will likely be really useful, and never to implement it similar to that, in fear of falling behind civilization.
Thank you for rigorously reading our work. We sincerely hope that this information will make it easier to with the productive use of the AI system for your corporation.
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