The Challenges to Tackle Before You Start With AI
Artificial Intelligence and the technology behind it are growing at a furious pace. Marketers have realized its vast potential and are striving to extract the technology’s opportunities in full. There are numerous advancements being made in this regard, and many organizations have taken center stage of the AI world with in depth data analysis and data discovery solutions.
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Potential of AI
The potential of Artificial Intelligence is obvious. Adobe has estimated that they can expect 31 percent of all organizations will start using AI over the coming 12 months. This statistic is backed up by the fact that there are more startups than ever focusing their operations on AI and the services that it provides to the masses.
Roughly 61 percent of organizations that follow innovative strategies have turned to AI for taking extracts from the data that they previously might have missed. Innovation is an earmark of Artificial Intelligence, and startup ideas that believe in this ideology can seldom live without the oxygen that is AI.
Not only are marketers confident about AI, but consumers are also starting to grasp its vast potential. About 38 percent of consumers are beginning to believe that AI will improve customer service. With this growing awareness and popularity, we can expect these numbers to increase further down the line.
Challenges before you Start with AI
Organizations are finding it hard to find their footing under the four V’s of big data; Volume, Variety, Veracity and Velocity. Over 38 percent of the analytics and data decision makers from the market reported that their unstructured, semi structured and structured data pools made an increase of 1,000 TB in the year 2017.
The growth of data is increasing rapidly, as are the initiatives organizations are taking to extract value from it. Herein lie numerous challenges that organizations must overcome to extract full value from AI.
These complications are:
Getting Quality Data
Your inference tools would only be as good as the data you have with you. If the data that you’re feeding your machines isn’t structured and flawless, the inference gained from it would barely make the cut for your organization. Thus, the first step of the process is to have quality data.
Without the presence of trust in the quality of the data, they wouldn’t proceed with their AI initiative. This demonstrates the importance of data quality in AI, and how it changes the perspective of stakeholders involved.
The pareto concept applies here, as data scientists are bound to spend almost 80 percent of their time making data ready for analysis and then the remaining 20 percent for performing analysis on the prepared data. The creation of these data sets for the ultimate analysis is key to the overall success of the program, which is why scientists have to allot their time.
The 80/20 phenomenon has been noted by many analysts online, who believe that 80 percent of a data scientist’s valuable time is spent finding, reorganizing and cleaning up huge amounts of data.
Getting the Best Talent
Once you have quality data you need to understand the importance of recruiting and retaining the best talent in the industry. Since AI is relatively new, the labor market hasn’t matured yet. Thus, you have to be patient in your search for the right talent.
Two thirds of the current AI decision makers present within the market struggle with acquiring the right AI talent for their firm. With hiring done, 83 percent of these companies struggle with retaining their prized employees. The talent shortage obviously goes beyond all technical flaws, as firms need a wide range of expertise to handle AI systems. What is understood here is that traditional recruitment practices are barely implementable, and that organizations need to look for other options.
Access to Data
With the increasing rate of data regulations on the horizon, any organization can easily end up on the wrong side of the law if proper measures are not taken. The GDPR or the General Data Protection Regulation by the European Union is one of the most advanced and up-to-date privacy policies for data at the state level. Complying with such policies is mandatory, as non-compliance can leave you in a dire situation.
Trust and Data Transparency
There is a trust deficit and the market for AI and analytics isn’t showing any signs of decreasing over time. While the market has increased and progressed by leaps and bounds, this trust deficit still stands as it is and isn’t showing any signs of decreasing.
Strategies You Can Follow to Start with AI
With the complications mentioned above, we most definitely will not leave you hanging here. There are certain strategies you can follow for widespread AI implementation. These include:
Create an AI Vision
Organizations that know what to expect from their AI campaign fare better than those that have no idea about this technology and are just getting involved because their competition has. An AI vision can also act as a list of objectives for the future, so you can tally your end goals with what you planned out before.
Build and Manage Customer Journey Centric Teams
The end goal or the mega vision behind AI is to improve customer experience and add value to your offerings. To do this better, you can make customer journey centric teams that follow customers throughout their journey and improve their experience along the way. Your task goes beyond just making a team, as you will also have to monitor their progress moving forward.
Data Accessibility and Culture
While three fourths of all businesses want to be data driven, only around 29 percent can agree that they are good at connecting their analytics and data to actively generate insights. If the data you have isn’t ready for you to get actionable insights, unite your organization around that analysis and make business decisions based on that.
Data accessibility and culture are necessary for your organization because accessible data enables you to focus on business decisions, move on quickly and build an informed culture where data helps you make better decisions and take better actions.
End-to-End AI Lifecycle Management
End-to-end AI lifecycle management relates to the management of data from its extraction to when it is presented in the form of actionable insight. The process entails different stages like the acquisition, storage, dissemination, learning and implementation of the data. By implementing end-to-end management, you can ensure that your data is always in safe hands.
AI is the future for generating actionable insights for your organization. With the correct tools you can get your desired results and overcome the initial complications.
Link: The Challenges to Tackle Before You Start With AI