According to a recent survey from Demandbase, 80 percent of executives predict that Artificial Intelligence (AI) will revolutionize marketing by 2020. Several organizations are already making the necessary moves to ensure that its AI strategies align with their existing as well as future objectives, data and security. As a result, it becomes mandatory for employees to stay updated with AI concepts and implement it in their work.
Here are some basic steps to learn and implement AI at work. Take a look at the following pointers to get a better perspective:
It comes as no surprise that coders are ready to embrace the change by learning how artificial intelligence tries to replicate human intelligence. For enterprises, training their in-house developers to understand AI can turn out to be smart move if they want to be one up their competition.
That said, you can select from an array of resources and get introduced to the latest AI concepts. These include, but are not limited to, remote workshops and online courses such as Udacity’s Intro to AI, edX’s online AI course, AI Access Foundation’s open-source code directory at AIResources.org, and the mailing list for Open.ai.
2. Give Importance to Data Governance
On average, we create about 2.5 quintillion bytes of data daily. This is equivalent to over 90 years of HD video! Moreover, according to IDC, there will be 44 zettabytes of data by 2020.
With that kind of data, you can’t move forward without AI if you’re aiming better data governance.
For the uninitiated, poor data governance can lead to –
Several data-related risks
In the long term, these can damage a company’s hard-earned reputation.
Also, let’s not forget that data plays an important role in different industries such as healthcare enterprises, which have to deal with enormous amounts of data and technology regularly. Keeping track of this huge database and understanding it can be overwhelming without the intervention of AI.
Along with data governance, it provides a framework for automating and managing data and its context in the healthcare ecosystem. It also helps healthcare organizations by removing administrative burdens, tracking incremental changes, and improving analytics along with data-driven decision-making processes.
3. Reduce Workload
The mainstays of AI are that it redefines roles in an organization and reduces the workload and can be implemented in several ways.
For instance, in 2014, the Associated Press (AP) announced that it used automation technology to produce business news report. The idea was to use technology and get journalists to focus more on investigative and interpretive journalism instead of writing more reports.
In 2016, it reported that it’ll cover Minor League Baseball games nationally using AI and software from Automated Insights and data procured from MLB Advanced Media.
In 2017, as AI took some more steps ahead, AP is using its automated journalists’ ability to craft stories, which are at par with stories written by a human.
These point to the tremendous potential that AI has in store for the future; and as exemplified by AP, it is not just about ‘cutting the workforce’, but about ‘cutting the workload.’ It hints at the fact that project management with AI is no longer a distant dream and can be easily applied in various departments in organizations.
The catch is that AI tools will still rely on human to fill the gaps. They still need a supervisor who can overcome the limitations of machine learning as AI only tries to replicate human intelligence, but can’t completely replace it.
Ergo, there has to be trained project managers who’s going to take the responsibility of the entire process.
4. Forecast Sales with Predictive Analytics
Predictive analytics is a term that’s often used interchangeably with AI. It has the power to transform sales for enterprises with effective forecasting with the help of cloud-based technologies and machine learning algorithms. Overall, it helps firms deliver contextual sales to their customers and drive performance.
The American Express Company is known for applying big data analytics to forecast its clients’ behavior. It makes sense because a company that has a database of more than a hundred million credit cards globally will need AI and machine learning to put the data to good use, like it did in 2010 when it started leveraging big data for delivering value to customers and coming up with innovative products.
Likewise, as a sales person, you can put AI to good use such as for working on the leads for a more efficient follow-up process and business analysis. It saves hours on data-crunching and helps you aim for faster outreach to the best prospects.
You can also use it to explore the best opportunities and even recommend the right product to your target audience. This, in turn, leads to improved results each month.
5. Boost Security
With the advent of sophisticated machines and technology, cybercrime is also growing at breakneck speed. The recent ransomware attack that set a panic wave across nations is an apt example of why robust security mechanisms can never be an afterthought for any enterprise.
It helps firms manage their security flaws through constant learning from the data and insights, filtering through enormous piles of data, detecting threats, and offering instantaneous solution to the threats. Surely, this is beyond human capability!
A case in point is that of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx, which has an artificial intelligence platform called AI2 to its credit. The firm claims that it can predict cyber-attacks better by getting human experts to provide continuous inputs.
You’ll need similar platforms or develop your own AI solutions to avoid the security breaches in your organization in the future.
These are some of the ways in which you can step up your AI game without breaking a sweat. What do you think of them? Have you implemented them already in your organization? What were the pitfalls you had to face? Let us know in the comments.