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The world of business is constantly evolving and each day, some new technology comes out that threatens to make older ones obsolete. For businesses that wish to stay competitive in this environment, they must constantly look towards way to improve themselves and stay at least on pace with all other businesses. This is where the development, and evolution of Artificial intelligence has become a key factor for businesses. AI’s and machine learning have the potential to revolutionize how business processes work, reducing costs to a great extent, while maximizing both efficiency and profits. It is important now, more than ever, for businesses to realize the importance of using AIs and machine learning in business.

The applications of AI in business are extremely varied and it is important to discuss them:

  • Data Visualization and performance tracking:

When question over what course a business should take when launching a new product or service, the data that will be needed by the business will not be confined to just a single database. Here, machine learning will be of great help, allowing decision makers to put forth any questions that they have and receive prompt and detailed answers that can be easily understood.


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Looking at data in a visual form always helps make better decisions. This is because most of the data transmitted to the brain is in a visual form as it is easier to comprehend. So, the more the focus on making data available to all members, the better businesses will get at hitting key performance indicators. Business leaders should focus a portion of their team's energy on getting comfortable with visual data. Give every member of the organization access to the information that they need to self-assess their effectiveness – even if it isn't fully optimized. We are already seeing machine learning platforms evolve that automate critical reporting.

Sisense Pulse, for example, is a platform that simplifies the process of reviewing business intelligence, automating the creation of visual reports and improving the chances that an organization can successfully track and exceed their key metrics. Active monitoring allows the platform to immediately notify important personnel when there is an occurrence of an anomaly in data that either negatively or positively impact key performance indicators.. This helps corporate first responders immediately get to work to solve problems before they become a major issue, or to double down on initiatives that are yielding great returns. These platforms are designed to communicate in a way that they simply plug in to the way current corporate communications work.

  • Marketing:

Machine learning is also a great asset when it comes to marketing and all things related to it. Firstly, it bring the phase real time marketing to life. The response time of machine learning is unlike anything that has ever been seen before. Marketing is routinely changed for different kinds of consumers based on the data that firms have on them. Facebook’s retargeting advertisements is one of the best examples of machine learning being used to develop marketing techniques specific to each customer.

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Machine learning also ensure that there is no waste of effort. It ensures that marketing efforts only reach the people who are likely to be induced by it. Sending it to people who will not have any interest is marketing waste and machine learning ensures this does not happen by constantly monitoring the habits of different consumers. Machine learning also certainly reduces the costs associated with marketing. No longer do businesses have to spend tons of money on market research and surveys and test audiences to get the data that they want. Machine learning is constantly monitoring data online to give firms exactly the information they want, allowing them to optimize their marketing strategy, and thus saving a lot of costs associated with outdated marketing techniques.

  • There is also an exciting, albeit somewhat scare, byproduct of the increased reliance on AI. This happens to be that firms will need less man hours to compile reports and guide decision making. Why spend both excessive time and money on man hours when machine learning can do the same job in much less time. This does happen to be a big reason behind the general public fear of AIs taking over human jobs and making them redundant. This does not happen to be the case. Just as stagecoach manufacturers had to pivot to new industries after the Model T, data-entry personnel will find ways to use their experience to provide value to companies that no longer require as many hours of manual data mining.

It is arguable that most organizations have already steam lined their human resource departments. Every organization's primary goal is to ensure that it operates efficiently and with maximum profitability. The increase an AI use shouldn’t mean that we talk about a loss of a large number of jobs. Instead, the discourse should be about continuing a trend towards tech integration and more importantly, an effective use of human talent.

There's a reason that union membership is falling. Employees are working within corporations that are reaping the rewards of improved employee engagement. Engagement can only be improved by giving employees access to the critical information they need to understand their impact on the organization.

  • Automating finance:

A big advantage of machine learning, especially where finance is concerned is that it can expedite “exception handling” in many financial processes. For example, when a payment without an order number is received, a person must sort out which order the payment corresponds to, and determine what to do with any excess or shortfall. AI allows for a significant increase in the number of invoices that can be automatically matched by constantly monitoring existing processes and recognizing different situations.


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This, in turn, allows organizations to reduce the amount of work that they outsource to service centers and give more space for staff in finance to focus on other strategic tasks

These are just some of the more commonly known and currently used applications of machine learning in the business. As machine learning continues to evolve, the application of it to business process will evolve as well, allowing business to optimize their workings constantly and work in the best way possible.


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