Category: General

 
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How to Advertise Your Website to Your Target Market

Building a website is only the first step in your digital marketing process. Next challenge is to get people to visit your website. Luckily, there are many different online advertising platforms you can use to speed up this process.

Online advertising is a multi-billion dollar industry. According to Statista, the global digital advertising expenditure will amount to over 335 billion by 2020. However, not many marketers succeed in developing effective and efficient digital ad campaigns.

Improve your email marketing

Most marketers fail to generate better ROI (Return on Investment) because they lack the necessary data to properly target the right audience for the ads. Don’t make the same mistake.

If you’re thinking about promoting your website with digital advertising, do it properly. Begin your online advertising campaigns by first conducting marketing research and then follow these steps.

Do your market research
You shouldn’t blindly create ad campaigns targeting random people on the internet. Before you create the ad, you should learn more about your target audience and find out which platforms they spend their most time on. This process is called market research.

There are several ways you can conduct a market research. You can create a survey and ask questions directly from your audience. You can spy on your competitors to find where their website visitors come from and what kind of content they produce to generate traffic. And you can track your own website visitors to study their behavior as well.

Create buyer personas
Once you learn more about your audience and where they are at, you can then use the data you’ve gathered to identify your ideal customer. It’s also known as creating buyer personas.

A buyer persona is a complete profile of your perfect customer that you create to help craft better content, a better marketing strategy, and create more effective ad campaigns.

For example, if your business is related to dog food, your buyer persona should include all the information related to the ideal dog owner, including their age, gender, employment, location, etc. This will allow you to figure out if they would be interested in your product or not.

Create your buyer persona as detailed as possible to target the right people through your ad campaigns. Create surveys, conduct tests, use Twitter analytics and Twitter lists, and use data from Facebook Insights to learn the right information about your customers and create an effective buyer persona.

Which advertising platform is best for you?
Now that you have a buyer persona, you should now be well-educated on your target audience and what kind of people to target with your ads.

You now need to pick a platform to create your ad campaigns based on your buyer persona to target your ideal customers. Whether they’re business professionals who mostly use LinkedIn, teenagers who enjoy posting pictures on Instagram, or academics who use Google Search for studies, there are many different platforms you can use.

The good news is, almost all these platforms offer advertising opportunities at affordable costs.

  1. Google AdWords
    Google AdWords is the first choice of online advertising among marketers and for a good reason as well. It’s most effective and versatile platform for targeting customers.

According to a study conducted by the University of Pittsburgh Marketing, a small business managed to generate a sale of $20,000 as a result of a $100 AdWords campaign created by a student. Ask any marketer, that’s an incredible ROI worth investing on.

What makes AdWords great is that it’s open to all types and sizes of businesses. From big brands such as Fiat, Colgate, and John Deere to small startups, Google AdWords allows you to show ads to your potential customers by targeting specific keywords and search terms.

Setting up an AdWords campaign is easy, but it requires careful research and planning. For example, you need to do proper keyword research to find the most effective and low-competition keywords to target through your ads. You can also create ads as both text and display ad formats as well.

  1. Facebook Ads
    If your target audience spends most of their time on social media, you can target them through Facebook Ads. In terms of creating cost-effective ad campaigns, Facebook is the best.

Facebook Ads not only allows you to create ad campaigns to target your audience on both Facebook and Instagram at the same time, it also helps you to target your ideal customer and gather more data as well.

For example, Facebook allows you to create custom audiences by uploading your email list segments to find your email subscribers on the social network. Then, you can create effective ad campaigns to target those people. Facebook also allows you to create lookalike audiences based on your custom audience to find more people with similar interests for a wider reach.

Creating ads with custom audiences will also give more insights into your sales and learn more about the customers who are actually interested in your products.

  1. Influencer marketing
    Influencer marketing is another effective strategy you can use to reach new audiences. Using this strategy, you can ask influencers in your industry to promote your products and services to their followers to quickly generate traffic and build brand awareness.

You don’t have to pay big celebrities to get a better reach. In fact, according to a survey, 30 percent of consumers are more likely to buy a product recommended by a non-celebrity influencer.

This is a great strategy to not only reach massive audiences with a limited budget, but it’s also a great way to overcome the big obstacles placed by Ad-Blockers.

Other ways to promote your website
Online advertising is a shortcut you can take to quickly promote your business and website. However, it will only generate results for a very short time.

Consider investing in long-term marketing strategies, such as developing a blog to grow your email list, optimizing the website for SEO, guest blogging on popular publications, and creating effective social media campaigns.

Whichever method you use to promote your business, keep collecting data to refine and optimize your campaigns to generate better results each time.

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Crafting Your Customer Avatar


These are the phrases that are used interchangeably to describe the fictional, generalized representations of the persona that is most likely to buy from you.

It is critically important to the success of your marketing, sales, product development, and delivery of services that you have a deep understanding of who your Customer Avatar is. You’ve likely heard the phrase, “You can’t hit a target you haven’t set” this applies beautifully to the importance of having a clearly defined Customer Avatar.

Having a deep understanding a clearly defined Customer Avatar will help you:
Determine what social platforms they are spending their time on so that you know where your business should be present and active.
Be more effective in your advertising. Your marketing dollars will be well spent when you know where to advertise and who to target to maximize your exposure.
Better connect with your Avatar with your copy because you will have an understanding of their pains, pleasures, desires and wants.
Deliver and develop better products / services because you are able to anticipate your markets needs, behaviors, and concerns.

SAMPLE DOSSIER
Sally is a solopreneur who is age 35 and older who has been in business for 1 year or more. Sally works alone and runs all part of her business.

Sally has a passion for serving others and loves that she does, but she is starting to see that her dream of freedom, flexibility and control are getting pushed farther out each day. She loves the fact that she owns her own business and that she does have some flexibility but she feels like her business owns and controls her (instead of the other way around).

Sally is successful enough that she is earning close to $100k a year but she is starting to find herself spending less time doing what she loves and more time dealing with the business side of her business. Sally is at the point where she is overwhelmed by the day-to-day activities of running the business – yet she wants to grow. Her business is no longer rewarding because she is doing things that she is not good at.

Her vision is to become an entrepreneur with the intention of growing her business by hiring a team that can do the things she isn’t good at and doesn’t want to do and also by automating the mundane tasks in her business that are important but suck up a lot of her time.

She is ready to take on the role of marketing as her full focus. Sally is keenly aware that marketing and systems is the key to taking her business to the next level. Her focus is in growing revenue, creating systems, and positioning her business to scale. By implementing these strategies she will create the cash flow in her business that she needs to hire and add stability.

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AI process automation offers benefits, but challenges remain

Enterprises are starting to employ machine learning tools as part of their AI automation strategies, but several key challenges stand in the way of effective usage.

Thanks to rapid improvements in machine learning tools, AI applications are just now starting to make inroads in industrial processes, promising to improve older industrial automation protocols built around expert systems.

AI process automation tools that simplify the workflow of front-line employees present a big opportunity for businesses, but several challenges remain. Enterprises are still grappling with making vast stores of existing data available to AI platforms. There are also several challenges when bringing agility to AI application development and improving training data quality for machine learning algorithms.

Speakers at the Re-Work AI in Industrial Automation Summit in San Francisco discussed how enterprises are taking on these challenges.

Focus on pain points

Broken systems and downtime are among the biggest drivers of AI adoption in industrial automation.

“One of the keys for our customers is that they have experienced some incident, like a failure,” said Drew Conway, CEO of Alluvium, a company that makes machine learning tools for analyzing the performance of industrial equipment.

In many cases, data that could predict large-scale equipment failures in industrial settings is available prior to the failure, but the human experts viewing it don’t recognize key signals indicating it is likely.

“All of this data is falling to the ground,” Conway said. “A big problem is figuring out how to build tools that work with that data in a way that is valuable.”

Building a better algorithm to detect problems involves more than simply analyzing sensor data. It’s important to capture expert operator feedback and institutional knowledge to identify potential issues and alert the operators when a problem occurs. Conway said industrial automation is in need of better ways to blend existing approaches to machine learning with expert-driven systems that can provide operators with more actionable feedback.

“We realized that if we could get people in the control room to use software they trusted, it would grow usage,” Conway said.

This involves not just predicting problems, but relating these predictions to operators’ understanding of how machines work and their different potential failures so operators can take preventative measures.

Streamlining manufacturing processes

The core principles behind Agile development started in the manufacturing sector as part of lean production processes. For the most part, this has been driven by people identifying sources of waste in manufacturing processes.

Now, enterprises are starting to use AI applications and Agile software development practices to develop AI process automation strategies, said Greg Kinsey, vice president at Hitachi’s Insight Group. This is being driven in large part by the rise of industrial IoT and better data management practices.

Traditional lean manufacturing processes work well to optimize highly standardized production lines that don’t change much. But they can suffer problems when a production line is constantly adapting in response to market pressures, said Kinsey.

For example, Hitachi has been working with one company that produces polymers. The marketing department found it could significantly increase sales by making custom blends for a variety of uses. The problem was yield would drop by 30-40% each time the production line changed the blend it made.

Hitachi worked with the company to use machine learning algorithms to figure out how to adjust the settings for the equipment for each new production run, which reduced the drop in yield to less than 10%.

Agile machine learning for new data sets

The hard part of this AI process automation wasn’t not finding the right data; Hitachi worked with the polymer company to identify almost 300 different data streams that might relate to yield. But it wasn’t as simple as compiling all these data sets to train algorithms. Each data set had to be cleaned, calibrated and synchronized with other data sources to produce useful results.

Hitachi worked with the company on an Agile development process that started with the minimum viable data sets, Kinsey said. In the discovery phase, they assessed the predictive value of a few critical variables.

“Once you have a hypothesis, you can think about the data you need and then do the hard work of cleansing, labeling, ingesting and aligning that with the tasks that engineers need to do,” Kinsey said.

Hitachi representatives typically spend a month or two on the discovery phase, during which they deliberately try to avoid talking about applications. In the second phase, they begin to look at specific applications. This is done to formulate a hypothesis and create a minimum viable data set for a potentially larger AI process automation use case.

One of the biggest challenges is making sure you have the right mix of personalities on your team to tackle the different aspects of process automation problems. Highly innovative people are creative, and even though they may make some mistakes, they frequently bring new perspectives to problems. Solution-oriented people look for a stable process. The creative types play a stronger role in the discovery phase, while the solution-oriented types play a stronger role in the deployment phase.

Filling in the data gaps

In many cases, the data required to identify rare but expensive failures does not exist, said Dragos Margineantu, AI chief technologist at Boeing. Airplanes and the maintenance crews that service them collect vast troves of data. But airplanes are rarely grounded or breakdown in flight, so there is not much recorded data about what to look for related to edge cases that might cause a plane to break.

“No matter how much data you collect from real-world processes, it is typically incomplete,” Margineantu said. “We have data sets from customers that operate that have not had a single rejected takeoff in four years. This is an event that happens rarely.”

Building better algorithms for industrial automation sometimes requires finding ways to make sense of data stored in manuals and tapping into the knowledge of experts. It frequently demands a broad survey of potential sources of knowledge rather than simply building a bigger data set.

AI architecture required

Going forward, Margineantu believes AI process automation will require the development of special application architectures designed for other types of enterprise applications. These could be built using components that can be switched out, like microservices running on Docker containers. The beginnings of these kinds of architectures are already being used in domains like autonomous cars that use the Robotic Operating System framework.

An AI architecture can make it easy to develop and deploy a machine learning algorithm and then quickly switch it out when a better algorithm comes along. Today, Margineantu finds Boeing spends a lot of time developing the application infrastructure that wraps around each new machine learning algorithm.

Robustness is important

It’s also important to focus on robustness rather than just accuracy. Systems should be designed to alert humans when an algorithm has trouble reaching a conclusive prediction or recommendation, especially when AI decisions impact industrial equipment.

For example, if an algorithm is trained to identify cats and dogs in pictures, it may struggle with an edge case that includes a picture of a bear. AI systems will have to know how to respond when challenged by new classes.

“If you see a bear, you would like the systems to respond, ‘I don’t know,’ or ‘give me more information,'” Margineantu said.

In the long run, this kind of robustness is likely to be built by groups of algorithms that work together.

“I want to remind you that all competitions in machine learning are won by ensembles, since they provide for more robust outputs,” Margineantu said.

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Everything You Need to Know About Automation for Small Business

So, you want to implement automation into your business model? Every morning an article flashes across your iPhone about the benefits of automation and RPA, or as it’s commonly known, robotic process automation.
Automation is the process is giving artificial intelligence repeated tasks that usually involve huge volume and turnover. Giving a large part of the workload sounds great in theory, but so did many other technologies that are not used anymore. For business owners who are new to automation, there are a lot of question.
Which roles will become automated? Is full or partial automation the best fit for your business? How do you ensure that a smooth transition into automation with current employees? Will you need to expand or evolve the roles as employees work with AI?
All of these questions are valid. Having doubts is a healthy sign of not having Shiny Object Syndrome. Shiny Object Syndrome affects small business owners across the world. Symptoms may include wanting the latest and greatest technology without a proper strategy, lack of training, and research into the realities of automation.

Thankfully, there is a cure. The treatment options listed below should be taken together, if possible. Skipping one step may result in further discomfort for you and your business.

  1. Automation Academy: Automation has many benefits and some risks. The only way to know whether your business should be investing in new technology, is to learn more. A software company called WorkFusion runs an automation course, and although they are selling the product, they offer free sign ups for anyone is curious about RPA, or repeated process automation.
  2. Open Source: Anyone can start building AI and automation frameworks with open source tools. These DIY projects probably won’t be as good as something created by a company that specializes in automation, but they are a start. Tech Beacon pointed to nine open source automation frameworks. Even if you choose not to develop the structure yourself, poking around in open source can help you understand this field better and be more confident in choosing an automation partner.
  3. Read the Reviews: Almost every business is on some kind of review site, and automation is no different. Captera is a website that helps enterprises find software. The site features customer reviews and rankings of each product. There are also case studies on software company websites. Although they are edited to make the company look good, they are still a useful resource.

Since Ford’s assembly lines, we have been steadily marching towards a future where machines do tasks for people. Automation is the next step in that process, but that does not mean every business needs to buy into it without the proper research. By doing a little human legwork, small companies can make a smooth transition into automation.

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Three benefits of AI and automation for consulting businesses

Artificial Intelligence (AI) and automation are disrupting businesses across the globe. As these new technologies develop, many companies are increasingly thinking about integrating AI or at least automation into their operations. According to a study by Narrative Science. the number of companies implementing AI, within the space of one year, nearly doubled with an increase from 38% to 61%. Of these companies, a quarter use AI for predictive analytics, and 22% use it for machine learning.

When considering whether to implement AI or automation technology, it’s important to understand the difference between the two. This is certainly not an easy task as AI is often mistaken for automation due to the fact that AI often involves an element of automation and vice versa. As a result, it can be difficult for the average lay person to clearly identify the differences between these technologies. Machine learning is an algorithm that allows devices to “learn” based on data, i.e. using artificial intelligence to improve algorithms.

How can consultancy firms benefit from AI and automation?
While AI and automation have significantly disrupted some industries, most notably manufacturing and customer service, they have also undeniably had an impact on consultancy firms. Generally, the effects of these new technologies on consultancy firms are positive, and if harnessed correctly, AI and automation could significantly enhance how these firms operate as well as the services they provide to clients.

Data collection
AI technology can process, handle, and analyse massive amounts of data far more efficiently and faster than the average human. As such, it can provide more accurate insights into many areas of business including sales, operations, supply chain and more. For consultants, such information can be used to augment their offerings and services to clients, enhance clients’ ROIs. When used for consultancy purposes, information on for instance sales channels, customer journeys and client behaviour can help marketeers and consultants tasked with sales identify new opportunities and develop more effective strategies for advertising campaigns. For partners, AI can be tapped to streamline the delivery of projects, on the back of more effective resourcing and prioritisation.Three benefits of AI and automation for consulting businesses

Streamline admin tasks
Probably one of the most tedious aspects of any consultant’s job is processing routine paperwork. Whether it relates to manually creating client invoices, processing payroll or creating progress reports for clients, admin tasks can slow down a consulting business. A recent report by Sage revealed that the average small business, including smaller consultancies, spends 120 days per year on admin – time which would otherwise be spent growing their client base and creating new opportunities.

Robots are much more efficient at handling routine admin tasks than humans. Robotic process automation, also known as RPA, devices can help companies with a wide range of admin tasks; for example, creating and delivering invoices, matching incoming payments with the correct invoice, record-keeping, and much more. Some, more high-end AI devices, can even decision-makers make more logical and consistent business decisions or to ensure regulatory forms are completed to avoid non-compliance fines.

Improved productivity
Automating routine admin tasks can also increase consultancy firms’ productivity. Consultants can streamline mundane processes with financial software or schedule meetings, record conversations, and make restaurant reservations with a virtual personal assistant like Zoom.ai. Removing these responsibilities from junior staff members’ workloads means that they can focus on more rewarding work resulting in higher engagement and productivity levels across the whole consulting firm.

Consultants that want to use automation to boost their operations and performance can tap into a host of tools and software solutions. Using If This Then That (IFTTT) for instance, professionals can create processes to automate almost any task including logging time spent for time tracking or saving email attachments to Google Drive. The latest information sharing and collaboration platforms such as GoogleDocs or GSuite support automated document sharing, while a tool such as infusionsoft allows for automating emails to relations and staff.

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Automation, AI, and Machine Learning Are Changing Business Operations

Machine learning the concept that, once data is introduced to a computer, it can make decisions based on the input, has grown by leaps and bounds in the past few years. Machine learning enables predictions based on large quantities of data. The more data, the better the predictions. Add in AI and robotic automation, and the future of business looks very interesting.

AI and Machine Learning
While machine learning and AI are often conflated, they are two different concepts. It’s not quite the same. AI is meant to simulate intelligent thought, while machine learning is more about using data for prediction. AI like IBM’s Watson can use machine learning for analyzing big data and sharing its insight across a company, utilizing then-unheard of amounts of data to draw conclusions.
“The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more,” according to the University of California, Riverside. “Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.”
Here’s a relatable example: You are searching for something to watch on Netflix. There’s a “recommended for you” section based on previous movies and shows you have watch. Algorithms have used the data — what you have watched — to predict other movies and show you might be interested in watching.

Natural Language Processing
Another major application that is still evolving is speech recognition, and natural language processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.
AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world, there are tools using NLP to create stories that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business earnings reports and localized election coverage.
Robotic Process Automation.


RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.
Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.
The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.
RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.
Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. There have already been more than 3 million robot-assisted surgeries in the past two decades.
AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.

Machine learning the concept that, once data is introduced to a computer, it can make decisions based on the input, has grown by leaps and bounds in the past few years. Machine learning enables predictions based on large quantities of data. The more data, the better the predictions. Add in AI and robotic automation, and the future of business looks very interesting.

AI and Machine Learning

While machine learning and AI are often conflated, they are two different concepts. It’s not quite the same. AI is meant to simulate intelligent thought, while machine learning is more about using data for prediction. AI uses machine learning for analyzing big data and sharing its insight across a company, utilizing then-unheard of amounts of data to draw conclusions.

“The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more,” 

“Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.”

Here’s a relatable example: You are searching for something to watch on Netflix. There’s a “recommended for you” section based on previous movies and shows you have watch. Algorithms have used the data — what you have watched — to predict other movies and show you might be interested in watching.

Natural Language Processing

Another major application that is still evolving is speech recognition, and Natural Language Processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.

AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world,  that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business reports and localized election coverage.

Robotic Process Automation

RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.

Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.

The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.

RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.

Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. 

AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.


Another major application that is still evolving is speech recognition, and natural language processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.
AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world, there are tools using NLP to create stories that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business earnings reports and localized election coverage.
Robotic Process Automation
RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.
Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.
The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.
RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.
Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. There have already been more than 3 million robot-assisted surgeries in the past two decades.
AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.

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