Remember that feeling of spending hours on repetitive tasks, wishing there was more time for your job’s strategic, creative aspects? AI-driven automation is no longer a futuristic fantasy; it’s here, a powerful tool to streamline those mundane processes and free you up to focus on what you do best.

Forget about AI replacing human ingenuity—it’s here to supercharge it. This exciting era of human-machine collaboration unlocks a vast potential, empowering businesses to make sharper decisions, develop groundbreaking ideas, and leave the competition in the dust.

Read on to discover how AI can transform your workforce, optimize operations, and propel your business to a future of unmatched success.

Streamlining Workflows: Repetitive Tasks on Autopilot

Many businesses struggle with repetitive, time-consuming tasks that bog down employees and hinder productivity. AI-driven automation offers a compelling solution. If your business faces challenges integrating these solutions, contact ProSource’s tech support or other reputable IT services companies. They can assist in implementing these technologies seamlessly.

Below are ways AI can streamline your workflows:

Automating data entry and processing

Manual data entry is prone to errors and consumes valuable employee time. AI can automate data entry from various sources, including invoices, forms, and emails, improving accuracy and efficiency.

Streamlining report generation

Generating reports can be tedious. AI automates data analysis and report creation, freeing up employees to focus on strategic initiatives.

Simplifying customer service interactions

AI-powered chatbots can handle routine customer inquiries, reducing wait times and improving customer satisfaction.
Automating these repetitive tasks empowers your employees to focus on higher-value activities that drive business growth.

Scaling Operations with Agility: Adapting to Change with Ease

The business environment is constantly evolving. AI-powered automation solutions offer the agility to adapt to changing market demands and customer preferences.

Here are the key ways AI enhances operational agility:

Increased process flexibility

AI can adapt workflows based on real-time data. This allows businesses to respond quickly to changing market conditions or customer needs, ensuring they remain competitive and responsive.

Improved scalability

AI automation can easily scale up or down to meet fluctuating workloads. This ensures efficiency without sacrificing quality, making it easier to manage peak times and slower periods effectively.

Enhanced innovation

By automating routine tasks, AI frees up resources for research and development. This enables companies to explore cutting-edge technologies and business models, fostering continuous improvement and innovation.

To integrate these solutions effectively, consider partnering with managed services providers. They can offer the expertise needed to maximize the benefits of AI-driven automation, ensuring your business remains agile and competitive.

Enhanced Decision-Making: Data-Driven Insights for Informed Choices

Effective decision-making hinges on access to accurate and insightful data. AI excels at analyzing vast amounts of data, uncovering hidden patterns and trends that might escape human observation.
Below are the key ways AI enhances decision-making:

Predictive analytics

AI can analyze historical data and identify patterns to predict future customer behavior, market trends, and potential risks. Imagine a retail store using AI to predict demand for seasonal products. This allows the store to optimize inventory levels, preventing stockouts and maximizing sales.

Improved risk management

AI can analyze financial data to detect anomalies and identify potential fraudulent activity. This enables businesses to take proactive measures to mitigate financial risks and safeguard their assets.

Personalized marketing strategies

AI can analyze customer data to understand buying habits and preferences. This allows businesses to create targeted marketing campaigns that resonate with specific demographics, leading to higher engagement and conversion rates.
By leveraging AI-powered data analysis, businesses can move beyond intuition and guesswork. Data-driven decision-making strengthens business strategies and fuels long-term success.

Redefining Human-Machine Collaboration: A Symphony of Skills

The future of work lies not in replacing human workers with AI but in harnessing the complementary strengths of both.
Here’s how AI and human expertise can work together to create a powerful collaboration:

Enhanced employee productivity

AI automates repetitive tasks, freeing up valuable human time for higher-order thinking. Imagine a marketing team where AI handles data entry and social media scheduling, allowing human marketers to focus on developing creative campaigns and analyzing customer engagement.

Upskilled workforce

AI can personalize training and development programs, equipping employees with the skills they need to thrive in the AI-powered workplace. This might involve AI analyzing employee performance data to identify skill gaps and recommend relevant training courses.

Improved employee experience

AI can automate administrative tasks like scheduling and data entry, alleviating employee burnout and fostering a more fulfilling work experience. This allows employees to focus on meaningful and engaging tasks, leading to higher morale and job satisfaction. By fostering human-machine collaboration, businesses unlock the full potential of AI, driving innovation and achieving superior results.

Conclusion

AI-powered automation presents a transformative opportunity for businesses to streamline workflows, optimize operations, and make data-driven decisions. Companies can unlock a new era of innovation, agility, and competitive advantage by fostering a collaborative environment where AI amplifies human ingenuity. As AI technology continues to evolve, businesses that embrace this powerful partnership will be best positioned to navigate the ever-changing landscape and propel themselves towards a future of unparalleled success.

It’s forecasted that the algorithmic trading market will reach $31.49 billion by 2028. The Covid-19 pandemic positively impacted automatic trading, as more people looked into new money-making ventures.

Since then, the industry has gone from strength to strength. People enjoy being able to make rapid decisions while reducing human error.

However, as is the case with anything new, you shouldn’t dive right in without having a full understanding. So, with that being said, let’s take a look at some of the essential takeaways for automated trading in this blog post.

What is automated trading?

Automated trading involves using a program to execute trades based on exit and entry conditions you predetermine.

As the trader, you’ll combine technical analysis with your own parameters for your positions, such as guaranteed stops, trailing stops, and orders to open.

From start to finish, your trades will be managed automatically, which means you don’t have to spend as much time monitoring your positions.

Automatic trading means you’ll be able to carry out many trades in a very short amount of time. A further benefit is that you can remove emotion from the process. Many trading mistakes are made when traders allow their emotions to take over, but you don’t need to worry about this with automated trading. This is because your trading rules will already be built in via the parameters you’ve set up.

Essential takeaways for automated trading

To help you fully understand what automated trading is and how it works, we’ll take you through the essential takeaways below:

1.   Automated trading executes trades at precise moments

Automated trading has been designed to make sure you get the timing right.

We’re busy people these days, and it’s hard to do everything manually. You could be at work or picking up the children from school, meaning you miss out on a trade. Well, you don’t have to with automated trading.

Of course, automated trading also gets the timing right in terms of ensuring your trade is executed at the right moment, i.e. when all of the conditions are right.

Ultimately, algorithmic trading combines financial markets and computer programming to execute trades at precise moments.

2.   You need to have some background knowledge to get started

Many people mistakenly believe automated trading has been designed to help you get rich without needing to do anything yourself. If it were that easy, we’d all be rich, right?

You still have control when partaking in automated trading. After all, you determine what rules to set.

So, what do you need to get started with automated trading?

• Coding capabilities
• Financial market knowledge
• Network access
• Computer access

These are the four essential ingredients you’ll need to get started. It certainly helps to spend some time honing your skills and doing background research if you don’t feel confident yet.

We advise having the following technical requirements to begin algorithm trading:

• Historical data for backtesting, depending on how complex the rules are
• The infrastructure and ability to backtest the system after you have built it but before it goes live
• Market data feed access that the algorithm can monitor so it can place orders
• Network connectivity
• Access to trading platforms so you can place your orders
• Knowledge about computer programming so you can program the required trading strategy – alternatively, you can use pre-made trading software or hire a programmer

And, don’t forget, many great demo trading account options will enable you to test your strategy before you risk real funds.

3.   There are a number of trading strategies you can deploy

You can utilize several different trading strategies if you want to capitalize on automated trading. Some of the most common are:

• Index fund rebalancing – As index funds have specified rebalancing periods to bring holdings on par with respective benchmark indices, this presents automated traders with a great opportunity. You can capitalize on expected trades offering 20 to 80 basis points profits, which will depend on how many stocks are within the index fund prior to rebalancing.
• Percentage of Volume (POV) – Until you fully fill the trade order, the algorithm will keep sending partial orders based on a defined participation radio and based on the volume traded in the markets.
• Arbitrage opportunities – Arbitrage means capitalizing on price differences across various markets to make money. If a security, commodity, or currency is priced differently in two different markets, traders will purchase the cheaper version and then sell it at a higher price to make money.
• Time Weighted Average Price (TWAP) – The TWAP trading strategy will break a larger order up, releasing dynamically determined smaller chunks of the order to the market by utilizing time slots (which are divided evenly), between a start and end time.
• Trend-following strategies – Again, as the name suggests, this type of trading strategy simply involves riding the trend, i.e. you purchase when the price is increasing, and then you sell when the price starts to go down.
• Strategies based on mathematical models – There are a number of different proven mathematical models you can use, such as the delta-neutral trading strategy, which enables trading on a combination of options and the underlying security.

4.   Emotion is stripped out of trading

One of the reasons why automated trading is so successful is because it takes the emotion out of the equation. One of the biggest issues people have with trading is their struggle to keep emotions in check. This results in us trying to chase losses or holding on longer than we should. The outcome is rarely a good one.

With automated trading, you will have pre-determined rules, which the software will execute automatically on your behalf. Therefore, you never need to worry about emotion getting in the way of your trades again.

5.   There are some drawbacks to consider

There are always drawbacks to consider when it comes to automated trading, so you do need to keep these in mind when determining whether or not this is right for you.

• Limited customization – Automated trading systems depend on pre-defined instructions and rules, which can restrict the ability of traders to customize their trades to suit their specific preferences or needs.
• High capital expenses – Developing and implementing algorithmic trading systems can be expensive. Traders may need to pay ongoing fees for data feeds and software.
• Regulation – Automated trading is subject to numerous regulatory requirements, which can be time-consuming and complex to comply with.
• Market impact – Big automated trades can considerably impact market prices, resulting in trade losses when you cannot adjust trades in response to these changes.
• Technology dependence – You’ll depend on technology, including high-speed Internet and computer programs. If there are technical failures or issues, it can cause disruption to your trading efforts, causing losses.

These drawbacks aren’t highlighted to warn you away from automated trading. In fact, it’s quite the opposite! Automated trading presents great opportunities, but you must manage it effectively to ensure the best possible results.

Reduce human error and make rapid trading decisions with automated trading

As you can see, automated trading allows you to make rapid trading decisions so you can react to the market almost instantly. With speed often comes more mistakes, but this is not the case with automated trading, as the machine elements also helps to reduce errors.

FAQ about automated trading

• How do I start automated trading? – You should begin by understanding the market. Diving right in is never a wise idea. Once you’ve honed in on a good strategy, back-test it. Next, select the right platform, go live, and continue to evolve and adapt.
• What platform is the best for automated trading? – There are many automated platforms, and the best one depends on your goals. Oil Profit, for example, is ideal for those wanting to trade oil markets. Other platforms with a good track record include Immediate Edge, NFT Profit, Bitcoin Loophole, and Bitcoin Prime.
• Does automated trading work? – Automated trading can work, but as is the case with anything in this world, there are always risks. Nothing is guaranteed. However, automated trading does have a high success rate because it removes emotion from trading, which is critical.
• Can you lose money with automated trading? – Yes, it’s always possible to lose money when trading, even when trading automatically. Of course, there are some things you can do to put the odds in your corner, for example, effective backtesting and validation methods, as well as implementing risk management techniques.

Customer onboarding refers to the process of which your customer gets introduced to your brand, product, or service. For example, when you receive a set of headphones from a specific brand and in the box is a manual, that manual is considered as part of the customer onboarding process. It basically teaches the customer how to make the most out of your product.

If you run a business, this should be a part of your customer service. It will make the customer’s experience smoother and keeps them loyal to you. Customers who are dedicated to your brand can be valuable word-of-mouth marketers, and you’re guaranteed to always get a sale out of them.

Here are more reasons why you should have a customer onboarding process to keep loyal customers:

1. It’s Expensive to Get New Customers

You spent all this money getting the attention of your customers. Make that dollar spent count by taking care of them. More often than not, a customer leaves not because the product was bad but because customer service is terrible. And when you lose a customer, that’s potential sales lost and more money than you have to spend advertising for new customers.

2. It Highlights Your Value

Part of making a customer happy is demonstrating the value of your service. You made the sale, now your customer decides whether it was worth choosing you over your competitors. With a good onboarding process, you will make your customers feel special. It shows that you care about their experience with your product. Even if your price may be a bit higher than the other brand, if customers see value, they’ll stick with you.

3. It Answers Pain Points

Customers have pain points. This refers to the problems that they face with businesses. For example, with banks, a pain point would be having to visit the branch just to make a simple transaction. The solution? Online banking.

Another example of a pain point would be having to enter a six-digit pin every time you unlock your phone. There’s now a solution that exists which is biometrics used as identification and security. With just a touch of your thumb, you can open your phone is less than a second.

A customer onboarding process addresses customer pain points ahead of time. If you’re good at knowing your customers, you already know what these common problems are and have a solution ready. However, you can always improve by asking for feedback. You will often find this at the end of a guide or a FAQ where you’re asked if you had a question in mind that was unanswered.

For a really good onboarding process, you always want to get the customer’s feedback. They have an objective view of your business and can spot problems that you may not.

4. It Encourages Brand Advocacy

A customer will often not renew a subscription or contract due to an experience that happened early on in the buyer’s journey. Simply put, they didn’t see the value when they signed up with you. So from the very start, one foot is already out the door.

By having an onboarding process, you demonstrate value and keep your customers longer. You can sell more products and services to them. And if you’re consistent at providing great value, they can become brand advocates. These are your lifelong customers who will recommend you to their family, friends, and coworkers.

We all know how important word-of-mouth is. Customers are more likely to listen to the recommendations of people they know personally than the stud they see on TV. So pay attention to your onboarding process and determine whether it’s providing value to your customer or not.

What benefits have you gotten from having a customer onboarding process? Share your thoughts in the comments below.

In our last article we discussed the fact that automation and algorithms are created by humans, and so, therefore, can be biased. For our final article on the subject of automation and where it’s taking us, we want to examine the human’s role in the automated society, and how important it is for us to fully understand it.

Different Interpretations of Humans

The Industrial Revolution brought machines to common use in the world. Where you might have had 50 people engaged in a certain task, those 50 people were then replaced by 1 machine. When all was said and done, though, you still had a human being controlling what the machine was doing. Advancements have been consistently made since then, and increasingly more human functions have been replaced. Today machines can compile and analyze data, and suggest strategies and directions to us.

But as we said in the last article, humans are the ones designing and programming the algorithms that are behind all this. They can be influenced by gender, age, race, culture, nation, upbringing and mindset. Also, a very important part of all this is the world view taken on the human—how is this person viewing the world?

The future will depend on how we interpret the directions we are getting from automation. It is therefore very important that we have a realistic view of human beings.

There are many different descriptions of the human being. For the purposes of economics, we can narrow them down to 2 distinct views:

1. As stated in the Austrian School of Economics, the human being, the individual, is the active agent behind anything that takes place in the world. The founder of the Austrian School of Economics, Carl Menger (1840-1921) described the human individual as the final source of reality, as opposed to society as a group. Another of the Austrian School’s leading lights, Joseph Schumpeter (1883-1950), actually created a term for this concept: methodological individualism.

2. Another very different (and highly unrealistic) concept of the human being, known as Homo Economicus, was evolved in mainstream economics. The concept was first advanced by British economist John Stuart Mill (1806-1873). Italian economist Vilfredo Pareto wrote about it also.

This theory basically states that humans all have the same reactions, and can, therefore, fit very neatly into economic equations. Homo Economicus is said to have 6 habits:

1. Self-interest.
2. Rational in action.
3. Maximizing benefits.
4. Reacting to environmental conditions.
5. Equipped with established preferences.
6. Fully informed.

Comparing the Concepts

The Austrian School, from the beginning, has been completely opposed to the concept of Homo Economicus. Why? Very simply, because it’s impossible for individuals to fall into these mere 6 “pigeon holes.”

Let’s just make an example of a couple of computer programmers—one quite young, and another past middle age. Without much looking, you’re going to discover that they have very different preferences. The younger one would be after that new car, the latest smartphone, or the latest model appliances. Normally the older person would not have these same preferences, as they have completely different desires from life.

The Austrian School tells us that there are short-term and long-term preferences. The older a person becomes, the shorter-term become their preferences, simply because their life expectancy is shorter. For example an 80-year old, normally speaking, is not going to purchase a home.

Preferences can be broken down even further. Someone in their twenties, when it comes to automobiles, will be more likely to want a sports car. In their mid-30s when they might have children, preferences usually go in a different direction—they’ll want a vehicle more suited to a family, such as an SUV.

Taking a look at habit #6 above, are we really all “fully-informed”? The answer is, of course, no. The Austrian School tells us that we’re never fully formed about anything—and evidence of this can be seen on a daily basis. If people were truly fully informed, they wouldn’t make many of the wrong decisions that they make.

This also strikes right at #2 above—that our actions are based on rational decisions. Just look around at the world, and ask yourself how true that is. How many people really make rational decisions?

According to the Austrian School, there are three factors involved in decision-making:

Egoism, dealing with the person themselves
Altruism, dealing with what a person does for other people
Mutualism, dealing with cooperation with others

Another of the Austrian School’s leading lights, Friedrich Hayek (1899-1992), takes all of this even further and says that the rationality of behavior is limited by a person’s capacity to perceive, and their own principles of perception. This says that we cannot possibly have all of the knowledge that we need. So no, we’re not “totally informed” and therefore cannot make totally accurate predictions about the future.

Objective Versus Subjective View

Another leading economist from the Austrian School, Ludwig von Mises, in his book Praxeology, speaks about the theory of human action—basically that human action cannot be predicted. Therefore we cannot know how a human being will behave. He also points out that most reality is subjective, not objective.

This can be seen in history. The history of humankind is totally subjective—it’s an interpretation by the historian. This becomes obvious when you read 3 different accounts of the same historical event by 3 different people. You can discover the truth of subjective reality for yourself through the old game of “telephone” in which you line up 10 people, whisper a sentence in the first person’s ear, and have them pass it on by whispering it to the next person, and so on down the line. Then have the 10th person relate what was passed onto them. It will usually be totally different than the sentence started by the first person.

Subjective Views and AI

In that subjective realities vary so greatly, how can we then depend on Artificial Intelligence programmed by humans with such differing views and paths of thinking? (Note that I don’t have an answer to this question—I am simply raising it).

Once more we’ll return to the Austrian School’s Joseph Schumpeter, who pointed out that there is no difference in race, sex, religion, class, or nationality. Every human being is the same, but operate from different motivations rooted in egoism, altruism, and mutualism. We cannot predict because these motivations are subjective. Therefore when someone is programming an algorithm, who knows what that person is thinking and how they’re interpreting the world?

Another question might be, how many people will have to fine-tune the decisions we will make based on decisions issued by artificial intelligence?

Changing Preferences

Today, after 140 years, people are increasingly realizing the fact that there is a real problem with the theory behind Homo Economicus. Through the tremendous number of studies on human behavior, we know that human behavior is unpredictable. Why? Because a human, in a single instant, can change their preferences.

A sudden event can change everything with regard to a human’s perception and preferences. Someone could have an accident, or lose a partner, and it would change much. Or on the positive side, what about winning the lottery? I’m quite sure that the life preferences of the 24-year-old millennial who recently won 768 million dollars have radically changed!

Trying to predict human behavior was already a complex undertaking at the beginning of the 20th century. But we now have almost 4 times more people on Earth than we had then. When Menger and Schumpeter were writing, let’s say in the year 1920, we didn’t even have 2 billion people. Today we’ve got nearly 8 billion, and trying to predict behavior is a mathematical impossibility. In fact, we cannot predict what one human being is doing—and now we have nearly 8 billion of them.

Human Thinking Danger to AI

This being the case, how accurate can our computer algorithms, or the data they generate, actually be? Who will correct the analysis?

I’m not saying we should cease creating artificial intelligence or these algorithms, not by any means. I’m just bringing up the risk factors for the future. We should be aware of them.

The “computer” in our mind is very different. Every word that you’re reading automatically brings an association to another text you have read at some time, or experience you have had, or something from your education. It is all this information that artificial intelligence and algorithms don’t have.

These particular weaknesses of human beings are what bring the danger, as I see it, to automation, to artificial intelligence.

With all our faults, though, we’re still in a supreme position to machines. We create them after all. One question becomes, will the machines eventually take over? A computer, essentially, doesn’t make mistakes, while humans do. That’s what makes it possible to fire rockets perfectly off into space and to have them guided. For this and for many other areas of life, we rely (and will increasingly do so) on technology that makes no mistakes.

In a previous article, we took up the self-driving car, which is an example. It will one day make very much sense, simply because it doesn’t make mistakes. A human driver might be on the freeway and someone calls them and yells, “Your mother died!” In that moment the driver irrationally stomps on the brake or the accelerator or just stops paying attention to the road. The driver creates an accident—where the self-driving car wouldn’t.

I believe it might be necessary to remove human decision from where—as in the case of the car, above—it is a risk factor. More broadly, we can see that the computer might be fully informed in comparison with the human being who is, as we’ve demonstrated, never fully informed.

Application to Sales

Which brings us, finally and once again, to sales. How many salespeople are upset because they reach out to prospects with calls and emails and get no response? Well, have you ever seen a time when a computer didn’t respond when you asked a question? A computer is running 24/7, has all the information and is totally informed.

It might behoove companies to actually leave the buying choices for big purchases to a computer. A computer, with no emotional bias whatsoever, could compare product to product, price to price, feature to feature, benefit to benefit, and make highly reliable comparisons. There is a consumer app now marketed for this purpose called Honey that is all the rage. This concept could potentially extend to more complex purchases.

I should note, however, that there have been a few predictions—one by Forrester comes to mind—that sales jobs would be drastically reduced over time because of automation. I don’t agree with this prediction at all, and in fact, I see sales jobs increasing. Yes, automation has already replaced some lower-level B2C sales positions, but with more complex B2B sales, live reps will always be needed. And even in B2C, we still see an increase in sales reps at places like Apple stores, don’t we? In an increasingly automated world, that human touch becomes more needed than ever.

Who Is This Human?

So once again, it all comes back to: who is this human being? How do we interpret the human being, and how realistic is the world view of that human being?

In answering this question, we have to make all our decisions for the future. I do realize this is very philosophical, but in my opinion, AI is very much like philosophy because—and we will see this very soon—it will affect every part of our lives.