Trending February 2024 # Ai In Automation: Discover Automatable Tasks With Ai In 2023 # Suggested March 2024 # Top 9 Popular

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The jobs that can be mostly automated include

predictable physical labor

white-collar back-office work: data collection and processing

Machines can now perform the activities involved in these jobs better/cheaper than humans. These activities include tasks that involve manipulating tools, extracting data from documents and other semi-structured data sources, making tacit judgments, and even sensing emotions. In the next decade, driving is likely to become automated as well, enabling one of the most common professions to be automated.

What share of jobs can be automated?

Based on McKinsey and PwC’s analysis, ~20% of business activities can be automated using today’s technology. PwC estimates this automation wave to take place until the late 2023s and that automation could reach 30% of all existing jobs by mid-2030s.

Example occupations and automation potential according to McKinsey:

PwC estimates 20% of jobs to be automated by the late 2023s and 30% of jobs to be automated by the mid-2030s. PwC divides this transformation of automation into three main phases: algorithm wave (to early 2023s), augmentation wave (to late 2023s), and autonomy wave (to mid-2030s). Simple computational tasks are automated, and analysis of structured data is conducted in the algorithm wave which we are currently in.

The next phase is the augmentation wave. Automation of repeatable tasks and dynamic interaction with AI will be common in this period. Also, semi-automated robotic tasks like moving objects in warehouses are a part of this phase.

Lastly, the full automation of physical labor will become prominent in the autonomy wave. Using AI for problem-solving in dynamic real-world situations that require responsive actions like transportation and manufacturing is the main focus. While the technology is expected to reach full maturity on an economy-wide scale in the mid-2030s, PwC estimates ~30% of the jobs in all sectors will be automated by that period. Below, you can see a figure that shows the automation potential in different sectors:

Automation can raise productivity growth by 0.8 to 1.4% annually with the current AI-powered automation tools by reducing errors and improving quality and speed, and in some cases achieving outcomes that go beyond human capabilities. Thus, companies are inclined to automate their tasks to improve their productivity.

As we group these occupations into categories, we see that the top three categories have a large potential for automation. These activities are:

Predictable physical labor

Data processing

Data collection

This article will investigate each category to understand the automation potential and how businesses can automate their tasks under these categories.

What are the jobs most prone to automation? Jobs requiring predictable physical labor

McKinsey states that performing physical activities in predictable environments has the highest potential for automation. It predicts that 81% of such activities are prone to automation with current AI technologies including robotics.

Physical labor activities are divided into predictable and unpredictable activities. Machines are better than humans at performing predictable activities as they don’t get bored and can tirelessly perform repetitive and predictable activities. However, unpredictable activities require the human level of flexibility in adapting tasks that are still not available to machines.

The highest probability of automation jobs requires lower education levels and includes repetitive tasks. This is quite expected while repetitive tasks provide a predictable environment for the machines and they can successfully perform low-skill tasks without any breaks. Deloitte has occupations with the highest probability of automation in the following table.

A PwC report on automation indicates that machine operators and assemblers become a prominent occupation with high automation potential. While their task can be automated by 64% according to the report, PwC estimates that businesses can achieve this potential by 2035.

While self-driving cars are trending, jobs in the transportation industry are at the potential risk of automation according to the same study by PwC. By the mid-2030s, 50% of existing jobs in the transportation industry could potentially be automated.

Manufacturing is another industry that is prone to the automation of predictable labor. As Bain predicts that automation in manufacturing will grow by 55% from 2024 to 2030, companies are working on smart, fully automated factories that will have accelerated and continuous production. Ericsson will run its first one in early 2023, however, the plant will initially have a staff of ~100 before it becomes fully autonomously operating.

Data processing

Data processing is the second work activity that has the highest potential for automation. Businesses can automate 69% of their time at data processing, according to McKinsey. This process includes storing, manipulating, preparing, and distributing data. Automated data processing will enable increased business effectiveness and lower costs.

Numerous customer-facing processes such as loan applications, customer service queries, account upgrades of telecom customers, etc. are dependent on data processing. Automation will enable the processing of large amounts of information with minimal human interaction and sharing it with the right audiences leading to faster, less error-prone data processing. This will improve the customer experience.

Even investor-facing processes can be prone to errors that harm companies’ both reputation and finances. There are numerous cases, including one that made a famous trader more famous, of trading typos that result in millions of losses.

Beyond stakeholder-facing processes, business decisions rely on data analysis and reporting. Historically, executives relied on manually produced reports for decision-making. Today, an increasing share of reports are produced automatically. Faster, less error-prone data analysis will improve the quality of business decisions.

HR’s tasks, such as:

Assessing and creating newcomer data,

CV screening,

Data cleansing,

Payroll processing, etc. With automation, HR can remove process delays and reduce costs by 65% compared to an offshore-based FTE in the Shared Service Center.

Can be automated. This lowers process delays and reduces costs

Data collection

A common example is accounts payable. Most companies currently manually capture data from invoices even in developed markets like the EU since these documents are not fully standardized and digitized:

Share of e-invoicing in EU as of 2023:

Current automation technologies are capable of introducing ~80% automation to accounts payable while most companies rely on legacy, template-based Optical character recognition (OCR) systems that enable only 10-15% automation. OCR is a software technology that enables us to convert scanned hardcopy documents and images into editable digital texts which can now be stored, searched, transferred, and sorted. However, OCR does not create key-value pairs that are ready to be inserted into databases. Deep learning-based solutions address this gap and identify key-value pairs and tables in papers, receipts, contracts, or books so they can be inserted into databases. Feel free to read more on this from our article on automated invoice capture.

If you want to read more about AI, these articles can also interest you:

If you believe your business will benefit from an AI solution, feel free to check our data-driven hub of AI solutions.

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And let us guide you find the right tools for your business

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Autogpt Tutorial: Automate Coding Tasks With Ai

AutoGPT is an AI tool that automates coding tasks using GPT. Install Python and Pip, add API keys, and install AutoGPT to get started. Tutorials are available online for Windows, macOS, and Linux.

Are you tired of spending countless hours on repetitive coding tasks? Have you ever wondered if there was a way to automate these tasks and streamline your workflow? Look no further than AutoGPT, an open-source application that uses the GPT-4 language model to perform complex coding tasks and achieve goals with minimal human input. In this tutorial, we will guide you through the installation process and demonstrate how AutoGPT can revolutionize the way you approach coding.

AutoGPT is an Xcode Source Editor extension that enhances productivity by leveraging the capabilities of GPT-4, an AI language model developed by OpenAI. It is an experimental, open-source Python application that uses GPT-4 to act autonomously. AutoGPT is essentially an AI tool that creates different AI agents to meet specific coding tasks. It defines an agent that communicates with OpenAI’s API, and this agent’s objective is to carry out a variety of commands that can automate coding tasks. AutoGPT uses GPT’s superior text creation as part of its interesting breakdown of the AI’s phases. The tool can be set up on a local computer, and users can define the role of the AI and set goals for it to achieve. AutoGPT is an autonomous version of GPT-4 that can think and do things itself.

Also Check: What is Auto-GPT, and why does it matter?

There is no specific information available on the system requirements for AutoGPT. However, it is mentioned that Python is the only requirement for AutoGPT.

To install AutoGPT, follow these simple steps:

Download the ZIP file from Github.

Extract the ZIP file and copy the “Auto-GPT” folder.

Open the command prompt and navigate to the folder location.

Run the command “pip install -r requirements.txt” to install all the required libraries to run AutoGPT.

Finally, run the command “python -m autogpt” to start AutoGPT on your system.

On the first run, AutoGPT will ask you to name the AI and define its role. You can also set goals for the AI to achieve. Once you have completed the setup, AutoGPT will use the GPT-4 language model to perform tasks and achieve goals.

See Also: Auto-GPT vs AgentGPT: Understanding the Differences

It can perform a task with little human intervention and can self-prompt.

AutoGPT’s reasoning ability is similar to that of humans, making it a highly capable AI model.

It can complete tasks that you know nothing about, making it very versatile.

AutoGPT can interact with both online and local apps, software, and services like web browsers and word processors.

However, AutoGPT’s practicality may be limited due to the expensive GPT-4 model it uses.

The cost per task completion can be high, even for small tasks.

AutoGPT stands out from other AI tools because of the following reasons:

Independent operation: Unlike other AI tools, AutoGPT operates independently, which means that you no longer have to direct or steer the model to meet your needs. Instead, you can write your objectives, and the AI will do the rest for you.

Interact with apps and services: AutoGPT can interact with apps, software, and services both online and locally, like web browsers and word processors. This feature allows users to automate complex tasks that previously required human intervention.

Open-source project: AutoGPT is an open-source project that anyone can contribute to or use for their own purposes. This accessibility makes it easier for developers to use AutoGPT in their own projects and to improve the technology.

Breakthrough technology: AutoGPT is a breakthrough technology that creates its own prompts and enables large language models to perform complex multi-step tasks. Its unique abilities make it a valuable tool for a wide range of industries, including marketing, customer service, and content creation.

More Also: How to Use AgentGPT and AutoGPT

AutoGPT currently supports Python, JavaScript, and Swift, but the development team is constantly adding support for new languages.

Yes, you can modify the AI’s behavior by setting goals and defining its role during the setup process.

AutoGPT’s experimental nature poses challenges for users. The learning curve and potential issues require patience and persistence, as the developers work to refine and improve the software. However, the benefits of using AutoGPT make it worth the effort.

AutoGPT is a game-changer for coders who want to streamline their workflow and automate repetitive coding tasks. With its autonomous nature and use of GPT-4 language model, AutoGPT is a powerful tool that can save you time, reduce errors, and increase productivity. By following this tutorial, you can install and set up AutoGPT on your local computer and start enjoying the benefits of this tool.

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Robotic Process Automation: Confluence Of Automation And Ai

We have gathered information about Robotic Process Automation: Confluence of Automation and AI

We hear about robotic process automation (RPA), machine learning, and artificial intelligence (AI) daily in every industry. We learn what these technologies mean for the world as we know it and how they will benefit society, industry, and humanity.

Robotic Process Automation: Confluence of Automation and AI is a book written by Jyoti Sekhar Banerjee, B. Tech, M.E, Ph

We’ve heard a lot about the possible entanglements, the destruction, misery, and the death of human resourcefulness as far as we might be concerned; all while tech giants like Elon Musk and Bill Gates talk about how AI and robotics will take over jobs.

I consider the implications that RPA will have on the financial services sector, which is the industry and market space in which I spend the majority of my time. Technologists discuss RPA initiatives at every meeting and product evaluation I attend. My inquiry is, how far can support organizations take automation and AI?

To set the stage, let’s begin with a brief definition. RPA is the use of software with automation and AI and machine learning capabilities to handle repetitive, high-volume tasks that require human intervention. This could be as simple as software that finds an error, analyses it, and then has the intelligence to fix it without having to be used by a person. Many industries, including fund administration, can greatly benefit from this kind of automation.

RPA has the potential to shorten reporting cycles in the fund administration industry from months to days, allowing investors to see and analyze trends more quickly and base investment decisions on intricate calculations and trends. As they add more fund clients, fund managers see RPA as a means of streamlining accounting and valuation processes. According to Deloitte, it can lead to improved service, fewer mistakes, increased audibility, increased productivity, and lower costs. It makes it possible to have a workforce that is automated in a variety of ways around the clock.

More sophisticated tools are taking the place of the outdated methods that relied on Excel sheets and macros. Additionally, functions like dashboarding, workflow, and proactive system and process monitoring are becoming increasingly important components of technology infrastructures thanks to these new tools. Additionally, these “new” tools frequently need to interact with older systems, which is not possible alway. To extract, format, shape, and distribute the data in a way that a downstream system can consume, necessitates human interaction. This process is being automated with RPA in a more controlled, efficient, and less labor-intensive manner. RPA bots can, for the sake of simplicity, completely automate human actions like opening files, entering data, and copy-pasting fields.

As they provide the next logical step to further reduce costs, RPA and AI will continue to expand. Administrators’ shift to outsourcing has plateaued. To further drive down their gigantic workforce, they will see AI and robotization tackle undertakings that once required individuals – like pursuing choices as well as leading and finishing processes all the more productively.

1. Increased customer satisfaction as a result of increased accuracy, efficiency, and error reduction

2. Ability for employees to concentrate on activities that add more value and eliminate manual and low-value activities as well as overhead ones

3. More real-time data evaluation and analysis results in data sets that are both cleaner and more complete

The need to quickly assimilate and analyze data, identify trends and make decisions also grows as the volume of unstructured data continues to increase. The ability to automate tasks like system health checks and issue identification and prediction before data delivery will become a reality as technology, and RPA in particular becomes a more integral part of administrators’ service offerings. All of this helps to build a workforce that can better serve customers and is stronger and more valuable. They can do this with more detail, more information, and better capabilities that ultimately strengthen relationships.

Trends In Artificial Intelligence (Ai) In Cybersecurity

The world runs on data, and humans alone could never monitor or safeguard all of it.

When applied thoughtfully, artificial intelligence (AI)-enhanced cybersecurity can add essential layers of protection for modern enterprise networks. 

Research firm Technavio expects the AI-based cybersecurity market to grow by $19 billion from 2023 to 2025. The company cites the increased complexity of enterprise networking environments, which often include a mix of legacy, on-premises infrastructure, and cloud resources, all of which need to be accessed remotely. AI approaches add efficiency and accuracy and reduce the impact of the ongoing worker shortage in this field. 

As organizations have become more comfortable with autonomous applications that help streamline workflows and reduce human error, it’s only natural that we would see more AI in cybersecurity adoption as well. These five trends in cybersecurity underscore the overall shift toward AI business applications across many fields:

As workers around the world were sent home from their offices to work remotely in 2023 during the COVID-19 pandemic, cybercriminals were already lying in wait, ready to exploit vulnerabilities widened by the mass influx of unsecure network connections. Those same tactics have played out across the SecOps field, which has been dealing with a significant skilled worker shortage for several years. 

(ISC)2 estimates the cybersecurity market is in need of about 3 million qualified workers, according to its 2024 Cybersecurity Workforce report. Additionally, the report shows 64% of the cybersecurity professionals surveyed said their organization is impacted by the cybersecurity skills shortage. 

When SecOps teams are lacking in staffing, vulnerabilities naturally increase. No human could keep up with every viable threat, as cybercriminals know. 

AI is playing a role in these situations. Sophisticated AI-driven algorithms can recognize patterns of attacks, suspicious email activity, and identify the most vulnerable network endpoints. AI can also tackle repetitive, error-prone tasks, like data labeling, and generate automated reports for human analyst review. All of these features will help to reduce SecOps teams’ bandwidth, so team members can focus on other security functions. 

Identity and access management (IAM) is becoming more important than ever with the increased adoption of zero-trust security frameworks, which require every network user to be authenticated, authorized, and continuously validated. 

AI can greatly reduce the amount of manual labor required to carry out these goals by introducing smart automation into security systems. AI can monitor and analyze user activities, including typing and mouse movements. It can also power supervised algorithms and unsupervised learning, both of which help SecOps teams identify anomalous behavior. 

AI can improve security across the customer authentication experience as well, from the point of account creation and login to interacting with service accounts. AI monitoring of these activities helps organizations to assign risk scores related to potentially suspicious events, instead of simply locking users out or terminating their connections mid-session. This more nuanced approach improves efficiency and helps analysts zero in on genuine threats. 

See more: Artificial Intelligence (AI) In Cybersecurity 2023

Blockchain adoption has been increasing dramatically, as cryptocurrencies have become more widely understood. Grandview Research values the global blockchain technology market size at around $3.67 billion in 2023 and expects that figure to skyrocket, growing at a compound annual growth rate (CAGR) of 82.4% from 2023 to 2028.

Bitcoin and other crypto coins are built on blockchain solutions that keep transactions secure and decentralized. Blockchain is also used within the medical field to better secure and monitor access to electronic records. 

Advances in AI-powered blockchain have reduced the need for time-consuming secure sockets layer (SSL) and transport layer security (TLS) “handshake” methods that involve verification keys. Instead, newer systems can analyze data chains in bulk using high-powered AI, which is a much faster and far more secure process overall.   

AI can apply regulatory rules and requirements to data across complex networks, which is a quicker, more foolproof compliance method versus manual search processes.

AI-based data processing will be critical as over 300 million new regulations are expected over the next decade, according to LogicGate.

Enterprises can use AI to track regulatory agencies worldwide to help monitor and maintain ongoing compliance, as rules change and new rules are adopted, LogicGate says.

Regulatory compliance enhanced with AI is a smart investment, considering the financial ramifications of being found out of compliance with broad data laws, like GDPR and HIPAA.

As more organizations move portions of their data to the cloud, cybersecurity has become more complex. Many legacy systems are incapable of monitoring cloud data, but newer AI-enhanced cybersecurity is specifically designed for the cloud. 

Hybrid cybersecurity solutions involving AI that are able to monitor and analyze data across multiple environments will become a must. Many organizations have been getting by with an ad hoc approach, where enterprise data is pulled from various architectures, compiled, and then analyzed by a software platform. Not only are these approaches complicated and expensive, they are also prone to missing important data. 

See more: Top Performing Artificial Intelligence Companies of 2023

Top Responsible Ai Predictions To Look Out For In 2023

Responsible AI is essential for organizations to meet customer satisfaction with principles

The constant innovation and development with artificial intelligence (AI) have transformed the workforce of so many industries with new opportunities to boost productivity. Thus, organizations need to be responsible with AI with Responsible AI as a governance framework to document the challenges around artificial intelligence. Organizations should follow emerging trends of Responsible AI to achieve fairness and trust in the highly competitive market. Let’s explore some of the top Responsible AI predictions to look out for in 2023.

Top Responsible AI Predictions for 2023 Accelerating Governance

Accelerating governance is one of the top Responsible AI predictions for 2023. Artificial intelligence is dynamic in nature with constant improvements and developments. Organizations need their government to function at a rapid speed like this technology. Responsible AI toolkit should be all-time on track of AI model performances and look for new potential risks throughout the process. One of the trends of Responsible AI is to boost company governance efficiently and effectively to eliminate errors and risks.  

Enhanced Ethical AI

One of the top Responsible AI predictions is the enhanced Ethical AI in organizations. It will help in creating smart frameworks that can assess and plan for AI models to be fair and ethical towards the goals of company strategies. Being responsible means being more ethical towards the products and services in the global tech market. End-users should have a strong understanding of their ethical concerns or doubts about artificial intelligence.  

More Cultivation of AI Models

Another trend of Responsible AI is providing an opportunity to cultivate AI models more to enhance productivity and boost efficiency. Organizations can utilize the principles of Responsible AI to cultivate AI models as per the needs and wants of end-users. Employees need to focus on appropriate real-time data and seek improvement to fulfill all the needs to have a successful Responsible AI in a company.  

Adopting Bias Testing

One of the top Responsible AI predictions is that more companies will adopt bias testing and eliminate inadequate tools and processes. There are multiple open-source machine learning tools and frameworks with stronger ecosystem support. Responsible AI can be leveraged with these tools focusing on bias assessment with mitigation, especially in non-regulatory use cases.  

More Focus on Explainability

The constant innovation and development with artificial intelligence (AI) have transformed the workforce of so many industries with new opportunities to boost productivity. Thus, organizations need to be responsible with AI with Responsible AI as a governance framework to document the challenges around artificial intelligence. Organizations should follow emerging trends of Responsible AI to achieve fairness and trust in the highly competitive market. Let’s explore some of the top Responsible AI predictions to look out for in 2023.Accelerating governance is one of the top Responsible AI predictions for 2023. Artificial intelligence is dynamic in nature with constant improvements and developments. Organizations need their government to function at a rapid speed like this technology. Responsible AI toolkit should be all-time on track of AI model performances and look for new potential risks throughout the process. One of the trends of Responsible AI is to boost company governance efficiently and effectively to eliminate errors and chúng tôi of the top Responsible AI predictions is the enhanced Ethical AI in organizations. It will help in creating smart frameworks that can assess and plan for AI models to be fair and ethical towards the goals of company strategies. Being responsible means being more ethical towards the products and services in the global tech market. End-users should have a strong understanding of their ethical concerns or doubts about artificial intelligence.Another trend of Responsible AI is providing an opportunity to cultivate AI models more to enhance productivity and boost efficiency. Organizations can utilize the principles of Responsible AI to cultivate AI models as per the needs and wants of end-users. Employees need to focus on appropriate real-time data and seek improvement to fulfill all the needs to have a successful Responsible AI in a chúng tôi of the top Responsible AI predictions is that more companies will adopt bias testing and eliminate inadequate tools and processes. There are multiple open-source machine learning tools and frameworks with stronger ecosystem support. Responsible AI can be leveraged with these tools focusing on bias assessment with mitigation, especially in non-regulatory use cases.Organizations need to put more focus on explainability to follow Responsible AI efficiently. There cannot be a complex AI model performing that is difficult to explain to stakeholders. Responsible AI needs organizations to have a strong understanding of artificial intelligence algorithms and the process to provide predictions. Thus, if a company puts more focus on the explainability of AI models, it is easier to follow Responsible AI principles and meet customer satisfaction in a long run.

3 Ways Artificial Intelligence (Ai) Will Transform Ecommerce In 2023

Unless you have been marooned on a desert island, chances are you know what Artificial Intelligence (AI) is and have experienced the benefits of machine learning is having on customer experience and business operations

Despite being around for decades, AI is currently one of the most popular topics in business with Gartner predicting that by 2023 AI will be a top five investment priority for more than 20%  of CIOs.

Join Joey Moore, Director of Product Marketing and Greg Moore, Manager for Personalisation and Campaign Strategy of Episerver for a practical webinar on The 3 key ways to improve the customer journey.

AI is currently closing the gap between detecting patterns from large data sets and predicting intent (a role traditionally reserved for human marketers and merchandizers). AI-powered technologies are replacing the manual work traditionally completed by merchandizers to make product recommendations and marketers to make ad spend decisions. For now, AI will not completely replace all human effort, but it will dramatically improve the effectiveness of ecommerce teams that use it while enhancing the experience of shoppers who purchase from AI-centered ecommerce businesses.

Today, AI enhances the shopper’s experience by delivering personalized product recommendations

The primary way AI has taken the online world by storm is by delivering highly personalized experiences shoppers desire. Using AI to personalize the customer journey is a huge value-add, and those that have implemented personalization strategies see sales gains of 6-10%, a rate two to three times faster than other retailers, according to a report by Boston Consulting Group.

Many ecommerce websites use AI-powered product recommendations engines to target and personalize which products are displayed to customers. A variety of machine learning algorithms have been developed by technology vendors (including Episerver) that alter the recommendations based on the stage or channel a customer is shopping in. For example, algorithms that prioritize top selling products may be used on a product details page while algorithms that prioritize complimentary products may be used on the cart and checkout pages.

2024: Ubiquitous personalization, voice & chatbots, and the introduction of predictive analytics 1. Ubiquitous and automated personalization using machine learning algorithms

AI-powered personalization tools are now extending into new stages in the customer journey such as personalizing search results on ecommerce websites to each individual shopper and injecting personalized product recommendations into new digital touchpoints such as shoppable mirrors. Personalized site search tools like Episerver’s Personalized Find automatically change the sort-order of product searches based on the behavior of the user who is conducting the search, which has the potential to dramatically improve relevancy of search results and lead to higher conversion.

2. Voice commerce and chatbots powered by natural language processing

But the most transformative AI-powered tools we will experience as consumers will use natural language processing (NLP) to understand and respond to voice and live chat interactions. NLP chatbots can be embedded and used through major messaging applications to act as customer service agents without needing a human. Chatbots make it easy and fast for customers to reach retail businesses using the same messaging services they use daily.

NLP voice commerce tools and virtual personal assistants (VPAs) will also transform how consumers interact with ecommerce websites, by allowing them to search and purchase using voice commands in scenarios where a customer doesn’t have a touchscreen or computer. In fact, by 2023, Garter Research predicts end-user spending on VPA speakers will reach $2.1 billion, growing at a compound annual growth rate of 43% from 2024 through 2023, while by 2023, 30% of our interactions with technology will be through “conversations”.

3. AI will empower marketers to make better decisions with predictive analytics

While AI has and will continue to transform how we shop online, the most revolutionary changes to ecommerce will be how online retailers and B2B companies analyze customer journeys and make marketing and merchandizing decisions. Predictive analytics will allow AI to take over discernment and prediction historically reserved for human judgment.

Propensity to purchase: As ecommerce leaders, we would love to know who wants to buy something and when they want to buy it. One example use case allows a marketer to see how many digital interactions such as page views or emails sent before a stagnant customer segment will reactivate and make a purchase. Next best action will allow marketers to create a segment of customers approaching the purchase threshold to offer a targeted promotion.

Product sales analysis: Ecommerce leaders would also love to know which products are going to be popular if they are put on sale and which products are probably going to be sold with or without discounting. Predictive analytics can analyze previous sales data to allow a merchandizer to see what products are likely to sell next month if they are put on sale.

In 2023, data will be the new currency

The technology that enables this level of analytics requires a centralized repository of all customer data including browsing behavior and purchases. In the emerging field of AI-powered ecommerce, data is the new currency. Ecommerce departments must centralize their customer and order data and enrich this data profile by integrating their marketing, ecommerce, and back-office technologies together. Some companies are years into these initiatives but most have not even begun. Episerver is piloting a Customer Data Management platform which will enable marketers and merchandizers to run predictive analytics use cases like the ones described above.

AI will be a game changer

Advances in artificial intelligence have changed our lives and the impact of AI in retail and ecommerce is undoubtedly picking up speed. AI is starting to have a significant effect on the way ecommerce businesses attract and retain customers. A survey by Gartner predicts that by 2023, 85% of a client’s relationship with a business will be managed without interacting with any human. While ecommerce giants like Amazon, Walmart, and eBay have used these capabilities behind the scenes for years, more modest ecommerce teams can do the same, dramatically improving the effectiveness of the teams that use AI while enhancing the experience of shoppers who purchase from these businesses. AI is becoming more and more sophisticated by the day. And ecommerce businesses are now able to harness that power to improve consumers’ shopping experience.

Smart Insights are holding their fourth digital marketing Summit on the 12th December, where they will explore the key digital trends for 2023. Join Joey Moore, Director of Product Marketing and Greg Moore, Manager for Personalisation and Campaign Strategy of Episerver for a practical webinar on The 3 key ways to improve the customer journey.

In this webinar, you will learn the value of personalization to businesses. We show you how it can improve your customer’s online experience by giving them an individualized journey resulting in improved satisfaction, increase loyalty and ultimately more profit to your business.

We will walk you through the 3 key ways and show real-life examples for each:

How you can implement your personalization strategy by using:

– Content Personalisation

Sign up for your FREE  place today! 

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