The adoption of Artificial Intelligence (AI) is rapidly increasing across industries, offering new opportunities for businesses to automate processes, improve decision-making, and enhance customer experiences. To leverage these benefits, organizations need to be AI ready. Let’s focus on the steps you need to take to prepare your organization for AI implementation, focusing on Azure Machine Learning, Azure Data Factory, and Azure DevOps as essential technologies.
Understanding AI Readiness
AI readiness is the degree to which an organization is prepared to integrate AI technologies into its business processes and operations. It involves a comprehensive assessment of the organization’s current capabilities, infrastructure, data, and workforce, followed by the development of a strategic roadmap to address identified gaps and opportunities.
Key Components of AI Readiness
- Strategy and Vision: Develop a clear understanding of how AI can support your business goals and define a strategic vision for AI adoption.
- Data Infrastructure: Ensure that your organization has access to the necessary data and infrastructure to support AI initiatives, including data storage, processing, and analytics capabilities.
- Talent and Skills: Assess your organization’s current AI talent and identify areas where additional guidance may be necessary to support AI initiatives.
- Governance and Ethics: Establish policies and guidelines to ensure the ethical use of AI and to address potential risks and challenges associated with AI implementation.
Leveraging Azure Technologies for AI Readiness
Building the Model
Azure Machine Learning is a cloud-based service that enables organizations to build, train, and deploy machine learning models at scale. By using Azure Machine Learning, organizations can:
- Access a wide range of pre-built AI models and algorithms.
- Develop custom models using popular machine learning frameworks and libraries.
- Automate the entire machine learning lifecycle, from data preparation to model deployment.
- Monitor and manage AI models in production, ensuring optimal performance and ongoing improvement.
Data is Key
Azure Data Factory is a cloud-based data integration service that allows organizations to ingest, prepare, and transform data from various sources into a format suitable for AI and machine learning applications. Key features of Azure Data Factory include:
- Support for a wide range of data sources, including on-premises, cloud, and hybrid environments.
- Robust data transformation capabilities, including data cleansing, aggregation, and enrichment.
- Seamless integration with other Azure services, such as Azure Machine Learning, Azure Data Lake, Azure SQL and more.
Complete the Cycle with MLOps
Azure DevOps is a suite of tools and services designed to support the entire application development lifecycle, from planning and coding to deployment and monitoring. By integrating AI and machine learning projects with Azure DevOps, organizations can:
- Streamline the development and deployment of AI models and applications.
- Ensure consistent and reliable AI model performance by implementing continuous integration and continuous delivery (CI/CD) pipelines.
- Monitor and manage AI models and applications in production, addressing issues and opportunities as they arise.
Building AI-Ready Organizations
To fully leverage the benefits of AI, organizations need to develop robust data and analytics capabilities and adopt a data-driven mindset. This involves:
- Data Strategy: Develop a comprehensive data strategy that outlines the organization’s goals and objectives related to data management, analytics, and AI.
- Data Governance: Implement data governance policies and procedures to ensure data quality, security, and compliance.
- Data Integration: Integrate data from various sources, both internal and external, to create a unified view of the organization’s data assets.
- Data Analytics: Leverage advanced analytics and AI technologies to extract valuable insights from the organization’s data, driving better decision-making and improved business outcomes.
AI readiness is crucial for organizations to harness the full potential of artificial intelligence and gain a competitive edge in today’s rapidly evolving business landscape. By developing a clear AI strategy and vision, investing in the right data infrastructure, building the necessary talent and skills, and implementing strong governance and ethics policies, organizations can prepare themselves for a successful AI-driven future.
Leveraging Azure technologies like Azure Machine Learning, Azure Data Factory, and Azure DevOps can significantly streamline the AI readiness process, enabling organizations to build, deploy, and manage AI solutions more effectively. By focusing on data and analytics and adopting a data-driven mindset, organizations can further enhance their ability to capitalize on the opportunities presented by AI.
If your organization is embarking on its AI journey and seeking expert guidance to help you become AI-ready, feel free to contact us. Our Data and AI team will be more than happy to assist you in developing a customized roadmap for AI adoption tailored to your unique business needs and objectives.
The Old World of Corporate Budgeting and Its Shortfalls
Corporate Budgeting has traditionally been an activity complained about by business leaders as being an unproductive and non-value-added task. Despite that annual budgeting was a prudent financial activity for the organization to properly plan investments or satisfy bank requirements, that sentiment commonly shared by businesses still had some merit.
Silos and Lack of Ownership
The process tended to be conducted in a silo that fell primarily on the Finance department without properly engaging leaders across the business, which would naturally lack the ownership by business leaders to hold themselves accountable for those targets. Alternatively, when business leaders were engaged in the corporate budgeting process, many times it was seen as being disruptive to normal business activities without proper structure, guidance, and data. Then after the business leaders spent time preparing the budget, that would be the last time they would see or hear about it until next year’s budgeting cycle.
Manual and Time Consuming
Budgeting and reporting are still largely done manually and in spreadsheets, which is very time-consuming and leaves little time for valuable Finance resources to provide value-added analysis. In many cases, organizations will build large coporate budgeting workbooks with many spreadsheets requiring inputs from budget contributors. These budget workbooks are then sent out by email or loaded onto a file share, where people fumble around entering their budgets while trying not to break formulas and links. The Finance team then spends time tracking down the workbooks and keeping people on track to complete them by a deadline. At which point the Finance team spends time manually pulling together all of the budget data across the various workbooks to compile a rolled-up view of the budget for review. If further revisions or drafts are required, then it must go through this whole cycle again. While there are opportunities to simplify it, living in a “spreadsheet and email” world does not lend itself to agility and innovation.
Stagnant and Not Relevant
From a reporting standpoint, the annual budget would rapidly lose relevance as the year progressed because it did not capture assumptions that needed to be reassessed as well as new information that was unknown prior to the start of the year. The budget eventually becomes meaningless to the business leaders and did not enable better decision making such as being able to assess, plan, pivot the business and understand what the forward-looking impact would be.
The old world of coporate budgeting and reporting clearly has had its challenges and shortfalls. However, organizations have been forced to re-think the process due to increasing business demand and competitive pressure.
FP&A, xP&A & Where Things Are Going
Organizations have been increasingly investing in Financial Planning and Analysis (FP&A) practices and resources, which focuses on the ongoing feedback cycle of planning, budgeting, forecasting and analytics. This movement has helped graduate organizations from an incongruent annual process to something that was more continuous and integrated into the business rhythm.
Integrated Into Business Rhythm
Many organizations have instituted a formal FP&A process and assigned resources to help manage the process of better distilling high-level corporate plans into detailed driver-based budgets, continuous forecasts of the financial outlook as the year progresses, and value-added analysis for all levels of the organization. In addition to forecasting, re-forecasting has become an increasingly important component of FP&A where the forward-looking periods are more frequently re-assessed and adjusted to factor in new information such as changes to business, competitive factors, cost drivers, and new opportunities. This is done to provide an outlook that is more in-line with reality to better enable business leaders to be proactive, drive growth and mitigate risk, rather than working with a stale budget. Making FP&A an integral part of the business rhythm, makes it less disruptive and better set up for success.
Broader Scope and Collaboration
FP&A has grown into something more holistic, collaborative across business, and real time – which is now being referred to as Extended Planning & Analysis (xP&A). While FP&A tools (corporate budgeting, planning, forecasting, and reporting) were previously only used and owned by Finance, they are becoming increasingly available across all business units and functional areas of the business (Sales, HR, Operations, IT, etc.). FP&A tasks used to be sole duties owned by a Finance resource and are now becoming listed in the job descriptions of business leaders. This is helping the organization plan better by assigning accountability and ownership is held at the right levels of the business and equipping them with the tools and training to succeed.
Enhancing Business Processes with Technology
Modern FP&A & xP&A platforms are now providing more functionality than many organizations could wish for compared to what their spreadsheets and email can offer.
Historically organizations rely on analysts to extract data from systems and manually update reports and distribute to the business. These platforms are integrated with key source systems (such as ERP, CRM, and POS) and continually updated with the latest data (financial and non-financial), enabling organizations to monitor their performance and update forecasts in a timelier fashion.
Another aspect of this is better utilizing non-financial data to enhance planning and monitoring. Certain non-financial data points are critical drivers that help us build our plans, such as headcount, time sheets, membership/subscriber count, production volumes, raw material consumption, transactions processed, etc. Other data sets can help supplement reporting and monitoring as potential leading indicators of if we are on track to achieve our goals and help better understand our performance in a more holistic way. Examples could include customer growth/retention, customer satisfaction, employee engagement ratings, absenteeism, social media sentiments, etc.
We are now able to set up early warning triggers and alerts based on business logic and thresholds, that were only available with complex custom coded applications. This can draw people into the system to act based on how performance of a particular business is trending, intervene with spend that is exceeding the pace that was planned for, and address trends in headcount levels that are required to deliver on forward looking commitments. So much can be done here by leveraging the understanding of the business and its drivers to set up alerts that can notify us before it is too late.
Given all these advancements in xP&A providing more enhanced and integrated planning and analysis, being able present the reporting and analytics in a digestible and summarized fashion is tremendously valuable to leadership. These platforms provide rich data visualization capabilities as well as the ability to replace static PDF report packages of the past with interactive flip books with drill-down capabilities. These changes help leadership draw insights and act, without having to sift through static documents, spreadsheets and tables of data.
How To Get There
The natural next question is what the next steps are to get there. In summary, the goal is to:
- Make “xP&A” a part of every leader’s job description
- Update business rhythm to make time for this ongoing exercise
- Select the right tools/platform to support your organization
- Provide training and resources to set your leaders up for success
Naturally organizations may have doubts related to concerns about capacity constraints, perceptions of complexity, cumbersome planning process or design, availability of data and general understanding. Despite these concerns, it is more achievable than you can think, and this can be done in a phased approach without too much disruption.
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Marketing analytics measures the performance of marketing efforts. The goal of marketing analytics is to increase the effectiveness of advertising so that the advertiser gets the best possible return on their marketing investment.
The analysis of marketing techniques involves looking at data and finding information about customer activity, sales, engagement, and other factors. Machine learning provides a way to analyze large amounts of data.
This technology uses algorithms to find, categorize, and analyze data. It can automate sophisticated data collection and analysis tasks so that marketers have all the information they need to improve their campaigns right at their fingertips.
Furthermore, machine learning can provide predictive analytics that can use past and present data to foretell customer behavior and help foresee the effectiveness of planned marketing campaigns.
Understanding Machine Learning
Machine learning uses algorithms to perform data analytics and glean relevant information from vast amounts of data. It can also make predictions based on probability.
In marketing analysis, the predictive abilities of machine learning are quite useful; by predicting customer behavior, marketers can automate some tasks that would be time-consuming and expensive to perform manually.
For example, a company can deploy chatbots rather than actual customer service representatives to deal with customer questions and issues. These algorithms can also help predict customer behavior and target marketing campaigns, among other things.
Machine learning is a sub-field of the broader discipline of artificial intelligence (AI). Artificial intelligence covers all activities where machines carry out tasks or make decisions based on an automated set of parameters.
This involves giving computers or other machines access to large amounts of data and programming them to use this data to make decisions or perform analysis.
People often use the terms “machine learning” and “artificial intelligence” interchangeably. However, because marketing analytics involves processing and using massive amounts of data, people in this field use machine learning.
The Benefits of Machine Learning in Marketing
Machine learning can help improve customer experience, foresee customer needs, provide useful support, and help companies predict the effectiveness of marketing campaigns.
However, in some ways, machine learning has not lived up to expectations when it comes to marketing analytics. One of the main reasons for this is that companies do not have systems in place to handle the data that they receive. For example, they may not integrate the data from different sources, so they do not have enough information in one place to perform useful analytics.
Here are some ways that machine learning has already transformed marketing analytics.
Improving the Customer Experience
One of the most obvious ways that machine learning has improved marketing involves customer experience. For example, on e-commerce sites, machine learning enables personalized shopping recommendations based on real-time data.
A machine learning algorithm collects data about the customer’s browsing history. It finds patterns in this data and uses it to make personalized recommendations to each shopper. A good machine learning algorithm can produce recommendations based on real-time data, so the recommendations change each time a shopper visits a new page within the site.
Predicting Customer Needs
The predictive aspects of machine learning can forecast customer needs. By combining machine learning with big data, which requires large data sets from all across the internet, marketers can get enough information to make reasonably accurate predictions about future demand, industry trends, or customer needs.
Being able to predict customer needs can help businesses gain a competitive edge. For example, a company could create a product or a service that meets projected customer demands. Such early development could help them gain a competitive advantage in their industry.
Machine learning can also help define existing needs or areas of opportunity, such as gaps in service or product offerings. New products related to existing ones can help a company maximize revenue without having to develop entirely new products or services.
Optimizing Marketing Content
Advertising is not an exact science, but machine learning brings a scientific aspect to marketing campaigns by allowing analysts to see large amounts of data to measure marketing effectiveness.
Machine learning optimization can take a few different forms. First, marketers can use machine learning algorithms to compare advertisements or marketing strategies. These tests are sometimes known as A/B tests because they compare two different ads side by side. With enough data, marketing team members can easily see which choice performs better before investing in widespread ad placement.
Machine learning can also help provide insights about how users interact with an ad or other marketing content. This data lets marketers measure the success of a campaign and helps them make changes to marketing content to increase engagement.
Providing 24/7 Customer Service
Customer service can be an expensive operational cost for companies. Machine learning applications such as chatbots can help reduce these costs. Chatbots can answer basic questions and interact with customers on a fundamental level.
Chatbots can also collect data about customer concerns. This data can help companies fix a flaw in their product or service or improve chatbots by giving them more data to offer better customer service.
Chatbots can provide 24-hour service, and they can limit costs because they can handle customer issues without requiring an actual customer service agent. Companies can hire fewer customer service reps and lower their operational costs.
How to Enhance Your Marketing Efforts With Machine Learning
The process of adopting machine learning can vary depending on the needs and culture of a company. In some cases, you may need employees to buy into the idea of using artificial intelligence to aid marketing efforts. Management may need to take steps to convince experienced marketing personnel that the insights and tools available can improve marketing campaigns overall.
The next step is to integrate the machine learning applications into everyday operations. As with all technical solutions for businesses, this process involves teaching employees the technical aspects and, in the case of marketing, how to apply what it provides to existing practices and strategies.
IT employees, data scientists, programmers, or outside contractors will handle most of the technical aspects of machine learning for a marketing department. Most training, therefore, will involve making marketing personnel aware of the tools that they now have at their disposal when creating marketing campaigns, testing advertising, and measuring results.
Cloud-Based Data Collection & Analysis
Machine learning is a data-intensive undertaking. A cloud-based system provides the best infrastructure for combining machine learning with marketing analytics.
In the cloud, you can easily collect data from different sources and store it in one centralized place. All members of the marketing team can access the data from their offices or remotely on other devices. This easy access can increase productivity and make collaboration between people in different locations possible.
With a cloud-based system, you need a reliable cloud solutions provider to handle the platform and ensure that everything runs smoothly. The first step for a company that wants cloud-based systems is to move its current operations and workflow to the cloud. A third-party provider can make the cloud transition seamless.
A cloud services provider can also help you implement new machine learning tools and make changes that can help you get the most out of your system.
Make Changes Based on Actionable Insights
Machine learning uses data to provide insights that marketers can use to make changes or take specific steps. These insights can lead to operational improvements.
In addition to analysis, machine learning can help with operations. For example, an algorithm can help an e-commerce site or video streaming site offer personalized recommendations to each shopper or viewer based on their browsing and buying habits. This example of an operational application of machine learning has changed the customer experience on some of the world’s most popular websites.
Also, rather than replacing advertisements, machine learning can enhance campaigns by providing additional data. Data from a test of two marketing web pages, for example, can help marketers choose the most effective ads. They can also fine-tune elements within an ad by measuring customer interactions and behaviors.
With these tools, marketers can fine-tune a campaign while it is in progress. Also, they can gain enough data from current operations to inform future campaigns.
There’s a new kind of workhorse in the workplace, but it’s not human. It’s called artificial intelligence (AI) and machine learning, and it’s quickly changing the future of work.
As a business owner, artificial intelligence allows you the opportunity to maximize productivity while minimizing expenses. It closes the growing gap between structured and unstructured data in company operations, and machine learning can include everything from physical tasks to speech recognition, problem-solving, and learning abilities. AI puts you on the cutting edge of development and technology for your industry.
This is your detailed guide about artificial intelligence in the workplace.
AI Is Taking Over (and Creating) Jobs
In 1930, John Maynard Keynes wrote about “technological unemployment,” but it was the National Bureau of Economic Research and Boston University’s 2017 study that really grabbed the attention of the business world.
It found that just one robot for every 1,000 workers could reduce the employment-to-population ratio by up to 34% while also creating a decrease of 0.25-0.5% in wages.
What Jobs Will AI Replace?
Factories aren’t the only places losing jobs to technology. With many systems already automated, that creates a perfect fit for AI to take over. In Belgian hospitals, a receptionist robot model is already at work, standing over 4ft tall and understanding 20 different languages.
Other jobs that are being replaced by AI technology include these popular roles.
Math is one area that can easily be automated with computers being taught to perform complex math equations. Software and internet programs for DIY accounting, Quickbooks, FreshBooks, and the ever-growing assortment of newer programs that pop up every year eliminate the need for professional help.
Robots are being devised to handle both intricate surgeries and more basic procedures, which frees up doctors and medical personnel for more complex cases. AI also helps with automated data entry and collection for more efficient recordkeeping and higher patient satisfaction.
Factory jobs for humans are on the decline, with robots taking over many construction and manufacturing roles. This is especially evident where repetitive tasks are concerned because AI can also carry physical demands to provide greater efficiency and lower overhead with no human error.
Self-driving cars are already here, so self-driving services for passengers are likely not that far off. This will be an excellent resource for courier services and mail and food delivery, that can use robots with wheels or that travel by air.
Logistics and transportation is another area that will soon be overrun by robotic innovation. Robots, drones, and self-driving vehicles all make a compelling case for faster and more cost-effective delivery.
These are dangerous positions that can be replaced by the flying technology of drones that deliver automated reporting without human error. AI can furnish other things that humans simply can’t provide, like heat sensors or video and photo capabilities. With basic security and monitoring out of the way, military and security personnel can be employed for more cognitive, strategic purposes.
With a threat to all of these industries, you may be wondering what is left, but AI is creating new jobs, too.
Jobs That AI Will Create
Now that the more mundane, repetitive work is out of the way, there is a greater need for creative skills. Decision-making, empathy, and social skills are not items that AI cannot replicate.
AI will also create new jobs, such as these.
1. IT facilitator
This position will oversee robotic operations and ensure that everything is accurate and working in conjunction with other tools, programs, and resources.
2. AI Development Manager
Businesses will need someone to research and integrate all tools and programs within the company’s infrastructure. Someone will need to sell and set up this technology and then perform ongoing maintenance as well.
3. AI Healthcare Technicians
To work in conjunction with this new technology, there will be a need for special healthcare techs who can set up, support, and analyze AI data. This role will empower nurses to provide better care, thanks to technology that better identifies and monitors a person’s health.
4. Government AI Analysts
Local, state, and federal governments are already incorporating AI technology into government operations. This will create a growing need for AI analysts who can implement new systems and find ways to improve through a detailed study of AI-driven data.
5. Modern Transportation Controller
This is a take on the traditional transportation controller but expanded to include emerging technology such as drones, self-driving cars, trucks, and buses. There will be a need for someone to set up, program, and monitor this infrastructure to ensure there are no technical snags that could create havoc on America’s roadways and in its airspace.
6. Augmented Reality Designers
With a rise in AI technology, people will be more interested than ever in augmented reality and its abilities to transform everyday life in areas like entertainment, training, and growth. Designers will need to provide fresh, current content on an ongoing basis, which will include brainstorming, writing, and building these programs.
These are just a few of the ways that AI can create jobs in the workforce, but there are so many more that we cannot yet fathom as technology continues to transform the world as we know it.
How Artificial Intelligence Improves Workplace Productivity
Despite how it may seem, AI is creating technology that is, in turn, creating more jobs. According to a recent study from analytical firm McKinsey & Company, AI could create up to $15.4 trillion annually across 19 business sectors.
It has successfully changed the way that businesses are able to work, providing a more flexible workplace. The more monotonous, wearisome tasks are being replaced by robotic powerhouses that are able to replicate human action and response.
AI’s Implications for Cloud Computing
Cloud computing with AI allows you to benefit from greater accessibility for your entire company. By embedding AI into an internal IT infrastructure, it allows smaller companies with limited cash flow to jump on the AI bandwagon. It also allows for a far wider audience to access AI technology via remote cloud commuting from anywhere. Companies can now access tools and software that were unavailable before and at a far more affordable price.
The cloud delivers greater efficiency, too, now that companies can work smarter. You can not only automate standard work processes but also use automation to eliminate recurring tasks. AI can not only alert you to potential issues but it can also pinpoint and resolve these issues independently for a far more secure and reliable network for your whole company.
Cloud computing delivers better data management to collect, store, and analyze. Many companies are incorporating AI into Software-as-a-Service (SaaS) platforms, which allows you to use large amounts of data, such as those collected from studies, trials, or reports, to make better decisions for your company.
How to Prepare for the Future of Automation in the Workplace
AI can be an invaluable addition to the workplace, but it’s also enough to make your colleagues wrought with fear. AI is making waves in a lot of fields, but it’s enhancing them for the better, and that is what employees need to understand.
Address AI With Your Workforce
As a leader, it will be your responsibility to address the addition of AI solutions to your company. AI usually signifies a threat to job security, so your staff may be nervous about the changes, and you should be prepared to address the matter truthfully and tactfully. You can support them with training and education on what AI means for their jobs.
It’s important to point out how AI will transform workplace culture. Employees are more likely to enjoy their jobs more because there is less of a focus on repetitive, mundane tasks and more of a concentration on creative outlets.
Companies should consider investing in proper training to ensure your business is getting the most out of your new AI-enhanced infrastructure. After all, these changes will mean nothing if you can’t maximize your investment.
Stay Up-to-Date on AI Innovations
AI has many incredible features that can transform your business for the better, but first, you must understand what it has to offer. This is a constantly evolving field with new technology, so it is important to stay on top of new developments so you can start one step ahead of the competition.
To best understand how AI can work for your business, take the time to educate yourself and others in the workplace on the benefits of AI, as well as developing trends and special features for your business.
A cloud solutions provider can help you not only migrate to the cloud but also work with you to show you to operate in the cloud, as well. For many companies, cloud innovation is best delivered by a professional AI consultant who can help you remain abreast of emerging trends while also helping with things like education and training.
The world is rapidly changing every day, and AI is the best way for your business to get onboard for a better, more capable workplace.
6 Surprising Industries Ripe for Disruption by Artificial Intelligence
Artificial intelligence (AI) can simplify the lives of workers in myriad fields by performing tasks that are typically done by humans in a fraction of the time cost. A 2019 survey by Gartner shows that 37% of organizations have implemented some form of AI, skyrocketing its investment by 270% in the past four years. Nevertheless, industries have room to grow when it comes to adopting and innovating cloud-based operations through machine learning and other AI-powered technologies.
Automation alleviates repetitive or even dangerous tasks for manufacturing workers, while effectively streamlining operations for the businesses running such facilities. These discoveries, however, are not new, and instead can be seen throughout much of the 20th century. Notably, in 1951, George Devol conceived of the first industrial robot — the Unimate — a mechanical arm capable of moving an object from point A to point B. Otherwise known as the Programmed Article Transfer device, this mechanical arm came to fruition in the 1960s after Joseph Engelberger pitched the product as one that prioritized workers’ safety.
Today, AI not only performs manufacturing tasks like assembling goods, but also helps conduct quality control, facilitate predictive maintenance, and reduce material waste, among other beneficial uses.
Despite the advantages surrounding the use of AI in manufacturing, one significant downside remains. Oxford Economics predicts that up to 20 million manufacturing jobs worldwide will be lost to robots by 2030. AI’s ability to cause job displacement makes the recognition of digital disruption important for industry executives and leaders to understand. With machines performing typically human-powered tasks at half the cost and twice the speed, companies may be more prone to side with the machines. Between 2000 and 2010, siding with AI was all too familiar for manufacturing workers as the industry plummeted by a third, causing nearly 6 million Americans to lose their jobs.
While private companies, such as IBM’s Watson Education, have created AI systems to personalize learning strategies and improve students’ academic performance, how educators leverage these tools is entirely up to them. One teacher has begun implementing AI in the classroom to auto-grade students’ schoolwork and provide insight into where they need help. School administrators are utilizing AI through virtual assistants; this technology can announce teacher absences, which classrooms need substitutes, and what forms need to be signed.
Although AI may be useful in the classroom, internet accessibility at home remains an obstacle for students across the U.S. Nearly 15% of U.S. households with school-aged children lack access to high-speed internet, creating a “homework gap” between those with access and those without. Furthermore, AI may only be beneficial as a supplement, and not as a replacement, to real one-on-one teaching and mentoring.
There are still far more ways to utilize AI in the education sector before it truly revolutionizes the ways in which students learn. Policies regarding its application and accessibility must be prioritized.
3. Hiring and Employee Development
The hiring process is one more area of focus ripe for disruption by AI. The technology is essentially transforming the way in which companies recruit. With its ability to efficiently filter through applications, choose the best applicant for a position, and even seek out potential applicants, AI is providing a plethora of advantages to companies — especially their human resource departments.
A recent survey conducted by IBM found that 50% of human resource executives understand cognitive computing has the ability to transform HR operations. Despite implementation, many businesses have yet to scale AI’s usability; a McKinsey report finds that only 21% of respondents embedded AI into multiple business units or features. As such, there is still much to be done to successfully incorporate AI into businesses and fully revamp companies’ hiring process.
Similarly, AI can be used to help develop existing employees’ talents. By pinpointing their strengths and weaknesses, AI can effectively inform managers on what their employees could improve, as well as provide personalized training programs that focus on their missing skills.
The potential uses for AI in healthcare are seemingly endless. With myriad departments inside healthcare facilities, advanced technology could provide workers with the collectivity and consistency that they need to perform their jobs well. Just about every department inside healthcare facilities could benefit from AI.
Clinical stage, AI-powered biotechnology company BERG utilizes the technology to forward the discovery and development of breakthrough medicines. In a 2018 Neuroscience conference, the company revealed their findings on treating Parkinson’s disease, and how they used AI to discover a correlation between chemicals in the human body that were previously unknown.
Diagnosing and treating patients, helping senior citizens live fuller and healthier lives, and managing massive collections of patient records are just a few examples of how AI can help this sector. Despite all the advantages of AI in the healthcare sector, real challenges exist in its implementation. Electronic medical records, for instance, have not been as successful as originally thought to be, primarily due to high levels of data entry among healthcare professionals. Interoperability must remain at the forefront of developers’ minds going forward with the industry’s growth.
5. Business Intelligence
AI offers businesses the opportunity to replace old tools, become more innovative, and ultimately change the ways in which companies use their data, by means of automation, data analytics, machine learning, and natural language processing. Retrofitting businesses’ intelligence systems with this technology could help find important data points or patterns that were previously unknown.
The whole purpose of business intelligence systems is to provide companies with insights that aid in decision-making. Big data analytics effectively does this; companies using the product are 5 times more likely to make faster decisions. Banks, for instance, are utilizing AI-driven business intelligence practices to outperform competitors and receive meaningful information in ways that were previously unfeasible.
Data may only be one way to improve business efficiency, but it is an excellent starting point for any company or department.
6. Customer Service
Providing product recommendations, personalizing advertising, and handling simple customer queries are just a few examples of how AI can be used to augment the customer experience. Companies can implement these practices through the use of chatbots, virtual assistants, and other AI-powered technologies.
Chatbots, for instance, provide real-time solutions to customers without the need for human interference. This software application may be used for answering questions, troubleshooting, or interacting with potential customers. It’s 24/7 availability makes it possible for customers to receive immediate answers to some of their most commonly asked questions. For more complex questions, chatbots can direct customers to the proper department.
Personalized advertising and product recommendations are two other ways AI is revolutionizing customer service. AI’s ability to amalgamate individual customer data provides valuable insight on personal preference, as well as geographical location, weather, and even events nearby — each of which may be used to tailor a site’s content to their unique visitor needs. Customer service, as a result, is one field that would highly benefit from disruptive innovation.
The industries outlined above must all be primed for a digital transformation if they are to truly succeed in harnessing AI’s capabilities.