Author: Great Learning

Artificial intelligence is expected to grow at a CAGR of 52 percent between 2017 and 2025, owing to the strides in the innovation and application of AI.

Recent advancements in cloud computing and big data have allowed for this growth of AI in all industries. Improving efficiency and productivity also brings better performance. AI also improves decision-making within an organization by providing critical information.

For some organizations, leveraging AI means exploring select parts of their enterprise to craft AI use cases. While this approach can help you follow trends quickly, it is not the roadmap to AI authority.

To become a true AI-fueled organization, you might need to rethink processes and human-machine interactions within your working environment.

Top-level executives should also be vested in the idea of deploying machine learning and other cognitive technologies across the organization’s core processes to adopt insight-driven decision-making across the enterprise.

Enhance Productivity Across Organizational Teams

Here are ten specific ways organizations can improve overall productivity with the applied science of AI.

1. Automate Hiring – Employer branding as a major effort for organizations these days. This is the reason why they need to be proactive at hiring and make the process seamless. AI and ML can help do that by automating part of the process to improve recruiters’ productivity and help them sort relevant applications in lesser time. With AI and ML in hiring, recruiters will not have to read one CV again and still be able to shortlist the right talent for their enterprise.
2. Track on-screen time – Companies can improve the productivity of employees by implementing an AI and ML enabled time tracker that automatically detects their work hours and helps them keep track of billable hours. In the professional services space, timesheets are hard to maintain and track. With AI and ML, no more hindered productivity from noting down effective hours on each project.
3. Investigate frauds – Within the financial institutions of today, there is a growing concern toward scams and similar attacks. Intelligent automation using robots can help finance and banking companies to detect fraudsters and prevent revenue loss due to malicious intent.
4. Employee Onboarding – Since all onboarding activities remain almost the same year after year, companies are now shifting these from employees to robots. Several emails and repetitive tasks can be automated with the efficient use of technology. This application of AI and ML can significantly improve employee productivity by freeing them from low-value services.
5. Customer Engagement – The widespread use of AI and ML-powered chatbots is not unknown to anyone. Chatbots are helping enterprises automate parts of their conversation with customers, allowing for 24/7 presence at reduced costs. Conversational marketing is gaining ground and companies are able to address global clientele with a multi-linguistic chatbot- improving employee productivity and allowing them to interject into interactions when the need be.
6. Sales and Conversions – Within sales, teams have been traditionally picking up customers for getting on a call depending on logic, but also guesswork. AI and ML can take the guesswork out of this equation by telling sales reps what they should prioritize in order to gain maximum leverage. As a result, sales teams are becoming more productive and converting leads faster.
7. Maintenance – Equipment manufacturers and suppliers are now offering assets-as-a-service, delivering outcomes instead of machinery only. These companies are using AI and ML in the form of sensors embedded into equipment to detect defects before time. Where uptime is mission-critical, equipment manufacturers need to predict the need for maintenance. By scheduling and predicting maintenance in advance, companies are achieving improved productivity by minimizing downtime and saving cost and time on unnecessary maintenance activities.
8. Employee Engagement – To keep employees encouraged and productive, companies are planning solutions geared by AI and ML to manage their workforce- both in-house and remote. With AI and ML, companies are monitoring employees in real-time, streamlining conflict resolution, accelerating learning and development activities, and providing continuous support and encouragement to employees.
9. Demand Forecasting – McKinsey says that inventory reductions of 20% to 50% are possible with machine learning. With AI and ML, we can test models of production and outcome possibilities and precisely analyze data when adapting to new products, disrupting supply chains, and spikes or lows in demand. Sophisticated drones can accomplish so much in less time than a human within a warehouse.
10. Regulatory Compliances – A lot of industries face stringent compliances that are critical for them to follow. Since compliances remain a moving target, companies are looking at AI and ML to help them navigate changes in regulations and ease compliances for them. By intelligently automating compliance in IT systems, organizations can leave them to the robots without bias, mood, and subjectivity- which is ideal.

 

Artificial intelligence and machine learning are creating a personalized experience for everyone involved with a business. These technologies are fast-forwarding us to the future where the customer is the centric objective and businesses base decisions on facts rather than on guesswork.

Since AI initiatives juggle with a lot of data, that is one of the top three greatest issues with AI adoption companies face, according to Deloitte’s second annual State of AI survey respondents.

Training machine learning is another challenge that can be solved with sophisticated algorithms, neural networks, and clean, relevant, massive data. Defining the ethics of AI and balancing convenience with security also remains a top challenge for AI and ML implementation.

Nevertheless, enterprises are excited about the avenues that open up for them to improve productivity across the board with applied artificial intelligence and machine learning.

Education is rapidly changing around the world. The new generation of learners are born with digital acumen and the ability to adopt technologies quickly.  Software solutions and advanced devices are facilitating smart classrooms. Smart solutions for education have begun a wave of transformation in this space.

Traditionally, schools and higher-education institutions carried out tests and compared the scores of each student against their peers’. We are now beginning to understand that the world of learning is so much wider than classroom tests.

There is so much that kids and adults can learn on their own. To accommodate these shifts, education is transforming, thanks to AI.

The Challenges with Legacy Education Processes

Today, all educational institutions grapple with the following critical challenges:

• Providing quality education at scale – Often, educational institutions remain within the confines of their walls. They don’t have the right infrastructure to make education available to larger groups of people beyond their geographies. Also, imparting quality education is a challenge when the knowledge of your teachers is limited.
• Making education more accessible – Helping students in rural areas access education has always been a challenge. With infrastructure constraints, there is a need to find out ways we can make education available to remote areas of the world.
• Reducing costs of delivery – While making quality education available at scale, institutions might need to invest a lot. This is an issue for institutions with finite resources!
• Optimizing teachers’ time – Teachers are burdened with administrative tasks such as planning teaching materials, marking and grading assessments, checking facts and the sources of the submitted assignments, and so on. This takes time away from teaching and renders the job less efficient.

Many edutech solutions are hitting the market every day, but the most exciting avenues of disruption come from AI. Artificial Intelligence promises to solve the pressing challenges in the educational space.

As AI techniques such as Natural Language Processing, Machine Learning, and Voice and Speech recognition become more and more sophisticated, they are set to transform education for its instruction and administration capabilities.

But, what exactly can AI do for Education?

Let’s explore!

Grading and Assessment Management

Teachers spend the majority of their time working on grading and marking assessments. That means a lot of their fruitful, efficient hours are wasted on an activity that can easily be automated.

In an effort to reduce this burden, institutions are using artificial intelligence-led solutions for automatic essay scoring, reducing the reliance on multiple choice-based questions. These solutions are adding to the holistic learning patterns and ensuring better student comprehension.

Resolving In-class Queries

As classes get more and more crowded, teachers have little time to address individual questions students might have. Using AI-powered virtual assistants in classrooms can resolve this issue. Using virtual assistants (VA), students can check their own answers freeing the teacher from repetitive questions that can be simply looked up on the internet.

This method also empowers teachers to record and analyze the kind of questions students ask the virtual assistants. This way, teachers can identify weaknesses in comprehension and learning, course-correcting their strategy for teaching.

Personalizing Communication

As the scale of education grows, personalizing communication with students and their guardians becomes increasingly difficult. Giving students, parents, and teachers access to the right information at any time from anywhere is an added challenge.

AI-fueled chatbots can tackle both gaps head-on. With chatbots, information can be accessed from anywhere by anyone at reduced costs. A chatbot could do the work of 10 recruited staff saving you thousands of dollars and improving overall efficiency.

Chatbots can record conversations with each individual, personalizing the experience more and more every time.

One-on-one Customized Guidance

University education mostly happens in lectures with a lot of students. Therefore, dedicating time to individuals or even small groups can be tricky. Personal virtual tutors powered by AI and ML can do the job!

A private virtual tutor can customize learning for each individual, taking into account their speed of learning and comprehension- providing the necessary support where needed.

Student Enrolment

A lot of the student enrolment activities remain the same year after year. Automating these tasks with AI makes a lot of sense for educational institutions. Personalized communication with students can help them enroll in universities with ease.

A round-the-clock, integrated chatbot can answer their many queries before admissions and streamline the entire process of application and enrolment. This would free teachers and other staff from administrative and repetitive work that deserves to be automated.

Plagiarism and Authorship Detection

Graduate and undergrad students often succumb to malpractices such as plagiarism to attempt and win a degree with no effort. Since it is cumbersome for teachers to check each submission for originality, AI can help!

AI-powered software can help detect plagiarism and assert authorship based on the writing style, grammar, punctuation, and many other factors. By automating this part of education, degree allotment and assessment checking can be made more efficient.

There are still challenges AI and ML have to face before they can be commercialized for use in educational institutions.

Here are a few examples of the above:

• Teacher’s resistance to change in the wake of the fear of losing their jobs.
• Challenges in adopting AI technologies at scale after getting buy-in from stakeholders.
• Lack of easy access to funds to set up smart teaching within schools and universities.
• Ensuring the security of AI-based solutions so that users are comfortable providing all the data these systems need in order to get the right ROI.

Even though there are challenges down the road, a few companies are taking leaps and strides in making AI accessible, plausible, and affordable to institutions.

Educational organizations willing to take the plunge into AI will gain a massive competitive edge in the market and attract both students and teachers- thereby improving educational standards.