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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.