09 Oct Artificial intelligence for enterprise applications
Enterprises are upgrading their AI programs from trial events to full-blown deployments across its various functions.
AI’s recent interventions in the ecosystem of corporates has let them accomplish far more in less time, creating compelling and highly personalized customer experiences. It even helps in predicting outcomes based on data to drive greater profitability for the business.
What AI does is it leverages the massive amounts of process, operational and transactional data of enterprises to tell enterprises how they can be more efficient, productive and save costs as well.
By applying natural language processing, computer vision, machine learning or deep learning, AI systems can intelligently respond to actions or situations, drive insights that can help to improve processes, as well as improve customer reactions.
But just how does it apply to your enterprise?
Common techniques of AI in enterprises
There are many theories, methods and algorithms for AI solutions, but not all are meant for the enterprise framework. The most common AI techniques for enterprises are:
Machine learning: Machine learning is an important aspect of AI and the success of AI programs is very much linked to the growth of machine learning initiatives by enterprises. Machine learning is an algorithm that learns and gets experience from data to uncover hidden patterns for predictions or obtaining useful information. Machine learning lets enterprises predict future trends like their future sales by running the historical sales through a machine learning algorithm. Once the system picks up the pattern, it will get better are predicting future sales.
Deep learning: Deep learning enhances the idea of neural networks incorporating many deep layers to understand and learn complex non-linear relations, making it stand out from the traditional machine learning methodology. Deep learning uses numerous deep layers to understand abstract concepts, getting inputs from the lower data layers. A deep learning networks acts as feature extraction layer that holds a classification layer on the top. Feature extraction is the most powerful aspect of deep learning algorithms and is a process that is automatic and multi-layered. Deep learning networks learn from large numbers of labeled examples. It detects errors and the weights between the data are adjusted to optimize the process and create a tuned network.
Reinforcement learning: Systems automatically learning from the situations and choosing an optimal path for itself to meet its objective. The system is based on rewards where the learner is not told what action to take but is rewarded for the right decisions. This method applies to how students learn – he/she is rewarded when he/she excels and punished for failures. Data scientists and AI professionals can explore and contribute to this niche area.
Natural language processing: NLP is the ability of machines to comprehend human language in multiple formats like written text, speech or video. Since natural language does not have any fixed structures, storing and processing such data is rather difficult. NLP is hot topic currently and enterprises are investing in research in this area to let it evolve into a discipline that helps reduce instances of fraud detection, bring down security breaches, automates customer/employee assistance and mine unstructured data.
Automated machine learning: Setting up statistical routines and automating machine learning. The system executes the best algorithm on the bases of the data sets provided. The system also simplifies the data by hiding its complexities while sharing it with people. When a user provides an automated system, the automated machine learning system understands the data, create various mathematical models and returns a result arrived at using the model. Automated machine learning is a complex science since the system must learn the patterns within the inputted data and derive values as well optimize its parameters using complex statistical and machine learning models.
Application of AI in industries
Proponents of AI have never been more hopeful about the discipline as they are in the current times. AI has seen an unprecedented rise in research, investments and real business applications around it. Some of the major applications of AI in industries are:
- Healthcare: Data mining for identifying patterns in healthcare records, carrying out accurate diagnosis and treatment of medical conditions, treatment plan selection, medical imaging, medication management, drug discovery and robotic surgery.
- Banking and financial services: Loan application processing, custom investment suggestions, identify sectors/companies aligned to long-term needs and objectives from social media accounts, customer experience chatbots, insurance plan creator, claims processing and fraud detection.
- Retail and e-commerce: Product recommendations, customer experience chatbots.
- Logistics and transportation: Sorting and packaging products, supporting the quickest shipment route and supporting the last-mile delivery, self-driving vehicles, scheduling, routing and traffic light management.
AI is evolving and is certainly disrupting process in other major industries as well. Today, we are part of the age where machines understand and anticipate what users want or are likely to do in the future.
Business functions already using AI
Though still in early stages of adoption, enterprises are keen to adopt AI and are excited about its possibilities.
Sales: AI lets sales professionals get insights that can improve their sales functions. AI helps improve sales forecasting, predict customer needs and improve communication. Intelligent machines can help sales professionals manage their time and make timely communications with prospects.
Marketing: AI helps to develop marketing strategies in enterprises and plays a crucial role in implementing them as well. AI sorts customers based on interests, demographics and can target ads to them based on browsing history, powers recommenders and can let you give customers what they want when they want it.
Research and development: AI enables research into problems and develop solutions never imagined before. AI brings in the possibility of discoveries, ways of improving products and services as well as accomplishing complicated tasks with ease. With AI, R&D activities can be more strategic and effective.
IT operations: Often the first interaction most enterprises have with AI is in its IT operations. AI is commonly used for analyzing IT system log file errors as well as to automate routine processes. The AI system identifies issues with systems that the IT team can help rectify so IT systems don’t face a downtime. With IT systems getting more complex, AI is helping to improve its performance, stability and services.
Human resources: Recruitment or talent management, AI can turn anything around. AI can personalize hiring experiences, help HR teams with data-based decision-making, simplify candidate screening and the recruitment process. AI chatbots can also answer common questions around company policies and benefits.
Contact centers: Organizations are using AI technologies to enhance human capability in this area. AI can help their human counterparts collect the right amount of data from customers to understand them better and deliver a much more enriched experience during every interaction. The data collected can be used to learn more about customers, predict their intent and do the next best action.
Manufacturing: Data analytics can help enterprises track their inventory. And that’s not all. Predictive intelligence can help enterprises anticipate demand, manage production quantities and sensors on equipment can predict needs for maintenance. AI can also flag areas of concern in the manufacturing process and machine vision can check quality control at manufacturing facilities.
Accounting and finance: AI is a massive factor in cost reductions and efficient operations in enterprises. AI frees up human finance professionals from repetitive duties, reduces errors and helps enterprises get real-time statuses of financial information with NLP capabilities.
Customer experience: Enterprises have been using big data and AI to improve their customer interactions, making them more personal enhancing sales and customer experience and relationships. Tailored recommendations, customer service chatbots are ways to give a great experience to your customers.
How can enterprises enable an AI/ML function?
AI and ML adoption is in its early stages, and companies are working through the challenges of understanding how these technologies can benefit their business. You can move your business forward with Nuvento’s AI practice.
While large businesses are investing more in research to explore crucial business areas that can be improved with AI, a vast majority of small and medium businesses (SMBs) are yet to accelerate their rate of AI adoption.
Here’s what all enterprises can do to get started:
- Assess business capabilities: Your business goals can be around providing better customer service, reducing waste in the supply chain, or accelerating your innovation pipeline, think about where you are missing insights that lets you achieve these goals and transform your business. Having the vision of what you want your business to achieve through AI and ML is just the start
- Build the skills needed in your organization: If you want to expand your AI and machine learning capabilities in-house, you can hire experts to set up the process.
- Get help from a skilled team: You’re clear on your goals but don’t have a team to set up the processes in place, you can get in touch with our team of AI experts who’ll handhold you through the process of setting up your data straight so you can drive your machine learning initiatives in products and processes.
Nuvento’s AI experts can embed AI into your applications to let you create intelligent and automated solutions to real business challenges.