Aricent helps companies get the most out of an industrial-scale automation by leveraging AI and machine learning
The word automation was first coined in 1946 by an engineering manager at Ford Motor Company to describe automatic devices and controls in mechanized production lines. Seventy-two years later the definition now includes computers and robots that leverage artificial intelligence (AI) to engage in making highly complex decisions independent of humans. AI includes a number of subcategories, including deep learning, machine learning (ML), natural language processing (NLP) and robotic process automation (RPA).
The accelerating pace of AI adoption into many markets—automotive, consumer, finance, manufacturing, medical, retail and others—expands the definition of automation far beyond the mechanization of production and other rote transactional functions. Powerful AI-enabled machines—in combination with advanced robotics, sensors, cloud computing and analytics—can deliver orders-of-magnitude improvements in productivity, agility and business value that were unimaginable a decade ago, let alone in 1946.
Some areas where AI and ML (which allows computers to learn without explicit programming) include more productive and innovative design and development, leaner supply chains, more productive assembly lines with fewer machine failures, according to McKinsey & Co.
However, Aricent has found that many companies that start the journey towards intelligent automation are not leveraging the full potential of the technology and so are not reaping maximum business value. We have identified five reasons for this failure, along with the necessary steps to overcome them.
- First, define your strategy
Before you take the first step on your AI-enabled automation journey, it is essential to develop a well-defined and clearly articulated strategy. This includes identifying and measuring the primary reasons for adopting AI. Most important, intelligent automation should be leveraged for competitive advantage in the market and not just to reduce operational cost. So, it is important to measure both direct and indirect benefits before deciding on the strategy or business case.
- Organizational Readiness
The next step on the journey is to ensure that the organization is ready for intelligent automation. This involves defining the systems, processes, and roles and responsibilities of employees to leverage the potential of intelligent automation fully.
The infrastructure includes compute, storage and connectivity. For example, a legacy WAN network may need to be replaced by a software-defined WAN network that is more compatible with the intelligent automation architecture. Or a hardware datacenter may need to be replaced or supplemented by a cloud-based, virtualized datacenter.
Another critical requirement for organizational readiness is education. Employees need formal training to ensure they understand the requirements for managing intelligent automation. The training should include everyone from senior managers to front-line factory staff. Utilizing the services of an intelligent automation partner can be critical during the early rollout stage.
- Sensors and Monitoring
Intelligent automation is only as good as the inputs it receives and it’s connections. Installing a network of Internet-of-Things (IoT) sensors that connect to all relevant machines and devices, processes and systems ensures 24/7 monitoring, which is necessary for managing AI-automated processes. Sensors are the eyes and ears of the system. An industrial network of smart meters that are wirelessly connected is far more prepared for intelligent automation than an organization with legacy meters. Today, there are a variety of low-cost sensors available to measure and communicate almost anything from temperature to pressure to the presence of chemicals in the environment.
One requirement for any sensor network is a robust security capability to monitor and thwart malicious activity that could disable or shut down the network. Hence, ensuring fool proof security operations is critical as well.
- Data Collection and Processing
AI-based automation is useless without a powerful analytics engine that can process the massive amounts of data coming in from the network of devices and sensors. It is vital that this engine is up to the task, both regarding the ability to manage the volume of structured and unstructured data and the ability to process, store and communicate the data rapidly. This is critical as the analytics engine is essentially the nervous system of intelligent automation corpus that ensures all the dots are connected in real-time.
- Finally, Add the AI
Only after you have successfully addressed the prerequisites outlined above will you be ready to add the AI brains to the system. Leveraging AI to make intelligent decisions will enable autonomous actions throughout the automation-ready infrastructure by leveraging the IoT sensor network and analytics engine. Machine learning software will process the volume of data and generate useful insights that lead to more efficient, cost-effective and faster production that drives business value.
Intelligent automation is not just about automating the last mile or bolting on AI or RPA capabilities to fine tune an existing automated process. Rather, this kind of intelligent transformation requires a strategic change throughout an entire enterprise. When artificial intelligent capabilities are methodically and “intelligently” combined with the appropriate infrastructure—compute, storage, sensors, analytics and security—you will be able to leverage the full power of intelligent automation, ensuring your enterprise is future-ready.