When artificial intelligence and machine learning are added to systems it opens the door for better opportunities for businesses across every industry. The intelligent tools and solutions provided by these technologies not only yield significant outcomes but also give rise to innovation to thrive ahead of a competitive edge. The technology like machine learning which is technically a subset of artificial intelligence stands out with its innovative features and capabilities it provides to the user.
Earlier ML had to be hand-coded and hand-wired and if coder needed to change anything, he had to stop and rewrite the algorithm. Contrary to this, today, coding is much easier and such algorithms are available as a service or an API.
Undoubtedly, the embedded AI and ML technologies make a process more intelligent and enable it to take over repetitive and mundane tasks which are usually time-consuming. Consequently, with the systems growing intelligent, digital integration of them has become more convenient through data, robotic process automation, and machine learning platforms. With each segment of integration, the system climbs the stairs of better and different management approach through technology, innovation and paves way for the creation of intelligent enterprise.
Well, continuous evolution in business models is essential for the creation of an intelligent enterprise. In present times, most of the industry work is achieved through the amalgamation of automation and manual tasks often weakening the process and making it more labor-extensive and time-taking.
Specifically, in manufacturing, with significant changes in geopolitical situation and trade agreements, the professionals need to quickly find and source different suppliers to trade their components within a couple of days to protect their production from standstill influencing their margin. Other than the rapid search and assessment process, the company’s flexibility also becomes a matter of concern in the changing world.
In such challenging situations, embracement of AI and ML technologies in the existing business process provides an edge to the system to evaluate and analyze areas that may concern customers.
How AI Influences Employers Before Employees?
As technology offers competitive differences, employers are expected to affect more by artificial intelligence than their employees. But it’s not that simple, they have to make certain changes to avail the perks of such advents.
Interestingly, in the US, 22 percent of organizations allocated parts of their profit to AI, in their annual reports in recent months.
Several studies show that the organizations who are doing most with ML have witnessed 43 percent accelerated growth than those who haven’t yet started using AI technology.
However, as AI has become core to various business strategies, data governance and data management still is a persistent challenge to overcome. Leadership becomes a key in this context and excelling companies should first improve data management to begin the successful AI voyage. Bad training data will restrict the effectiveness of technologies regardless of the great algorithms.
If an organization desires to attain AI/ML success, it needs to consider transparency, data quality, ownership, and governance. With new advancements come new rules and companies need to design principles focused on leveraging transparency in AI processes.
Innovation is the Mantra for Success
An innovative and smart approach to tackle business challenges can profit organization and bring success to the queue. Companies should take a business problem specific to any one customer and extract the elements from it which are common across the industry. Next, they require to define the methodology and technologies used to solve it and package them to make it available as an industry innovation kit. These packages can also be embedded in other applications to address common industry challenges.
Better outcomes cannot be created until business solutions do not provide intelligent technologies for operational processes. Innovation including pre-designed tech packages, enabled by best practices and process frameworks, can solve business problems and strengthen the core processes to manage everyday tasks. AI and ML technologies can also help automate those processes which seem difficult to be completed.
Design Thinking Can Yield Better Outcomes
Design thinking practices and user-led approach derives insights, co-create ideas and enhance fresh technological innovation to the industry. The approach is all about open innovation and instant design development. Design thinking is capable of delivering results in weeks and with lesser risks, unlike those innovation processes which take 2-4 years to exhibit productivity.
In the past few years, the design-thinking approach has proved itself as a key to successes in discrete manufacturing, energy and natural resources, and consumer industries.
Most of the experts also envision ML within AI as the most intelligent component a company can use as they reduce repetitive task and take care of heavy lifting. While excelling with the advancements of ML, some are already benefiting from their first initiative infusing ML whereas others are building new business models and improving operational efficiency through IoT, blockchain and predictive analytics. It is high time that organizations should envisage AI and ML appropriately and accurately into their digital transformation strategy.