In the second of a two-part series, you’ll learn how companies are successfully using Artificial Intelligence and how to take advantage of these new capabilities.
In the past, Machine Learning (ML) had to be hand-coded and hardwired. If a change to an algorithm was necessary, the process had to stop while the code was rewritten. Now, thanks to the addition of Artificial Intelligence (AI) and ML, systems have new opportunities to run simple business processes. Today, coding is easier and ML algorithms are available as a service or an API. Embedding intelligent technologies like AI and ML into business systems can make processes more intelligent and can take over repetitive work to relieve knowledge workers.
As systems become more intelligent, it is much easier to digitally integrate them through data, robotic process automation (RPA) and machine learning. This opens the door to new management practices using technology application and new service paradigms to improve and transform business performance ultimately resulting in an intelligent enterprise. Data is the glue that will integrate digital processes, with AI tools will help seamlessly transform the processes.
If an intelligent enterprise is at the end result of successful digital transformation, this means our business ecosystems will need to change and business models will be in constant flux and evolution. At present, most work tasks are accomplished through a combination of automation and manual tasks. Manual tasks require human intervention or decisions, and seamless parts are automated. The connections between manual and automated tasks may have weaknesses and are often labor-intensive and time-consuming. When involved with automation, one should examine areas of how AI and ML can support human decision-making. Let’s look at good processes for intelligent data transformation.
The impact of AI
In the United States, 22% of companies have allocated parts of their profits to AI. According to a recent study that SAP conducted in conjunction with the Economist Intelligent Unit on AI adoption, the organizations doing the most with ML have experienced 43% more growth on average than those who either haven’t started using AI and ML or who aren’t using it well.
While AI is becoming core to business strategy, data governance and data management remain a persistent obstacle. The most important step is the leadership and proper training when it comes to improving data management. No matter how great the algorithm, bad training data will limit the effectiveness of AI and ML. Transparency, data quality, ownership, and governance make all the difference for AI and ML success.
Avoid the pitfall of the mythical “moonshot promise”
The “moonshot promise” is a strategic misconception that AI and ML are for things that will solve giant problems and contribute to full industry disruption.
Take online shopping for example. AI and ML didn’t holistically change the retail industry, rather it simplified the process for customers. Consumers were fine heading to the mall for the latest fashions but were excited to be able to order clothes from the comfort of their own home.
It simply took two steps out of the old process and infused some ML. In fact, contrary to the “moonshot promise” of new technology, most people want to do the same things they have always done, only faster, cheaper and with much more data and personalization.
With news technologies comes the need for design principles that focus on providing transparency on how AI came to its answer and ones that focus on proactively dealing with regulatory and ethical dilemmas. Key questions to ask are:
- Who is going to pay attention to managing data?
- How does AI come to this decision?
- Who trained the system?
- What data is the basis?
- What confidence does the AI have in the data quality and relevance?
- Do I trust this decision?
Read More: AI Beyond 2020: What Makes the Tech Tick?
Factors for Success
The first step in being successful, is to identify a business problem that is specific to one customer, find the elements of that challenge that are common within the industry then define the elements of a methodology and use technologies to solve it. Packaging them and making them available as industry innovation kits or embedding them into other applications helps other customers more easily address common challenges.
Business solutions need to provide intelligent technologies for business processes to create better outcomes. A business solution comprised of a pre-orchestrated tech stack, enabled by best practices and process blueprints, can solve business problems and complement the core processes and the tasks people manage every day. For processes that are difficult to complete, new technologies like ML and AI can help automate them.
Here are some key requirements:
- Do you have the training data or the business examples to train your AI & ML?
- Can you afford for your best business process experts to make their time available to train the ML in your secret sauce or how you do things?
- Is the strategy clear and focused on scaling knowledge augmentation across the enterprise? The goal should be to implement ML initiatives enterprise-wide, rather than within individual business units or functions.
The Power of Design Thinking
Design thinking best practices take an outside-in, user-led approach to uncover insights, co-create ideas, and accelerate new technological innovations to market. It’s all about open innovation and rapid-design development, delivering results in weeks with fewer risks.
During the past year, we’ve seen how the design-thinking approach and industry best practices are key to successes in discrete manufacturing, energy and natural resources, and consumer industries. Some recent examples of successes:
- Customers call a support line less to fix issues because the technologies can now self-analyze and fix problems
- Reduces downtime by 40% because equipment can predict and self-heal failures
- Decreasing costs by as much as 68% by integrating AI and cloud computing during the manufacturing process
- Increasing employee retention with the help of analytics and ML
The early adopters are in the Machine Learning report, are already profiting from their first initiatives that infuse ML into their processes. Others are creating new business models and improving operations with IoT, blockchain, or predictive analytics. It’s time for all organizations to start thinking about how AI and ML fit into a strategy for digital transformation.
Read More: The Future of AI: Are Jobs Under Threat?