There are some interesting similarities between introducing Machine Learning in your company and building a new house. When you would decide to build a new house you would not only have to buy the materials, but you also would have to hire the skilled talent who can get the job done.
That exactly is the lesson many senior executives, including their CIOs, around the world are learning about their plans to implement Machine Learning technologies that can analyze and improve performance without direct human intervention. Despite investing in Machine Learning most companies do not havethe talent, data quality and budgets to fully leverage the technology. So the question is how to have a successful implementation of Machine Learning in your organization?
First please be aware that capturing greater value requires more than investing in technology. It is also necessary to make significant organizational and process changes, including approaches to talent, IT management and risk management. So before you would start you must first prepare your organsiation for that disruptive change. The three most significant challenges in that respect for you will be :
1. Designing an organizational structure to support data and analytics activities;
2. An effective technology infrastructure;
3. Ensuring senior management is involved.
Organizations that can manage these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.
To ensure a succesful Machine Learning implementation the following five steps should be taken:
1. Improve data quality
Ensuring the quality of data is a common obstacle to machine learning adoption. Poor data leads to machines making poor decisions, which can lead to increased risk. CIOs need to consider implementing solutions that simplify data maintenance in order to accelerate the transition to machine learning. The first step should be to consolidate redundant legacy and on-premise IT tools into a single data model.
2. Establish value realization
Articulate the business value of all technology goals, then determine how best to reach those goals. This includes examining existing processes to identify which unstructured work patterns will benefit most from automation. Determining where fragmented data “lives” will enable you to identify how automation will lead to gains in productivity.
3. Create the best possible customer experience
Using machine learning for automation will boost operational efficiency, but do not overlook the ROI of accelerating decision making (without sacrificing accuracy) to improve the customer experience. Start by envisioning the customer experience you want to create, then prioritize investment against those elements of business processes that could most improve the customer experience. Machine learning allows organizations to personalize advertisements, call-center interactions and even products and services for individual customers -- and to predict what they want next.
4. Set and measure metrics
CIOs understand the value that machine learning offers, but the other members of the senior executive team and board may not. CIOs must therefore set expectations, develop metrics of success before beginning the implementation process and prepare a solid business case to present to the leadership team when requesting the necessary funding. Metrics will need to change as you adopt Machine Learning capabilities and reap the benefits of intelligent automation.
5. Understand the effect on your Corporate Culture
How employees’ roles will change as the organization introduces Machine Learning requires the senior management to adjust their hiring and training processes. What would require data science, engineering, math and critical thinking. This transformation will likely be uncomfortable for some employees.
An important part of your Change Management strategy should be to make sure to communicate the value Machine Learning will bring to their day-to-day work. Take away their fears that the machines will take away their jobs, but instead would free them of tedious manual processes and would allow them to focus on more strategic projects.
It’s important also to understand that CIOs are not immune to that uncomfortable feeling. Their roles must evolve as well from being responsible for keeping the lights on when it comes to operational matters to an executive who has a much broader engagement across the business and, therefore, a new level of strategic importance.
Realizing a ROI on Machine Learning requires planning and disciplined follow-through -- all while adjusting employees to how rapid and ongoing technology changes will affect their day-to-day work. Following the five steps described above will ease that transition.