About the series
Fact or fiction? breaks down common myths and misconceptions about digital transformation, while showcasing KPMG leaders’ perspectives on related topics such as artificial intelligence and blockchain, and the impact of these emerging technologies on the workforce, businesses and society.
Using AI effectively requires a methodical strategy and plan, aligned to business needs, supported with the right talent and leadership. This means the entire organization is responsible for implementing AI.
Slowly but inevitably machine learning is starting to influence our daily lives. Whether you ask your home virtual assistant to check the weather forecast or cede some of the planning (and even driving) of your daily commute to your “smart” automobile, it is machine learning that is making life easier.
Yet while we seem to have embraced machine learning at home, understanding and embracing its potential in the enterprise remains challenging. The desire is there, experiments are happening, but there is difficulty in getting real change into production.
Many organizations are not yet making the transformational changes driven from machine learning that will be needed in order to succeed in the coming years.
At the same time, organizations are starting to make moves that act as building blocks for imminent change and transformation. With that in mind, KPMG has identified four trends that demonstrate how machine learning is starting to bring real value to the workplace.
The rise of the virtual assistant will transform the workplace
By 2020 an estimated 80 percent of business-to-customer conversations will be conducted by machines. That will have enormous implications for all organizations both in terms of business processes and also future staffing needs. Executives will need to have a clear vision and also a strong culture in order to effectively plan and manage this shift to machine driven intelligent interaction.
Machine learning will combat rogue behavior
Processes where companies are monitoring enormous amounts of data for specific trends and patterns to identify anomalies or rogue behavior are ripe for the application of machine learning. This might be for things like anti-money laundering, revenue leakage, customer segmentation, and more. This is where machine learning is uncovering insights that couldn't be identified before at a pace that is exponentially faster and more effective than historical approaches.
Businesses will gain new visibility into unstructured data
Machine learning offers organizations the chance to gain deep insight into large volumes of unstructured data, automate and accelerate existing business analysis as well as streamline and bring greater consistency to customer interaction. One area where we are seeing this heavily is in the contract management lifecycle. Utilizing ML and NLP-led processes to extract, compare and analyze the data locked up in contracts is proving to have tremendous value for procurement organizations, and is rapidly moving into the legal function. But organizations will only realize those benefits if they have a strategy that scales across both technology and business processes, and is ready to adjust for the changing roles and capacity needs.
Converting unstructured data will transform regulatory compliance
Increased regulatory compliance is an area where machine learning and natural language processing/understanding are beginning to radically change the data landscape (especially in the financial services and life sciences sectors). When applied in this way, machine learning is helping to increase the speed and effectiveness of compliance initiatives, manage risks and reduce the regulatory burden.
Each of these four areas provides value to an organization seeking to move forward with machine learning and add incremental value that can scale to be truly transformational.
Machine learning is here to stay, so let’s embrace the early incremental value areas now, to scale to real change in the future.