AI, ML & ‘Robot’ Business Spending Will Hit $232bn by 2025 Says Report
A recent KPMG reports claims that whereas business spending on artificial intelligence (AI), machine learning(ML) and robotic process automation (RPA) technologies is $12.4bn this year, it will increase to $232bn in 2025.
Ready, Set, Fail?
The report, entitled “Ready, set, fail? Avoiding setbacks in the intelligent automation race” highlights how the potential of AI technology is already being examined by 37% of enterprises, and how its uptake is expected to accelerate over the next three years, with all enterprises using the technology to some extent, 49% of enterprises using it at scale, and 29% using it selectively. Currently, 13% of enterprises are missing out altogether on the opportunity of using AI to add value to their business.
Can’t All Be Like Leading ‘Digital First’ Companies
The report accepts that while most businesses can’t realistically expect to be leading ‘digital first’ companies, such as Amazon with its one-click experience linked to a complex back-end and digital supply chain, they can make good ground from now on by acting quickly, understanding the need for urgency, and defining and executing a comprehensive AI strategy.
What Is Digital First?
A ‘digital first’ / digital by default approach involves giving priority to new media channels and technologies to improve the business by bringing it into line with the needs and behaviours of today’s consumers. It involves adopting a whole new way of looking at the business in order to add the skills, and to change to culture and mindset in order to make it more effective.
What Is Robotic Process Automation (RPA)?
While many of us are now familiar with the terms artificial intelligence (AI), and machine learning (ML), the report also focuses on ‘robotic process automation’ (RPA). This refers to an emerging form of business process automation technology that uses software robots or artificial intelligence (AI) workers.
Instead of software developers producing a list of actions to automate a task and interface to the back-end system using internal application programming interfaces (APIs) or dedicated scripting language, RPA systems develop the action list by watching the user perform that task in the application’s graphical user interface (GUI), and then they perform the automation by repeating those tasks directly in the GUI.
Expectations High But Readiness Low
The KMPG report shows that even though managers’ expectations are high for AI use in their company in the coming years, the readiness to implement AI is low. The reasons for this include the fact that two-thirds of enterprises lack the in-house talent, and half of businesses are still struggling to define goals and objectives for AI.
Also, the 33% of respondents in KPMG’s study said that management are lacking readiness to implement AI because of a concern over AI’s impact on employees.
According to the report, even though readiness is low, the investment needed for intelligent automation is available, and is expected to increase over the next 3 years, with 32% of organisations having approved more funding for robotic process automation, and 40% saying that they will increase spending on artificial intelligence by at least 20% over the next three years.
What Does This Mean For Your Business?
Artificial Intelligence holds many opportunities for businesses, and those businesses that have moved successfully to a digital first approach appear to be reaping the benefits in terms of competitive advantage and profitability in the modern marketplace.
There are many ways in which businesses can meet high marketplace expectations for AI. These include:
– Long-term planning with a sequence of steps, beginning with prioritised projects that can realise scale in one or two years, with the help of C-level buy-in and sponsorship. This can lead to a successful transformation built on new blueprints and architectures for operating models and business models.
– Taking a comprehensive and holistic approach to automating the service delivery model.
– Taking another look at the whole operating model and how AI can be best adopted and applied to the core business. This involves looking at the operational and technology infrastructure, organisational structure and governance, and people culture. This can be supported by measurement and incentive systems, and implemented in a way that causes minimum disruption to existing business processes.