If data is the new oil, artificial intelligence (AI) can arguably be its best drill, able to uncover insights and mine real business value from the huge and complex data sets that typify modern organisations.
Enterprises are not blind to the massive opportunities that can be extracted: according to the latest Gartner data, enterprise adoption of AI has grown 270% over the past four years. In the last year alone, AI adoption has essentially tripled within enterprises of all sizes.
That’s not surprising, considering 85% of global CEOs believe AI will fundamentally change the way they conduct business within the next five years.
Until recently, the majority of business decision-making was predominantly driven by human-centric capabilities. Much of it involved intuition, experience, judgement and reliance on skills and expertise. Business leaders were more likely to rely on a mix of these elements and a healthy dose of ‘gut feeling’ to make decisions over certain strategic objectives, key business outcomes, product or service capabilities, and more. But the human brain is error-prone and subject to certain inescapable biases.
Data collected by computers over the past half a century has enabled more evidence-based reasoning and decision-making. But it has – until recently – still required human processing. That was until the commercialisation of AI.
What AI offers is an opportunity to process vast amounts of structured and unstructured data accurately and without any of the cognitive biases from which the human brain suffers. Deployed effectively, AI gives organisational decision-makers the ability to greatly improve their decision-making and creates opportunities to automate processes, boost productivity, uncover meaningful insights from large data sets and improve overall innovation efforts.
Where is AI being used?
According to the IDC, IT operations is the leading business area for AI deployment, followed closely by customer service and fraud and risk management. Top industries include healthcare, which is predicted to see a 49.7% compound annual growth rate between now and 2026 to reach over $8bn in value.
Integrating AI into your business is not as easy as 1-2-3. A quarter of global organisations that are already using AI report a failure rate of up to 50%. These organisations cite lack of skills, unclear business value and unrealistic expectations as the main reasons for this failure.
There are ways to improve the success rate of enterprise AI incorporation. Integrating your AI deployment into a broader intelligent enterprise strategy that rapidly transforms data into insight and supports process automation, innovation efforts and great customer experiences can accelerate time-to-value and increase the opportunities for AI to deliver value.
For enterprises of any size, it all starts with data.
Data is the foundation of AI success
At an enterprise level, successful incorporation of AI into the ecosystem largely depends on the quality of the data that the algorithm has access to, and the strength of the platforms that provide that data.
To establish a strong data foundation that can continuously utilise to deliver AI-imbued business value, enterprises need to look at five key aspects:
- Defining an integration strategy that allows the organisation to embed AI into the end-to-end business processes of the organisation;
- Establishing a holistic data platform that eases the process of data management across large and complex organisational structures – ensuring the one version of the truth everyone agrees on inside the organisation;
- Developing a full understanding of the organisation’s data, including where it comes from, which business processes it represents, quality levels, and the Five Vs of big data: volume, velocity, veracity, variety and value;
- Defining appropriate governance and compliance policies for internal and external requirements, and building governance controls into data management operations; and
- Ensuring a positive customer experience by simplifying the use of analytics and visualisation tools, encouraging self-service to speed up adoption among internal end-users.