Why data is key to ensure AI isn’t just a more intelligent ‘garbage in, garbage out’ process
Today, enterprises are going through challenging times. Customers are increasingly demanding personalised experiences and ‘predictive’ service quality. Enterprises need to be speedy, adaptable, agile and lean to deliver these experiences. As a result, artificial intelligence (AI) comes to the rescue of these organisations as they enable them to simplify complex operations, empower humans, create unified experiences, build smart, efficient processes and provide effective service orchestration to manage expectations.
The benefits of a bigger brain
AI is expected to be pervasive and present across various industries and verticals addressing use cases spanning the depth and breadth of enterprise functions. AI is likely to be most impactful in data-intensive industries such as technology, media, telecoms, consumer and financial services, where its immense data-crunching capabilities, combined with other technologies, will provide a major advantage.
While most use-cases being explored today centre around automating repeatable and low-level tasks, AI is increasingly being explored to address complex human-centric tasks around cognition and industry specific requirements. With recent developments and breakthroughs, enterprises are able to step up their game and realise AI’s true potential. Furthermore, current fears around humans losing their jobs are likely to prove largely, unfounded and untrue. Indeed, various analysts and executive surveys are predicting AI to create more jobs than it eliminates. With transactional tasks taken care of, humans will be free to focus on more skilled roles and play an increasingly strategic role, driving greater satisfaction.
The devil is in the data
Before these benefits can be achieved, enterprises must first successfully implement AI; however, as the waves of transformational innovation that have rolled in over the past few years have shown us, no new technology comes without its own challenges and AI is no different.
Successful AI implementations need to ensure that AI engines are fed with the resources needed to make them truly intelligent. “Garbage in, garbage out” rings especially true - without relevant inputs, AI will not be equipped to deliver a positive impact, as no level of algorithm sophistication will overcome a lack of data. Of course, there are other challenges to overcome, such as training the algorithms and people using them, but the initial top priority for enterprises looking to adopt AI should be identifying the relevant data sources and making them accessible.
Tapping into expertise
Another challenge being faced by enterprises beginning their AI journey is that there is no internal precedent to follow. While AI can work wonders for the enterprises, at times, precise use cases and efficiency points elude even key and prominent implementations. This can lead AI implementations to miss their objectives and benefits.
Having convinced key decision-makers on the potential benefits, and secured the funds for AI, this failure may impact the entire AI journey for the enterprise - both current and future. This is where the assistance of a trusted IT partner with expertise in AI deployments can add great value, giving access to a wealth of expertise and advice on the challenges enterprises will face and how they can be overcome, easing the burden on internal teams. With issues around these complex technologies demystified, enterprises can stride forward with their AI implementations for desired results, confident that it will prove a success.
- » New collaboration aims to explore VR and AR for engineering and automotive design
- » Organisations jumping head first into digital transformation projects – with wasteful results
- » Microsoft adds Slack to list of official Office rivals in 10-K report
- » ERP budgets: Reduce risk and prepare for successful implementation
- » Siemens snaps up Mendix in $730m deal to ‘strengthen digital enterprise leadership’