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Businesses need to fast-track automation of core processes to enable access to advanced data analytics capabilities
If anyone was ever in doubt as to the impact AI/ML will have, not just on business but on society as a whole, they need look no further than the clamour surrounding OpenAI’s ChatGPT tool. As Kweilin Ellingrud, senior partner at McKinsey Shanghai reflected post-Davos, the World Economic Forum’s annual event in Switzerland, “it will affect every one of us, the question is how fundamentally?”
That’s the point with AI/ML. It’s not a question of ‘when?’ It’s really a question of how quickly organisations can take advantage of AI/ML to make significant impacts on their operations? With so many unforeseen factors influencing economies and industries, primarily knock-on effects from the war in Ukraine, it is difficult to react quickly. This is mainly because it takes time to find, select and purchase technologies, let alone integrate them into an organisation.
Understandably, legacy technology is a huge barrier to progress and business leaders may be reluctant to take a rip and replace approach due to existing investments. It can lead to inertia, driven by uncertainty in IT strategy and a fear of failure through making expensive mistakes.
There’s also the issue of data. To take full advantage of AI/ML tools, organisations need accessible data. They need that data de-centralised and put to action where it is needed most to enable accurate decision making in an organisation. Data silos, manual processes and a lack of internal digital skills can lead to an environment lacking in confidence and capability. But there is hope. As Paul Daugherty, group chief executive at Accenture recently said; “in an uncertain world, technology innovation is the one certainty that you can rely upon to tackle seemingly intractable challenges and accelerate business and societal progress.” He points to cloud, artificial intelligence and the metaverse as “defining technology trends that will shape our future,” but importantly stresses that leading companies are turning to these technologies “to foster resilience, accelerate growth, optimise operations and reinvent their capacity for innovation.”
That means organisations are doing this now, to address the challenges of economic uncertainty and to use technology to find any advantage, in either more efficient operations or innovation in products and services.
Overcoming key challenges with augmented analytics
The key is to find a way to remove costs and focus on the core business strategy and special projects. By modernising business intelligence capabilities and moving to an AI/ML-as-a-service model, businesses can quickly get up to speed with advanced analytics. This increases intelligence and delivers the right information to the right people in the right format, to make quick, informed decisions that make a difference.
By design, this would also democratise and de-centralise data. It doesn’t demand a rip and replace transformation but can help organisations prioritise data, despite legacy technologies, meaning silos become less restrictive. It would also lead to a demand in improved data literacy, as data is utilised where it is needed most and not in a central location, controlled by a few data science specialists.
More people in the organisation can therefore be data driven, which helps to unify the business and deliver on common goals.
This is augmented analytics; using low-code/no-code tools (which more often than not leverage AI and ML) to automate tasks performed during the analytics workflow. Ultimately, this augments the user experience and can actually span most areas of data and analytics, from data ingestion and data preparation to analytics and ML model development.
It means multiple users can discover insights that could have otherwise gone unnoticed within existing data. It can also help users explore wider data sources, to enhance analytics capabilities, all while minimising human biases and accelerating insight and decision making. This can lead to both reduced operational costs but also more accurate, in-depth decisions. As David Wright, partner in Deloitte’s intelligent automation team recently said “organisations that moved beyond piloting intelligent automation tell us they have achieved an average cost reduction of 32 per cent in the areas they have targeted, up from 24 per cent in 2020.” He adds that this also “opens the door for better human to machine integration.”
This is where AI/ML-as-a-service, as part of an augmented analytics platform designed to deliver self-service intelligence at the fingertips of any user, can have a huge impact on organisations. By speeding up the intelligence process through streamlined data access and preparation, it is possible, using advanced automated tools, to join-up and visualise data quicker and easier than ever before.
Given the economic challenges this year, the ability to deliver smarter, more dynamic workflows will be a differentiator. With this holistic approach to data management, delivering faster, better-quality insights, organisations can plan and react to market changes more effectively. And that could make all the difference in the face of volatile economies and uncertainty in supply chains.