See the Future … Faster
Global disruption continues to change us, our society and the way we do business. From computing big data to creating decisioning models, businesses are using analytics in bold new ways. Even now – amid geopolitical risk, climate change, supply chain breakdowns and economic inflation – analytics can turn disruption into opportunities. Our new currency for growth and survival is scaled and accurate decision-making.
When thinking about the future of AI and advanced analytics, it is easy to contemplate the continued advancements in areas such as natural language processing, conversational AI, predictive modeling, complex simulations, and computer vision. These advanced capabilities will certainly continue to make positive impacts on businesses and society. But the stark reality is that the volume of data being created daily is far exceeding the collective human capacity to make sense of it. To overcome this information overload, organisations need to apply the right analytic and modeling techniques to support transparent and explainable decisions. This trend is driving even more innovation and productivity in analytics and AI – to ultimately see the future faster.
At the same time, emerging technologies, like generative AI, blockchain and simulation, are disrupting traditional industries, offering future-focused innovation and converging into the next iteration of the web. These technologies are causing an explosion in the rate, complexity and volume of data, and creating an even more pressing need for analytics, machine learning and AI to help make sense of it all.
I believe we will see growth in the following areas in 2023:
1. Data management becomes automated with AI.
We continue to see organisations struggling to keep up with the speeds and feeds of their data, spending 80 per cent of their time simply wrangling data and 20 per cent of their time performing analysis and modeling. Over the next decade, one of the largest impacts AI can make to overcoming the information overload is by automating data management processes, so customers can spend 80 per cent of their time performing analysis, deploying more models into production, and scaling decision-making.
2. AI model marketplaces emerge.
Coming soon are industry-specific AI model marketplaces that enable businesses to easily consume and integrate AI models in their business without having to create and manage the model lifecycle. Businesses will simply subscribe to an AI model store. Think of the Apple Music store or Spotify for AI models broken down by industry and data they process.
3. Digital twins, synthetic twins and simulation take center stage.
A rising trend in the industry is the use of digital twins that collect data from analog objects to create a historical view of operations. Businesses are also exploring the use of synthetic twins – applying machine learning capabilities to digital twins and running simulations to see and test various outcomes. The next generation of the analytics life cycle, or ModelOps life cycle, will be simulating complex systems to help prepare for any possible scenario or disruptive event. From there, businesses can make rapid and resilient decisions to minimise risk and maximise profits.
4. AI-driven decisions demand trust and explainability.
As organisations ramp up their adoption of AI models in their organisation, trust and explainability will be the number one expected feature. You can’t deploy hundreds of AI models in a business if the users or consumers don’t trust the results. AI-driven decisions must be defendable and explainable, especially when AI makes a recommendation or decision that is surprising or unintuitive. All you have to do is enter a question into ChatGPT and there are two emotions you feel: first, shock and awe at how sophisticated the response is, and second, concern around misinformation, plagiarism and information harm at scale.
5. Blockchain simplifies the mundane but increases risk.
Blockchain and cryptocurrency are disrupting traditional payment methods across multiple industries. We are going to see blockchain move away from high-dollar activations toward simplifying mundane and everyday processes like reading an article behind a paywall. Enabling microtransactions for online activities will give control back to content providers and offer new incentives for content owners to develop new markets and create new financial relationships with customers. However, as cryptocurrencies and blockchain continue to digitise traditional financial instruments, the failures of FTX and others have exposed gaps in capital transparency, risk modeling and resiliency during market volatility. Furthermore, from a fraud perspective, digital payments have created new avenues for money laundering.
As we continue to navigate the changing landscape and increasing complexity of information overload, bold new ways to use analytics and AI will help us see the future faster, turn disruption into opportunity, and achieve a competitive advantage in the new world.
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