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Over the last few decades, technology has slowly shaped our world into one that’s different from what our grandparents grew up in. Some of that change has been about the gadgets in our homes and in our pockets. Much else has been driven by researchers and scientists using powerful supercomputers to answer life‑changing questions and make groundbreaking discoveries in life sciences, physics, chemistry, and astronomy.
But the pace of change is about to accelerate. The global datasphere is growing exponentially and according to IDC’s Global DataSphere forecast, the amount of data created over the next three years will be more than the data created over the past 30 years, and the world will create more than three times the data over the next five years than it did in the previous five!
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I’ve always believed that if AI is the rocket-ship, then data is the fuel. The insights uncovered from a well-executed data analysis program is at the very core of ensuring the quality, relevance and impact of an AI-enabled automation strategy.
AI has the power to help organisations make meaningful, value‑added predictions and respond quickly to changing market conditions and customer demands. Today, AI technologies have come into the mainstream, allowing businesses with foresight to deploy them to gain valuable real-world benefits. Perhaps because of this, according to IDC, over 90% of new enterprise applications will embed AI by 2025. But at the same time, “only 14.6% of firms report that they have deployed AI capabilities into widespread production.” This indicates a wide gap between companies’ ability to move from proof of concept into full production. Now more than ever, companies need technology that empowers them to extract valuable, accurate and timely insights from their data.
The making of an AI strategy starts with realising it’s a journey
For most companies, there is a huge gap between ambition and execution. The key differences lie in recognising the transformative value of data, defining a relevant AI use case, putting in place AI-enabled and AI-enabling infrastructure, and approaching the relationship to AI as an incremental journey, not a destination.
A first step in this journey is understanding that the deeper, larger and more complex our data stores and streams become, the more critical the role of AI becomes. But data is often messy — it’s duplicated, incomplete, geo-biased and requires data engineering — so simplifying data acquisition, management, access and protection are all critical. It’s all about separating the signal from the noise and ensuring you have the right skills, tools and use cases in place to do so. Data is everywhere, but not always where you need it, so it’s becoming more and more essential, in the age of edge and IoT, for example, to move compute capabilities to where the data resides.
Given this reality, the AI journey requires the consolidation of data for analytics and then builds from there, creating analytics-based applications to drive the intelligence, modeling and inferencing that is driven by data.
So, how can you start doing more with AI and ML technologies to influence the future arc of your organisation? As with every new technology, AI comes with unique challenges, especially in the realm of data and compute. While the path to AI is different for all organisations, here are some common steps in the AI journey:
- Outline the business goals and align the company’s AI strategy to define the use case(s)
- Determine data availability and prepare the data for AI analysis and action
- Understand and integrate infrastructure requirements
- Determine steps to build models with validation methodologies
- Establish tracking tools and systems
- Adapt and scale the strategy over time
As you determine your best next steps on the journey to adopting AI solutions, investing in modern infrastructure building blocks is necessary to store, protect and execute against the valuable data that is the fuel for AI. Look for purpose-built, intelligent, AI-capable systems and solutions that take control of data to deliver deeper insights to transform decision making and drive business growth.
Pushing the boundaries of AI requires intelligent data management
Many of the world’s most innovative companies, are well on their way with AI, gaining immediate and long-term benefits, from infrastructure management to product innovation. From delivering better healthcare outcomes to using algorithms to enhance fraud detection in finance to autonomous driving technologies and smart manufacturing – the list of AI use cases is endless and only continues to grow.
In this region, the market opportunity is significant as governments lay out national AI policies and transformation agendas. In fact, according to PwC, AI can contribute $320 billion to the Middle East economy by 2030. So, as businesses rush to capitalise on this opportunity, they must stay focused on unlocking the value of data. Data is growing at an astronomical rate and it’s impossible to take full advantage of it manually to get insights to win. Automation can help provide faster, better and deeper data insights.
Competitive advantage over the next 10 years depends on how much of the right data you leverage, and how rapidly and accurately you use it to drive business success. Organisations that capitalise fully on the data opportunity will be the ones that leverage the benefits of AI, to achieve greater competitiveness and success in the next data decade.
