Have you unlocked your data goldmine yet? Here’s how AI trends shape the future of business
What do untapped data goldmines, the democratization of AI and intelligence manufacturers mean for your business? Recent insights and 6 tips to approaching AI adoption.
In the rapidly evolving technology landscape, few topics have garnered as much attention and excitement as Artificial Intelligence (AI) and its subset, Generative AI (GenAI). Recently, a keynote featuring Jensen Huang, CEO of NVIDIA, and Ali Ghodsi, CEO of Databricks, shed light on the transformative potential of these technologies. Here are some highlights from the keynote, along with reflections from some of our experts.
The Data Goldmine is your untapped resource
Jensen Huang’s description of proprietary data as a “goldmine” for enterprises resonates strongly with many discussions in board rooms as well as media. Every company, regardless of size or industry, sits on a wealth of data accumulated over years of operation. However, until recently, the ability to extract meaningful insights from this data has been limited.
The introduction of advanced AI and machine learning (ML) technologies over the past decade has changed this paradigm. Now, companies have the tools to dive deep into their data, uncovering patterns, trends, and insights that were previously invisible. But more importantly, such a capability isn’t just a nice-to-have; it’s becoming a critical factor in maintaining competitiveness in an increasingly data-driven market.
The democratization of AI
One of the most exciting developments in the AI landscape is the rise of open-source models. Tools like Llama 3 and contributions from companies like Databricks are making AI more accessible than ever before. This democratization is enabling a global movement where companies of all sizes can make use of the power of AI.
“We’re really excited about this trend. AI shouldn’t be the exclusive domain of tech giants or companies with massive R&D budgets. Every business should have the opportunity to leverage AI to improve their operations, enhance customer experiences, and drive innovation. And every business should be on their toes, looking at where to start”, says Shahin Atai, Head of AI at HiQ Sweden.
The future of business belongs to “Intelligence Manufacturers”
Perhaps one of the most thoughtful insights from the keynote was Jensen Huang’s prediction that every company will become an “intelligence manufacturer”. This shift represents a fundamental change in how businesses operate and create value.
In this new paradigm, companies won’t just be producers of goods or services; they’ll be “producers of intelligence”. By leveraging AI to process and refine their data, businesses will be able to generate insights that drive decision-making, improve products and services, and repackage existing as well as innovate new offerings.
The HiQ Perspective
At HiQ, we’re seeing these trends play out in real-time as we work with our clients. Here’s our take on the current landscape and what it means for businesses:
1. Data Complexity: Many of our clients are grappling with complex data environments, often using several siloed databases. This creates “operational knots” that can hinder efficiency and data insight generation.
2. AI as a Spending Priority: We’re seeing AI and GenAI becoming top spending priorities for many organizations. This shift is driving new demands for accelerated IT infrastructure and modernized data management capabilities.
3. Need for Preparation: While the potential of AI is exciting, many organizations aren’t yet prepared to fully leverage it. There’s significant upfront work required to assess AI readiness and determine where AI can deliver the most value.
4. Importance of Engagement: Successfully implementing AI isn’t just about technology; it’s about human engagement. It requires collaboration across different lines of business to identify use cases and uncover real value strategically and ethically.
5. Start Small, Think Big: As Jensen Huang emphasized, the key is to “just get started”. We advocate for an approach where organizations begin with small, focused AI projects while developing a broader strategic vision.
Getting started with AI: how should businesses approach AI adoption?
1. Assess Your AI Readiness: Before diving into AI projects, it’s crucial to understand your current capabilities and limitations. This includes evaluating your data infrastructure, technical skills, and organizational culture.
2. Identify Your “Data Goldmine”: Work with different departments to understand what unique, valuable data your organization possesses. This could be customer interaction data, operational metrics, or industry-specific information.
3. Start with high-impact use cases: Look for areas where AI can deliver significant value quickly. Customer service automation, as highlighted in the keynote, is often a great place to start, or as Jensen Huang put it: “Right now, we’re seeing data grow about 10 times every five years. I would not be surprised to see data grow 100 times every five years because of customer service.”
4. Invest in Data Infrastructure: Ensure you have the necessary infrastructure to support AI workloads. This may involve upgrading storage systems, implementing data lakes, or adopting cloud technologies.
5. Foster a Culture of AI Literacy: Encourage employees across all levels to learn about AI and its potential applications in your industry.
6. Partner Strategically: Consider partnerships with technology providers and consultants who can bring expertise and accelerate your AI journey.
“The AI revolution is here, and it’s transforming the way businesses operate. By viewing your data as a goldmine and leveraging AI to unlock its value, you can position your organization at the forefront of this new era of intelligence manufacturing”, says Shahin Atai.
At HiQ, we’re committed to helping our clients navigate the AI landscape, whether you’re just starting your AI journey or looking to scale your existing efforts.
You have just finished the second article in our AI series. In the first part, we guide you through the changing AI landscape. Read the article here
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