When evaluating how to implement or create artificial intelligence tools, companies need to analyze the highest value use cases and plan to build a strong base of support and talent.
Any effort to implement AI will be based on three main parts: data, infrastructure and talent.
- Data they generate information but require access to large sets of them. The power of machine learning is often correlated with the amount of data available. At this stage, access to large numbers is a prerequisite for getting the most value out of machine learning tools.
- Infrastructure, both software and hardware, you must have your space to run machine learning models effectively. Cloud service providers are well positioned to expand their AI infrastructure offerings and offer solutions that can be used alongside open source software. For some companies, moving training data to the cloud would be too expensive or impossible due to regulation or other business reasons. For these enterprises, a large amount of computing power will be required, and sometimes hardware acceleration with GPUs, FPGAs, or ASICs will be required.
- The talent it is vital to making effective use of machine learning. While not every company will look to build an internal AI organization, having access to experienced data scientists is key to driving the value of AI. Machine learning is a difficult subject that requires experience.
- Turning AI into core product and service offerings creates competitive differentiation. Companies need to produce their in-house expertise to build a robust infrastructure capable of handling AI development.
In many cases, operationalizing a strategy requires a significant capital investment.. If building an internal solution is not an option, adopting third-party tools may be a suitable alternative. Companies that have not yet been able to differentiate their products with AI can still take steps to improve and automate basic operations. Operational efficiency is also a competitive advantage as:
- Differentiated customer service through advanced bots and virtual assistants
- Smarter forecasts for financial planning, inventory management and sales
- Automated HR processes through streamlined recruiting, automated talent management, and personalized benefits
- Increased sales force productivity through automated sales, intelligent customer engagement, and destination marketing
- Simplifying legal tasks with AI due diligence and contract review, assisted legal research, and automated IP monitoring.
Not all companies share the same priorities. While some may find that an automated customer service solution adds more value to their business, smarter forecasting in inventory management may matter more to others.
a very near future
AI is no longer just about theoretical research in academic institutions or R&D labs; it is already a fundamental technology that will change society and lead to decades of innovation. From the way we get to work, to how doctors identify and treat disease, AI is poised to forge a future of infinite new possibilities.
Companies that implement an AI strategy now will be better positioned to take advantage of the opportunities that arise. AI is already transforming the way we do business, and for companies large and small this could mean an unsettling change.
However, the convergence of accessible technology and an active ecosystem suggests that companies are more ready than ever to participate in this new wave of innovation.
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