AI/ML with AWS
Are you deriving business value from your investments in artificial intelligence?
You’ve seen the ways cloud migration, modernized data platforms, and analytics can help you make decisions. Now it’s time to rethink your approach—by experiencing the power of an AI-fueled organization.
The moment for “what if” is over.
Real enterprises face real challenges and pursue real opportunities, and artificial intelligence (AI) and machine learning (ML) are ready to produce results you can measure.
Now is the time for a hard look at the differentiated business value these evolving technologies can help you achieve. That will take a unique combination of capabilities, but you don’t have to do it alone. Deloitte & AWS are here to help you on the journey.
The turning point
Working together, Deloitte and AWS bring AI/ML out of the test lab and into the crucible of everyday accomplishment. It takes knowledge of your industry and organization, alongside knowledge of the technology, to help you turn the corner from “we have this” to “we use this.” Yesterday, it was impressive that you added the ability to analyze documents by the millions. Or perhaps you consolidated disparate data sources, launched a chatbot, or built a computer vision demo—all feats that would have been front-page news not long ago. The question today is: What value did it deliver?
The answers spring from your core strategy—from inventing new products and services to managing your workforce, from reinvigorating your supply chain to enhancing regulatory compliance, or even creating a whole new business model. In the Age of With, it takes a practical ecosystem of applied technology to unlock the barriers that stand between you and the places data modernization and AI/ML can take you next. Thanks to their shared practical experience, Deloitte with AWS can help you realize power with purpose.
Where do you stand? Where are you heading?
According to the most recent State of AI in the Enterprise, a significant and growing percentage of organizations are displaying the behaviors of an "AI-fueled organization."
Transforming but not fully transformed, this group within the research has identified and largely adopted leading practices associated with the strongest AI outcomes. They average 5.9 out of 10 possible full-scale deployments of different types of AI applications, and 6.8 out of 17 possible outcomes achieved to a high degree.
Complementarily, Gartner defines this level of maturity by impact: “Companies in this stage rely on AI to do significant lifting for the business.” Companies at this level use AIML pervasively and responsibly.
Pathseekers have adopted capabilities and behaviors that are leading to success, but on fewer initiatives. In other words, they are making the right moves but have not scaled to the same degree as Transformers. They average 1.9 out of 10 possible full-scale deployments of different types of AI applications, and 6.2 out of 17 possible outcomes achieved to a high degree.
Characteristics of pathseekers, according to analysts, are those that have adopted machine learning and AI-driven automation in their day-to-day. They have the infrastructure but not necessarily the vision or ability to scale.
Getting a late start in building AI capabilities seems to characterize this group in the study; they are the least likely to demonstrate leading practice behaviors. They average 1.6 out of 10 possible full-scale deployments of different types of AI applications, and 1 out of 17 possible outcomes achieved to a high degree.
By definition, “Starters” match up to Gartner’s definition for this level of maturity as well: If AI is in motion but still rather experimental, consider your organization at the beginning and a little behind the curve.
A significant amount of development and deployment activity characterizes this group; however, they haven’t adopted enough leading practices to help them effectively achieve more meaningful outcomes. They average 5.6 out of 10 possible full-scale deployments of different types of AI applications, and 1.4 out of 17 possible outcomes achieved to a high degree.
Companies in this stage have ideas, but not strategies, for how to use AI in their businesses. The good news is these organizations can take advantage of what has already been successful: Models, algorithms, and engines are accessible—with a solid partner—when you are ready… just don’t wait for too long.
See where you land by benchmarking your organization against the research on the current State of AI in the Enterprise.
Artificial Intelligence and Machine Learning hotspots
Machine Learning Operations
Machine learning is like any other tool—it has to operate within rigorous quality controls. MLOps takes AI/ML by the hand to help instill the discipline and repeatability that wide-scale production requires.
Conversational AI
Tools like natural language processing (NLP) have the potential to transform communication—but first they need to integrate with the rest of the business. Solutions such as Deloitte’s TrueServe™ and AWS Contact Center Intelligence (CCI) can put you on the cutting edge.
Computer Vision
Like the human brains they emulate, AI/ML systems need eyes. Training machines to analyze visual information at exponential scale and speed has vast potential impacts in areas like supply chain optimization or worker safety.
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