Artificial intelligence (AI) is now powerful enough to realize all the sci-fi fantasies of its proponents. The technologies are already around us but are not widely deployed, integrated or delivering value beyond precise point solutions. Persistent companies and researchers can bring these separate pieces together in a seamless way to create the kinds of experiences that previously could only be imagined.
We believe AI has real meaning only when it provides a multi-dimensional experience that recognizes and combines rational, emotional and cognitive levels of intelligence across a larger process.
A Differentiated Approach to AI
Just as no one company can make a smartphone and all of its underlying components, no single company can claim to “solve AI.” The human brain has various dimensions of intelligence, including creative intelligence, social intelligence, perception intelligence and emotional intelligence.
Our applied AI approach revolves around creating an ecosystem of AI solutions that augment and simulate each of the major dimensions of human intelligence while also staying connected to each other through a system of intelligence.
An ‘Applied Artificial Intelligence’ Definition
The “intelligence” in AI should perceive and behave in human and familiar ways, while the “artificial” provides scale and repeatability not otherwise possible. When properly applied, AI will augment and enhance, rather than automate and replace, our human experiences.
In practice, anything AI does — image recognition, a chatbot, a medical recommendation, walking on uneven pavement and stairs, even distinguishing a child’s laugh from a cry — could be or has been performed by people for thousands of years.
What AI offers is the ability to scale this intelligence to levels that take advantage of the enormous amount of data that pervades people, processes, organizations and things (which we call Code Halos).
The resulting benefits are remarkable in both their impact on life and the relatively low technical hurdles that must be surmounted. These advances in technology can improve the routing of roads and transit to get commuters home more quickly and safely; reduce accidents; increase the accuracy of medical advice; boost farm yields; increase manufacturing quality and safety; and lower the hurdle to completing tasks for complex life events.
The introduction of humanized AI will change business models, propelling companies to new heights if used creatively — or drive a company to irrelevance if ignored.
Implementing Applied AI
When implemented to its full potential, artificial intelligence (the technology) will become applied AI (the practice) — and will be as much a part of the fabric of our life as electricity, transportation and communications.
So, how does the “artificial” become “applied”? We believe the biggest gains come from integrating what would otherwise be isolated AI technical advances to create transformative ideas for life and commerce.
Applying Artificial Intelligence to Business, Social and Personal Situations
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Karthik Krishnamurthy, Global head of Cognizant Digital Business’s Analytics and Information Management Practice sheds light on how Applied AI is solving both social and business problems.
How Applied Artificial Intelligence Will Drive the Automotive Industry
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In this second installment of our Artificial Intelligence podcast series, Karthik Krishnamurthy discusses a potentially larger and immediate impact of Applied AI, driver assistance and its benefits.
Artificial Intelligence and Real-Time Flood Forecasting
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By predicting floods and quickly warning potential victims and first respondents about impending disasters, intelligence systems prove to be quite viable. Learn how in the third episode of our Applied AI series.
Automated Customer Service Agents
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In this series, Karthik Krishnamurthy talks about how companies applying AI for transformation will lead the way, enhance customer relationships, and advance their corporate and social missions.
Unpacking Artificial Intelligence for Organizational Excellence
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Find out if your organization is mobilizing around AI the right way, in this last of applied AI podcasts series with Karthik Krishnamurthy, Global Head of Cognizant Digital Business’s Analytics and Information Management Practice.
Identifying AI Opportunities
Because the opportunities of AI are nearly endless, organizations need a mechanism with which to build a wish list, trim it to a short list and get started.
We recommend building multiple suggestion lists and then merging them. Look at AI opportunities using these tools:
AI by trend. Research market and social trends and consider ways that AI could participate.
AI by value addition. Think of the ways in which AI can help — reducing costs, increasing revenue — and use that to find opportunities for applied AI to expand your best thinking.
AI by example. Look at successful cases and assess comparable situations in your business or personal life.
AI by process. Consider moments when customers or suppliers interact with your company, and how those moments stitch together into overall experiences.
AI by channel. Consider how AI engages the information it needs, and returns answers and insights. Consider how AI can benefit from and enhance the channels you use.
Looking Forward: Guidelines For Applying AI In Your Organization
So how can AI apply to your industry, your company and your business unit? Ideas include:
Move beyond point solutions.
Evaluate your business model and ask what you could do if your best ideas, best thinking and best experiences could be scaled 10, 100 or 1,000 times across more points of interaction.
Expand the humanity behind the services or products your organization delivers, in terms of the benefits customers obtain, and the societal advances that can be gained by your stakeholders.
Imagine integration and discovery, guided by automated assistance and connected insights.
Our experience shows that AI solutions can and should be pursued in parallel. That is, the opportunities in AI are so diverse and so important that several good ideas should be launched simultaneously. Start small, fail fast, learn and recover as you embark on this journey.
A successful AI initiative typically follows three steps:
Identify AI ideas and opportunities, classify and prioritize, pick a short list.
Conduct due diligence on the readiness for these ideas within your organization.
Review your organization’s capacity and institute plans to scale up the necessary materials, machinery and model to deliver AI applications. (To learn more about our “3 Ms model", read our latest book What To Do When Machines Do Everything.)
Finding the platform and data to get started can be simplified by following the “1-1-1-1” rapid development timeline, which recommends the following schedule:
One day: Provision the space and computing power.
One week: Load data and use built-in reporting and functions to gain confidence with the data and the system.
One month: Customize and learn.
One quarter: Productize and launch.
The principle of “applied AI” encourages the use of existing vendor or open source capabilities. For example, cognitive technologies are provided by IBM Watson, Microsoft Cortana Intelligence Suite and Google’s Tensorflow (among others), in partnership with existing AI platforms, such as Cognizant’s BigDecisions AI edition.
Use freely available open source software to quickly develop solutions. Google, Microsoft, Facebook, Amazon and Yahoo have all released open source machine learning or deep learning algorithm libraries.