Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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The First Wave of Corporate AI Is Doomed to Fail
Artificial intelligence is a hot topic right now. Driven by a fear of losing out, companies in many industries have announced AI-focused initiatives. Unfortunately, most of these efforts will fail. They will fail not because AI is all hype, but because companies are approaching AI-driven innovation incorrectly. And this isn't the first time companies have made this kind of mistake.
Back in the late 1990s, the internet was the big trend. Most companies started online divisions. But there were very few early wins. Once the dot-com bust happened, these companies shut down or significantly downscaled their online efforts. A few years later they were caught napping when online upstarts disrupted industries such as music, travel, news, and video, while transforming scores of others.
In the mid-2000s, the buzz was about cloud computing. Once again, several companies decided to test the waters. There were several early issues, ranging from regulatory compliance to security. Many organizations backed off from moving their data and applications to the cloud. The ones that persisted are incredibly well-positioned today, having transformed their business processes and enabled a level of agility that competitors cannot easily mimic. The vast majority are still playing catch-up.
We believe that a similar story of early failures leading to irrational retreats will occur with AI. Already, evidence suggests that early AI pilots are unlikely to produce the dramatic results that technology enthusiasts predict. For example, early efforts of companies developing chatbots for Facebook's Messenger platform saw 70% failure rates in handling user requests. Yet a reversal on these initiatives among large companies would be a mistake. The potential of AI to transform industries truly is enormous. Recent research from McKinsey Global Institute found that 45% of work activities could potentially be automated by today's technologies, and 80% of that is enabled by machine learning. The report also highlighted that companies across many sectors, such as manufacturing and health care, have captured less than 30% of the potential from their data and analytics investments. Early failures are often used to slow or completely end these investments.
AI is a paradigm shift for organizations that have yet to fully embrace and see results from even basic analytics. So creating organizational learning in the new platform is far more important than seeing a big impact in the short run. But how does a manager justify continuing to invest in AI if the first few initiatives don't produce results?
We suggest taking a portfolio approach to AI projects: a mix of projects that might generate quick wins and long-term projects focused on transforming end-to-end workflow. For quick wins, one might focus on changing internal employee touchpoints, using recent advances in speech, vision, and language understanding. Examples of these projects might be a voice interface to help pharmacists look up substitute drugs, or a tool to schedule internal meetings. These are areas in which recently available, off-the-shelf AI tools, such as Google's Cloud Speech API and Nuance's speech recognition API, can be used, and they don't require massive investment in training and hiring. (Disclosure: One of us is an executive at Alphabet Inc., the parent company of Google.) They will not be transformational, but they will help build consensus on the potential of AI. Such projects also help organizations gain experience with large-scale data gathering, processing, and labeling, skills that companies must have before embarking on more-ambitious AI projects.
For long-term projects, one might go beyond point optimization, to rethinking end-to-end processes, which is the area in which companies are likely to see the greatest impact. For example, an insurer could take a business process such as claims processing and automate it entirely, using speech and vision understanding. Allstate car insurance already allows users to take photos of auto damage and settle their claims on a mobile app. Technology that's been trained on photos from past claims can accurately estimate the extent of the damage and automate the whole process. As companies such as Google have learned, building such high-value workflow automation requires not just off-the-shelf technology but also organizational skills in training machine learning algorithms.
As Google pursued its goal of transitioning into an AI-first company, it followed a similar portfolio-based approach. The initial focus was on incorporating machine learning into a few subcomponents of a system (e.g., spam detection in Gmail), but now the company is using machine learning to replace entire sets of systems. Further, to increase organizational learning, the company is dispersing machine learning experts across product groups and training thousands of software engineers, across all Google products, in basic machine learning.
This all leads to the question of how best to recruit the resources for these efforts. The good news is that emerging marketplaces for AI algorithms and datasets, such as Algorithmia and the Google-owned Kaggle, coupled with scalable, cloud-based infrastructure that is custom-built for artificial intelligence, are lowering barriers. Algorithms, data, and IT infrastructure for large-scale machine learning are becoming accessible to even small and medium-size businesses.
Further, the cost of artificial intelligence talent is coming down as the supply of trained professionals increases. Just as the cost of building a mobile app went from $200,000â??$300,000 in 2010 to less than $10,000 today with better development tools, standardization around few platforms (Android and iOS), and increased supply of mobile developers, similar price deflation in the cost of building AI-powered systems is coming. The implication is that there is no need for firms to frontload their hiring. Hiring slowly, yet consistently, over time and making use of marketplaces for machine learning software and infrastructure can help keep costs manageable.
There is little doubt that an AI frenzy is starting to bubble up. We believe AI will indeed transform industries. But the companies that will succeed with AI are the ones that focus on creating organizational learning and changing organizational DNA. And the ones that embrace a portfolio approach rather than concentrating their efforts on that one big win will be best positioned to harness the transformative power of artificial learning.