A new Accenture report, which looked at investments, revenue growth and acquisitions in the AI space, predicts that the healthcare AI market will reach $6.6 billion by 2021 from just $600 million in 2014.
· The rapid growth is driven by factors like move to population health, and greater acceptance of machines in healthcare delivery by consumers, driven by experiences in other services.
· Accenture’s survey of over 3000 consumers shows that one in five US patients have already used AI-powered healthcare services, like robots, virtual clinicians and home-based diagnostics.
· In keeping with the move in the wider healthcare industry from volume of care to value-based models, business and venture funds are funding development of products and systems based on AI.
Examples: Use of AI by Anthem and Cigna to curtail opioid addiction; funding by Optum Ventures of the startup Buoy Health that has developed an AI-powered digital health assistant that helps patients better understand symptoms and advises on next steps.
· Regulators are stepping up approvals of AI systems and related products.
Example: Approval of three of the seven robotics products developed by Bionik Laboratories, a venture whose solutions have been used for treating neurological disorders in over 200 hospitals in 20 countries.
The future of AI in healthcare
According to Bionik’s CEO, Eric Dusseux, there will be a steady evolution of AI both in the medical industry and beyond. Humans have long relied on technology to improve efficiency, productivity and process quality. Innovations like AI, machine learning and brain-computer interfaces will encourage continued usage of technology in the medical space to further optimize patient treatment and care.
AI: The theory and practice of building machines capable of performing tasks that require intelligence, using technologies like machine learning, artificial neural networks and deep learning.
Blockchain: A new filing system for digital information, which stores data in an encrypted, distributed ledger format. Encryption and distribution of data across many different computers enables the creation of tamper-proof, highly robust databases that can be read and updated only by those with permission.
The theoretical benefits of combining these two technologies haven’t translated as yet into real world applications, but this could change soon. Three ways why this should happen are:
1. AI and encryption work well together
The cost of securing the vast amounts of sensitive, personal data (think healthcare systems, banks or even Netflix) handled by AI systems is immense. Blockchain databases hold information in an encrypted state, requiring only the safety of the private keys – a few kilobytes of data – lowering the costs of handling AI data tremendously.
Conversely, an emerging field of AI is concerned with building algorithms that can process data while still encrypted. This complements the safety of blockchains, by eliminating the risk of exposure of unencrypted data.
2. Blockchain can help us track, understand and explain AI decisions
AI systems make complex decisions by assessing a large number of variables. In areas like banking or the retail industry, these still need to be audited for accuracy by humans, which can be very difficult. If decisions are recorded, on a datapoint-by-datapoint basis, on a blockchain, it makes it far simpler for them to be audited, with the confidence that the record has not been tampered with between the recording of the information and the start of the audit process. This is a step towards achieving the level of transparency and insight into robot minds that will be needed to gain public trust.
3. AI can manage blockchains more efficiently than human or conventional computers
Conventional ‘stupid’ computers apply brute force when working with encrypted blockchain data, requiring large amounts of computer processing power. AI can perform such tasks – like verification of Bitcoin transactions – more intelligently by becoming adept at cracking codes almost instantaneously if fed the right training data.
Clearly, the two ground-breaking technologies have the potential to become even more revolutionary together. They can enhance each other’s capabilities, while offering better oversight and accountability.
Not just the stuff of sci-fi films anymore, AI is transforming technology in people’s daily lives, Amazon’s Alexa and AmazonGo being important examples. According to a Gartner report, there are three key requirements that define AI:
1. It needs to be able to adapt its behavior based on experience.
2. It needs to be able to learn without being solely dependent on human instructions.
3. It needs to be able to come up with unanticipated results.
Based on these criteria, the AI that we deal with regularly, like Siri and Alexa, are examples of ‘weak AI’, as they are built to accomplish very specific tasks. ‘Strong AI’ or ‘General AI’, which is the end game, is still a distant dream.
Nonetheless, weak (or narrow) AI by itself packs in enough exciting potential to revolutionize the workplace. The opportunities it presents can however be squandered if its deployment is done without proper human governance. This governance will largely fall into the hands of corporate IT Service Management (ITSM) teams.
AI’s role in the ITSM revolution
At the same time, AI has the potential to transform ITSM too, allowing staff to delegate mundane tasks to AI software and focus on more strategic issues. A learning, conversational AI experience will be critical for AI technology to succeed, and will revolutionize ITSM in the following key ways:
1. An AI-automated front line
Currently, risk and uncertainty is rife in old-style self-service portals, making companies reluctant to divert their human ITSM front line resources away from basic phone handling. AI-enabled chatbots can help develop automated ITSM solutions that are better at customer query interpretation, assistance without human intervention and providing a personalized end-user experience.
2. Operational Efficiency
Good ITSM operates many vital ‘back-end’ processes like incident management and change management that keep IT systems running. AI can not only make this process more efficient – for instance, when connected to IoT devices it can be notified instantly if a smart device starts malfunctioning, without the end-user having to report it – but also make business aware of ITSM’s important place as an enabler.
3. All knowing AI
AI-powered ITSM can efficiently handle large volumes of data and decipher patterns, resulting in real-time insights, predictions of problems and recommendations to fix them. Plus, it can source answers to difficult queries from across the Internet as well as pool data from multiple organizations to provide better solutions.
Ultimately, humans will remain vital for delivering AI-enhanced IT services. AI’s rapid evolution will allow it to work alongside humans to create a more efficient workplace. It will also allow IT staff to become business enablers and productivity transformers, while technology does the heavy lifting.
As AI finally begins to realize its long-heralded potential and change the business environment drastically in the process, companies need to develop an understanding of these fundamental changes and develop well thought out, but flexible, strategies to harness the strongest capabilities of both men and machines.
After decades of unfulfilled promise, artificial intelligence has finally begun to realize its potential, driven by massive gains in processing power and better data collection methods. Developments in natural language processing and computer vision have played particularly important roles in helping machines perform tasks traditionally reserved for men. AI has also caught the public imagination over the years thanks to well-publicized events like Deep Blue’s chess victory over Kasparov, AlphaGo’s recent victory over Lee Sedol, IBM Watson’s victory in Jeopardy or even Google’s demonstration of self-driving cars. All this has prompted massive investments in AI-related areas in industries like finance, retail and health care.
Differences between human and machine thinking
Because AI systems ‘think’ and interact, they are often compared with people. Humans are fast at parallel processing (pattern recognition) and slow at sequential processing (logical reasoning); exactly opposed to this, while computers have mastered parallel processing in some narrow fields, their strength lies in superfast logical reasoning.
Human intelligence allows for different types of problem-solving capabilities. Compared to this, at current processing power growth rate, artificial general intelligence is a long-term possibility at best. AI excels at performing specific tasks, fast and thoroughly.
So while investment in AI is critical, it is useful to ask this question: How can business leaders harness AI to take advantage of the specific strengths of man and machine?
AI’s effects on traditional ways of doing business
On notions of competitive advantage
In the 1980s, a technology tool in itself (such as Wal-Mart’s logistics tracking system) could serve as a source of advantage. But, now, because of open source software, algorithms in and of themselves will not provide an edge. Other traditional sources of advantage, like positional advantage and capability, are also being reframed by AI. Companies need a more fluid and dynamic view of their strengths, instead of a focus on static aspects. These three examples show how traditional notions of competitive advantage are changing:
· Data – This is the raw material for AI systems, where large and early-moving companies like Google, Facebook and Uber have an apparent advantage. They have created massive data repositories, which helps them collect more data, and also leverage it for better ad-targeting or in self-driving cars. But other companies without these resources can still do well through collaborations, even with competitors, to create their own privileged zones. They need to figure out areas where sharing works, and where it may not.
· Customer Access – Traditional notions of customer access, like well-placed physical stores, are being replaced by AI-driven customer insights that help with personalized marketing and appetite prediction, generating higher revenues at negligible extra cost.
· Capabilities – AI-driven automation is encouraging the replacement of traditional segmented and discrete areas of knowledge by cross-functional capabilities and agile ways of working.
On decision making
· The speed of decision making is changing rapidly.
· Predictive analytics and objective data is replacing decisions based on gut feel and experience (as can be seen in stock trading, online ads, supply chain management and retail pricing).
On the role of human employees
Humans will not become obsolete, but will see rapid and major dislocations into new areas of work.
· They will be needed in large numbers to build the AI systems.
· They will be needed to help machines with common sense, social skills and intuition as well as for quality checks.
In such an AI-inspired world, strategic issues are enmeshed with organizational, technological and knowledge issues. Agility, flexibility and continuous education are important for winning strategies.
Getting started towards winning strategies
Companies need to identify the jobs that machines or men are better at, develop complementary roles and redesign processes accordingly. Plus, they should be willing to change and adapt strategies at short notice. This is true in general for all areas in today’s business world, but especially so for AI-enabled processes.
Instead of a brute force implementation of AI everywhere, companies could evaluate through four lenses whether AI can create a significant and durable advantage:
· Customer needs – Define the fundamental needs of your customers and see if AI can better address them
· Technological advances – Study if the right technology exists to address your requirements, and if you can make use of work already done instead of building a system from scratch
· Data sources – Create a holistic architecture that combines existing data with novel sources, even if they come from outside
· Decomposition of processes – Break down processes into relatively routinized and isolated elements that can be automated, taking advantage of tech advances and data sources identified above
These steps can be challenging for most companies. Setting up a center of excellence can help keep track of current and emerging capabilities, incubate technical and business acumen and disseminate expertise through the organization. It can then collaborate with the functions that would eventually put AI to use.
Thus, the full potential of AI can be achieved only if humans and machines solve problems together and learn from each other.
Since at least Turing’s time in 1950, intelligent computers have fascinated people. But it has taken decades for the right combination of factors to come together to move AI from concept to an increasingly ubiquitous reality. After a lot of talk in 2017, it is in 2018 that data generated because of mass use of Internet-connected devices, and algorithms that recognize patterns in this data, will lead to tangible ways in which AI affects all our lives. Some of these expected developments are:
1. Smart virtual assistants – Personal assistants based on AI will become smarter and more affordable. They will learn our daily routines and handle simple, but useful, chores like ordering groceries.
2. Multiple voice-based gadgets – The popularity of voice-based assistants will result in many devices at home across platforms. There’s exciting potential, but possible chaos expected as well.
3. Practical use of facial recognition – Going beyond security and biometric capabilities, facial recognition will start replacing credit cards, driver’s licenses and barcodes. No need to even line up at the payment counter at a store.
4. Basic AI terminology becoming commonplace – As AI permeates the enterprise, everyone from CEO through middle managers to frontline employees will start becoming conversant in basic terminology. This will help demystify the technology and open up possibilities.
5. Personalized media – Forget identifying songs that you will like – new services could start creating music from scratch based on your tastes.
6. Tailored news and market reports – Reports that don’t just recap market performance, but explain your portfolio performance, at any time, will soon be a reality. Newsrooms will also use AI in more innovative ways.
7. Wide use in healthcare – By end-2018, nearly half of leading healthcare systems will have adopted some form of AI within their diagnostic groups, not just in medical specialties, but even in hospital operations, solutions for population health and clinical specialties. The way patients experience healthcare globally will truly begin its transformation in 2018.