The internet is always speculating and wondering about what’s to come, in terms of access modes, once mobile and screens have ceased to be the latest shiny new thing.
The theory is that artificial intelligence (AI) is going to be the next evolution; and precisely because of that prediction about AI (plus its other uses), we witnessed an unprecedented technology gold rush in 2017.
The craze about AI had people investing millions of dollars into new products and services, and hand-wringing skeptics forming think tanks to postulate about the end of the world. But, for all of the speculation, what did AI actually do in 2017? What’s it doing now? And, what’s it poised to do in the coming year?
First, AI broke through the legitimacy barrier.
AI had its first victory as an up-and-coming technology when it broke through a crucial legitimacy barrier, proving to everyone that it is not only viable but also a competitive advantage when deployed well.
A report by PwC estimated that by 2030, AI could contribute upwards of $15 trillion to the global economy – more than the outputs of China and India today, combined. PwC’s statistic is one of many that suggest that our future with this technology is almost unimaginable. AI will destroy and create jobs, invent new industries, accelerate innovation to a new level and fundamentally change the way business is done across the board.
En route to that point, there are questions being posed: How does AI actually create value? What applications of the technology have worked so well that we believe it will change the future in such radical terms?
Interestingly, applications for sophisticated tasks like smoothing out supply chains or detecting risks are still maturing. What AI already does well and what we can expect to see a lot more of in the coming years is helping humans to interface with increasingly complex computers.
Sean Nolan, founder and CEO of Blink, weighed in on these issues during a chat we had the other day.“Humans in the workplace experience ‘technology fatigue’ because the computer they interact with all day is robotic — oddly organized, clunky, impersonal, and slow,” Nolan told me. “The reality is that computers are so powerful and store so much information, and we need them to do so much, that our ability to utilize them with just a mouse and a keyboard is becoming insufficient.
“AI comes along and opens up the possibility of creating a new interface between the complex, impersonal nature of the computer, and the intelligent, complex nature of its human operator.”
Next, AI is poised to take on immensely complex tasks.
By utilizing simple AI applications, like chatbots and micro applications, immensely complex tasks will — one day soon — be able to be reduced to simple commands. For example, instead of spending an hour searching a company’s servers, shared folders and cloud drives for every relevant document pertaining to a specific task, you’ll be able to simply ask a smartbot to do it for you. The task will be completed in seconds, saving valuable time — which PwC estimates will add up to $6.6 trillion in added efficiency by 2030.
While the benefits of added human productivity carry an obvious financial upside for the market, reducing AI to the role of human-computer mediator seems like a demotion for a technology that is supposed to change the world. This view does not properly account for the magnitude of that accomplishment. In fact, AI’s role as a mediator between humans and technology is perhaps its greatest triumph.
The myth that humans use only 10 percent of their brain power has been soundly debunked, but we know for a fact that we use only a fraction of our computers’ potential at any given moment. Afterall, our interactions with them are analog in nature; and, as a result, fall far short of achieving maximum potential.
AI’s untapped potential
AI as a user interface opens up vast untapped potential. This year, it’s chatbots and voice recognition. In 10 years it will be some version of Elon Musk’s Neuralink (one of his new companies, which is working on linking the human brain to a computer).
Today — right now — there are available a number of tools to help you interface with customers and users by implementing AI. They include:
1. Chatbots. Chatbots can provide the additional layer between you and your consumers to answer their questions, and to interact with potential or current customers.
2. Website builders. There are now new platforms like Grid, which use AI to build your entire website.
3. Smartbots. Smartbots are another form of machine, which can be used internally, like a personal assistant, to handle tasks like scheduling your calendar. This is how X technology works.
“What businesses need to know about AI is that the application they need to use it for first is workforce productivity,” said Nolan. “This single competitive advantage will determine which companies survive the first round of AI and which ones do not. Thinking about AI as UI [user interface] and a means to access the untapped potential of our computers is what we can expect to see in 2018.”
Meanwhile, the urgency to adopt AI is very real. Companies that successfully incorporate it as UI in their businesses will reap enormous dividends while their competitors will struggle to modernize. In a new report titled AI is the New UI, Accenture made the following observation, summing up my point perfectly:
“Getting started can be as simple as using AI to bring more human-like interactions into existing interfaces,” the report stated. “But if businesses want to do more than just keep pace, there’s no time to waste . . . The early adopters are already pulling ahead, but many of the necessary tools are openly being shared. The question to answer is simple: What could a company accomplish if every interaction with technology was an intelligent one?”
10 Artificial Intelligence Trends to Watch in 2018
Artificial intelligence (AI) is the new technological frontier over which companies and countries are vying for control. According to a recent report from McKinsey, Alphabet invested roughly $30 billion in developing AI technologies. Baidu, which is the Chinese equivalent of Alphabet, invested $20 billion in AI last year.
Companies aren’t the only ones investing time, money and energy into advancing AI technology — a recent article in The New Yorker reported that the Chinese government has been pursuing AI technology aggressively in an attempt to control a future cornerstone innovation.
Considering that some of the largest entities in the world are focused on advancing AI tech, it is all but certain that 2018 will see significant advancements in the space. The following are ten AI trends to look out for this year.
1. AI will become a political talking point.
While AI may help create jobs, it will also cause some individuals to lose work. For example, Goldman Sachs expects self-driving vehicles will cause 25,000 truckers to lose their jobs each month, as reported by CNBC.
Likewise, if large warehouses can operate with just a few dozen people, many of the 1 million pickers and packers currently working in U.S. warehouses could be out of a job.
During the 2016 election, President Trump focused on globalization and immigration as causes of American job-loss, but during the 2018 midterm elections, the narrative could be about automation and artificial intelligence, as more working-class Americans struggle to adjust to the new landscape.
2. Logistics will become increasingly efficient.
We are entering a world in which it will be possible to run a 20,000-square-foot distribution center with a skeleton crew. Companies like Kiva Systems — now Amazon Robotics — use a combination of artificial intelligence and advanced robotics to provide big box retailers with unprecedented logistics solutions.
Warehouses of the future will look nothing like they do today — rather than being designed to accommodate human packers, they will be built for highly capable robots that can work 24/7 and don’t require lighting to see what they are doing.
Kiva Systems, which was purchased by Amazon for $775 million in 2012, creates learning robots that can efficiently find and transport items in Amazon’s warehouses. The technology is already being used today and is expected to play an increasingly prominent role in the company’s quest for faster, less expensive deliveries.
3. Mainstream auto manufacturers will launch self-driving cars.
Tesla was one of the first auto makers to launch a self-driving vehicle. In their effort to keep pace with Tesla, traditional automakers like Audi are poised to release their own self-driving cars in 2018.
The Audi A8 will feature self-driving technology capable of safely shuttling humans without driver input. Cadillac and Volvo are also developing advanced self-driving technology, which will become increasingly visible in 2018.
4. DARPA will develop advanced robo-warriors in plain sight.
The Defense Advanced Research Project Agency (DARPA) has pioneered a number of technological breakthroughs that have impacted our daily lives. The organization, which is responsible for developing new technologies to be used by the American military, was instrumental in developing the internet and GPS navigation — they are no stranger to innovation.
Today, DARPA is working with Boston Dynamics to develop a series of robots designed for “disaster relief,” though the technology could be used in a combat role as well. The Atlas robot, which received internet fame for performing backflips, is one of the AI-powered technologies in development.
5. Machine learning will aid knowledge workers.
While some are rightfully concerned that AI will put people out of work, AI technology also has the ability to aid employees, especially those in knowledge work.
Today, tools like Gong, Chorus and Jog are able to record calls made by sales and customer service representatives. “This technology can coach customer-facing service workers to speak more effectively, thanks to machine-learning algorithms. Expect AI to increasingly support white-collar workers in 2018 and beyond,” explains Carrie Christensen, Operations VP of Mint Solar.
6. Content will be created using AI.
Brands like USA Today, CBS and Hearst are already using AI technology to generate content. For example, Wibbitz offers a software-as-a-service (SaaS) platform that allows publishers to turn written content into video content through AI video production.
Publishers used to spend hours, if not days, creating content for their websites or for social media. Tools like Wibbitz are now helping publishers create compelling videos in minutes.
Similar to Wibbitz, the Associated Press is using a tool called Wordsmith, created by Automated Insights, to apply natural-language generation in order to create news stories based on earnings data. In 2018, readers can expect to see more media companies adopt natural-language and video-generation technologies.
7. Peer-to-peer networks will create transparency.
Machine learning is a form of artificial intelligence, and companies like Facebook are already using statistical modeling to help machines make informed decisions about what content to show you next. In order for the models to work properly, they require massive amounts of data and significant computing power.
With the rise of peer-to-peer networks — like the ones used by cryptocurrencies — even small organizations will have the ability to run advanced AI programs by harnessing the collective power of networked personal computers.
Presearch is one company that aims to use peer-to-peer networking and artificial intelligence to bring transparency to the world of search engines. Google controls nearly 80 percent of the search market, yet few people fully understand how Google determines what content is shown to a particular consumer.
Presearch plans to use cryptocurrency to incentivize participants to lend them the computing power of their personal computers. In return, the company promises to build a more transparent search engine platform. The startup has already raised $5 million in funding, and it is likely that they — and other organizations — will use AI and peer-to-peer networking to challenge large organizations.
8. Consumers will become accustomed to talking with technology.
It’s estimated that over 20 million Amazon smart speakers were sold last year, and if you add sales of other smart devices like Google Home and Apple Airpod, you realize that tens of millions of Americans are getting used to interacting with technology through voice commands.
In 2018, consumers will become even more comfortable with voice-based interfaces, as smart assistants become integrated into computers, smartphones and even televisions.
As someone who finally caved in and purchased an Amazon Echo, I can tell you first-hand that these devices are going to get even more useful as the technology advances.
9. Demand for data scientists will surpass demand for engineers.
According to IBM, demand for data scientists will increase to 2.7 million by 2020.
Why? Machine-learning AI uses probability to determine what the proper answer or decision is for any given problem. With more data provided to machine-learning platforms, the platforms will become better at making predictions.
As companies of all sizes strive to collect and effectively analyze data, there will inevitably be an increased need for talented data scientists capable of handling large data sets to aid AI platforms.
10. AI will fight challenging diseases.
“We are entering a time where a peer-to-peer network of computers could have the capability of solving some of the world’s most challenging health problems by collecting and analyzing human molecular data,” explains Ben Hortman, CEO of Bet Capital LLC. Now, what if those computers were powered by chips smaller than the head of a pin with secure, built-in AI and cryptocurrency technology? What sounds like something out of a science fiction novel, is now a reality thanks to Nano Vision.
The technology was inspired by two tech trends — blockchain and AI. Users are rewarded for participating in the program through a special Nano cryptocurrency, while machine-learning technology seeks to identify and analyze illnesses to enable new drugs, treatments and cures at a fraction of the time and cost.
The 5 Most Interesting Artificial Intelligence Trends for Entrepreneurs to Follow in 2018
New advances in artificial intelligence (AI) and machine learning (ML) research made by tech giants and academia have quickly made their way into businesses and business models, while even more companies are introducing established AI solutions like chatbots and virtual assistants. Following all that is happening in the dynamic world of AI is time-consuming for entrepreneurs who are busy running their own companies, so I’ve compiled a list of the most interesting AI trends entrepreneurs should keep an eye on in the coming year.
AI content creation
The trend toward humanization of big data and data analytics will continue in 2018 with new advancements in natural language generation (NLG) and natural language processing (NLP). Using rule-based systems like Wordsmith by Automated Insights, media outlets and companies can already turn structured data into intelligent narratives based on natural language.
Making relationships in data understandable to people beyond data science teams will further democratize AI and big data, leading to the era of automatic generation of insights. The same technologies are already enabling automated content generation in news coverage, social media, marketing, fantasy sports, financial reports and more. In the coming year, automated content generation is likely to gain more traction in news reporting and marketing, helping companies instantly respond to emerging trends, news and events by creating the relevant content for their audience and clients.
The rise of capsules AI
Capsule networks (CapsNet) is a new form of deep neural networks proposed by Google’s lead scientist Geoffrey Hinton in a recent paper. In a nutshell, a capsules approach aims to overcome the shortcomings of CNNs (convolutional neural networks) that have been the de facto standard in image recognition for many years. CNNs are good when images fed to them are similar to those used during training. However, if they are asked to recognize images that have rotation, tilt or some misplaced elements, CNNs have poor performance. CNNs’ inability to account for spatial relationships makes it also possible to fool them by changing a position of graphical elements or the angle of the picture.
Conversely, capsule networks account for spatial relationships between graphical elements and understand natural geometric patterns that humans grasp intuitively. They can recognize objects no matter from what angle or point of view they are shot. Commentators predict that capsules will be the next major disruption in image recognition and computer vision. In particular, new capsule networks will dramatically outperform CNNs and other image recognition models and will be able to counter white box adversarial attacks designed to trick neural networks.
Until very recently, training of machine learning models was made in a centralized fashion on remote cloud clusters. AI companies had to manually collect large training data sets and feed them to ML algorithms run in data centers equipped with dedicated hardware (e.g. GPUs) for machine learning. The main downside of this centralized model is the difficulty of making rolling updates of AI software and implementing continual training using the constant stream of incoming data generated by users and applications.
In April 2017, however, Google made a decisive move towards solving these problems when it announced a new Federated Learning approach to be used in Gboard, Google’s Android keyboard. This novel approach enables mobile users to collaboratively train a shared ML model with their user data on Android devices. What Federated Learning really does is crowdsource ML training to millions of mobile users by making AI models directly available on devices. Moving AI training to mobile can help solve the high latency and low throughput connection issues involved in centralized learning.
Decentralized AI can also gather momentum with the development of edge computing that moves intensive computations from remote cloud applications to the network edges where digital devices sensing and collecting information are installed. Moving data processing and analysis to the “field” solves the problem of high latency and low throughput associated with sending data over the network. Many edge devices need advanced learning, prediction and analytics capabilities to function efficiently. This is where AI and ML have an opportunity to shine. Using AI on the edge is especially critical for drones and driverless cars, which need to run real-time deep learning without the network connection to avoid the disastrous or even fatal consequences of network failure.
To close the existing gap in AI for the edge, companies like Movidius (acquired by Intel in 2016) are creating AI co-processors and edge neural networks that can be used for obstacle navigation for drones and smart thermal vision cameras. In the coming year, we are likely to see more innovation in low-power computer vision and image signaling hardware and software specifically designed to enable AI on edge devices like security cameras and drones.
AI leveraging offline data
Data generated online is currently one of the main sources of insights for data analytics and AI-based solutions. However, major retailers like Amazon have already ventured into an unchartered territory of offline data collected by small digital devices like sensors and actuators in stores and malls. In Amazon Go grocery stores, for instance, these devices already track customers’ movements to see how long the customers interact with products. Data collected by Amazon sensors is stored in the Android app and Amazon account, which are required to shop in Amazon Go stores. In this way, Amazon accumulates mountains of data about consumers.
Using this data, AI algorithms can draw insights about consumer preferences and behavior to create automatic price-setting mechanisms and introduce more efficient marketing, product placement and merchandising tactics. Sources of offline data, however, are not limited to grocery stores. Using drones and the internet of things, AI companies will gradually transform the entire physical space we live in into a giant source of data for ML algorithms and models.
The rise of on-device AI: Core ML
Running AI software or training ML algorithms on mobile devices has been recently regarded difficult due to battery power constraints and limitations of mobile computing power. In 2017, however, we witnessed the move towards on-device and mobile AI heralded by CoreML, Apple’s ML library designed for iOS 11.
CoreML ships with a variety of trained ML models (e.g. for image recognition, text detection, image registration and object tracking), which can be easily integrated into iOS applications. All models are optimized for efficient on-device performance using low-level Apple technologies like Accelerate and Metal. As a result, iOS developers now have a powerful ML functionality at their fingertips, which promises to make AI/ML apps mainstream on mobile devices in 2018.
The current pace of innovation makes it almost impossible to stay on top of the AI trends, but understanding the terminology and the applicability of the machine learning advancements becomes a must for business owners in 2018. Using this knowledge, entrepreneurs can navigate the landscape and truly benefit from the improvements, even if they seem to be incremental.
Free the Data!
“What good are wings without the courage to fly?” These words of wisdom come to mind as I consider the open-source craze among leading artificial-intelligence technology providers.
Top firms, including IBM, Google and Facebook, have opened the source code of their artificial intelligence software tools, making them available for developers to use in their own devices and applications. This is most certainly a good thing, for the companies themselves and for the AI business generally.
However, open source is only part of the equation. Unlike previous generations of software, AI algorithms are worthless without a dataset to work on. And in contrast to their open-source code policies, these companies maintain a closed-data stance, hoarding their vast information repositories as a competitive advantage for developing better AI technology.
Essentially these companies have given us wings — but have denied us the sky. What the top tech firms need is the courage to stop hoarding information and embrace open data, giving the rest of the world access to the information required for AI cognitive engines to attain their full potential.
The data-rich get richer.
In the age of AI, a new 1 percent is arising. This upper, upper crust consists of companies blessed both with machine-learning technology and with large quantities of information.
Some companies have been dubbed “the Superrich” of the AI business, including Google, Facebook, Amazon and Microsoft. It has been reported that, while there are very few of these companies in the world, they have a massive advantage over everyone else in the machine learning space because they have access to vast amounts of clean, structured data.
Such data is needed to train machine-learning algorithms, giving them the basic information they need to function on their own in the real world. For example, an object-recognition algorithm designed to recognize cats in photos will be trained by reviewing massive numbers of images depicting felines. These images need to have some structure, i.e., they must be tagged with keywords that properly indicate they are depicting cats.
The larger the quantity of training data, the better the algorithm will perform, with more information providing more examples that can be used to find patterns. Conversely, inadequate quantities of training data can produce algorithms that deliver substandard results—sometimes to the extreme embarrassment of their creators.
Because of this, the usefulness of an AI algorithm is intrinsically tied to the availability of high-quality data. In this regard, AI algorithms are fundamentally different from other types of software, whose code is valuable on its own without any additional data.
Thus, when a company open-sources an AI cognitive engine such as a translation tool, it’s not the same as open-sourcing a piece of traditional software, like a spreadsheet. Without also providing access to the data, open isn’t really open.
Such data-denial is no accident. Rather, it’s part of a deliberate strategy to maintain a competitive advantage. With AI models well known and well distributed, the data set is the one commodity that can be locked away and kept from rivals.
That’s why top technology players are hoarding data. For example, IBM didn’t buy The Weather Channel’s data operations because it wanted to know if it’s going to rain in Tallahassee tomorrow.
Weather is the number-one factor driving global GDP. By combining The Weather Channel’s vast repository of climate-related information with its Watson AI, IBM can take the lead in forecasting the weather for private businesses, allowing it to do everything from predicting winter energy demand to forecasting crop yields.
This gives IBM a huge market impact and a built-in advantage that will be hard for other companies to match.
Google, Facebook and others hold similar advantages in their respective areas, possessing vast quantities of consumer and social-media data that can be used to train highly-valuable AI tasks, from sentiment analysis for marketing to object-recognition for photos, to natural language processing for user interfaces.
Examples of open AI software tools offered by technology powerhouses include:
Google’s TensorFlow, which is designed for building and training neural networks.
Microsoft’s Computational Network Toolkit, which can be used for applications including machine translation, image recognition, image captioning, text processing, language understanding and language modeling.
IBM’s SystemML, which can be used to create customized machine learning software.
Facebook’s deep-learning technologies, which the company has donated to an open source software project known as Torch.
With such initiatives, these companies are essentially giving away software that’s the product of enormous investments in manpower and intellectual property. However, these efforts are far from altruistic; by proliferating their technologies, companies aim to build large communities of developers accustomed to using their tools, establishing them as standards in the AI market.
Furthermore, with the real value of AI locked up in their proprietary data, these companies have little to lose by giving away their software tools.
So how can companies be convinced to give up their prized data for the greater good of the AI business? One example can be found in an Uber initiative called Movement, which opens up data collected by the company’s fleet of cars regarding urban traffic patterns. Via the Uber Movement website, city planners can gather information to help improve traffic conditions.
What’s in it for Uber? The company doesn’t conduct road planning and construction itself, so providing this information to planners allows the government to make changes that improve driving conditions. This results in an improved user experience for Uber vehicles.
For AI tech companies with large treasure troves of data, there may be other opportunities to open up access to information in order to stimulate broad societal benefits. These benefits could indirectly boost demand for their technologies.
The AI market is ready to take wing — now all the big players need to do is give clearance for takeoff by having the courage to open up their data.
6 Critical Questions to Help Businesses Cut Through the AI Hype
It’s always gratifying when experts confirm what you’ve suspected. Research firm Gartner put a shot of reality into our morning coffee this past summer with its critical analysis that artificial intelligence (AI) had reached the “peak of inflated expectations.” Frankly, I think a lot of technology vendors are blowing smoke about their capabilities.
If your expectations were for Elon Musk-worrying AI, dial them back to the level of business software. It’s actually less mundane than it sounds. AI already has changed how we use data to make sense of our world. It’s also become a corporate fashion. For every AlphaGo Zero there are a thousand firms — startups and established companies — sticking the AI label on their wares like pinstripes on 1980s cars.
I don’t doubt that the last few years have seen significant and rapid progress in AI. What I mistrust is the crescendo of hype, which echoes the tech bubble of the late 1990s and early 2000s. The risk: buying into over-exuberant promises rather than products with a proven return on investment (ROI). Hype clouds our judgment, sometimes intentionally.
If you share my skepticism, but likewise sense an important opportunity and want to avoid excessive caution, here are six questions to help you tune your BS detector.
1. What business problem am I trying to solve?
This is the most important question, and it has nothing whatsoever to do with AI. Granted, a few firms will find value in experimentation, but open-ended projects should be treated with extreme caution. Better to clearly define the business problem you want to solve.
You should evaluate any business investment against three criteria: Will it increase revenue, reduce costs or mitigate risk? Anchoring new technology to at least one of these fundamentals will establish its value. After that, assigning ownership and accountability is the best way to keep a technology initiative on track.
2. Why do I need AI to solve this problem?
Maybe you don’t. True AI acquires and applies knowledge and skills. It’s good for situations in which variability and novelty exist, but it’s difficult to build and therefore commands a premium. Consider the complexity of a self-driving car navigating busy city streets. Does your business problem involve continual unpredictability?
Machines that get incrementally better at a task sound compelling, but you need to focus on the outcomes delivered and not the technology used to achieve them. Will you gain a margin of improvement that makes the cost of AI worthwhile? Figure out a test to evaluate the size of the margin. Run it on paper and again in a proof-of-concept project. Make sure AI earns its premium.
3. Do I have sufficient data to use AI?
The best AI solutions outperform people at specific tasks such as recognizing cancer cells in scans or finding errant traders in an investment bank. But teaching a machine to make sense of messy and inconsistent data requires extensive training. AI uses models to make sense of the world and generalize. Finding enough examples to build a good model can be difficult.
Healthcare systems or banks can draw on extensive historic data. Can you? Even if you can, trawling through it to find relevant examples can be time-consuming and costly. To overcome this obstacle, some companies have started to work on AI model-training software that makes the process quicker and cheaper. Even so, it’s a tough assignment whenever data is scarce.
4. Should I build or buy an AI solution?
If you’re trying to embed AI into your own products or services, an in-house capability might make sense. Still, don’t underestimate the resources and specialized expertise involved. A carefully selected partner may offer a quicker path to glory.
If you’re tackling a known business problem, you’ll likely be better off working with an experienced vendor. Don’t be blinded by technology; whatever you buy will need to be customized or adapted to your environment and requirements. Focus on your vendor’s understanding of your business domain and the type of data the consultant will need to leverage.
5. How well does the vendor know my domain?
Some vendors claim AI makes domain experience irrelevant. Don’t believe it. It’s quicker and less stressful to work with a consultant who doesn’t need to learn your business from scratch.
Check any potential vendor’s relevant experience and partnerships. Can the vendor’s leaders give production examples of comparable problems solved for others? If your problem truly is unique, seek experts who bring experience in dealing with parallel challenges — perhaps in a different industry with similar sorts of data.
6. Is there a proven ROI?
In Gartner’s report, the “peak of inflated expectations” is followed by the similarly whimsical “trough of disillusionment.” AI will lose its luster as technology buyers look past blithe promises and start demanding proven results.
In my opinion, that’s merely sensible business practice. So why be patient? Ask to see ROI measures today. Your smart money is on solutions that don’t involve an extended learning curve for the buyer or the vendor.
While I’m obviously a bit of a cynic, I’ve seen firsthand the difference that genuine AI can make. Machines can take on tasks that are important but arduous for people — and do them better. It does more than save time or give a nudge to performance. It opens space for a shift in organizational change that reaps far greater rewards. That’s the true promise of AI, but realizing it takes more than clever software.
Amara’s law states that “we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” It’s sound advice. Leaders face the twin challenge of deciding if an investment in AI can create value in the near term while figuring out how to adapt their organizations for a world in which AI is ubiquitous.
Fostering positive experiences and making investments that deliver a healthy ROI will help reveal what’s at stake and lay the foundation for deeper change ahead. Heeding business fundamentals rather than hype will allow organizations to make smart choices today and build their experience of AI for the future.
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