Last year, Boris Evelson (@bevelson) and Michele Goetz (@Mgoetz_FORR), analysts at Forrester Research, noted many business executives were contemplating how artificial intelligence (AI) could be leveraged to improve their organizations. They concluded, “Failing to act is not an option in most organizations.” They went on to note that even though implementing AI operations may be an imperative, “The truth is, AI is hard!” A year earlier, Forrester predicted AI would spark a business revolution and, despite AI being hard, Evelson and Goetz predicted the revolution would continue to unfold. According to David Weldon (@DWeldon646), Gartner analysts insist AI continues to be one of the most disruptive business technologies. He writes, “Gartner says that disruptive emerging technologies will play a major role in reshaping business models as they change the economics of all organizations. … Artificial intelligence was by far the most mentioned technology.” Gartner analyst Andy Rowsell-Jones agrees AI can be disruptive. He’s just not sure disruption will lead to a business revolution. “On the surface this looks revolutionary,” he told the audience at Gartner’s 2018 ITxpo. “However,” he continued, “this bump in adoption rate of AI may indicate irrational exuberance instead. While CIOs can’t afford to ignore this class of technologies, they should retain a sense of proportion. This latest batch of AI tools is yet to go through its trough of disillusionment.”
Tempering irrational exuberance about AI
Gil Press (@GilPress) notes, “In its 2019 predictions, Forrester tries to temper the ‘irrational exuberance for AI adoption’ with a dose of reality, looking forward by observing how companies automate their work today while experimenting with adding intelligence — artificial and human — to analyzing data and making decisions.” Press summarizes the findings of two Forrester reports which make the following predictions about AI:
- It’s the data, stupid: “Most companies will find out that to realize their expectations from AI — exaggerated or not — they must invest in creating ‘an AI-worthy data environment.’ 60% of decision makers at companies adopting AI cite data quality as either challenging or very challenging — it’s their top challenge when trying to deliver AI capabilities.”
- Automation and intelligence convergence is the new thing: “More than 40% of enterprises will create state-of-the-art digital workers by combining AI with Robotic Process Automation (RPA). … By the end of 2019, automation will eliminate 20% of all service desk interactions, due to a successful combination of cognitive systems, RPA, and various chatbot technologies.”
- No brain, no gain: “Two-thirds of AI decision makers struggle with finding and acquiring AI talent, and 83% struggle with retention. The solution may lie, at least partially and paradoxically, in the problem itself: Companies will use AI to find scarce AI talent.”
- Bringing humans back in: “10% of enterprises implementing AI applications will add knowledge engineering to the mix — human wisdom and expertise — to ‘extract and encode inferencing rules and build knowledge graphs from their expert employees and customers’.”
- Humans need not apply but they will reinvent themselves: “One-tenth of startups will begin life with more digital workers than human ones. 10% of US jobs will be lost to automation — but the equivalent of 3% of today’s jobs will be created. Automation will contribute to better employee experience as rote tasks come off humans’ plates.”
- Searching for trust in explainable AI: “There will be increasing demand for transparent and easily understandable models. 45% of AI decision makers say trusting the AI system is either challenging or very challenging.”
- Centralization in, chaos out: “40% of enterprises will have automation centers in place. Change management, unpredictability, control, auditing, and security issues will raise governance concerns throughout 2019. In response, enterprises will invest in central coordination — automation centers — designed on the basis of unifying frameworks.”
Evelson and Goetz conclude, “CIOs will realize that new technologies like AI require hard work.” Understanding that simple truism should be enough to temper irrational exuberance. Evelson and Goetz go on to predict, “Forward-looking organizations will create new roles and processes to take full advantage of them – not by simply shifting away from old architectures, but by redesigning their whole operating models to suit the new wave of technology.”
It doesn’t take a seer to know AI will continue to affect our lives and the places we work for both good and ill. Ben Lorica (@dliman), Chief Data Scientist at O’Reilly Media, notes, “Open your Facebook feed, a newspaper or turn on the news and you’ll likely see something about the dangers of machine learning, the increasing amount of fake news or even the dangers of AI on our privacy. Yet, these technologies are continuing to develop and thanks to new developments in automation and machine deception — they will continue to shape the use of AI over the coming year.” Lorica goes on to make nine predictions about AI. They are:
1. New technologies will enable partial automation of tasks. “While full automation might still be a way off, there are many workflows and tasks that lend themselves to partial automation.”
2. Artificial Intelligence in the enterprise will build upon existing analytic applications. “Companies have spent the last few years building processes and infrastructure to unlock disparate data sources in order to improve analytics on their most mission-critical analysis, whether it is business analytics, recommenders and personalization, forecasting, or anomaly detection and monitoring. … Expect early forays into deep learning and reinforcement learning … in areas where companies already have data and machine learning in place.”
3. UX/UI design will become critical. “Many current AI solutions work hand in hand with consumers, human workers, and domain experts. These systems improve the productivity of users and in many cases enable them to perform tasks at incredible scale and accuracy. Proper UX/UI design not only streamlines those tasks but also goes a long way toward getting users to trust and use AI solutions.”
4. Hardware will become more specialized for sensing, model training, and model inference. “2019 should see a broader selection of specialized hardware begin to appear. Numerous companies and startups in China and the US have been working on hardware that targets model building and inference, both in the data center and on edge devices.”
5. Hybrid models will remain important. “Most end-to-end solutions are hybrid systems. In 2019, we’ll begin to hear more about the essential role of other components and methods including model-based methods like Bayesian inference, tree search, evolution, knowledge graphs, simulation platforms, and many more.”
6. Investments will be made into new tools and processes. “Tools for machine learning development will need to account for the importance of data, experimentation and model search, and model deployment and monitoring. … In 2019, we can expect many new tools to ease the development and actual deployment of AI and ML to products and services.”
7. Challenges around machine deception will increase. “We’re still in the early days of machine-generated content (fake images, video, audio, and text). At least for now, detection and forensic technologies have been able to ferret out fake video and images. But the tools for generating fake content are improving quickly so we must ensure that detection technologies are able to keep pace.”
8. Questions will be raised around reliability and safety. “It’s been heartening to see researchers and practitioners become seriously interested and engaged in issues pertaining to privacy, fairness, and ethics. But as AI systems become deployed in mission-critical applications including life and death scenarios, improved efficiency from automation will need to come with safety and reliability measurements and guarantees.”
9. Access to more data will help companies to take advantage of data they didn’t generate. “Services for generating labelled datasets are beginning to use machine learning tools to help their human workers scale and improve their accuracy. And in certain domains, new tools like generative adversarial networks (GAN) and simulation platforms are able to provide realistic synthetic data, which can be used to train machine learning models. Thanks to new and secure privacy preserving technologies, organizations can take advantage of data they didn’t create themselves. Consequently, smaller organizations will gain the ability to compete by using machine learning and AI.”
For most companies, AI will be used to improve decision-making. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), assert that if you can improve a company’s decision making you can dramatically improve its bottom line. They explain, “The best way to understand any company’s operations is to view them as a series of decisions. … Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance.” Since AI will be a prominent decision aid, in the future, humans are more likely to perceive AI as a benefit than a threat. AI will create some redundancies, but, more often, it will augment, rather than replace, employees.
 Boris Evelson and Michele Goetz, “Predictions 2018: AI is tough stuff and many organizations will fail at it,” Information Management, 20 November 2017.
 David Weldon, “AI, cybersecurity top list of most disruptive technologies,” Information Management, 23 October 2018.
 Gil Press, “AI And Automation 2019 Predictions From Forrester,” Forbes, 6 November 2018.
 Ben Lorica, “Artificial intelligence trends for 2019,” IT Pro Portal, 22 November 2018.
 Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.