AI-driven Digital Transformation of the Business Enterprise2022-03-162022-03-16http://cortexgroup.ai/wp-content/uploads/2019/04/logo_header.pngCortex Grouphttps://cortexgroup.ai/wp-content/uploads/2022/03/ai-driven-digital-transformation.png200px200px
10. AI-driven Digital Transformation of the Business Enterprise; Towards AI-driven Digital Transformation
11. The Agile AI-driven Business Enterprise; Assisting Businesses on their AI-driven Transformation Journeys
12. AI is Changing Business Processes
13. AI impacting the Workplace, Employment, and the Job Market
14. The New AI-driven Face of Business and Customer Services
15. AI-driven Cybersecurity for the Business Enterprise
10. AI-driven Digital Transformation of the Business Enterprise
There is no doubt that Artificial Intelligence will increasingly become a transformative and disruptive driving force in business, not only impacting all aspects of the business enterprise, business models and most business sectors, but also give rise to novel market opportunities and affect industries of the future where we will see more AI-powered codification of money, markets, and trust, the weaponization of code through AI-driven cybersecurity, and the development of radical markets. It has been estimated that AI will add $13 trillion to the global economy over the next decade.[i] Having been directly involved in developing and delivering impactful AI software solutions in a number of industries and business sectors across the globe over the last two decades, it has been amazing to see how business value drivers such as customer growth, retention, risk, productivity, efficiency, throughput, yield and quality can be impacted as more data becomes available and business are getting better instrumented and connected. This is further assisted by the significant increase in computing and data storage and processing capability, and the AI and smart technology toolboxes are being strengthened by the utility and capability of more powerful algorithms using all available structured and unstructured data.
As enterprises expect AI to enable them to move into new business segments or to maintain a competitive advantage in their industry, rethinking of industries and the enterprise itself is needed. We now see an evolution of the markets with respect to more informed consumers, faster and scalable marketplaces, dynamic and vibrant businesses, and leaner operations. Significant advances in AI are helping the creation of new industries and business segments by taking a fast adoption journey to move from discovery to commercial application to a new industry. Some early examples of AI driving new industry segments include GPS-driven ride-sharing companies, hyper-personalized online shopping platforms focusing on microsegments, intelligent virtual assistants driving conversations with customers as well as within the enterprise, recommendation-driven streaming channels, and adaptive learning based educational companies. We have also seen a tremendous increase in AI-focused startups with investments growing 1800% in the last six years.[ii] These developments are putting more pressure on executive management of enterprises to act swiftly in making strategic shifts to monetize these new business opportunities and adapting their business models as the acceleration of AI adoption and its applications spawn the creation of new industries and business segments. Although the current focus of AI applications is mostly on optimizing efficiencies in existing industries, the most formidable long-term economic use of AI will likely be in solving large, complex, and open problems that could be the foundations of new industry segments. For this, business leaders and AI strategists need to spot important trends, keep track of state-of-the-art AI developments and act quickly around new possibilities.
We also need to specifically rethink the impact of human-computer interaction, automation, jobs, the workplace, and cybersecurity, amongst many other factors that are impacting business value drivers, employees, and customer experience. It is also evident that customer facing businesses need to offer personalized customer experiences at scale, which is beautifully illustrated by the success of the internet giants like Google, Amazon, and Alibaba and their ability to deliver personalized experiences and recommendations. By using AI to build a dynamic real-time 360-degree profile of customers as they interact through mobile apps, intelligent virtual assistants and online web portals, providers of goods and services can quickly learn how their AI-driven predictions can fit customer’s wants and needs with ever-increasing accuracy. When we flip through recommendations on Netflix or Amazon, or search on Google, most of the AI-based calculations are happening in high-powered processors inside remote data centers (in the cloud) with handheld or desktop devices acting as the interface and communicating the results. This will change as AI algorithms become more efficient and capable of running on low-power devices at the “edge” where custom processors designed to carry out real-time analytics on-the-fly close to the point where data is gathered and used. With the cost of hardware and software continuing to fall, AI tools (augmented by IoT, cloud and edge computing, virtual and augmented reality, and so on) will increasingly be embedded into our vehicles, appliances, and workplace tools, giving these devices of every shape and size the ability to learn for themselves. As IoT integration will allow for the development of environments where solution providers and consumers can interact, it will likely also be possible to design experiences over products, which will affect business models further.
Towards AI-driven Digital Transformation
For any business to stay relevant and thrive given the swift pace of change and disruption in the Smart Technology Era, it needs to be transformed into an AI-driven business and have increasingly more real-time intelligence built into on all aspects of its internal operations, customer needs and impact, and competitive and collaborative forces in the ecosystem in which the business operates. For businesses to move towards AI-driven automated decision-making, they need to overcome the barrier of information quality. However, accurate data is becoming increasingly available with better quality sensors, improved connectivity, and an increase in smart technology and methods of simulating real-world processes and mechanisms in the digital domain. We will see an increase in the availability and accuracy of real-world simulations, which in turn will lead to more powerful and accurate AI systems. With computers now powerful enough and trained on accurate-enough data to do simulations in the digital world, the expense and risk of testing AI systems in the real world can also be reduced. For example, we have seen how simulations help businesses working on the development of autonomous vehicles to gain thousands of hours of driving data without vehicles even leaving the factory, which in turn leads to increases in data quality and significant reduction in cost. Given the nature of Tesla’s software-defined electric vehicles even more accurate real world driving data is captured. Whether or not they are Autopilot enabled, the data from Tesla vehicles is sent directly to the cloud and used to generate highly data-dense maps that they claim are more accurate than alternative navigation systems. The better a company can mine all available internal and external data across its operations, value chain, customers, and ecosystem to create real-time dynamic simulation models of all aspects of its business, the better it would be able to optimize the business over short, medium, and long-term windows and adjust its course where required. This is relevant across all industries.
Across industries, businesses and organizations are assessing ways and means to make better business decisions utilizing such untapped and plentiful information. As the world gets instrumented, data is generated at an exponential rate, whereas data utilization increases relatively linearly in relation to data generation. With evolving AI technologies that can unlock value from growing data sets and more and more business use cases come into the fray, there is a need for innovative approaches that takes into consideration the required data infrastructure, computing (both in hardware and software), AI tools and platforms, processes, organizational alignment, and roles. As enterprises look to innovate at a faster pace, launching novel products and improving customer services, they need to find better ways of managing and utilizing data both within the internal and external firewalls. Organizations are realizing the need for and the importance of scaling up their existing data management practices, overcoming siloed execution, and adopting newer information management paradigms to combat the perceived risk of reduced business insight or lack of impactful solution deployment. So, an organization’s ability to analyze that data to find meaningful insights and operationalize AI-driven solutions are becoming increasingly complex. Many of the current business success stories have come about with companies enabling analytic innovation and creating data services, embedding a culture of innovation to create and propagate new database solutions, enhancing existing solutions for data mining, implementing predictive analytics and machine learning techniques, complemented by the creation of skills and roles such as data scientists, AI or machine learning engineers, data science developers, big data architects, data visualization specialists, and data engineers, among others. These enterprises’ experiences in the AI and Big Data analytics landscape are characterized by agility, innovation, acceleration, and collaboration.
Another key aspect of leveraging smart technology is to also understand where it can be used, when it can be used, and how it can be used. Business value drivers can typically be categorized as strategic or efficiency related. Some strategic drivers include the generation of new business opportunities through exploratory analysis to uncover hidden patterns, proactive decision making and gaining operational insights via predictive analytics, forecasting customer and market dynamics, speeding up strategic decision making with real-time AI-driven analysis, and better decisions through cross-organizational analysis to quantify the estimated impact of decisions. On the other hand, efficiency value drivers typically include continuous improvements, the reduction of costs on people, processes, and infrastructure and tools that do not enhance an agile and smart data-driven business, and increasing automation to reduce efforts needed to extract, consolidate, and produce reports. Other efficiency drivers include to complement or retool skills of employees to emphasize problem solving and recommendations, developing data-driven decision-making culture, eliminate redundant tools, data stores and processes, and standardize metrics and streamline processes. We already see how robotic process automation is used to do repetitive work such as filling in forms, generating reports and diagrams and producing documentation and instructions. Although this leaves employees to spend more time on complex, strategic, imaginative and interpersonal tasks, we would need to get used to learning new skills and working alongside AI-powered tools and bots in our day-to-day working lives. The IDC predicts that by 2025, 75% of businesses or organizations will be investing in employee retraining in order to fill skill gaps caused by the need to adopt AI.[iii] Some example business drivers that I have encountered across multiple industries include increasing operational efficiency, effectiveness and revenue; creating strategic value via faster, better and more proactive decisions, enhanced scalability, new business models, and revenue growth opportunities; enhance customer experience via real-time, on demand, digital, personalized service delivery, assistance and advice which is enabled via 360 degree insights about the customer; and targeted sales and marketing. A business can increase its productivity through increasing automation, improving processes, and ensuring equipment availability. To increase revenue, an industrial business is focused on increasing throughput, yield, and quality, whereas a consumer-facing business is more fixated on cross-selling, up-selling and recommending products and services to its customers. To drive efficiency and effectiveness, a business also needs to reduce its risk which might include process and equipment failure, customer churn, fraud, waste and abuse, and cybersecurity risks. For a business to lower costs, it needs to eliminate redundancy, reduce energy and raw material usage, and have more cost-effective operations and maintenance.
Some of the key characteristics of businesses that are early adopters of AI are those that are digitally mature, adopting multiple smart technologies, including AI in core activities, have a focus on growth over savings, and have executive-level support for AI. In the chapters to follow, we will see how AI can create business value across the value chain via smarter research and development and forecasting, optimizing production and maintenance, targeted sales and marketing and enhancing the user experience. The key elements of successful AI-driven digital transformation include the following catalysts to accelerate the path to business value generation: Vision or Intent (which involves scanning the use-case horizon and sources of value; articulate business needs; and creating business cases, strategic plans, and performance metrics), Data (which entails breaking down data silos in the data ecosystem; deciding on the level of aggregation and pre-analysis; and identifying high-value data and data availability), Technology (which identifies first-for-purpose AI tools and platforms, partner or acquire to plug capability gaps; and taking an agile “test and learn” approach), Process (which encompasses integrating AI into workflows and workplace processes through change management; and optimizing the human/machine interface), and People (which is about adapting an open, collaborative culture and organization; building trust in AI insights; developing AI implementation skills; and reskilling the workforce to ensure complementarity). To help guide a business on its digital transformation journey in operationalizing AI solutions, I have been recommending and using an adapted agile version of the IDC’s Big Data and Analytics MaturityScape framework for many years.[iv] It is a framework of stages, dimensions, outcomes, and actions required for businesses to effectively advance along the five stages of Big Data and Analytics or AI implementation competency and maturity: Ad Hoc, Opportunistic, Repeatable, Managed and Optimized. The Ad Hoc stage is more experimental in nature and characterized by ad hoc, siloed pilot projects, undefined processes, and individual effort. The business outcome is typically value obtained through new knowledge and learning. The Opportunistic stage is more intentional where there are typically defined requirements and processes, but unbudgeted funding as well as project management and resource allocation inefficiency. At this stage knowledge value grows and business value opportunities become visible as a business outcome. The next stage of competency and maturity is the Repeatable stage where there are recurring projects, budgeted and funded program management, documented strategy and processes, and stakeholder buy-in. Here we see business value being realized but the business outcome remains localized to business units. When a business moves to a Managed stage, the project, process and program measurement influence investment decisions and standards emerge. The business outcome is typically that new product and service opportunities transition to business plans and execution. The final stage is Optimized where AI-driven continuous and coordinated process improvement and value realization leads to previously unattainable business value being continuously produced. This framework allows a way to assess the AI operationalization maturity and competence of a business as it transitions through the various stages over time for each of the five dimensions which can be visualized in a radar chart: Vision or Intent (strategy, budgeting, justification and culture), Data (quality, completeness, trust, and timeliness), Technology (deployment, adoption, performance, and functionality), People (skills, organization, collaboration, and training), and Process (data management, data analysis, governance, and measurement). With this framework a baseline can be used to define short- and long-term goals, prioritize smart technology, budget as much for integration and adoption as for technology, plan for improvements, make employee investment decisions, and bring business value into view. As the assessments can be done on a team, business unit, and business level, gaps in current AI competency and maturity levels can be uncovered among functional and cross-functional teams, business units or between business and IT groups. This allows for all the stakeholders to collaborate in advancing the organization toward a common goal of building a smart technology data-driven thriving business that continuously delights customers.
11. The Agile AI-driven Business Enterprise
To thrive in the Smart Technology Era, businesses need to be nimble which enables business to cope with continuous change in an increasingly complex, rapidly shifting, uncertain and unstable world. This agility is key for businesses to survive and thrive sustainably with today’s marketplace. The following three “laws” for operating in an agile fashion describes the key ingredients in this regard: the law ofthe small team where work should typically be done with small autonomous cross-functional or interdisciplinary teams on relatively small tasks in short cycles with continuous end user feedback; thelaw of the customer implies that everyone in the business is laser focused on delivering continuous new value to the customer and make the necessary organizational changes to support this; and the law of the network where the whole business is truly agile and seen as a fluid and transparent network of high-performance teams that are collaborating towards the common goal of delivering superior value to customers[i]. As companies apply the three laws of agility, the actions that promote scale in AI also creates a virtuous circle where interdisciplinary teams initially start collaborating with their diverse skills and perspectives combined with the user input needed to build effective customer solutions, absorb new collaborative practices across the business, and move from trying to solve siloed problems to completely reimagining business and operating models. The more the business adopts the test-and-learn agile approaches for AI-driven pilots and solutions, the faster innovation happens across the organization with decisions augmented by AI also happening faster and closer to the coalface. AI-driven businesses that operate in such an agile way creates a competitive edge for them in the marketplace. As we have seen with the coronavirus pandemic a case can be made that only agile businesses will survive.[ii] It further accentuates the importance of agility in the Smart Technology Era, where we see new ways of working, living, playing, and learning where everything is different. So, businesses either adapt or die whilst they are continually bombarded with a stream of strategic opportunities and risks whereas their business agility needs to be constantly upgraded.
12. AI is Changing Business Processes
AI is already changing many business processes across the enterprise and its value chain all the way from sales, marketing, and the customer interface to research and development, human resources, recruitment, legal services, auditing and security. AI can be used as a valuable tool for business process management that aims to create an efficient and effective workflow for the business enterprise by helping to automate repeated tasks through robotic process automation, improve user interfaces through personalized intelligent virtual assistants, enhance decision-making within business processes (e.g., should a customer be sent a product recommendation or get a follow-up call) and analyze massive data sets through data mining and predictive analytics. Well-designed AI systems should augment people and allow them to focus on decision making, innovation, and higher-value work that reinforces the role they play in driving business growth. As businesses move to outcome-based approaches where agreements are based on the business value created by the work, we will see a change in business processes that have traditionally been driven by service level agreements. The more AI is being democratized, the more business will be able to apply it in various business processes across the enterprise.
13. AI impacting the Workplace, Employment, and the Job Market
Given the current state-of-the-art in AI, the introduction of AI in the workplace will initially focus on augmenting and helping employees to do their jobs better and not necessarily replace them. AI-based technology is creating new ways for employees to maximize their interactions with customers and increase their productivity. Clearly tasks within jobs will change as more repetitive and mundane tasks will be automated. While few jobs are fully automatable, one study shows that 60% of all jobs have at least 30% technically automatable activities.[i] So which jobs or tasks within jobs will be harder to replace with AI technologies? This would not only be jobs or tasks that have minimal routine or repetition, but likely also ones that require creativity, are difficult to learn through simple observation, require hands-on manipulation, do not involve the use of large data sources, are dependent on human interaction and interpersonal communication, and require social perception. Kai-fu Lee, an AI expert and founder of Sinovation Ventures has the opinion that “every job which takes less than 5 seconds to think will be done by robots”.[ii] Martin Ford reckons that we will get into a situation where any kind of job that is routine or repetitive on some level will disappear.[iii] It is evident that the demand for uniquely human skills will grow. Although millions of jobs will likely be displaced, the World Economic Forum’s Future of Jobs Report projects in the order of hundreds of millions of new jobs that will be added over the next few years that requires skills in both emotional intelligence and technical intelligence.[iv] Some examples of new AI- or smart tech-related jobs over the next few years will likely include AI trainer, voice user experience designer, ethical and human use officer, data detective, AI-assisted healthcare technician, a health wellness coach, ethical sourcing manager, AI chatbot designer, AI digital market expert, AI business and public sector strategy consultant, creativity coach, tech-addiction counselor, business behavior manager, AI business development manager, man-machine training manager, financial wellness coach, cybercity analyst, augmented reality journey builder, digital tailor, and much more.[v] In her book, Les Métiers du Futur (Jobs of the Future), published in 2019, Isabelle Rouhan believes that 85% of the jobs that we will have in 2030 do not exist yet.[vi] In an attempt to reveal what our future labor market will look like, she introduces us to new professions that will appear this decade such as robot monitors (managing and configuring algorithms for robots), neuro manager (helping with employee welfare via neuroscientific methods), ethical hacker (fighting against cyber-attacks) and digital detox therapist (helping a generation that has forgotten what reality looks like with general well-being and mental health).[vii] Some professions are disappearing while many others are expected to emerge over the next few years as our evolving technology is transforming society with our consumption patterns and lifestyles that are following suit. Good advice to anyone is to be flexible in the short-term and adaptable in the longer term.
As more and more employees depend on the insights of AI to do their jobs more efficiently and effectively, developing an AI ready workforce will be a competitive advantage and create significant value at both the individual and business enterprise level. It is also becoming increasingly important to upskill people to learn how to work with AI. Once key business problems that can be addressed by AI have been identified, it is important to build an agile cross-functional team of stakeholders to educate employees on the business benefits of implementing these AI-driven solutions and also identify new skills and jobs needed in the workplace as part of this. As artificial intelligence in the workplace will fragment some long-standing workflows, it will also effectively create human jobs to help integrate those workflows. There needs to be an understanding in HR of how to use AI across the employee life cycle, what learning opportunities need to be implemented for key job roles and reinvent the HR function itself. As AI and smart technology are helping to create a knowledge-based economy, we will see more AI tools that not only helps the workforce to perform tasks more efficiently, but also capable of intelligent automation with self-learning features and assisting employees to innovate. Instead of relying chiefly on a candidate’s credentials, we will also see more skills-based recruitment that involves the setting of specific skills and competency requirements for a job.
Some of the barriers to adopting AI technology in the workplace include finding properly educated and skilled people, concerns over data privacy, data availability that is also due to software-as-a-service offerings, ongoing maintenance due to the data-driven nature of AI solutions, and limited proven applications. Once there are effective and successful AI solutions references where everyone benefits, it is easier for a business to overcome bias and trust issues. AI must therefore earn human trust to thrive. Cost-savings presented by these solutions also gives businesses the opportunity to upskill their current employees. The focus should be on augmentation and AI as a workplace helper and not on replacing human workers. It would likely change the amount of time employees spent at work, how they work, and provide more room to be creative.
Another important perspective of the impact of AI on employment is provided by Richard Baldwin in The Globotics Upheaval where he predicts that white-collar jobs (which are jobs involving cognitive skill such as pattern recognition and the acquisition, processing and transmission of information) will be swept away faster by digital change than in any previous economic transformation.[viii] According to him the explosive potential comes from the mismatch between the speed at which disruptive energy is injected into the system by job displacement and the system’s ability to absorb it with job creation. On the other hand, Richard Freeman, an economics professor at Harvard University predicts that few businesses will be able to make sweeping changes such as replacing their accounting department with a few people managing the AI-driven accounting software and completely change the way it is doing reporting and controls. AI’s impact on the workplace would likely not be like a sweeping tidal wave, but more mosaic in nature across industries. However, external factors such as the Covid-19 pandemic might accelerate sweeping changes as a drive towards digitizing businesses, transformed business models, and a leaner and more agile workplace dynamics takes hold. Advances in machine learning and AI software in general over the next decade would likely lead to more of the white-collar jobs, with their currently defined job descriptions, being swept away by the smart technology driven digital change and potentially also make these sweeping changes with respect to the current jobs easier to do.
AI has the potential to dramatically remake the economy, where we will see new startups, numerous business applications and consumer uses, as well as the displacement of certain jobs and the creation of entirely new ones. Whereas traditionally new businesses would appoint full-time employees to take on roles in business development, sales, marketing, product development, design, customer support, and administration, there are increasingly more flexible options available in the API economy of AI services (which can act as so-called white-collar robots) and outsourced talent of freelancers (including foreign freelancers and telemigrants) which together offers significant gains in productivity and efficiency and huge cost savings. As we reimagine business processes, we will also see closer collaboration between AI and human workers, where humans work more like humans and less like robots, and a collaborative intelligence where there is a reliance on AI-driven decision support systems to help create work efficiencies. AI-driven automation, of which the pace and extent will vary across different activities, professions, salary ranges and skill levels, can enable growth and other benefits on the level of entire economies where productivity acceleration is very much needed especially with the declining share of the working-age population in many countries.
As we contemplate the impact of AI on the job market, there are sobering thoughts from several authors that have written on this subject. The possibility of large-scale technological unemployment has been discussed at length by Calum Chace in The Economic Singularity which refers to the concept of an economic singularity where we need a new economic system to address the situation of AI-driven machines rendering most humans unemployable.[ix] Martin Ford also discusses technological unemployment in his books, The Lights in the Tunnel and Rise of the Robots and emphasizes that we are on the verge of wholesale automation of white-collar jobs and hollowing out of middle-class jobs.[x] Erik Brynjolfsson and Andrew McAfee in The Second Machine Age help to validate the discussion of technological unemployment where they discuss two phenomena which they coin as “bounty” and “spread”.[xi] “Bounty” is described as the “increase in volume, variety and quality, and the decrease in cost of many offerings brought by technological progress”, whereas “spread” is the inequality of labor markets and wealth and “ever-bigger differences among people in economic success”. The latter has also been described as the “great decoupling” where we have on the one hand a steady growth in worker productivity over the last few decades, but stagnant growth in median income and employment (with the United States as one of the prime examples). With the economic gains of the information revolution the top 1% in the US has approximately doubled its share of the national income over the last 40 years and have almost as much wealth as the bottom 90% combined. The question is whether bounty and its economy of radical abundance (as also elaborated by Peter Diamandis and Steven Kotler in Abundance and Bold) will overcome spread by ensuring that most people are comfortably off and inequality is less of a factor.[xii] We are clearly currently extremely far from such a scenario. Brynjolfsson and McAfee recommend some interventions which could maximize the bounty whilst minimizing the spread. In Machine, Platform, Crowd they put more emphasis on the way AI and smart technology leads to structural changes in the economy and the kinds of jobs available because of that.[xiii] In 21 Lessons for the 21st Century, Yuval Harari mentions that instead of competing with AI, humans’ jobs can be created by servicing and leveraging AI, but this would not solve the problems of unemployed unskilled laborers or prevent remaining jobs to be safe from the threat of future automation. [xiv] Kai-fu Lee in AI Superpowers shares similar sentiments and discusses two kinds of job loss, the first being one-to-one replacements that is typically captured by economists using a task-based approach where a single AI-driven product or service can replace a specific kind of worker; and the second ground-up disruptions where AI start-ups are reimagining an industry from the ground-up and looking for new ways to satisfy the fundamental human need driving the industry.[xv] He is also concerned that the AI era, if left to its own devices, will shake the foundations of our labor markets, economies, and societies and divide the world into the AI elite and the rest as well as AI-rich and AI-poor countries. This leads into topics such as a universal economic safety net, losing our jobs to AI versus losing control over our lives, avoiding digital dictatorship and related matters which I will discuss more in later chapters.
14. The New AI-driven Face of Business and Customer Services
AI is changing the face of business and the workplace. Whereas the future of human-computer interfaces is likely to involve the use of AI in combination with a range of other evolving technologies such as augmented, virtual, and/or mixed reality, gestural computing, robotics, holograms, and emotional recognition, AI-driven chatbots and intelligent virtual assistants with more advanced natural language processing capabilities is becoming more central to how businesses engage with their customers and employees at scale. Whereas there was some disappointment with the initial technical brittleness of chatbots earlier on when high expectation were created, AI looks set to power 95% of customer interactions by 2025 (with the banking industry that could see the success rate of chatbot interactions reach over 90% by 2022).[i] Market Insider projects that the global conversational AI market size is expected to grow from 4.8 billion in 2020 to 13.9 billion by 2025.[ii] According to Gartner, by 2022, 40% of employees will consult AI agents before making decisions in day-to-day business.[iii] As businesses are trying to meet the requirements and demands of their customers on a 24 hours a day, 7 days a week basis, AI can help to meet the modern consumer expectations with fast response times and proper answers and recommendations for their needs and solutions to their problems. AI-driven chatbots can also easily handle most customer support activities that are repetitive in nature and resolve specific issues. We are now seeing AI shifting toward building intelligent systems that can collaborate effectively with people, including creative ways to develop interactive and scalable ways for people to teach robots or AI systems. As many people have already grown accustomed to touching and talking to their smartphones, it is also becoming evident that people’s future relationships with machines will become ever more shaded, fluid, and personalized. As we have seen with intelligent virtual assistants and advisors, research is now shifting towards developing intelligent systems that are trustworthy, human-aware, and able to interact with people through dialog, not just react to stylized requests.
15. AI-driven Cybersecurity for the Business Enterprise
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Background LinkedIn Articles on Democratizing Artificial Intelligence