AI as a business Partner

Paradigm Shift in Business Partnerships: Leveraging AI and Robotics for Strategic Transformation

1. Introduction to Paradigm Shift in Business Partnerships

The most recent paradigm shift in business partnership has arisen due to technological advancements. Today, artificial intelligence and robotics are more likely to be the new business partners in global trade. From coordinating pipelines to processing sensitive data, AI and robotics offer outstanding quality and customer satisfaction to a scale that can hardly be realized by human interaction. Consequently, a transformation is not only inevitable in the manner the BFSI segment is structured but also in terms of the reskilling that is needed for employees and potential workers who are addressing future demands. The first question that arises is during the designing of a digital experience, should an AI be identified as a strategic partner of a business?

Information technology investments took decades to achieve the desired impact, more precisely 26 years from 1980 to 2006, albeit at high costs. The BFSI segment is currently in the midst of a revolution that mobilizes AI's aligning abilities. AI is reverberating in the form of chatbots, robo-advisors, or in the functions of regtech and insurtech along with different applications by autogpt and agenting. An AI firm, along with a few other co-firms, has demonstrated that AI outperforms humans in the fields of data processing, call center, and process automation while delivering the highest quality. By now, over 100 robots are deployed, each handling a wide range of transaction processing that previously relied on a mix of human intervention and complex IT systems. Robots have also provided better solutions suitable for financial regulations such as Basel II and anti-money laundering rectification. The results observed are based on objective statistics, for instance, that AI is more reliable in respecting the over-the-phone memorandum of understanding with clients.

1.1. Understanding the Need for Transformation

We live in a hugely dynamic, fast-paced world. Markets are increasingly unpredictable, and strategic goals are forever shifting, partly thanks to huge technological and societal changes. Most people live in an on-demand world, using real-time apps or digital platforms for everything from the way they consume media to the ways they work, date, and receive services. People expect an instant connection with everything around them, and they expect that connection to keep getting stronger, faster, and smarter. People now increasingly live their lives online through their conversations with digital assistants, their web searches, or automated messaging conversations.

After decades of specific investment into labor arbitrage economies and supply chain globalizations as the principal means to reduce business costs, humanistic industries can seem increasingly devoid of what is truly smart about the raft of new digital technologies. These have been employed by other vanguard industries to transform their cost structures, business models, and customer value propositions. Major shifts have been experienced, with efficient and unique customer experiences based on scale and accessibility. New digital solutions are the lifeblood of those corporations, designed and running to do without traditional business skills and areas of cost support. They have invested in a broad range of new digital tools at an extraordinary pace, making them highly relevant to employee experience, satisfaction, flexibility, and creativity. And they have delivered digital scale and accessibility through flexible and predictable operating costs.

2. Leveraging AI and Robotics to Automate Administrative Tasks

Many enterprises are discovering a variety of technical benefits in working with AI. We are only beginning to understand the major impact of AI as a strategic transformation tool that can shape, reform, and improve how we do business. It is essential that complex tasks be in the hands of knowledgeable decision-makers. But is it necessary that these administrative tactics be performed by these individuals as well? Many of these predictable tasks support the business operations and essential functions of large-scale enterprises or institutions. The potential for improvement by automating these administrative tasks quickly becomes apparent. While there are some limits to improving the performance of qualified employees in the completion of professional-level administrative tasks, the vast resources recently directed towards developing and implementing AI can substantially improve the efficiency of these cheaper, more routine tasks.

The challenge is how to optimize these administrative tasks through AI and robotics tools. The best interactions between knowledgeable professionals and these enabled informational workforce tools will benefit from procedures, protocols, and standards. To optimize the leverage of AI and robotics towards administrative automation to realize its full potential, the enterprise must carefully develop an enabling environment. The actual time and effort of setting up the structural and practical facilities required by AI to automate a specific administrative task is relatively modest. However, to bring consistent levels of predictability, assuredness, flexibility, and efficiency to the task, the enterprise must clearly determine which tasks are required, then carefully design, plan, sequence, and program these tasks as part of a business process. High-performance interaction between the business professional and AI requires technically simplified means to organize the issues facing them both. Then, as part of that organized system, the means to easily perform the logistical details in a standardized format to achieve the results necessary for the professional to successfully accomplish the task.

2.1. Identifying Repetitive Administrative Tasks

Has your personal administration increased more than your ability to manage it? One possible change agent can be artificial intelligence. Several basic aspects of having AI as a business partner to offload such administrative activities are discussed. There is a special focus on the use of intelligent robotic assistants. The full power of bots can be achieved only through a good understanding of their strengths and limitations. There is also a clarification of what AI can be relied upon to manage and what is expected to remain under human supervision. AI can be integrated as part of a more holistic platform that includes better use of available digital assistants.

The usual approach to leveraging AI is driven by a single objective of cost savings and associated reduction of headcount. In contrast, we propose here a strategic paradigm shift. The main emphasis is on an organizational transformation stimulated by the experienced business pressure from increasing volumes of administrative activities, enabled by the change agent role of advanced AI. The new era is one where AI and humans are business partners. Such a partnership with AI in the role of a true business enabler should be seen as a modern version of an organization.

2.2. Choosing the Right AI and Robotics Solutions with AIMA tool from iaidl

Gartner predicts that by 2021, 80% of emerging technologies will have AI foundations, and we can already see the emergence of AI vending machines, smart glasses, drones, or autonomous trains shaking up industry models. Needless to say, only best-of-breed AI and robotics solutions used in a business context are boosting the efficiency, productivity, and competitiveness of the companies deploying them. This is the ultimate must, and cherry-picking the best suppliers out there is a strategic enabler for the next decade. It’s also a road plagued with difficulties and potholes. Evaluating AI and robotics companies is different from evaluating traditional solution providers, and both investors and buyers are frequently burned by a lack of benchmarking and a lack of empathy between stakeholders.

In 2020, we believe the companies from tech hubs: Israel, Massachusetts, dubai, Washington, South Korea, California, and a few others are the best choice. We have met over 50 AI companies from the region since 2018. Given the financial backing, though, the matrix could guide you to over 20,000 AI companies worldwide. Our process of making the research into a meaningful, actionable guide for investors and businesses is nothing else than the “FIT-RATE” methodology: Familiarize; Identify; Test; Rate. In 2019, we used this trusted framework, which includes internal due diligence as AIMA tool at a public and open-source stage. The release was put together by a dedicated global consortium and licensed under a permissive license. You can find it and use any part of it for a specific purpose, without the hassle of paperwork, corporate know-how, or years of in-house development.

3. Shifting Focus: From 80% Admin to 80% Strategy with Quick Wins

While AI and RPA tools have been shown to deliver impressive productivity gains when used on a purely utility basis, several studies and projects at a variety of companies have shown they can deliver similar or even more substantial gains by delivering fresh, high business value analytics. The implication is that while lower priority, easier-to-implement actions are being undertaken, higher priority transformational Quick Wins are being identified and actioned. To put this concept into perspective, let us consider the hypothetical state of the art of the technology today. Following this is a description of a proof-of-concept project at a large multinational corporation that demonstrates the concept of Quick Win in broad terms.

Superintelligent AI or virtual staff members are not yet available. It is generally accepted that even a human expert working cannot possibly make timely use of the vast amount of data potentially available to assist in most strategic management decision-making nor effectively span the wide array of issues such a decision might require. Yet, in recent years, we have seen a steadily increasing number of successful instances of how AI models and RPA software tools can be deployed to undertake specific tasks, such as reviewing large data sets in extraordinary detail and combining data from disparate sources to present relevant, current, comprehensive information at a level of sophistication and accuracy greater than any previous systems. Starting with the simplest future implementation outcomes, typically, the desired staff augmentation will be to take a small but time-consuming tactical admin-based task and deliver big data results better and faster than the human information clerk provided. Not only is the information provided more timely and accurate, but the primary employees, commercial managers, and technical experts have time to better review, understand, and plan their business strategies.

3.1. Developing a Strategic Roadmap for Transformation

Every organization is unique with a specific culture and strategy, so it is natural that their transformation journey should also be unique. The transformation journey for the organizations that believe that data-driven work has and will continue to change their business and workforce comprises weaving the following four broader elements into their roadmap through customization based on their unique culture, strategy, and specific industry and technology scenarios: development of augmented workforces; implementation of new business models; modernization of their IT systems, data architecture, and IT operations; and enterprise risk management for their data-driven businesses.

The organizations that effectively weave these broader elements into their roadmap will be speedier in their journey as learning can happen at lower risk by developing and deploying incremental solutions. They also go way beyond short-term gains, building organizations that are sturdy and adaptable in the long run. The specific journey that is right for your organization can be developed by answering the following questions to ensure that the strategy, culture, and specific technology and industry scenarios are woven into the roadmap.

consequently, we can build the milestones as follows to a strategic partner roadmap that aligns with the key objectives outlined in the paradigm shift in business partnerships, leveraging AI and robotics for strategic transformation , as the follow:

?1- AI serves as an advocate for employees.

2- RPA is utilized for automating routine activities, also known as "process zeroing".

3- AI acts as a catalyst for change.

4- AI is positioned as a partner in various tasks and operations.

3.2. Implementing Quick Wins to Demonstrate Value

In many companies, especially in large-scale enterprises burdened with legacy technology and inflexible IT solutions for heavily fragmented systems, it is simply not possible to implement AI and machine learning enterprise-wide all at once. Another reason is the highly varying data quality and the immaturity of digital competencies in many departments or functions. Finally, as seen in financing and legal, data universes are small, which does not allow for the perfect application of machine learning yet. Instead, we propose a strategy of initiating the process by influencing selective parts of organizations often deemed quick wins. These quick wins later serve as credible proof-of-concept across the company. Once that is done, a continuous learning and adaptation process should take place with the discovery and exploration of new business questions and their subsequent verification.

This calls for a business model that sets an appropriate AI and machine learning analytical process. The enabler is typically a plan for programmatically creating data-driven models. It starts with a team strategy design and an initial operation put in place, and after labs are used for fast idea transfer, culture change management, and strategic focus on savings. A good model for learning could, for example, include: an expert team in commercial, risk, fraud, operations, sales, controlling, data, and pricing; a business model with value together with a related business use case plan; a target data set and the corresponding data understanding; a program tailored for high-priority and easily explainable use cases with simple techniques.

To achieve these goals, it is crucial to develop a strong understanding of AI business strategy, industry trends, and the language used within the industry. This includes conducting market analysis, gaining a competitive edge, and increasing knowledge and literacy in AI.

4. Enhancing AI Acumen in Business Partnerships

The perception of AI is undergoing change globally. While there is increasing acceptance of AI and robotics in the business precincts, the plethora of acceptance has moved beyond back-office operations to product and service areas. AI is now recognized as a business-driving technology available to industry players to help improve business practices. Consulting firms have started imposing management challenges on industrial leaders to develop AI skills among their workforce to benefit from what AI brings to the table. Skills certainly improve performance, especially in using higher technologies for expanded benefits. Most industries continue to look toward technology companies and consulting firms to offer them AI-based solutions. This is, in a way, good as these organizations have invested in AI by developing AI tools and gaining experience in unleashing its potential. They are best suited to address first-level client requirements about AI. But at the same time, industries should rise to the level and build AI awareness within their teams, to question what is served and not just accept it. House teams need to subjugate lethargy and demonstrate humility to evolve, dominated by AI. Such measures will instill control and reduce the risks involved. Further, building a capable workforce will ensure protection against a lack of know-how, abnormalities, and depression that may result from uncertainty around job security signaled by AI. Then, a sharp industrial skill floor would prompt consulting firms to diagnose and provide apt solutions, particularly regarding AI proficiency. Improvement in workforce qualifications, updating role understanding to complement the automated capabilities, and leveraging the augmented operational capabilities will progressively guide the industry toward focused digital transformation.

4.1. Building AI Literacy among Business Leaders

Companies practicing continuous innovation for systems should consider creating an internal Robotic Lab to ensure that sufficiently advanced talent, AI developers, and cognitive scientists always have the infrastructure, skills, and opportunities needed to transform their ideas into established, human-empowering, and profitable business capabilities. By systematically removing the components composing tasks, companies can also enhance the quality and throughput of systems working alongside humans in business processes featuring complex knowledge work. Companies interested in building early AI advantage also realize that AI has culturally different modes of thinking and that effective AI utilization requires the design of a new AI-fluent business environment in which demand for complementary AI capabilities thrives. It therefore becomes crucial to expedite the building of AI literacy on the business side of every corporation. When business leaders and operational personnel begin to understand what machine learning can do, that it is surprisingly easy to try, and that it will quickly become much more powerful, the pace of change will simultaneously accelerate and modify their strategic thinking and focus. Business line leaders, market segment leaders, business architects, and experts possessing both knowledge and wisdom are well equipped to visualize and create natural language interaction capabilities that can reach transparent consensus with corporate stakeholders. In the age of AI, building AI literacy among business experts and leaders is less about understanding better the capabilities of specific AI models and more about sensing and imagining potential applications where AI solutions are most beneficial to most actors. It is therefore incumbent upon AI experts to constantly be on the lookout for where AI could add significant value, to decipher the important design features in projects expected to create transformative economic and societal value, and to share context-aware data-to-experience case study information with the organization's knowledgeable process experts.

4.2. Training Programs for AI Skill Development and iaidl.org programs

Organizations and industry associations sometimes run targeted sessions to create awareness and encourage individual members to explore and invest time in developing these skills. It is important to address such initiatives towards the larger academic ecosystem. As for existing resources, another unique initiative for AI skill development is now being leveraged by individual professionals and organizations to ensure readiness for defining and delivering AI business partner-led strategic transformation programs.

Individuals could also spend time familiarizing themselves with different AI capabilities and features across various companies. The idea is to become "AI multi-skilled," the sort of person capable of embracing a new language or platform within weeks and focusing on building and creating value through applications. This is strategically imperative for professionals across various ranks, especially those in customer experience or process automation areas. The idea is to build ground zero language literacy for AI analogs and interfaces that facilitate manipulations and proficiency in all computing functionalities and general logic-created algorithms enabled to understand natural language.

5. The Role of AI as a Partner in UAE

The role of AI as a partner is to mine and interpret data and uncover significant insights that go beyond descriptive analyses alone. This can lead to enhanced innovative decision-making that is more timely. Furthermore, AI can be leveraged to monitor performance to provide feedback on which strategies and courses of action are working. That will allow for better chatbot behaviors to be developed. An AIP approach can unearth deeper values at play, and this can lead to the transformation of the organization by altering strategy, so that it leads to a higher value and deemed partnership. In this way, a chatbot is the visible manifestation of AI to the employees discussing matters with it. Therefore, the way a chatbot 'looks and feels' as a collaborator is a critical factor in influencing employee relations and ultimately its acceptance as having a valid partnership role. Despite the potential power of AI as a partner, to date there has been no research to suggest how one can operationalize the building of a partnership capability with AI tools.

5.1. Current Landscape of AI Adoption in UAE Businesses DNA

Technological development, socio-cultural context, economic vitality, and global competitive pressures are driving forces stimulating the rise of artificial intelligence (AI) solutions within organizations. While the adoption of AI solutions at the regional level is still low, companies are recognizing both the need to incorporate AI as a business partner and the potential to impact business functions, operations, and activities. This section provides insights into the actual level of AI adoption at an organizational level within different business segments in the United Arab Emirates.

The UAE is among the ten most digitally advanced countries in the world and the first in the region. In response to the transformative social strategy, in the last few years, the UAE government has continuously emphasized the importance of AI in what is widely regarded as the fourth phase of the industrial revolution and its role in the development of the UAE’s businesses and economic growth. The UAE government has taken several initiatives related to the adoption of AI as a principal component of its growth, transformation, and future-proofing strategy in key sectors. In 2017, the Emirates Artificial Intelligence strategy was launched to increase government performance and efficiency. Concurrent with this, the Smart Dubai Vision 2021, Dubai Blockchain Strategy, and other similar technology-driven initiatives have been gaining momentum.

5.2. Regulatory and Ethical Considerations in AI Partnerships

As organizations move from early explorations of AI to more sophisticated implementations, well-defined and resilient ethical standards will become increasingly important, owing to the increasing role that AI will play in their operations across business units and at all levels in the corporate hierarchy. Of course, while time-saving automation is a key attraction of AI, important questions arise. Does every such opportunity really translate into cost-saving optimization and hence maximize returns to shareholders? Are there stakeholder repercussions? What ethical considerations should be taken into account in deciding when and how to let technology make decisions that affect people directly?

First, as corporations are ultimately created by and accountable to governments, ethical considerations about AI go hand in hand with regulatory concerns. Such ethical considerations are inevitable, given that AI increasingly operates autonomously, impacting individuals and society in ways that are far more significant than older-generation, rules-based automation technologies. Regulations will arrive, albeit at varying speeds across industries and geographies, but legal and compliance teams within corporations are already taking steps, shouldering part of the burden. Executives should consider that missing out on providing best-of-breed contributions to the design of AI regulatory frameworks would mean that they – as corporations implementing AI and themselves impacted by its implications – will have to adapt later on. Such contributions could be required by law in the future.

6. Zeroing Process: Streamlining Operations with AI

In the world of process improvement, everybody is familiar with the terms Lean, Six Sigma, Design for Six Sigma, and Process Reengineering. These processes are specifically used to identify and remove waste within any process. When utilizing AI in a business process, it is critical to remove any excess calculations from the zeroing process by utilizing AI to the fullest. The zeroing process is so named because the measurement standards in the process are now so refined that the required data can be "zeroed in" to give a quick and precise determination of the future direction of the process. This will save years of reprogramming new AI data mining software to upload the correct information into the AI system each time a new measurement standard is implemented.

Process operations change because the introduction of the AI application has allowed for more waste to be moved, and data being approved through the process is now quicker each time as the uncertainty of processing data quality is removed. The methodology for AI integration requires a change in management involvement to be successful. The process management is explained in the chapter. The traditional attempts to manage technical projects sufficiently by those in authority positions are usually very successful when the AI process intended to be used is known in advance and well documented by statistical probabilities. To do this, measurement standards are in place and qualified for new operation standards. Many hardcopy and digital documents still exist from other projects that attempt to manage the future operation standards more efficiently by predicting future operations with statistical methods. However, both of these methods have been known to ultimately fail because what technical operations might seem incredibly simple to do with a higher level of technical depth are excruciatingly slow and painful to execute over time.

6.1. Process Optimization through AI Integration

AI is increasingly finding applications in streamlining business processes, continuously reducing waste, and adding value. At every level, from tactical to strategic operations, AI is bringing massive advantages to organizations that are leveraging these techniques. AI is making several operational tasks easier to perform at a much faster pace with fewer failures, leading to lower costs. Banks use AI to read scanned checks, perform OCR on the written text, extract the handwritten and printed characters, and then validate each character and the signature in order to validate the check for deposit and verify the signature. Most AI systems struggle to process illegible handwriting and scribbles, and this may not be a suitable use case for the technology. However, in the case of the printed text on checks that often contain machine-printed characters or handwritten amounts of money such as the date and signature, AI has been a blessing.

There are many business processes that were developed in the manufacturing era and need reorientation in the service era. The long-term future of any service company lies in its ability to institutionalize the deep integration of AI technologies and reorient service processes into their daily operations. This shift from human work to AI/human teaming works so effectively because AI turns out to be much cheaper and faster to scale, and because the benefits of trained human experts, managers, and front-line staff increase in tandem with the level of AI assistance. You should picture AI as a company standing for Augmented Intelligence instead of Artificial Intelligence. This perspective shifts the sensitivity of AI solutions from one of fear about machines taking over the workplace to one of confidence about small AI partners boosting human productivity. Because small AI makes each human practitioner vastly more effective, the practical effect of deep machine learning software becomes right-sized for a wide range of environments.

6.2 dubai as zeroing hub

Dubai as the regional zeroing hub: Apart from sharing a time zone with a fast-growing region, Dubai has emerged as the natural zeroing hub lying somewhere between Europe, Africa, CIS, and Asia. Located at the crossroads of the world, Dubai has global links with the presence of 12,500 companies in Dubai's Jebel Ali Free Zone alone, including 113 of the Fortune 500. It strategically has the largest airport, Al Maktoum, before the opening of Hong Kong's new airport, and sea/air port, with an expressway under construction, scheduled to open in the next few years. Dubai is the only cargo hub in the world that even has a hotel in the airport itself, where customer executives can be accommodated while their cargo is rerouted. The regional headquarters of four of the top 10 IT companies, as well as the top five shipping companies, are located in Dubai. The infrastructure is unexcelled and continuously improving with forward-looking leadership.

Dubai plans five separate airports altogether, a leader in designing airports for both cargo and passengers. Its present cargo facilities are close to the Jebel Ali Free Zone and port complex; also, to some of the region's companies. Dubai is free of the security, real estate, recruitment, and other business travails and natural fears that face a company wanting to conduct these pilot projects in other parts of the world without the blocking of politics and ready gold that the Dubai officials can provide. Dubai offers a uniquely secure and simplified path towards trying future designs while also developing new government and physical infrastructures for the emerging UAE Knowledge Society.

7. Becoming an AI Business Partner

The advent of AI business partners. Companies have begun to deploy AI in customer-facing applications, such as chatbots, and over the years, AI engines have exhibited surprisingly human-like capabilities in those settings. Yet, these applications are still very much traditional cases of exploiting AI engines as tools, empowering users to deliver intended tasks. However, a new thrust of industrial automation is emerging, driven by real-time learning of AI engines connected to robotics. These applications may already have labels that are here to stay — AI as an executive, AI as a manager, AI as a colleague, and AI as a business partner. In this chapter, we will propose a structure for AI as a business partner focused on how AI engines can be designed to effectively contribute to achieving strategic objectives for the business.

Using AI (and now also robotics) to accelerate the pace of industrial automation is not new. Traditional CAD-based CASE and ERP/MRP, and the ERP/MRP third revolutions focused on tightly specifying and scheduling manufacturing hardware. Because it is still straightforward to document workflow through formal logical rules, one rationale to integrate industrial automation with AI was the hope to articulate implicitly defined best practices for relatively low complexity problems. Nevertheless, tasks that integrate AI and robotics always exhibited high logical or lower physical complexity.

The capability of the largest professional services firms eventually evolved to provide clients with skills they learn by solving client problems, but the business model was strikingly different from that of technology platform companies. Algorithms and code were the crown jewels of the technology platform companies, and technology professionals were essentially the key ingredients the firms would manage to maximize the value of those digital assets. To deal with challenges of scale and complexity, the firms had created structures or domain practices that enabled large-scale professional services. Each domain practice included the set of assets and processes that would eventually operationalize local implementations of the best practices embedded into those software products developed by technology professionals. The implementation of the business transformation included not only the technology but also the additional ingredients of process models and training. At least for complex business decisions, the core skills of the technology professionals in those domain practices gave them the power of judgment that was far from the narrow and specific focus of problems addressed by traditional analytics. These best practices could be depicted as business strategies articulated by simple business models.

It may be hard to evaluate how much of that design was driven by a deep understanding of patterns in the data, and how much of that design simply reflected the insights of the domain practice professionals. In any case, the capabilities already achieved by these best practices are a clear realization of the potential for AI engines as support for those strategic objectives. These strategic objectives arise naturally from the way that the technology is operationalized in the businesses. The de facto assumption by the customer is in the ability to buy the strategic decision along with the technology. Given that professionals in the precision machine tools and robotics industries compete to offer high-quality technology equivalent for relatively complex tasks in a world of task implementation based on both narrow process models and significant professional judgment, there might exist significant business value when inferring from client interactions how to articulate narrowly defined strategic themes.

7.1. Key Skills and Competencies for AI Business Partners

The forecasts indicate that spending related to robotics worldwide could attain more than US$80 million by 2028. Additionally, cutting-edge artificial intelligence (AI)-based applications could actually become more practical and profitable sooner than most expect. Yet, while several organizations have executed fairly robust roadmaps when aiming to integrate AI and robotics into their operations, recruiting and developing the right talent is still a challenge. Indeed, there is a serious talent shortage for this purpose. There are hardly any schools in Europe with AI programs. The very few that do exist are full.

When teams are not prepared or have not developed the skills they require to take the fullest advantage of AI, then it is much less likely to succeed. Organizations have to prepare for the 'automation paradox,' where they can't automate the jobs they have become dependent on, but they also have the wrong type of workforce for the jobs they do have. It may seem counterintuitive, but the arrival of AI generates a robust demand for soft skills. AI might perform real-time reporting, but interfacing with the unit on the field is an ongoing challenge. And so, much depends on training the individuals who will eventually make contact with the workforce. That means that individuals with data skills and with IAIDL CERTIFICATION are crucial for effectively working with AI and robotics in business partnerships. These skills include ai and fmt understanding Some key skills and competencies for AI business partners include data analysis, machine learning, and programming. These skills are essential for effectively leveraging AI and robotics in business partnerships. Understanding of machine learning algorithms.could ultimately be replaced in their tasks by automation. Therefore, while some will need educating in special data skills, many others will be educated on how to use data thoroughly. This demands an education system that can support both sides.

7.2. Establishing Trust and Credibility in AI Partnerships

Trust is fundamental, especially in AI and the digital domain. Despite the obvious fact that AI has greatly advanced in a short time and is, beyond any dispute, a marvel of the human mind, it indeed surpasses past human performance. However, we haven't yet understood its nature. This is one reason why digitally illiterate societal actors feel disdain or fear for AI technology and why they often regard a digitally driven trust as naive or unrealistic. After making up our minds about the limitations of AI, let us not forget to be reasonable in our expectations about a technology that might revolutionize the course of our existing natural reasoning and intelligence as well as our futures if we let it down in its infancy. Truly, trust is fundamental. It is one of the first things children learn early in life and applies to all areas of daily experience. In essence, AI is not different when we consider the program of successfully educating, supporting, and utilizing people as talent in the digital 21st century, and when performing analytics on the wave of big data to search for meaning. This takes human traits like experience, knowledge, judgment, communication, transparency, sharing, honesty, humility, and respect for others' values. Then trust is simply a matter of context, of honest and reliable expectations that we will get the performance we need, even in difficult or emergent situations and conditions, and especially in the absence of alignment, direction, guidance, or control. Real-time AI differs from human intelligence in the knowledge creation process, often being statistical in the outcome of responses and especially in the rationale driving an automatic decision, although not in the ability to subsequently present its results or to follow negotiation principles of logic or rational decision-making.