Artificial Intelligence Briefing- The Impact of AI on Work

Those with the HR teams, training program, organizational structures, and adaptable staff will be best prepared for this fast-approaching reality.

Artificial Intelligence is already forcing leadership teams around the world to reconsider some of their core structures. Fundamentally. organizations need an AI Strategy to stay completive

How AI Is Changing the Way Companies Are Organized

Deloitte’s 2017 Human Capital Trends Report, draws on surveys from over 10,000 HR and business leaders in 140 countries.

Most of the changes are a result of the early penetration of basic AI and the preparation organizational need to do as AI emerges and matures.

Advances in technology and AI or now proven to result in:

  1. The restructuring of an organizations makeup
  2. Transformation of HR departments
  3. Development of new training models
  4. Reevaluation of hiring practices

“AI is definitely not eliminating jobs,
it is eliminating tasks of jobs, and creating new jobs.”

“The new jobs that are being created are more human jobs”

“Individuals that have very task-oriented jobs will have to be retrained, or they’re going to have to move into new roles”

 Josh Bersin, Principal and Founder of Bersin by Deloitte

More Human Jobs Will Be Retained

‘More Human Jobs’ require traits robots haven’t yet mastered, like empathy, communication, and interdisciplinary problem solving. “

  • The survey found that 41% of respondents have fully implemented or made significant progress in adopting AI technologies in the workforce
  • Only 15% of global executives say they are prepared to manage a workforce “with people, robots, and AI working side by side.”

New Human Resource Strategies

The Deloitte survey also found that

  • 56% of respondents are already redesigning their HR programs to leverage digital and mobile tools
  • 33% are utilizing some form of AI technology to deliver HR functions.

Early AI technologies and a looming AI revolution are forcing organizations to reevaluate established strategies.

  • Companies are now putting greater emphasis on cultural fit and adaptability, knowing that individual roles will have to evolve along with the implementation of AI.
  • On-the-job training has become more vital to transition people into new roles as new technologies are adapted
  • HR’s function is quickly moving away from its traditional evaluation and recruiting function—by efficiently using big data and AI software
  • A greater focus on improving the employee experience across an increasingly contingent workforce.

Collaborative and Team-Oriented Culture Shift

The integration of artificial intelligence tools is transforming organizations to become more collaborative and team-oriented, as opposed to the traditional top-down hierarchal structures.

“To integrate AI, you have to have an internal team of expert product people and engineers that know its application and are working very closely with the frontline teams that are actually delivering services.”

“When we are working AI into our frontline service, we don’t go away to a dark room and come back after a year with our masterpiece.
We work with our frontline day in, day out.”

Ian Crosby, Co-Founder and CEO of Bench (a digital bookkeeping provider)

Moving Away from a Top-Down Structure and Toward Multidisciplinary Teams

organizations are moving away from a top-down structure and toward multidisciplinary teams to adapt to changing technologies.

  • 32% of Deloitte’s 2017 Human Capital Trends respondents said they are redesigning their organizations to be more team-centric, optimizing them for adaptability and learning in preparation for technological disruption.

Finding a balanced team structure, however, doesn’t happen overnight, explains Crosby. “Very often, if there’s a big organization, it’s better to start with a small team first, and let them evolve and scale up, rather than try to introduce the whole company all at once.”

A Workforce That’s Adaptable to Change

Beyond checking the boxes of the job’s technical requirements, organizations are now looking for candidates that are ready to adapt to the changes that are coming.

“When you’re working with AI, you’re building things that nobody has ever built before, and nobody knows how that will look yet,” he says. “If they’re not open to being completely wrong, and having the humility to say they were wrong, we need to reevaluate.”

Decide Where to Place Human Employees and Where to Place AI

As AI becomes more sophisticated, leaders will eventually need to decide where to place human employees, which tasks are best suited for machines, and which can be done most efficiently by combining the two.

“It’s a few years before we have actual AI, it’s getting closer and closer, but AI still has a big problem understanding human intent.”

Rurik Bradbury, LivePerson’s Global Head of Research and Communication.

As more AI software becomes available, organizations must think of new hires in three different categories

  1. Human- Primarily Human
  2. Machine- Pure AI
  3. Cyborg- AI- Augmented Human

Artificial Intelligence, Management and Organizations

Recently many organizations have deployed Artificial Intelligence (AI), which has included neural networks, expert systems and voice-recognition systems.

Managers and developers understand very little about how management and organizations affect or are affected by the technology.

Using specific examples from practice and research, the interaction of AI, management and organizations is presented.

The Impact of AI on Organizations 

Some of the effects of AI on organizations include:

  1. Power shifts
  2. Reassignment of decision making responsibility
  3. Cost reduction and enhanced service
  4. Employee shifts within the organization
  5. Downsizing

Power Shifts 

The possibility of power shifts within an organization because of changes in the ownership and control of knowledge is inevitable

Reassignment of Decision-making Responsibility 

  • AI can change the ownership and responsibility for decision making.
  • For example, an AI system at American Express handles the majority of requests for expenditure authorization made with the American Express card.
    1. The system allowed American Express to auto­mate much of its credit authorization responsi­bility, removing the ownership of the decision from human authorization clerks.
  • In the field of personal loan and credit analysis, neural networks are now being used by many major credit card companies, including Citibank and General Electric Financial Services, to perform some of the credit-granting decision making.
  • Corporate secrecy means that details about these systems and their use are scarce.

Cost Reduction and Enhanced Service 

  • Implementation of AI systems can help reduce costs, enhance a service provided by the organization, or do
  • Automating authorization decision making, the Authorizers Assistant has allowed American Express to greatly reduce labor costs and better manage its provision of a card with no fixed limits.
  • These types of business benefits are now more acclaimed by management than the conventional benefits including
    1. Reduced decision making time
    2. Better use of expert time
    3. Codification of knowledge

Personnel Shifts and Downsizing 

  • AI can contribute to an organization's software maintenance expense and often requires a dedicated support staff.
  • The dynamic nature of knowledge the cost of maintenance and enhancement of AI applications may exceed that of traditional Information Systems.
  • AI can increase the number of overhead employees and reduce the amount of direct labor, and will typically result in both occurring.  

The Impact of Organizations on AI 

  • Organizational characteristics, including job design, process design and culture, affect the deployment of AI systems.
  • Certain organizational characteristics (e.g. size, techno­ logical awareness and IS budget) influenceadoption of Expert Systems.
  • We consider the issue from several viewpoints:
    1. User incentives to adopt AI
    2. External organizations
    3. Organizational structure
    4. Organizational support

User Incentives to Adopt AI 

  • AI systems are unlikely to be used if users do not have an incentive to adopt them.
  • For Example- CLASS (Commercial Loan Analysis Support System) provides com­mercial loan officers with support to evaluate a company's financial position, recommend loan covenants and document the commercial loan analysis.
    • Although the system demon­strated technical expertise, it provided few incentives for loan officers to use the system.
    • CLASS required loan officers to use computers in their problem solving and this did not fit with the corporate culture that precluded the use of computers in a loan officer's office.
    • The loan officers never developed a personal stake in the system.
    • The system confirmed their opinions, but never demon­strated personal gains for the loan officers, even though they agreed that the system would help them avoid bad loans. I
    • These factors had a significant ultimately led CLASS to recommission the system.
    • With appropriate incentives, this outcome would have been unlikely.

External Organizations 

  • As AI systems become larger and more visible, the possibility for outside organizations (including unions and regulatory agencies) to have an impact on their development and deployment increases.
  • For Example- the AT&T speech-recognition system, introduced above, offers a rare example of how an external organization can affect an AI implementation.
    • AT&T announced the implementation of its speech-recognition System during contract negotiations with the operators' union, Communication Workers of America (CWA).
    • The system became a signifi­cant factor in labor relations and negotiations.
    • Eventually, CWA negotiated a settlement that has AT&T giving operators, who were replaced by the new system, a 'crack' at other jobs within AT&T (Wall Street Journal, 3 July 1992).

Organizational Structure

  • With the emergence of self-managed teams, distributed responsibility and decentralized structures, there are new opportunities for AI because the technology facilitates
    • Decentralized decision making
    • More consistent decision making
    • Greater reliability in decision processes
  • For Example- Mrs Fields, Inc. uses Expert Systems to help manage its network of retail stores (Pancari et al., 1991).
    • Debbie Fields' (the founder) influenced deployment into the stores
    • The system allowed field managers to run the stores in the same way that she ran her first store 10 years ago- resulting in similar outstanding results

Organizational Support

  • The immediate management of the AI end-users and support staff hold the power to advance or inhibit AI systems.
  • Anyone of these may reduce operational use by
    • Limiting the number of users
    • Changing the composition of the target group
    • Withholding resources and/ or restricting the area to be affected within the organization.
  • Organizational support has been shown to have a positive impact on operational use.

The Impact of AI on Management

  • Expert Systems that result in product or service differentiation and cost reduction
  • Expert Systems have a direct impact on management strategies for gaining competitive advantage.
  • With some Expert Systems, management can take offensive or defensive actions for coping with competitive forces and create a defensible position for the company.
  • The strategies need not be limited to just products, but can include other actions, such as the development of a well-trained workforce.


  • The most prevalent examples of using AI for product differentiation are Expert Configuration Systems
  • These systems take a product specification or description and generate a parts list and instructions for putting the product together.
  • For Example- Carrier's EXPERT system, which produces designs for large complex air-conditioning units for multi-story buildings, and General Electric's Computer-aided Requisition Engineering (CARE) system.
    • These systems allowed salespersons to search a database for electric motors that meet customer specifications, or automatically design a new motor if there are no existing ones.
    • These systems result in
      • Fewer engineering errors
      • Reduce base costs
      • Reduce cycle time


  • Service organizations must maintain and effectively use their skilled workforce for sustained profitability.
  • Public accounting firms must disseminate changes in tax and accounting information to each of the accountants that perform those activities.
  • For Example- Coopers and Lybrand's ExperTax system guides accountants through the information-gathering process and helps them explain differences between statutory and effective (or computed) tax rates.
    • The system notes relevant issues, describes the importance of information requested and analyzes it to identify critical issues for audit and tax managers.
  • Auditor training is another benefit of Expert Systems
  • These types of systems can
    • Reduce labor costs
    • Increase accuracy
    • Provide product differentiation for basic 'vanilla' services, especially when the customer has little marketing information to differentiate providers.

The Impact of Management on AI

  • Management plays a key role in admitting AI into the organization and implementing it successfully.
  • The two primary ways management exerts its influence are
    • Acting as a champion for an AI system
    • Providing resources for its implementation


  • One of the primary issues in the implementation of AI systems seems to be the need for a champion to promote the use of AI.
  • Being a champion goes beyond just verbal support for the system, it includes a willingness to actively advocate the technology and make it a high priority in the organization.
  • Top management support and manager acceptance are important to Expert Systems implementation success, and favorably impact users' perception of management support and operational use.

Provision Resources

  • Organizational pressure to adopt Expert Systems, management support and provision of adequate budgets for the technology are positively related to Expert Systems adoption.
  • Management support includes provision of people, time and money.
  • By being the initial source of support, committing resources and making the implementation at least as important as other business activities, management can have a significant positive impact on successful deployment of AI.

AI and Other Information Technologies

  • AI systems will be components of larger business systems with confounding cost and benefit issues.
  • Information Systems planning will determine (at least in part) the nature and number of AI systems on an employee's desk.
  • Information Systems planning
    • Attempts to align computer-based systems with the needs of the organization
    • Helps organizations select what AI systems they want to build
    • Establishes the level of funding for them
    • Determines how they should be integrated with existing and planned databases, data entry systems, reporting systems, and decision support technologies.
    • Applies a top-down approach, beginning with business objectives and derive desirable architectures to support those objectives
  • Organizations with aggressive plans that include AI, will deploy the technology throughout their businesses.
  • The contribution of AI systems are more likely to be a function of how they integrate and interface with
    • Hardware
    • Software
    • Policies and procedures
    • Organizational business improved processes

AI and Cybersecurity


AI is emerging as our most powerful ally for cybersecurity, especially as it has become clear that relying primarily on humans to fight this war is a losing battle plan.

Cybercriminals have created one of the largest illegal economies in the world, generating $445 billion in annual profits and stealing more than a billion records of personal information, such as credit card numbers and health records, every year.

The most concerning fact, though, is that 80 percent of cyberattacks are driven by highly organized crime rings that freely exchange data, tools, and tricks of the trade. Cybersecurity experts just can’t keep up, and the situation will continue to be challenging with a projected 1.5 million security jobs to remain unfilled between now and the conclusion of this decade.

Cybersecurity experts need technology that augments their abilities by filling gaps in monitoring and identifying threats.

There is a growing understanding among security experts about the benefits of cognitive security. A recent survey by the IBM Institute of Business Value found that nearly 60 percent of security professionals believe cognitive security solutions can significantly slow down cybercriminals.

The same survey revealed there will be a threefold increase in the percentage of companies implementing cognitive-enabled security solutions in the next two to three years, from 7 to 21 percent. This won’t alleviate the need to hire additional cybersecurity experts, however, because the fight against cybercrime will require a closer alliance between human and machine.

Security and Big Data

Humans face a staggering volume of data and humans alone simply can’t consume all of it. The average organization sees over 200,000 pieces of security event data per day, with enterprises spending $1.3 million a year dealing with false positives alone, equaling nearly 21,000 wasted hours.

AI will help security professionals by sorting through all this data, using natural language processing to understand the imprecise human language contained in blogs, articles, videos, reports, alerts, and other unstructured data; connecting obscure data points humans couldn’t possibly spot; and making recommendations on remediation strategies based on those connections and insights.

Without AI, unstructured data will continue to be the Achilles heel of cyberdefense because it represents a huge blind spot, comprising more than 80 percent of all data.

Augmenting the expertise of cyber professionals, AI systems are learning how to monitor unstructured data to detect risks before they emerge. As they continue to learn, AI systems will be more adept at detecting the difference between a computer glitch and a malicious attack, alleviating the need for security analysts to waste their valuable time on wild goose chases.

Humans and Bots Working Together for Robust Security Analysis

Once an attack is identified, security analysts often turn to the internet for the latest ways to address it, generating thousands of pages of results that may or may not contain the solution. It’s a process that is neither fast nor accurate. In this stage of the fight, AI can play an important role analyzing reams of information, including unstructured data, to identify the most probable fixes — and do so in orders of magnitude faster than any human.

While we are just on the forefront of the cognitive era of security, progress is well underway in making this vision a reality. Cognitive tools such as IBM’s Watson are currently being trained to ingest and understand vast amounts of security data and research created for human consumption. Dozens of organizations are already working with this technology and helping discover new ways Watson can be used in the fight against cybercrime.

In the future, bots will seek out network vulnerabilities, diagnose them, and recommend ways to patch them — all while working seamlessly with cybersecurity experts, who will be even more valuable in the fight against cybercrime because they have been trained in the use of augmented intelligence.

Today, the often-automatic reaction to any mention of systems gaining intelligence is that the robots have come to take our jobs. In the war on cybercrime, reality could not be further from this view. AI will enable humans to deal with ever-increasing threats by augmenting our expertise — but it’s critical for people to first understand and accept the true definition of AI.

The Implications of Artificial Intelligence

Individuals and societies will be forced to deal with the disruptive threat of AI. Billionaire Elon Musk has a suggestion of how to avoid becoming irrelevant as Artificial Intelligence (AI) becomes more present in our work and personal lives.

Cyborgs are the Blend of Humans and Machines

The Tesla and SpaceX CEO said recently “that humans need to merge with machines to become a sort of cyborg.” Musk made these assertions recently at the World Government Summit in Dubai.

Elon Musk speculates that we will see a new layer of a brain able to access information quickly and tap into artificial intelligence.

"Over time I think we will probably see a closer merger of biological intelligence and digital intelligence"

"It's mostly about the bandwidth, the speed of the connection between your brain and the digital version of yourself, particularly output."

Elon Musk told an audience at the World Government Summit in Dubai, where he also launched Tesla in the United Arab Emirates (UAE).

Musk explained what he meant by saying that computers can communicate at "a trillion bits per second", while humans, whose main communication method is typing with their fingers via a mobile device, can do about 10 bits per second.

In an age when AI threatens to become widespread, humans would become useless, so there's a need to merge with machines, according to Musk.

"Some high bandwidth interface to the brain will be something that helps achieve a symbiosis between human and machine intelligence and maybe solves the control problem and the usefulness problem."

Elon Musk

AI will Displace Jobs

The more immediate threat is how AI, particularly autonomous cars that will displace jobs.

In the 2010s AI is starting to have an impact to our job security.

Musk predicts that in the next 20 years, 12 to 15 percent of the global workforce will be unemployed because of the impact of AI.

  • Initially through automation replacing higher and higher level jobs.
  • And then there will be technological unemployment.

The Coming Singularity

Musk touched upon his fear of "Deep AI" which goes beyond driverless cars to what he called "artificial general intelligence". Deep AI is "smarter than the smartest human on earth" and Musk called this a future "dangerous situation".

The precise cause of this change is the imminent creation by technology of entities with greater than human intelligence.

This breakthrough will be achieved through:

  • The development of computers that are "awake" and superhumanly intelligent.
  • Large computer networks (and their associated users) will "wake up" as a superhumanly intelligent entity.
  • Computer-human interfaces will become so intimate that users may reasonably be considered superhumanly intelligent.
  • Biological science may find ways to improve upon the natural human intellect.

What are the consequences of this event? When greater-than-human intelligence drives progress, that progress will be much more rapid.

What is the Singularity?

The singularity is a point where our models must be discarded and a new reality rules. As we move closer to this point, it will loom large over human affairs until the notion becomes commonplace.

One conversation centered on the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.



A Research Perspective: Artificial Intelligence, Management and Organizations, Intelligent Systems in Accounting, Finance, and Management, 1993.

Deloitte’s 2017 Human Capital Trends Report, Deloitte University Press, 2017.

Elon Musk: Humans must merge with machines or become irrelevant in AI age,, 13 Feb 2017 .

The Coming Technological Singularity: How to Survive in the Post-Human Era, Vernor Vinge, Department of Mathematical Sciences San, Diego State University, 1993.

Why AI must be redefined as ‘augmented intelligence’, Venturebeat, January 9, 2017.