AI’s Impact on the Future of Work: Transforming Human Achievement

Artificial intelligence (AI) is a big buzzword these days, and with good reason. Eager to tap into its power, businesses are investing in AI like never before, and all signs point to accelerated growth. For example, Constellation Research predicts that the AI market will surpass $100 billion by 2025, and an Economist Intelligence Unit executive survey found that 75% of companies plan to implement AI within three years.

As AI gains traction in the marketplace and the media, speculations abound about what this burgeoning technology will mean for the workforce. Many of us have grown up with science fiction fantasies about a future world where AI-driven robots and machines – with intelligence and efficiency that far exceed our own – gradually take over, eliminating jobs and replacing people. Some might be reminded of the friendly R2-D2 from Star Wars, while others feel threatened by predictions of gloom and doom perpetrated by figures like HAL from 2001: A Space Odyssey. Either way, we struggle with taking the broad term of AI and assessing how it will impact the future of work.

Of course, embracing new technologies can be challenging. And despite the enormous potential AI promises, the rapid change it evokes can cause even the bravest to balk. Yet as you go deeper into the use cases, you find that AI can enhance our value, complementing our capabilities and helping us work more effectively. For example, by automating many tedious and mundane tasks through AI, employees gain time to focus their efforts on more strategic activities that can drive major benefits. AI technologies also allow workers to curate and capitalize on the power of big data – enabling them to gain insights, uncover patterns, and make decisions with a level of intelligence and speed previously unattainable. And that’s just the beginning, since new AI applications that help users transform efficiency and optimize business outcomes are emerging at an ever-increasing rate.

Below is a framework and a 2x2 matrix that I have developed to highlight some of the ways in which AI technologies can assist us humans in our work. The horizontal axis indicates the type of user performing the task, with subject matter experts/deeply knowledgeable users on the left and casual/one-off users on the right. The vertical axis represents the task they’re engaged in, ranging from activities that are repetitive on the top to activities that are unique/one-off/differentiated at the bottom.

Let’s use some examples from procurement to illustrate how this applies to different tasks. While these examples are specific to procurement, the framework can be extended across other business areas as well.

The upper-right quadrant represents a casual user engaged in a fairly repetitive task, such as answering commonly asked questions like how to create a password. AI technologies can help the customer support agent deliver these answers by effectively automating much of the response, thus freeing them up to focus on more complex questions and other activities.

The lower-right quadrant represents casual users carrying out a task that they do once in a while, such as a new employee purchasing a laptop for office use. In this instance, AI technologies can analyze the request and recommend appropriate choices based on the user’s role, persona, department, and other relevant characteristics.

On the top-left quadrant are the deep domain expert users who are performing non-unique tasks that they may have to do frequently. Here AI technologies can assist the user through the process by building on existing history, knowledge, and data. For example, a sourcing category manager can provide the parameters of their sourcing event and get assistance to understand the type of sourcing event to run, the duration, how many suppliers to invite, and similar decisions to best meet the business goals.

Finally, the lower-left quadrant combines highly skilled users with very specific or subjective tasks, such as a contract negotiation with a supplier or commodity that demands deep domain expertise that is unique to every discussion. This is where AI can offer insights, relevant contextual data, and information such as past terms or history that amplify the user’s ability to efficiently complete the process and get the best business results.


AI technologies can have an impact across every area of work, enabling users to drive deeper engagement and adoption, make better decisions, retain top talent, and improve business outcomes. Using what I call the “Four As of AI,” you can see and share how AI might reshape the way work gets done in any organization. The bottom line? By bringing humans and technology together in a synergistic alliance, AI opens the door to unprecedented levels of achievement that can help all of us make the world a better place.

Embrace the future

Paul Michael Talbot

EVP, FinServ | Emerging/Converging Markets across Accounting, Banking, Finance, Insurance, Investment, Real Estate, & Technology

2 年

Thanks for sharing, Shivani!

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Satarupa Bose

Senior Manager - Product and Technical Program Management | ex-Oracle, Sun Microsystems

5 年

Synergistic alliance between AI and humans is key as you rightly point out Shivani Govil. Great framework and guide to apply to any workflow. Thanks for sharing!

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Vinod Malhotra

CTO | SaaS | Scale | Growth | Transformation | M&As

5 年

The "Four As" frameowork captures the essence well and is easy to remember. Shivani Govil - do also believe that adoption of each quadrant will happen at different time horizons, with Automation being the first and Amplification being the last?

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John Mark Williams

CEO at The Institute of Leadership

6 年

Cleverly, the matrix itself does the things it suggests AI can do. Very neat :-)

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Pratap Tambe

Cyber Risk Expert at Tata Consultancy Services | Risk Modelling Steering Group Member, Cybersecurity Focus

6 年

Repetitiveness or non-repetitiveness of tasks is one dimension of tasks. I would suggest outcome-defined or process-defined nature of tasks as an alternative way of categorizing tasks. Similarly Expert or Casual is one dimension of expertise. I would suggest more-deterministic, less probabilistic expertise and less deterministic, more probabilistic expertise as the other dimension. I am curious what your view will be for this type of 2X2 matrix. Please let me know.

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