Charting the Course of Artificial Intelligence

Jasika Walia and Noah Provenzano, Fischer Jordan


AI has been all the talk in recent years. Over the last decade, we have seen massive growth in this field, with an expanding breadth of use cases in government, corporate, and consumer contexts. AI has become a part of almost everything we do, and we often do not realize it. Investments in AI has been skyrocketing- doubling between 2020 and 2021[1]- as shown below.

A portion of AI’s growth is owed to some notable successes in R&D over the last 5 -10 years. It has performed exceptionally well in fields with widely available data and massive computational resources. These were the most high-return, low-risk analytical problems that were easiest to tackle first. The table below shows some familiar examples [2]. 


As the scope of AI grows, different views emerge on its path forward. However, predicting the course of any technology is a complex and error-prone undertaking. Still, there has been no shortage of efforts to try to map out the future of AI. Unfortunately, many of these attempts were forced to use broad figures or an arbitrary framework to make predictions. 

For example, Figure 2 is a hype cycle chart from Gartner [3] that estimates how long an AI application is expected to match its potential. Gartner acknowledges that applications can disappear and reappear anywhere on the chart. This is an interesting framework, and the expectations axis seems relatable; however, expectations are hard to quantify, hence it is difficult to make any future projections using this chart. 


For our analysis, we have used the following definition of AI:

“Artificial Intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.” [4] – IBM

Figure 4 further elaborates on what we are considering as AI.

Our research approach included the creation of a database of 60 AI applications. Each was rated with regard to data availability and development. To understand the rating scale, we put them on the X and Y axis of Figure 5 and divided it into 4 sections for discussion.

The size of its bubble represents the potential market size of the application and the percentage realized is represented by the color of the bubble. The larger the currently realized share of the potential market, the greener the bubble. This key is shown in Figure 6.

Using this key and the relationship between data availability and development, AI applications are plotted in Figure 7. The arrow on each application represents its predicted movement over the next 3 to 5 years. 

Figure 7. AI Applications by Development and Data Availability

A. Upper Left – Mature

These applications have shown significant progress and huge investments. Most of the low-hanging fruit here is gone as industry capitalized on these areas and saw great returns early on. 

Example – Advertising:

Online advertising has exploded due to AI developments. This field had incredible volumes of internet data waiting to be tapped into not that long ago. Some of the largest companies filled this void and offer highly sophisticated data-driven advertising.

B. Lower Left – Untapped

These applications are near-term opportunities as lots of data is available, but development potential still exists. We haven’t seen much funding in this space yet.

Example – Energy Market:

AI can be used to efficiently optimize energy distribution. This is such a large project that it will take a lot of work to apply AI across all areas of this industry. 

C. Upper Right – Ahead of the Curve

These applications were taken as the first attempt to have AI solve difficult algorithmic problems. As they progressed, they either got lots of funding and shifted to become less AI-reliant or are developing very slowly.

Example – Self Driving:

Self-driving cars have seen immense funding. Since there is pressure to see returns, these companies have realized where AI performs well in driving and where it does not. One example would be programming in particular scenarios to account for variability in results.

D. Lower Right – Future Opportunities

Much more progress with AI is required to develop these applications. We need better data collection, management, and possibly much more computing power. Investors need to be careful as returns may take a long time to be realized.

Example – Human Robots:

All of us have wondered when we will get to enjoy the services of robot butlers. However, the sheer number of situations where a robot like this will need to perform well is what makes it such a difficult task. 


Based on the characteristics of each quadrant in Figure 7, the predicted trajectory of AI applications in the medium term is represented by the arrows. If we apply these arrows, we see a predicted version of the chart in Figure 8

Figure 8. AI Applications by Development and Data Availability in 3 to 5 Years

The changes in this predicted chart are broken down by quadrant.

  1. Upper Left – These mature applications will grow at a steady pace. Since they have lots of training data available, they will continue to eat away their potential market size at historical rates.
  2. Lower Left – These untapped applications will develop the most because they have the data available and much more development potential. With time, their development will match the trend of the other data-rich applications.
  3. Upper Right – These ahead-of-the-curve AI applications will develop slowly as they have low amounts of training data for all possible scenarios. Since they were invested in early on and have more pressure to see returns, these will see some increase in data availability to meet their goals.
  4. Lower Right – These applications will progress faster than the upper right since there is much more room for growth. However, applications with very low data available for all possible scenarios will take a long time to develop regardless of how high the ceiling is. A large breakthrough may be required to see progress in those cases.


On taking a deeper look at the untapped quadrant in the bottom left, we can see expected growth in market sizes for the three most promising AI applications.

Figure 9. Expected Growth in Market Size for Promising AI Applications [12, 13, 14, 15, 16, Own analysis]

Based on our research, we believe that the future of AI cannot be captured in a homogenous manner. AI will progress depending on the specifics of the application and data availability is crucial to determining the rate of development. While this analysis presents a unique way to view AI and its growth, it is not nearly the end of AI development research. We hope to continue to see many others attempt to map out this difficult market.

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