Potentially AI (LSE:AGI) Piotroski F-Score: 5 (As of Jul. 18, 2026) — 67% Above Median

Potentially AI  (LSE:AGI) Piotroski F-Score Explanation

The developer of the system is Joseph D. Piotroski is relatively unknown accounting professor who shuns publicity and rarely gives interviews.

He graduated from the University of Illinois with a B.S. in accounting in 1989, received an M.B.A. from Indiana University in 1994. Five years later, in 1999, after earning a Ph.D. in accounting from the University of Michigan, he became an associate professor of accounting at the University of Chicago.

In 2000, he wrote a research paper called "Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers" (pdf).

He wanted to see if he can develop a system (using a simple nine-point scoring system) that can increase the returns of a strategy of investing in low price to book (referred to in the paper as high book to market) value companies.

What he found was something that exceeded his most optimistic expectations.

Buying only those companies that scored highest (8 or 9) on his nine-point scale, or F-Score as he called it, over the 20 year period from 1976 to 1996 led to an average out-performance over the market of 13.4%.

Even more impressive were the results of a strategy of investing in the highest F-Score companies (8 or 9) and shorting companies with the lowest F-Score (0 or 1).

Over the same period from 1976 to 1996 (20 years) this strategy led to an average yearly return of 23%, substantially outperforming the average S&P 500 index return of 15.83% over the same period.

Potentially AI Piotroski F-Score Related Terms

The historical data trend for Potentially AI's Piotroski F-Score can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

Potentially AI Piotroski F-Score Chart

Potentially AI Annual Data
	Trend	Dec16	Dec17	Dec18	Dec19	Dec20	Dec21	Dec22	Dec23	Dec24	Dec25
Piotroski F-Score		Get a 7-Day Free Trial					3.00	3.00	4.00	3.00	5.00

Potentially AI Semi-Annual Data
	Jun16	Dec16	Jun17	Dec17	Jun18	Dec18	Jun19	Dec19	Jun20	Dec20	Jun21	Dec21	Jun22	Dec22	Jun23	Dec23	Jun24	Dec24	Jun25	Dec25
Piotroski F-Score	Get a 7-Day Free Trial															4.00	0.00	3.00	0.00	5.00

LSE:AGI vs BLK, BX, KKR: Piotroski F-Score Comparison

For the Software - Infrastructure subindustry, Potentially AI's Piotroski F-Score, along with its competitors' market caps and Piotroski F-Score data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.

Potentially AI Piotroski F-Score vs Software Industry

For the Software industry and Technology sector, Potentially AI's Piotroski F-Score distribution charts can be found below:

* The bar in red indicates where Potentially AI's Piotroski F-Score falls into.

How is the Piotroski F-Score calculated?

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

*Note: If the latest quarterly/semi-annual/annual total assets data is 0, then we will use previous quarterly/semi-annual/annual data for all the items in the balance sheet.

Profitability

Question 1. Return on Assets (ROA)

Net income before extraordinary items for the year divided by Total Assets at the beginning of the year.

Score 1 if positive, 0 if negative.

Potentially AI's current Net Income (TTM) was -1.72. ==> Negative ==> Score 0.

Question 2. Cash Flow Return on Assets (CFROA)

Net cash flow from operating activities (operating cash flow) divided by Total Assets at the beginning of the year.

Score 1 if positive, 0 if negative.

Potentially AI's current Cash Flow from Operations (TTM) was -1.64. ==> Negative ==> Score 0.

Question 3. Change in Return on Assets

Compare this year's return on assets (1) to last year's return on assets.

Score 1 if it's higher, 0 if it's lower.

Potentially AI's return on assets of this year was -7.62831858. Potentially AI's return on assets of last year was -0.87865169. ==> Last year is higher ==> Score 0.

Question 4. Quality of Earnings (Accrual)

Compare Cash flow return on assets (2) to return on assets (1)

Score 1 if CFROA > ROA, 0 if CFROA <= ROA.

Potentially AI's current Net Income (TTM) was -1.72. Potentially AI's current Cash Flow from Operations (TTM) was -1.64. ==> -1.64 > -1.72 ==> CFROA > ROA ==> Score 1.

Funding

Question 5. Change in Gearing or Leverage

Compare this year's gearing (long-term debt divided by average total assets) to last year's gearing.

Score 0 if this year's gearing is higher, 1 otherwise.

Potentially AI's gearing of this year was 0. Potentially AI's gearing of last year was 0. ==> This year is lower or equal to last year. ==> Score 1.

Question 6. Change in Working Capital (Liquidity)

Compare this year's current ratio (current assets divided by current liabilities) to last year's current ratio.

Score 1 if this year's current ratio is higher, 0 if it's lower

* Note that for banks and insurance companies, there's no Total Current Assets and Total Current Liabilities reported. Thus, we use Total Assets and Total Liabilities to calculate current ratio for banks and insurance companies.

Potentially AI's current ratio of this year was 13.68461538. Potentially AI's current ratio of last year was 0.48706897. ==> This year's current ratio is higher. ==> Score 1.

Question 7. Change in Shares in Issue

Compare the number of shares in issue this year, to the number in issue last year.

Score 0 if there is larger number of shares in issue this year, 1 otherwise.

Potentially AI's number of shares in issue this year was 4.317. Potentially AI's number of shares in issue last year was 5.351. ==> There is smaller number of shares in issue this year, or the same. ==> Score 1.

Efficiency

Question 8. Change in Gross Margin

Compare this year's gross margin (Gross Profit divided by sales) to last year's.

Score 1 if this year's gross margin is higher, 0 if it's lower.

* Note that for banks and insurance companies, there's no Gross Profit reported. Thus, we use net income instead of gross profit and calculate Net Margin for this score.

Potentially AI's net margin of this year was 3.60669456. Potentially AI's net margin of last year was 2.02590674. ==> This year's net margin is higher. ==> Score 1.

Question 9. Change in asset turnover

Compare this year's asset turnover (total sales for the year divided by total assets at the beginning of the year) to last year's asset turnover ratio.

Score 1 if this year's asset turnover ratio is higher, 0 if it's lower

Potentially AI's asset turnover of this year was -2.11504425. Potentially AI's asset turnover of last year was -0.43370787. ==> Last year's asset turnover is higher ==> Score 0.

Evaluation

Good or high score = 7, 8, 9
Bad or low score = 0, 1, 2, 3

Potentially AI has an F-score of 5 indicating the company's financial situation is typical for a stable company.

Potentially AI Business Description