SYSTEMS AND METHODS FOR SOFTWARE AS A SERVICE MANAGEMENT USING MACHINE LEARNING AUTOMATION

1 . A method comprising:

determining current software-as-a-service (SaaS) applications being used by an organization;

obtaining criteria for each of the current SaaS applications including at least a cost, a renewal date, uplift, and cybersecurity;

generating a machine learning model for the organization that defines a functional fit based at least in part on the criteria and parameters of the organization;

evaluating replacement SaaS applications when one of the current SaaS applications does not comply with the functional fit, the replacement SaaS applications also having criteria; and

automatically generating new contract terms for any of the replacement SaaS applications.

2 . The method according to claim 1 , further comprising generating a dynamic playbook that is used to optimize a selection process for replacing or retaining SaaS applications.

3 . The method according to claim 2 , wherein at least a portion of the criteria of the current SaaS applications are defined by a user of the organization.

4 . The method according to claim 3 , wherein generating the dynamic playbook includes generating weighted metrics for the criteria of the replacement SaaS applications and the criteria of the current SaaS applications.

5 . The method according to claim 1 , further comprising activating an automated workflow application configured to procure a replacement SaaS application in accordance with an algorithmic workflow.

6 . The method according to claim 5 , wherein activating the automated workflow application comprises:

identifying end users configured to approve to select a vendor of the replacement SaaS application; and

receiving data representing approvals to select the vendor of the replacement SaaS application from the end users, wherein at least one approval is performed automatically.

7 . The method according to claim 1 , further comprising:

monitoring performance data for the current SaaS applications to calculate utilization thereof; and

selecting one of the replacement SaaS applications based on utilization data.

8 . The method according to claim 1 , further comprising:

monitoring performance data;

predicting data based on the performance data during an interval of time to form forecasted data; and

selecting one of the replacement SaaS applications as a function of the forecasted data.

9 . The method according to claim 1 , wherein the criteria include at least one key performance indicator (“KPI”) metric.

10 . The method according to claim 9 , wherein the criteria include data representing terms in an electronic document constituting requirements associated with an exchange of data.

11 . A system, comprising:

a processor; and

memory for storing instructions, the processor executing the instructions to:

determine current software-as-a-service (SaaS) applications being used by an organization;

obtain criteria for each of the current SaaS applications including at least a cost, a renewal date, uplift, and cybersecurity;

generate a machine learning model for the organization that defines a functional fit based at least in part on the criteria and parameters of the organization;

evaluate replacement SaaS applications when one of the current SaaS applications does not comply with the functional fit, the replacement SaaS applications also having criteria;

generate a dynamic playbook that is used to optimize a selection process for replacing or retaining SaaS applications; and

generate a dashboard that indicates recommendations of the current SaaS applications that should be replaced based on the dynamic playbook.

12 . The system according to claim 11 , wherein the processor is configured to automatically generate new contract terms for any of the replacement SaaS applications.

13 . The system according to claim 11 , wherein at least a portion of the criteria of the current SaaS applications are defined by a user of the organization.

14 . The system according to claim 13 , wherein the processor is configured to generate the dynamic playbook by generating weighted metrics for the criteria of the replacement SaaS applications and the criteria of the current SaaS applications.

15 . The system according to claim 11 , wherein the processor is configured to activate an automated workflow application configured to procure a replacement SaaS application in accordance with an algorithmic workflow.

16 . The system according to claim 15 , wherein the processor is configured to activate the automated workflow application to:

identify end users configured to approve to select a vendor of the replacement SaaS application; and

receive data representing approvals to select the vendor of the replacement SaaS application from the end users, wherein at least one approval is performed automatically.

17 . The system according to claim 11 , wherein the processor is configured to:

monitor performance data for the current SaaS applications to calculate utilization thereof; and

select one of the replacement SaaS applications based on utilization data.

18 . The system according to claim 17 , wherein the processor is configured to:

monitor the performance data;

predict data based on the performance data during an interval of time to form forecasted data; and

select one of the replacement SaaS applications as a function of the forecasted data.

19 . The system according to claim 11 , wherein the criteria include data representing terms in an electronic document constituting requirements associated with an exchange of data.

20 . The system according to claim 11 , wherein the processor is configured to:

evaluate invoices and subsequent payments for a customer;

evaluate payment history for the customer; and

generate a suggestion to shorten a time span for payment of an invoice based on the payment history.