SYSTEMS AND METHODS FOR GENERATING CHANGES TO WORKPLACE PLANS USING GENERATIVE AI

1 . A method for generating changes to a first plan, the first plan comprising a workload and a performance metric, the method comprising:

estimating whether the first plan is achievable, comprising:

comparing the first plan to at least one instance of workplace data, the workplace data indicative of instances of workload and performance metric, the instances being previous to the time of the first plan, and

wherein the first plan is deemed to be achievable if, for at least one instance of the workplace data, the performance metric of the first plan is less than or equal to a performance metric of the past instance;

if the first plan is not deemed to be achievable, generating a modified plan with a higher probability of achievability than a probability of achievability of the first plan, the modified plan comprising a modification to at least one of the performance metric and the workload;

creating a prompt for a generative artificial intelligence (AI) system, the prompt configured to ask for a message for a user, wherein the message states that the first plan should be replaced with the modified plan and states the modification to the at least one of the performance metric and the workload of the modified plan; and

receiving from the generative AI system, the message for a user.

2 . The method of claim 1 , further comprising:

creating, based on the modified plan, a staffing schedule for delivering the modified plan.

3 . The method of claim 2 , wherein creating a staffing schedule is carried out using a generative AI.

4 . The method of claim 1 , wherein the method is carried out for each of a number of interaction types, the interaction types comprising at least one of:

voice-based interactions,

text-based interactions,

video-based interactions, and

face-to-face interactions.

5 . The method of claim 4 , wherein, when comparing the first plan to at least one instance of workplace data, the workplace data and the first plan both relate to a same interaction type.

6 . The method of claim 1 , wherein the modified plan comprises a decrease to the performance metric.

7 . The method of claim 1 , wherein the generative AI system is a large language model (LLM).

8 . The method of claim 1 , further comprising:

outputting the modified plan for displaying to a user.

9 . The method of claim 1 , wherein the first plan and the workplace data relate to a contact center or call center.

10 . A system for generating changes to a first plan, the first plan comprising a workload and a performance metric, the system comprising:

a memory;

a processor, the processor configured to:

estimate whether the first plan is achievable, comprising the processor being configured to:

compare the first plan to at least one instance of workplace data, the workplace data indicative of instances of workload and, performance metric, the instances being previous to the time of the first plan, and

wherein the first plan is deemed to be achievable if, for at least one instance of the workplace data, the performance metric of the first plan is less than or equal to a performance metric of the past instance;

if the first plan is not deemed to be achievable, generate a modified plan with a higher probability of achievability than a probability of achievability of the first plan, the modified plan comprising a modification to at least one of the performance metric and the workload;

create a prompt for a generative artificial intelligence (AI) system, the prompt configured to ask for a message for a user, wherein the message states that the first plan should be replaced with the modified plan and states the modification to the at least one of the performance metric and the workload of the modified plan; and

receive from the generative AI system, the message for a user.

11 . The system of claim 10 , wherein the processor is further configured to:

create, based on the modified plan, a staffing schedule for delivering the modified plan.

12 . The system of claim 11 , wherein creating a staffing schedule is carried out using a generative AI.

13 . The system of claim 10 , wherein the processor is configured to carry out each method step for each of a number of interaction types, the interaction types comprising at least one of:

voice-based interactions,

text-based interactions,

video-based interactions, and

face-to-face interactions.

14 . The system of claim 13 , wherein, when the processor is configured to compare the first plan to at least one instance of workplace data, the workplace data and the first plan both relate to a same interaction type.

15 . The system of claim 10 , wherein the modified work plan comprises a decrease to the performance metric.

16 . The system of claim 10 , wherein the generative AI system is a large language model (LLM).

17 . The system of claim 10 , wherein the processor is further configured to:

output the modified plan for displaying to a user.

18 . The system of claim 10 , wherein the first plan and the workplace data relate to a contact center or call center.

19 . The system of claim 10 , wherein the modified plan is to be transferred to an automatic call distributor of a contact center, and wherein the automatic call distributor is to route contacts of the contact center to agents of the contact center in accordance with the modified plan.

20 . A method for assessing the feasibility of a proposed contact center work plan comprising a proposed workload and a proposed performance metric, the method comprising:

generating, based on the proposed contact center work plan and data indicative of previous feasible contact center work plans, a feasibility score for the proposed contact center work plan; and

where the feasibility score is below a threshold, generating, using a large language model, a modified contact center work plan for a user with a feasibility score above the threshold, the modified contact center work plan comprising a modification to one or more aspects of the contact center work plan.