Most businesses in operation today have notable and significant track records of successfully adopting Software-as-a-Service (SaaS) solutions, but too many of those same companies try to apply the SaaS implementation playbook to artificial intelligence solutions. That's a bad idea.
AI may seem similar to conventional SaaS, but the best practices for implementing AI, adapting your workflow to AI, and training staff to use AI all differ greatly from those same processes for traditional SaaS applications.
If you want to successfully adopt AI, your adoption playbook needs some updating.
How AI Implementation Is Different from SaaS
The standard practice for adopting SaaS applications -- or any conventional software -- is to identify a pilot group of users and allow them to test-drive the software for a relatively short amount of time. Once they've tried out the software for a few weeks or months and are comfortable with the system, the solution is progressively rolled out the rest of your organization.
This practice triggers a broader rollout of software based on pilot users becoming competent with the new system, but AI rollouts need to also be gated based on how well artificial intelligence has adapted to its users. AI solutions adjust and improve their performance based on user feedback, and AI pilot projects should have specific success criteria around the performance of the solution, not just how well users understand it. An AI should not leave the pilot phase until it has learned enough from early adopters to be truly ready for a wider rollout.
Moreover, when you do a phased rollout of AI, bear in mind that it will "learn" different processes and behaviors from different users, different use cases, and different departments. Your pilot group should have a broad, representative sampling of users from across your organization so the AI can learn how to handle all of your team members, not just the one department or one location that wanted to be in the pilot.
On the subject of training, when you prepare to rollout a SaaS solution, SaaS apps are immediately ready to ingest all your historical data from old systems. Sales data, customer contacts, email archives, calendar files, even code repositories can all be moved into a SaaS solution pretty quickly, so ramp-up times are short.
AI systems, however, benefit from specially annotated data. If you want an AI bot to help manage and mediate sales conversation, for example, you can't just dump all your old emails and chat logs into an AI algorithm. You must identify within each conversation who is the salesperson and who is the customer, what products were being sold, and whether the deal was actually won. That takes time, effort, and resources that are not accounted for in traditional SaaS rollout processes.
How AI Workflow Changes are Different from SaaS
With SaaS implementation, users are often given a "crosswalk" of processes and functions that explain how to accomplish tasks in the new SaaS solution as compared to whatever software your new SaaS app is replacing. In many cases, the basic workflow doesn't change, it just enjoys added features or accelerated performance.
Given AI's need for annotated data, everyday workflows should be enhanced to create annotation as part of regular work. For example, when managing a customer support case, you'll likely want to explicitly connect an inbound email or chat session to a customer record or account, so that an AI assistant can learn how the conversation relates to the customer's solution set and usage metrics.
Workflows in SaaS systems also don't typically offer fundamentally different functionality than any other conventional software solution, so very few tasks that weren't possible before SaaS are now on the table, and few things you did prior to SaaS can now be eliminated or avoided.
Artificial intelligence offers the ability to predict outcomes, classify data, or automate tasks that SaaS can't. Tasks that used to require human oversight can now feasibly be handed off to an AI agent, which means a whole new range of workflow changes are now possible that a SaaS adoption playbook would never consider.
How AI Staff Training is Different from SaaS
SaaS solutions are designed to help employees do their existing tasks better, so training on new SaaS applications is usually just a matter of acclimating your team to the new set of controls and features. AI solutions are designed to do things conventional software can't, such that your employee's jobs can fundamentally change. AI staff training involves identifying work that can be handed off to a SaaS agent, then training your staff to train that AI agent to do the job.
That said, most AI solutions today are not full-on replacements for human workers. They are assistants and consultants, designed to help do tedious administrative tasks or complicated but repetitive functions that are often a waste of a talented human's time. AI Staff training is about teaching your team to delegate the right items to AI, such that their workflow can evolve and expand, rather than simply adapt to a new user interface.
Start Your AI Adoption Journey
Talla is building a customer support AI agent that is designed to help your human support reps do their jobs more effectively. It's the perfect test case for how AI can improve your workplace performance and help develop your AI adoption playbook.
If you'd like to start your journey to AI excellence, contact Talla today.