AI implementation 101: How to prepare your sales team

The AI revolution is here; automation is taking over repetitive tasks and augmentation is enhancing our decisions. During this revolution, we’re saving time and money, and business teams are becoming more efficient and productive. However, you can’t participate in this digital transformation if you don’t properly prepare your organization with an AI implementation strategy.

AI isn’t just another tool to add to the tech stack. It brings a lasting impact to your organization. That said, sales teams and enterprises need to make careful considerations and preparations before starting implementation. Around 85% of AI projects and initiatives either fail or struggle to make their expected impact because of rushed implementation or general failure to prepare.

To do this, management and executive teams need to analyze the business pains that an AI solution could solve and start researching the right business intelligence tools to help streamline your sales team’s monotonous tasks.

Once you’ve generated a short list, you’ll need to create an AI implementation strategy, review your data quality, evaluate integration needs, and build a change management plan.

Creating an AI implementation strategy 

With so much hype building around AI, the sentiment is often “implement quickly and be done with it.” However, this type of implementation will not bring favorable results. AI systems are complex and require time, effort, and strategy in order to be successful.

The next industrial revolution (what we call “Business 5.0”) will be an overhaul of business process changes that will improve efficiency and productivity for years to come. Don’t think of sales AI as a “quick fix” — these technologies will bring lasting and impactful change.

When crafting a sales AI implementation strategy, it’s important to consider the following:

• Short- and long-term objectives
• Current KPIs and if those will need to change
• Who will own this technology (sales ops, CRM administrator, IT, sales engineer, etc.)
• A rough timeline of AI implementation milestones
• Obstacles that may prevent or delay AI implementation
• Installation and onboarding
• Training for end-users
• Review sessions to continue optimization

Implementing a complex AI platform may seem daunting, but taking the right steps in small increments will lead to more overall efficiency.

Reviewing your data quality

Having robust, accurate data is essential prior to your AI implementation strategy. More than 60% of enterprises have seen AI or ML initiatives fail because the data implemented was inconsistent and built out of too many conflicting sources. In order for any sales AI or ML system to produce results, it must learn from the mountain of customer data that exists in an organization’s CRM (or other sales database).

First, perform an audit of your data and consider these three things: data quality, data collection and storage, and security. When it comes to data quality, it’s vital that data is clean. AI uses data to make predictions, therefore if the sales data is inaccurate it will produce inaccurate results.

Assess how much data is missing or inaccurate. For example, if lead title or phone number fields are often left blank or if there are too many variations of a company name. Consider whether the AI system could function properly and bring the desired results with the current state of the sales data.

Cleanliness of data in itself can be a major business problem that AI can solve. If this is the case, consider implementing an AI solution with a data cleanliness component to increase sales data accuracy.

Second, examine the data storage and collection process. Unfortunately, in many organizations, customer and sales data exists in several siloed locations. Perhaps some information exists in the CRM or other database, others only in spreadsheets or emails. This is a problem as it prevents your organization from seeing a complete view of the sales process and pipeline.

Locate all the data related to the sales organization and decide where that data should be centralized.

And finally, consider security. Customer and business data should be kept under tight lock and key, both for the benefit of the company and its customers. No system should ever remove or have access to any of your sensitive data. Only integrate an AI platform that strongly encrypts data both during storage and transport. This promises that no human will be able to access, view, or use any classified information.

Evaluating your business intelligence tools’ integration needs

Sales teams work with a lot of tools. On average, an SDR uses six tools, each one adding its own step to the sales process and often bogging reps down.

It’s important not to implement an AI system that will just be another tool in the tech stack. The goal is to help with sales rep productivity, not get in their way and cause more confusion and work.

Make sure the business intelligence tools you select can easily integrate with the current stack in use. In your research, you’ll find that some AI platforms easily fit into any sales process while others are complex and have many requirements to get started.

Take inventory of the tools and processes the sales team has in place and how the new technology will fit. Ask these questions: Is there a third-party dashboard that the sales reps will need to sign into? Is it an encrypted layer that works in the background? Does the CRM, email, or other system need to sync?

Then, create a checklist of all the items needed and the people who will help before integrating. Ownership will be key.

Building a change management plan

Finally, make sure the entire sales organization is ready and aligned before the AI system is in place. Lack of expertise is a common roadblock to proper implementation — with 53% of employers citing it as a reason for their AI hesitation. Create a change management plan that gets everyone on board with this initiative.

Begin by aligning all stakeholders. Consider the different “owners” of information and technical implementation. Does IT need to be involved to install the software? Does the legal team need to analyze the security measures? Does a Salesforce admin need to be sure that the product can be customized? List out all stakeholders that will need to sign off in order to move the project forward. Doing this ahead of time will help you see where there might be potential roadblocks and to ensure you have the support you’ll need within your organization.

The sales reps (or other end-users) need to have an understanding of this project and how it will bring value to their roles. Otherwise, they will be less motivated to use the technology correctly and consistently.

Be sure they understand what is required of them during each phase of your AI implementation strategy. For example, there will likely be test users that will need to provide feedback, so make sure they understand why feedback is important and what kind of feedback is needed.

Once the AI platform is fully purchased and implemented, be sure to train all individuals on how to properly use it. This will need to be ongoing and built into your onboarding process. Included in this training should be what KPIs are to measure success and if KPIs of the sales reps will change after AI implementation.

More than 70% of organizations have experienced some stall or delay in their digitization efforts, with organizational readiness being a significant contributor. Ensure your company’s time, effort, and money aren’t wasted on stalled initiatives by properly preparing your organization ahead of implementation.