There’s no doubt that artificial intelligence (AI) provides a significant competitive advantage to businesses. Just look at Amazon – the company’s AI-powered recommendation system generates tens of billions of dollars in yearly sales, accounting for 35% of the company’s revenue.
When studying the impact of AI on business results, one McKinsey & Company survey discovered that “63% of respondents report revenue increases from AI adoption in the business units where their companies use AI, with respondents from high performers nearly three times likelier than those from other companies to report revenue gains of more than 10%.”
The right AI solution can help distributors better understand their customers, make more effective upsell and cross-sell recommendations, know when items are due to be reordered, make their sales reps more productive and improve lifetime customer value. The case for artificial intelligence is so compelling that 88% of chief sales officers (CSOs) have or are considering investing in AI analytics tools and technologies, according to Gartner research.
Implementing new technologies can be challenging – especially for businesses that have always done things the same way. Executives worry about wasting money on an unsuccessful deployment, while employees can be hesitant to adopt new workflows. A lack of funding, poor change management and low employee buy-in often contribute to a lackluster implementation.
Without taking the proper steps to implement this technology, your business could fail to see the largest possible ROI from their AI investment.
Taking the right steps, you can avoid common pitfalls and ensure your AI implementation is successful. In fact, Deloitte found that reimagining workflows, adopting new operational practices, leveraging change management and implementing an enterprise-wide AI strategy can make AI deployment more successful.
To ensure your AI investment is effective and profitable, follow these three principles:
Think Big, Start Small
When implementing new technology, it can be tempting to deploy it to your entire organization right from the start – especially if it presents the opportunity for high ROI and productivity improvements. But, introducing technology too quickly can result in adoption challenges such as increased costs and lower buy-in from employees and C-Suite executives. To avoid this, it is best to think big but start small.
For example, our team at Proton recently worked with a multi-billion-dollar distributor to make their counter and inside salespeople more proactive and productive, enabling them to sell more to each customer.
We thought big: what would it look like to do a company-wide deployment? What would that mean for the business in terms of revenue uplift? How would their sales reps be able to use our solution to be differentiated in the market?
We had big plans but knew the implementation would be more effective if we started small. So, to begin, we worked with about 150 of the company’s inside and branch salespeople before expanding. By training a small number of salespeople first, we could ensure they were comfortable with their new tools and workflows.
We wanted to show that it works for these reps to build momentum within the rest of the company. So, we deployed with those reps, and the results were strong. Early on, the reps using Proton grew 22% faster than other reps in the company. We had a meeting with the executive team, including the CEO, and by the end of the meeting, the CEO said, ‘Why are we not using this on every customer interaction?’
Another Proton client that found success with a phased integration approach was Benco Dental, the largest privately owned and fastest-growing U.S. dental supplies distributor. By thinking big but starting small, Benco Dental adopted AI in one sales channel. Once they mastered that channel, they continued to add sales channels one at a time. This approach helped them improve customer service and grow ecommerce revenue dramatically.
Set Measurable Targets
As you begin your AI implementation, define and set measurable metrics for success, both leading indicators (input metrics) and lagging indicators (output metrics).
Examples of input metrics include the number of pitches, site visits and how many people log on to the site.
Examples of output metrics include sales revenue, profit numbers and expenses.
For instance, look at how effective your salespeople are at upselling and cross-selling. What percentage of pitches are successful, and what is the average revenue generated per pitch? After you understand where your organization is, determine what outcome you want to see from your new technology.
Setting goals and metrics will help you measure the effectiveness of your integration and will give you insight into areas that may need improvement. Without measurable targets, you won’t know how much progress you’re making or have the necessary data to grow.
It is important to remember that this is not a “set it and forget it” process. Because technology, workflows, business goals and metrics are constantly evolving, your teams will need to monitor key performance indicators (KPIs) to identify fluctuations in performance.
In its most recent State of AI in the Enterprise report, Deloitte discovered that “transformers” are three times more likely to have an AI strategy than businesses that struggle to implement artificial intelligence.
In the same report, Deloitte recommends that “Organizations should develop dynamic ways of assessing their strategy to ensure it remains responsive to ever-changing market and technology developments. As the organization’s core business strategy and AI capabilities mature over time, leaders should continually sharpen their goals, moving beyond staying competitive to increasingly using AI and ML as competitive differentiators.”
When deploying your AI solution, track metrics such as:
- How many pitches are being made now vs. before implementation?
- How successful are upsell and cross-sell efforts?
- Is customer satisfaction improving?
- How many users are logging in regularly?
- In what areas could results be improved?
Align your KPIs across departments, communicate goals and use measurable results to track performance. With this data, you will have the knowledge to make critical adjustments and ensure your AI deployment is successful.
Align the Why
Once you have a plan and have set measurable targets, it is time to introduce your AI solution to your teams. In this case, proper change management is critical to success. Most salespeople and customer service reps (CSRs) are hesitant to change their workflows; they have done things the same way for years and are afraid that new technology will either replace them or make their jobs more difficult.
When it comes to technology, you only get one chance for a great first impression with your sales reps. Explain why you are introducing a new workflow and how it will help them. Align the “why” to each team member’s role. For instance, a sales rep may be more interested in raising their commissions, while a CSR may need a more streamlined way to help their customers.
As an example, the main implementation goal for one of Proton’s clients (a large industrial distributor) was to use AI to raise sales. But, when Proton’s team approached the customer service team for training, no one seemed interested in that goal. After spending considerable time training their team, we looked at the usage data and noticed that no one in that department was logging in and using their new tools.
We were trying to figure out what we missed. We did the training with them and told them how it would help them sell more, so what happened? Eventually, through conversations with their managers, we figured out that their team cares about the customer experience, not about selling more. They weren’t paid commission, so that’s not what they were focused on.
After realizing that the customer service team wasn’t driven by making sales, the Proton team went back and retrained them. This time, however, we focused on aligning the technology to the team’s “why.”
Instead of saying, “this is a software that can help you sell more stuff,” we said, “this is a software that can help you better help your customers.” That changed the game. They ended up selling more – a lot more. But it only came because we aligned the “why” with their “why.”
How to Ensure Ongoing Success
When implemented successfully, artificial intelligence will enhance the customer experience and make your organization more profitable and productive.
However, to ensure your business enjoys a successful AI deployment, be careful not to overwhelm your systems by deploying AI to your entire staff simultaneously. Instead, have a roadmap to implement new workflows in different departments over time. Set measurable targets, offer incentives and align your culture around a unified “why.”
Benj Cohen is the founder and CEO of Proton.ai.