Navigating the AI storm: How to harness the power of AI without being crushed by it

For many support leaders, the world before and after AI feels drastically different.

Rewind to before Q1 of 2023, and while the details varied, the challenges support leaders faced were largely the same as they had been for decades.

Before AI, support leaders were tasked with:

• Improving the customer experience with under-resourced teams.
• Finding ways to improve the cost-to-revenue ratio.
• Preventing team attrition (despite managing people with difficult jobs and low compensation).
• Representing customers’ needs to teams with competing priorities.

They were also expected to operate behind the scenes, often taking on tasks from other departments. Despite being essential for customer retention, support was still viewed primarily as a cost center, and leaders rarely had strong executive advocacy.

These conditions forced support leaders to develop highly valuable skills like creativity, scrappiness, and people leadership, but also put them in a difficult position for operating in the new AI-first world.

Now with AI:

Support leaders are still responsible for almost all of the above, but they’re now being asked to do more – and do it differently. They’re now expected to be AI experts and run large AI implementation initiatives while not letting operations slip. This by itself wouldn’t be unachievable by many support leaders, but the shift in expectations around how they now need to operate is proving to be the stormwall for many.

• They’re suddenly being asked to step out from behind the scenes to center stage and lead the company in its first large adoption of AI.
• They’re being asked to regularly communicate with executives who previously had little interest in their initiatives or ideas.
• They’re being asked to run high-lift, high-impact, cross-functional projects without the infrastructure in place to manage it.
• They’re also now expected to hit AI performance metrics that their executive heard somewhere was possible and that’s potentially completely unrealistic for their use case.

Oh, and if they fail, they’ll likely lose their job. And if they succeed, they could cause job loss for their team members.

When you consider all of these things at once, it could be tempting to curl up, wait for the AI change storm to pass, hope you’re still there when it does, then try to make the most of whatever debris of your support operation is left. In other words, you might be tempted to delay AI changes as long as you can, until eventually another team takes it over, then cross your fingers that they don’t completely destroy what you’ve built over the last several years.

But there’s a better approach: harnessing the storm’s energy to elevate your customer experience, your team, and yourself.

Harnessing AI’s momentum

This new era has the potential to reduce your support operation to a transactional, robotic experience – or to transform it into what you’ve always dreamt it could be. The result of what it does to your organization is mostly dependent on you and how you respond to the demand to implement AI.

You need to recognize this change in technology as one of the most unique opportunities of your career. Suddenly, you will have your executives’ attention, more access to product and engineering resources than ever before, and you won’t need to spend nearly as much time convincing other stakeholders why the change you’re requesting is best for customers and the company.

With the right plan, you can:

• Reframe your team from “cost center” to “value driver.”
• Expand services instead of struggling to meet basic metrics.
• Move from surviving to thriving.

So, how do you do this? Here are three key things I think every support leader should focus on.

Become the AI subject matter expert

Learn. You need to start by understanding what is actually possible with AI now, and what may be possible in the near future. You need to understand how the technology works at least a layer or two deeper than the average person using ChatGPT. You also need to know what’s required to implement an AI solution that’s more than just a glorified answer bot handling only the most basic customer questions.

From there, know the common pitfalls. AI is often advertised as being able to “do it all” with little effort from you. Even sales demos can look impressive with minimal lift from the vendor. But there are several pitfalls to watch out for:

Not digging deep enough with vendors

If you vet multiple AI vendors, you’ll notice their demos don’t vary much. The real test comes when you run a proof of concept or trial. I strongly recommend running multiple trials with different vendors to uncover real capabilities and better understand what’s truly possible.

Only finding a technology solution, not a partnership

While many technologies can deliver similar results, not all vendors are created equal. You need to ensure the partner you choose aligns with your values and that their approach fits your use cases.

Pick a partner who will give you the right level of attention after the sales cycle. Understand if they’re moving at a pace that suits your organization. And – last but certainly not least – get a sense of whether they’re committed for the long haul, or if they’re just in the space to be acquired in a year or two, which would force you to start over again.

Not knowing what good actually looks like

Ask multiple vendors about their average involvement rates and resolution rates. Ask what AI CSAT typically looks like for a team in your industry. Document their responses so you can build solid benchmarks to measure against. Having these data points on hand will be critical when you need to reference them with executives.

Not learning from others’ mistakes

There are plenty of examples of companies overestimating AI’s impact and underestimating the human resources still required. Some laid off hundreds of support team members – only to rehire them later – damaging their brand, wasting resources, and losing or giving away thousands and thousands of dollars. Move with purpose and pace, but don’t move so fast that you make these same mistakes.

Not communicating your plan effectively

You need to be able to articulate why deflecting, say, 50% of your inquiry volume doesn’t mean you should cut your support team by the same percentage. Your rationale could be as simple as the logistics required to cover operating hours and redundancy for service level agreements. More likely, it should also highlight the need to maintain staff for company growth and natural attrition.

Don’t forget all the extra work your team does outside of direct inquiries. Be prepared to explain how improved staffing levels could significantly enhance your customer experience. Whatever your reasoning, practice stating it concisely in a way that convinces executives.

Create a clear AI plan

Remember: your whole company is in uncharted territory with AI. Unless someone was specifically hired for their AI implementation experience in the last year or two, none of your executives have ever deployed AI in support.

This means that you, the support leader, are not just as qualified to create the plan for how AI should be used in the support organization as your executives, but you are probably the person best qualified to do it. In unfamiliar territory, it’s the person holding the map that guides the group to their destination. So in this case, I’m suggesting that you need to draw the map, be the holder of it, and tell your company where they need to go.

The specifics of every plan or map will vary by industry, company, and team, but every plan should include:

1) A vendor evaluation plan

Outline how you’ll conduct AI provider research and determine which move from demo to trial stage. Specify how many vendors you will review and within what time frames. You also need to define your criteria for what AI needs to accomplish and what effectiveness and quality metrics you’ll use to measure its performance.

2) Implementation phases

AI is not a “set it and forget it” tool. There’s no scenario where you plug it in and it handles everything you want. And because AI touches customers so quickly, it’s important to mitigate risk by rolling it out in phases.

These phases don’t have to be slow, but they should be deliberate. Consider different audiences, types of questions, and channels. By identifying rollout phases and presenting them in a clear timeline, you’ll show thoughtfulness and help cross-functional stakeholders plan resources accordingly.

3) How you’ll measure success

You’ll likely reuse some metrics from your evaluation plan, but go wider and deeper. Track AI involvement rate and AI resolution rate, which together give your deflection rate. Measure quality through CSAT and CX Scores, and run regular QA where possible. It’s also a good idea to track the impact on your support cost-to-revenue ratio, as your CFO will care most about this metric.

4) How your team will use reclaimed time

Probably the piece closest to each of our support leader hearts: we need to be thinking about what the successful implementation of an AI solution means for our teams. I can’t state this clearly enough, if you fail to plan for this, you will be pushed to let way too many of your people go.

Ask yourself: if my team suddenly had 20% more time, how could we improve the customer experience? How could we drive revenue or retention? If I had the space to upskill my team, what could they become excellent at?

Quantify that impact in terms of customer value, and set milestones or a timeline for upskilling or adding value-added work.

5) How you’ll report on progress

One of the main reasons I see support leaders fail is poor communication with stakeholders, especially executives. That usually comes from being siloed from most of the company or having a different communication style.

Regardless of the reason, if you hope to transform all of this energy around AI into a positive outcome for you and your team, you must communicate regularly, clearly, and concisely. I suggest partnering with your executive sponsor to decide the best method and cadence for you to share AI project updates, then err on the side of more communication, not less. You can’t afford to under-communicate.

Own the initiative at a higher level

Support leaders don’t have a problem taking ownership of new tasks or initiatives. In fact, if you’re a support leader reading this, I bet you’ve likely taken on projects from other teams where they never spoke to you about it again after handing it off. That’s both a compliment and a problem: it shows you can own things fully, but it also means you’ve had less practice running highly visible initiatives.

This one will be different. Here are some suggestions to own this initiative at a higher level than you’ve commonly had to before.

Project management

• Find a tool that works for you and your team that will be doing the majority of the executing, but that also enables you to summarize your project progress succinctly enough for other teams and executives to follow along.
• Ask some of your company’s product managers how they manage their projects. Try to learn some of their best practices while planting the seed that you’ll likely be partnering with them on this solution soon.
• Learn your executive sponsor’s – and other executives’ – preferred project update style, then tailor your approach to that.

Communication

Perhaps the most critical part of high-level ownership is communication. If you take nothing else from this, please remember to overcommunicate. Not with a lot of details, but with regularity. There should not be a week that goes by that your executive sponsor doesn’t know the status of the project.

A lot of us don’t like to be a bother and assume others are too busy to hear what we’re working on. But in this case, you should overcompensate for that tendency – confirm with your executive sponsor about the effectiveness and cadence of your updates, and if you’re on the fence about saying something or not, rather say it. It’s better to be told to send fewer updates than not enough.

What to communicate? It will vary by audience, but executives should be your top priority. Their time and attention are limited, but they care about this initiative. Adjust this as needed, but I recommend weekly or bi-weekly updates including:

• A brief summary statement.
• Three bullet points (impact statements).
• A link to the project plan.

This could look like:

• Saved customers 30K waiting hours M/M.
• Improved full resolution time by 30% M/M.
• Next initiative will improve X metric by Y%.

Showcase your thought leadership

Referencing your plan consistently is one way to demonstrate leadership. Another is showing awareness of industry trends and benchmarks, and how your team compares.

Calling back to the “Become the SME” section of this article, you should be able to mention these benchmarks proactively and retroactively:

• Proactively: as you launch your AI plan, you should set measurable goals, which reference industry benchmarks. Be prepared along the way to share your reasons for either planning to be behind or ahead of these. You may also find a way to tie in these benchmarks to your regular updates.
• Reactively: when executives find time to dig in deeper to your project plan, they will likely have several questions. Your ability to have a succinct, data-driven answer that references industry benchmarks will showcase your expertise in the area and earn their trust.

The storm is here – what will you do?

The pressure of the AI storm is growing and, barring any major AI catastrophes, is not dissipating anytime soon. This storm can either crush your team as you know it, or be the wind under your wings to elevate your organization to its maximum potential.

But it depends on you. Will you wait and risk someone else reducing your team to cut costs, or will you step up as the support AI expert, form a plan, and transform your team into a major value driver?

The chance – and the choice – are yours.