The topic of AI is more widespread than ever. But for organizations already deep into their intelligent automation journeys, the question isn’t if they’ll use AI. It’s how they’ll use it to solve existing problems and extend their previous technology investments.
This applies to Naviant customers, too. At a recent panel at the Nashville stop of Naviant’s Intelligent Automation Day conference series, IT leaders gathered to discuss:
- Their outlooks on AI
- How it’s impacting their intelligent automation journeys currently
- Key considerations that have helped them find success with AI
A recurring theme that connected all our speakers’ perspectives was taking a measured, strategic approach to AI, not chasing the hype.
But of course, that’s easier said than done.
So, let’s examine four considerations our panelists are prioritizing as they add AI to their intelligent automation journeys.
How IT Leaders Are Using AI to Enhance Their Automation Journeys
Consideration #1: Set Realistic Expectations About the True Cost of AI
For many people, AI wasn’t on their radar until the introduction of the free version of ChatGPT.
But as powerful as this technology is, all the hype surrounding its many use cases and its remarkable accessibility caused many to underestimate the cost of AI implementation at large. And without a reality check, this can lead to disappointment down the road.
One of our panelists called this challenge out, sharing that they’ve encountered misconceptions from stakeholders, like the belief that organizations can realistically use the free version of ChatGPT or that an AI call center solely comes with the one-time implementation fee.
In reality, AI implementation often involves ongoing infrastructure, licensing, and support costs. So, our panelist recommends educating stakeholders early on about these realities in order to set realistic expectations and avoid budget surprises.
Consideration #2: Balance Innovation with Responsibility
While our panelists all expressed intrigue in AI’s possibilities for their organizations, they agreed that it’s vital to take a cautious approach. This is especially pertinent when it comes to legal liability, ethical use, and data privacy, and even more so for those who handle highly sensitive employee or customer data or who are in highly regulated industries.
One panelist shared some of the top questions that their organization has investigated as they approach the possible uses of AI, like:
- Who owns the data used to train AI models?
- What happens if a breach occurs – who is considered responsible?
- How do we ensure that AI-generated outputs are accurate, fair, and secure?
In addition to asking these questions, the panel discussed two strategies that support this cautious approach:
- Some organizations are forming internal committees to evaluate AI tools, assess risk, and ensure compliance before deployment.
- Others are holding regular working sessions with end users and analysts to collaboratively define how AI will be used on a practical level and what safeguards are needed.
Both strategies involve meeting more frequently in the beginning as you’re getting things off the ground, and wind down into less frequent meetings, such as once a quarter.
This measured approach and commitment to regrouping and reassessing one’s strategy in the face of an evolving AI landscape will boost your AI initiative’s trust and long-term sustainability.
Consideration #3: Find Opportunities to Enhance Existing Workflows
When it comes time to finally implement AI, it can be difficult to know where to begin.
One high-impact strategy that’s relatively straightforward to get off the ground is to use AI as a natural extension of your existing workflows. Our panelists discussed various ways they’re doing this in their own organizations, like:
- One panelist shared that they use Microsoft Dynamics 365 Finance & Operations (F&O) as their ERP and rely on multiple legacy systems, and their procurement team is currently doing testing with Copilot to see how it can interface with the organization’s indirect PO procurement process.
- A panelist explained that its product development group has begun adding ChatGPT into its product development process to reduce certain tedious tasks.
- Another panelist’s accounts receivable team has rolled out RPA bots to simplify its AR process for certain cases. Some of its larger customers use Oracle, so it receives Oracle remittances for cash application, and it uses the AI-powered RPA bots to apply those payments across the multiple large customers and their groups. This way, the bots can reduce manual effort, improve accuracy, and free up teams to focus on higher-value tasks.
- A panelist in the healthcare industry is considering using AI in the diagnostic area, where they’d be able to plug in a diagnosis code and have it come back with recommended treatment plans and treatment options. This way, providers have a convenient tool to help them weigh all the options for their patients to ultimately provide more thorough, high-quality care.
These efforts are typically small-scale and tightly scoped, and one of the panelists even called this phase “the crawling stage.”
But it’s powerful: They’re already delivering measurable efficiency gains. This approach also supports the advice commonly shared by AI analysts of “chase the quick wins first.” In other words, when you can prove AI’s value with successful use cases your staff can relate to, it’s easier to get hesitant staff on your side and continue to get support for your endeavors.
Consideration #4: Empower Lean Teams with AI-Driven Insights
In smaller or resource-constrained teams, AI is proving to be a powerful force multiplier, making small teams a strong starting point for AI implementation.
One panelist explained that they’re literally a one-person team, so they’ve found immense value in AI and use it to simplify their life every day.
A primary use case they’ve found helpful is using AI to analyze unstructured data. For example, they got a heavy data request that involved all unstructured data, and without AI, it would’ve been a significant undertaking for one person. But the panelist decided to create a report, drop it into Excel, and then feed it to Copilot with a detailed prompt. Within just 15 minutes of receiving the request, the panelist had exactly what they needed.
The Takeaway: Start Smart, Stay Grounded
AI’s hype over the past few years has led some organizations to view it as a standalone technology initiative that’ll inherently deliver impressive ROI. So, they chase AI for the sake of it.
The truth is, going all-in on a new piece of technology without rigorous consideration of how it can fit into your unique tech stack, processes, and long-term strategy is bound to be resource-intensive and underdeliver.
As our panelists have discovered, asking smart questions, experimenting with purpose, and aligning AI with their existing strengths offers far more success. Sure, it might provide smaller wins starting out, but these little victories will make it easier to scale down the road.
AI isn’t a magic bullet solution. But in the hands of thoughtful teams, it’s a powerful tool to advance your intelligent automation journey.
