The business case for internal AI applications can be pretty compelling on paper. But to enterprise leadership teams, promises of reducing administrative bottlenecks and automated compliance checks may not be enough to get them to sign off.
Not even the possibility of instantly surfacing insights from thousands of organizational documents will necessarily convince them of AI-improved operational efficiency.
So how do you get leadership teams to pull the trigger on AI digital product development? First and foremost, you do not hesitate.
The initial anxiety with AI is not normally the upfront cost of building software. It is the fear of the unknown.
Post-launch, leadership teams want to know how the organization can prove employees are actually more efficient and productive for using it.
Valid ROI Questions
AI digital product development represents a significant financial investment. So post-launch fear is valid.
Leadership teams have questions about ROI. Perhaps the biggest handicap is a lack of understanding about AI apps and how they work. They are not set-and-forget tools.
They are tools that constantly need tuning in order to maximize performance. Continual performance monitoring and tuning are the keys to meeting ROI objectives.
AI Apps Create a Blind Spot
The last thing the software development team wants is a completed and deployed AI app they know is working but has no concrete means of proving its value to decision makers. Without that means, an internal AI app creates a blind spot.
Leadership does not know whether it’s working or not. They don’t know if investing in the app was worth the cost.
Traditional apps, like project management portals and CRM systems, can be monitored fairly easily.
They are straightforward, predictive apps whose use and productivity can be measured by looking at login rates, button clicks, task completion times, and so forth. But that is not possible with AI apps, according to GojiLabs.
As a leader in AI digital product development, GojiLabs builds apps with the understanding that their variables are entirely different. AI operates on a probabilistic model rather than a predictive one.
It must be capable of handling everything from free-form text to shifting and unstructured inputs. And because employees will interact with the software in unpredictable ways, the way it responds could be just as unpredictable.
Proactive Performance Monitoring Is the Solution
Proactive performance monitoring is what minimizes the risk of the deadly blind spot. It looks at key metrics capable of revealing whether an AI app idea proves itself worthy of being permanent. Here are three examples:
- Latency – One of the reasons for investing in an internal AI app is to allow employees to do what they do faster and more accurately. If latency is significant, an AI tool could prove to be too slow. Users might go back to doing things manually, if it is faster.
- Accuracy – Speed and efficiency are never replacements for accuracy. Unfortunately, LLMs are susceptible to data drift and outright inaccuracy, especially if the data that feeds them has not been properly audited and structured. Therefore, accuracy must always be monitored.
- User Perceptions – How employees use an internal app is critically important. By monitoring and analyzing everything from prompt patterns to feedback and work completion rates, the technology team can see where an AI product is delivering value, if at all.
To leadership teams, AI digital product development represents a long-term investment in software that absolutely must work.
But it is not enough to deploy the latest LLM and in aesthetically pleasing UI. Software developers must also create a means to continually monitor performance.
And with performance monitoring come the ongoing adjustments that continually make the product better.
