How AI Is Changing the Way I Support Mobility Negotiations

For years, mobility cost optimization has depended heavily on experience, data quality, and knowing which questions to ask. A carrier proposal may look attractive on the surface, but the real answer is usually hidden in the details: usage patterns, plan mix, overage exposure, device behavior, contract terms, and the customer’s actual business needs.

Recently, I have been using AI to support mobility analysis and contract negotiation work, and the impact is becoming very real.

AI is not replacing domain knowledge. In fact, the opposite is true. The real value comes when domain knowledge, intuition, and experience are combined with AI as a support tool. The expert still needs to know what matters, what looks suspicious, what assumptions need to be tested, and how to interpret the results. AI helps accelerate the process, organize the analysis, and generate repeatable workflows.

One example is a recent request to evaluate a carrier proposal. Verizon was proposing a $60 unlimited plan, but certain conditions had to be met. The question was simple: should the customer agree to these terms?

On the surface, a $60 unlimited plan may sound attractive. But in mobility negotiations, the better question is not “Is this a good rate?” The better question is: “Is this the right structure for this customer’s actual usage?”

Using the available datasets, we were able to quickly analyze whether the proposal made financial sense. We could compare the proposed unlimited plan against the current plan structure, review usage trends, estimate cost impact, and identify which lines would benefit from unlimited versus which lines may become more expensive. Instead of relying only on a high-level rate comparison, we could build a much deeper view of the proposal.

AI helped speed up that process. It helped frame the questions, organize the logic, create charts, and test different scenarios. When an analysis became repeatable, I could ask AI to help create a Python script so the same type of analysis could be run again in the future with new data.

That is where the opportunity becomes even more powerful.

In the past, a one-time analysis might have taken significant manual effort. Now, once the logic is validated, it can become part of a repeatable analytical process. That means faster turnaround, more consistency, and more time spent on interpretation rather than manual data preparation.

For mobility negotiations, this can be extremely valuable. A customer may receive a proposal with attractive headline pricing, but the true value depends on the details. AI-supported analysis can help answer questions such as:

Should unlimited plans be applied to all lines, or only high-usage lines?

Are lower-usage lines better left on shared data or lower-cost plans?

What are the hidden trade-offs in the proposed terms?

What conditions must be met before the offer becomes financially attractive?

What alternative deal points should be negotiated?

How does the proposal impact cost over 12, 24, or 36 months?

These are not generic AI questions. These are domain-specific business questions. The quality of the answer still depends on the experience of the analyst asking the question.

That is the key lesson for me: AI becomes much more useful when it is guided by someone who understands the business context.

The customer feedback has also been encouraging. Customers appreciate not just the speed of the analysis, but the depth. They can see the financial impact more clearly. They can understand the trade-offs. They can walk into negotiations with better support and more confidence.

In my opinion, this is where AI will create meaningful value in professional services. It will not simply be about generating summaries or automating basic tasks. The bigger opportunity is helping experienced professionals produce better work faster.

For mobility expense management, AI can support:

Data analysis

Scenario modeling

Contract review

Rate benchmarking

Optimization recommendations

Negotiation support

Chart creation

Repeatable Python-based workflows

But the human role remains critical. AI can help generate outputs, but the expert still needs to challenge the assumptions, validate the logic, and decide what matters for the customer.

The future of this work is not AI alone. It is domain expertise plus AI.

That combination can improve the quality of analysis, reduce turnaround time, and create better outcomes for customers. For mobility negotiations, where the difference between a good deal and a bad deal can be hidden in thousands of lines of usage and billing data, that combination is becoming a major advantage.

The more I use AI in this process, the clearer it becomes: AI is not just a productivity tool. When used correctly, it can become an analytical partner that helps turn experience into repeatable, scalable insight.