Consolidating AI Tools: When It Pays Off and When It Doesn't

Consolidating AI tools means reducing the number of paid AI tools active in a company by moving people onto a smaller set of platforms. It pays off when tools overlap on the same use cases and the saving covers migration and training. It does not pay off by default: decide use case by use case, with a simulation.

What does consolidating mean and why is it discussed?

In large companies AI tools accumulate through decentralized purchasing: an assistant in the productivity suite, a second tool for development, a third for writing. The result is fragmented spend, contracts with different expiry dates and little visibility of the whole. The general picture is in how to reduce AI license spend.

Consolidating does not necessarily mean reducing to a single tool. It can mean:

• eliminating tools that duplicate another for the same people;
• choosing a reference tool for general-purpose use cases and keeping a few specialist tools;
• concentrating volumes to renegotiate, where the contract allows.

Consolidation is a means. The end is to spend less for the same value, or get more value for the same spend.

When does consolidation pay off?

Consolidation makes sense when three conditions are met together.

1. Real overlap. Several tools cover the same use case for the same people, and nobody can explain why both are needed. Detect it starting from use cases, not from tools.
2. Equivalent quality. The tool that remains performs the task acceptably, verified on concrete cases and not on impressions.
3. Saving that covers the costs. The annual saving exceeds, within a horizon acceptable to the budget, the one-off costs of migration and training.

If one of the three is missing, reducing inactive seats is almost always the simplest and least risky step: it starts from measuring unused seats.

When does it not pay off?

There are four situations in which consolidation should be postponed or limited.

• Specific use cases. Software development, legal work, data analysis or tasks with particular quality requirements may need a dedicated tool. Forcing these people onto a general-purpose tool can cost more in productivity than is saved on the seat.
• Vendor dependency risk. Concentrating all spend on a single vendor reduces negotiating power and exposes the company to changes in terms, features or service availability.
• High migration and training costs. If workflows are integrated into the tool being replaced, or many people need training, one-off costs can absorb the saving for several years.
• Adoption still unstable. If use is growing or use cases are not yet clear, consolidating early locks in choices that should be revisited.

When in doubt, the prudent answer is to consolidate in stages: one department at a time, with a quality check before extending.

Decision criteria

To avoid decisions of principle, use a grid with explicit criteria, assessed for each use case or user group.

1. Use case coverage: does the candidate tool cover it without loss of quality?
2. People involved: how many active users would change tool?
3. Expected annual saving: difference between current spend and spend in the scenario.
4. One-off costs: migration of workflows, training, support during the transition period.
5. Payback time: one-off costs divided by annual saving.
6. Vendor dependency: share of spend concentrated on a single vendor after the change, and the possibility of exit.
7. Contractual constraints: renewal dates and flexibility on volumes, as in the checklist for renewing AI licenses.
8. Data and compliance risk: where data is processed and with what safeguards, a topic that involves the DPO under the GDPR.

A simple rule: consolidate where criteria 1 and 3 are favorable and payback is compatible with the budget; keep things as they are where criterion 1 is not met.

Hypothetical simulation: two scenarios compared

The example is hypothetical: values are invented for illustration and do not represent real prices of any vendor.

A company has two tools. A: 600 seats at €300 a year, spend €180,000 (600 × €300). B: 200 seats at €360 a year, spend €72,000 (200 × €360). Total spend: €252,000. Of the 200 users of B, 50 have a specific use case and stay on B. The other 150 would be moved to A. Hypothetical one-off costs: €100 per person for training and €12,000 for migrating workflows.

Scenario 1: the 150 users do not already have an A license.

• A rises to 750 seats: 750 × €300 = €225,000.
• B falls to 50 seats: 50 × €360 = €18,000.
• Spend after: €225,000 + €18,000 = €243,000. Annual saving: €252,000 − €243,000 = €9,000.
• One-off costs: 150 × €100 = €15,000, plus €12,000, totaling €27,000.
• Payback time: €27,000 ÷ €9,000 = 3 years.

Scenario 2: 60 of the 150 users already have an A license (double licensing).

• New A seats are needed only for 150 − 60 = 90 people: A rises to 690 seats, 690 × €300 = €207,000.
• B falls to 50 seats: €18,000.
• Spend after: €207,000 + €18,000 = €225,000. Annual saving: €252,000 − €225,000 = €27,000.
• One-off costs: 90 × €100 = €9,000, plus €12,000, totaling €21,000.
• Payback time: €21,000 ÷ €27,000 ≈ 0.78 years, about 9 months.

The same consolidation is unattractive in the first scenario (payback in three years) and very attractive in the second. What makes the difference is real overlap: that is why the analysis starts from user data and not from a price comparison. In the first scenario, moreover, dependency risk must still be considered: after the change A concentrates about 93% of spend (€225,000 ÷ €243,000 ≈ 0.926), against 71% at the start (€180,000 ÷ €252,000 ≈ 0.714).

The role of vendor independence

Deciding whether to consolidate requires comparable data across different tools: spend, seats, usage, use cases. If this data comes only from the vendor proposing to be the replacement, the comparison is biased. The independence of whoever measures is what allows scenarios to be simulated without a commercial interest in the outcome, including the "do not consolidate" scenario.

Even after the choice, keep the ability to switch: data and prompts in exportable formats, documented use cases, an evaluated alternative and clear exit clauses. Consolidation reduces complexity, but it must not become a constraint.

How to proceed

1. Build the inventory of tools, contracts and expiry dates.
2. Map use cases by user group.
3. Identify overlaps and separate specialist cases.
4. Simulate at least two scenarios with one-off costs and payback time.
5. Start a pilot department and check quality and adoption before extending.
6. Measure the effect with a baseline, as explained in how to measure AI ROI.

Where Delphis fits

Delphis is a platform, independent of AI vendors, that connects spend, adoption, impact and regulatory obligations. It reconciles costs and actual usage across several tools, detects overlaps by use case and simulates the saving of each consolidation scenario, including migration costs and payback times. Impact is measured with a before-and-after baseline, and data on people stays aggregated by role or department. For the approach, visit the Cost page.