AI Is Reshaping Consulting – How Much Can a Georgian Company Save?
The clearest effect of artificial intelligence on consulting is not yet a lower price tag. It is a redesign of the work itself. Research, document review, an initial financial model, meeting synthesis, and the first draft of a presentation once consumed many analyst hours. Generative AI can compress parts of that work into minutes. The commercial question is therefore no longer whether consultants will use AI, but who captures the productivity gain: the adviser who delivers the same scope faster at the same fee, or the client who buys fewer hours.
This matters acutely in Georgia, where many companies are too small to maintain full strategy, research, data, or transformation teams and periodically rely on outside experts. AI can bring selected tasks in-house, yet a software subscription does not create savings by itself. The company still needs a task map, secure data handling, quality control, and a commercial agreement that turns less time into less spending.
What the evidence actually shows
In a Harvard Business School field experiment, consultants using AI completed 12.2% more tasks and worked 25.1% faster on average, with substantially higher quality on tasks within the technology’s capability frontier. The caveat is essential: performance could deteriorate when users relied on AI outside that frontier. The 25.1% figure is therefore not an automatic discount for every engagement; it is evidence of task-specific productivity under controlled conditions.
An OECD survey across seven countries found that 31% of participating SMEs used generative AI. Among users, 65% reported better employee performance, 33% reported lower workloads, and only 14% reported reduced reliance on external contractors. Meanwhile, 83% saw no change in overall staffing needs. In other words, time and quality gains often arrive before cash savings. Spending falls only when the company also changes its workflow, sourcing, or contract.
| Indicator | Result | Interpretation |
|---|---|---|
| Consultant completion speed | 25.1% faster | Applies to selected tasks inside AI’s capability frontier |
| Tasks completed | 12.2% more | More output per unit of time, not automatically a smaller invoice |
| SMEs using generative AI | 31% | Adoption is meaningful but not universal |
| AI-using SMEs reporting lower workload | 33% | Time savings often precede budget savings |
| Reduced reliance on external contractors | 14% | Insourcing is possible, but remains a minority outcome |
| No change in overall staffing need | 83% | The dominant effect is work redesign, not immediate workforce reduction |
Sources: Harvard Business School, Navigating the Jagged Technological Frontier; OECD, Generative AI and the SME Workforce (2025). Results come from different studies and are not directly comparable.
A transparent savings model
According to BTU researchers – Consider an illustrative Georgian company that purchases 1,000 hours of external consulting a year at a client cost of GEL 150 per hour, for a baseline budget of GEL 150,000. These are modeling assumptions, not an official Georgian market average. The scenarios vary the share of work that AI can genuinely accelerate, the time reduction on that share, and the full first-year cost of licenses, implementation, training, data protection, and human review.
Gross value of saved time = baseline hours × AI-addressable share × time reduction × hourly cost. First-year net saving = gross value − implementation, tooling, and quality-assurance cost.
| Scenario | AI-addressable | Time reduction | Hours saved | Gross value | AI / implementation / QA | Net saving | Budget % |
|---|---|---|---|---|---|---|---|
| Conservative | 30% | 15% | 45 | GEL 6,750 | GEL 5,000 | GEL 1,750 | 1.2% |
| Base | 45% | 25% | 112.5 | GEL 16,875 | GEL 8,000 | GEL 8,875 | 5.9% |
| Intensive | 60% | 35% | 210 | GEL 31,500 | GEL 12,000 | GEL 19,500 | 13.0% |
The first-year net saving ranges from GEL 1,750 to GEL 19,500. The base case saves 112.5 hours and GEL 8,875, equal to 5.9% of the original budget. The intensive scenario’s 35% time reduction is an explicit modeling assumption, not an empirically established average for all consulting work. Crucially, an hour saved is a financial saving only if the buyer stops purchasing it, reallocates an employee to higher-value work, or materially shortens the project.
The contract determines who captures the gain
According to BTU researchers – Under time-and-materials billing, fewer verified hours can reduce the client’s invoice. Under a fixed fee, the adviser may capture most of the efficiency by delivering faster at the old price. The client may still benefit from speed or quality, but not from direct budget relief. This is why procurement design becomes as important as model selection in the AI era.
| Commercial model | Likely beneficiary | What the client should do |
|---|---|---|
| Time and materials | Client, if lower hours reduce the invoice | Record baseline hours, AI use, review time, and actual billing |
| Fixed fee | Often the adviser | Negotiate a lower price, faster delivery, or a measurable additional outcome |
| In-house team plus AI | Client, if external work is genuinely eliminated | Assign ownership, create a secure environment, and retain expert review |
| Outcome-based fee | Gain is shared | Tie compensation to an agreed result and quality threshold |
Where AI belongs and where it does not
The quickest savings usually come from repetitive, verifiable work: first-pass research, long-document summaries, meeting-note structuring, comparison of public competitor information, standard report drafts, simple data-cleaning steps, and presentation outlines. AI does not remove the professional; it lowers the cost of first-pass processing.
High-stakes judgments are different: legal or tax opinions, transaction valuations, cybersecurity architecture, workforce-reduction plans, regulatory engagement, or strategy under incomplete information. Responsibility, contextual knowledge, and independent judgment must remain human. A cheap first draft can become an expensive error when review and rework are excluded from the business case.
How a Georgian company should measure the result
A practical starting point is a controlled four-to-six-week pilot in one process. Before launch, record baseline cycle time, external invoices, error and rework rates, and the quality of approved outputs. During the pilot, separately track licenses, integration, training, security, human review, and correction costs.
The core metric is not the number of generated documents, but the total cost per approved outcome. If a report falls from 100 hours to 75, yet a senior expert spends 20 additional hours correcting it, the real gain is only five hours. If faster analysis brings a decision forward by two weeks, that time may have business value, but it should be reported separately rather than disguised as cash saving.
| Metric | Formula / definition | Purpose |
|---|---|---|
| Net hours saved | Old total time − new total time, including QA and rework | Captures hidden review labor |
| First-year net saving | Actual cost removed − full AI cost | Measures the financial result |
| Cost per approved outcome | Total cost ÷ accepted outputs | Keeps quality attached to volume |
| Error and rework rate | Outputs requiring correction ÷ all outputs | Detects quality deterioration |
| Saving-realization ratio | Actual cost removed ÷ monetary value of saved time | Shows whether time became cash |
Management conclusion
AI will not eliminate consulting. It lowers the value of standard analysis and first drafts while increasing the premium on problem framing, difficult contextual judgment, decision defense, and accountability. A Georgian company will therefore save more through precise task decomposition and contract redesign than through choosing the cheapest AI subscription.
According to BTU researchers – In our transparent model, the base first-year saving is GEL 8,875, or 5.9% of a GEL 150,000 budget. The full range- GEL 1,750 to GEL 19,500-shows that three variables matter more than the technology label: how much work is genuinely addressable, what safe implementation costs, and who owns the value of the saved hours. Without answers to those questions, AI may increase productivity while leaving the client’s budget unchanged.
Sources and methodological note
Principal sources: Harvard Business School, Navigating the Jagged Technological Frontier; OECD, Generative AI and the SME Workforce (2025); OECD, The Effects of Generative AI on Productivity, Innovation and Entrepreneurship; NBER, Generative AI at Work; and Financial Times reporting on AI-driven changes in consulting spending and contracting. The GEL 150,000 example, GEL 150 hourly rate, implementation costs, and scenario parameters are BTUAI modeling assumptions. They are not Georgian market averages, forecasts, or observations from a specific company.