Many finance teams today use AI mainly to do a job faster: reading invoice data, speeding up reconciliation, preparing a report in less time. These gains are real and measurable. But the question asked in board meetings is often a different one: "What does the AI investment contribute to the company's ability to make better decisions?"
The survey Gartner published on July 20, 2026 puts a number on the gap between these two views. According to a survey of 204 finance executives conducted in March 2026, 45% of AI investments in finance lean toward productivity and only 20% toward decision quality. In this article, we look at what this imbalance means and how the investment portfolio can be rebalanced toward decision quality.
We previously examined which finance processes deliver returns quickly in our article on finance automation. This article has a different focus: beyond speed gains, what AI contributes to finance's role in decision-making.
The data: where is the investment going, and what does the board expect?
The distribution Gartner shared falls into three groups. Of the participants, 45% say their investments lean toward productivity, 35% say they are balanced, and 20% say they lean toward decision quality.
According to Gartner, the problem is that this distribution does not line up with the priorities of boards. While finance executives put productivity- and efficiency-focused use cases first, boards give more weight to investments that support growth, improve decision-making and provide a competitive advantage. The result is what Gartner calls a "perception gap": the finance team reports progress in its use of AI, while the board sees limited strategic impact.
The ceiling on productivity gains
One finding in Gartner's assessment stands out: although investments that raise individual productivity or streamline transactional processes deliver measurable efficiency in the finance function, their returns have a ceiling for most organizations. Once a task has been sped up, the benefit generally plateaus if that speed does not change a broader business decision or meaningfully transform the way finance works.
In the same survey, functions that invest in AI initiatives creating new value propositions, products or markets were found to be more than twice as likely to report high realized value. This does not mean that productivity investments are unnecessary; it shows that a portfolio made up only of them limits the value.
What does decision quality mean in practice?
Decision quality may seem like an abstract concept, but Gartner's August 2026 assessment of forecasting makes it concrete. AI can update forecasts more often than ever before and generate new scenarios and early-warning signals. According to Gartner, however, the speed of forecasting has begun to outpace executives' capacity to interpret change with confidence.
For this reason, Gartner stresses that success should be measured not by speed but by decisions made with confidence under uncertainty. The recommended tools are as follows:
- Not every update calls for a decision. Which forecast changes require action and which only require monitoring should be determined in advance.
- Explainability builds trust. Forecast outputs should be tied to recognizable business drivers and transparent assumptions; a change that cannot be explained erodes trust.
- Guardrail thresholds separate signal from noise. Decision triggers, materiality thresholds, confidence intervals and challenge processes reduce overreaction to short-term fluctuations.
This approach shows that AI aimed at decision quality is not just a matter of building a model; it has to be designed together with the decision process, thresholds and responsibilities.
The role of human judgment
Decision quality also depends on the finance team's ability to question AI output. Gartner's September 2026 assessment notes that even trained finance employees are unsure how to apply AI to their daily work. The recommended path is to redesign the work: while AI prepares the first draft, employees review the output, question the assumptions, examine the exceptions and explain what the numbers mean. This division of labor is also a precondition for decision-quality use cases to work well.
In the same assessment, Gartner recommends tying learning to friction in the workflow: repetitive tasks, process bottlenecks and manual handoffs give employees the opportunity to apply AI to problems they already need to solve. The value of productivity projects shows up here as well; it is in this work that the team learns where the tool is useful and where it goes wrong before moving on to decision support.
A practical path: a checklist for rebalancing the portfolio
The steps below are a general framework derived from the findings in the sources; each organization needs to adapt it to its own decision processes and data maturity.
- Classify the current portfolio. Label each AI initiative in finance as "productivity," "balanced" or "decision quality." This three-way distinction, which Gartner uses, makes it easier to discuss the distribution with the board in a shared language.
- Put the outcomes the board expects in writing. List enterprise-level goals such as growth, capital allocation, risk and competitive position; match each initiative to the goals it contributes to.
- Identify decision-quality candidates. The areas Gartner points to are improving decision-making, running scenario analysis, identifying growth opportunities and creating reusable assets such as data, models and knowledge.
- Change the metrics. Instead of the number of pilots or hours saved, define metrics that track enterprise-level impact; cover both near-term benefit and long-term strategic value.
- Write the decision rules in advance. For forecasting and scenario tools, clarify the action thresholds, confidence intervals and challenge process before use begins.
- Make explainability a requirement. A model output should not go into a decision meeting if it cannot be explained which business driver has changed.
- Tie the productivity gain to a decision. Define which analysis or decision the time freed up in a faster process will be redirected to; otherwise the benefit hits the ceiling.
- Rebalance the portfolio periodically. Gartner states that the definition of success is a moving target that needs to be re-evaluated as business needs change.
Conclusion
The first wave of AI in finance delivered productivity, and those gains are valuable. But Gartner's data shows that a large share of investment is concentrated in this area, while boards expect a contribution to decision quality and growth. The way to close the gap is not to stop productivity projects; it is to add decision-quality initiatives to the portfolio, back them with explicit decision rules and measure success by enterprise-level outcomes.
From Orient Global's perspective, a sound starting point is to classify the current AI portfolio honestly under these three headings.
Why logistics experience matters in decision-quality work. Logistics is a daily exercise in deciding under uncertainty. Reorder points, safety stocks and exception alerts settle in advance when a change calls for action and when it can simply be absorbed, which is the discipline that step 5 of the checklist asks of finance. One of our consultants brings more than 30 years in operations, logistics and supply chain: at a technology distribution company of roughly one billion dollars in scale, he ran logistics, warehousing, technical service and administrative operations end to end, building and leading teams of 100+ people and delivering lasting gains in operational performance through KPI-driven programs. He has also designed and directed large-scale logistics and distribution networks across leading retail, logistics and distribution organizations, managing nationwide warehouse and transport operations and network and capacity planning, which are decisions built on forecasts and scenarios. An industrial engineer, he now brings that supply-chain discipline to SMEs through AI and automation. Meet our consultants.
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