Half of Companies Now Generate Value from AI—How to Turn Experiments into Workflow Redesign

A sales representative opens a customer inquiry in the morning. AI reviews past interactions and product information, then drafts a reply. The representative checks and sends it, deciding on any next steps. Changing work through AI starts with rethinking a single workflow like this.

According to Boston Consulting Group’s (BCG) “2026 Applied AI Index,” a survey of more than 1,300 executives and senior leaders across over 20 industries found that 50% of companies are already generating value from AI. A growing number are also moving from experimentation into production.

Simply handing out AI tools, however, does not guarantee results. The survey finds that leading companies focus their investments, redesign business processes, and scale across the organization while measuring results. What makes the difference is where and how they change work, rather than the number of tools they have deployed.

“Creating Value” and “Enterprise-Wide Adoption” Are Different Stages#

Among the companies surveyed, 7.5% were classified as “Future-Built,” the highest level of AI maturity. Another 41% were scaling AI deployment and finding value. Although half of companies are achieving some results with AI, enterprise-wide adoption remains limited.

A majority of Future-Built companies—61%—had moved from pilots to multiyear, enterprise-wide programs. BCG’s survey reported that these leaders achieved 2.4 times the revenue growth of companies lagging in AI adoption.

This does not mean that adopting AI automatically increases revenue. Leading companies select initiatives aligned with business priorities, assign owners, and define measures of business value. Crucially, they move beyond individual team experiments and extend proven workflows across multiple departments.

The survey found that Future-Built companies and those scaling deployment generated two to three times as much value from AI in both cost reduction and revenue growth as lagging companies. Industry differences alone cannot explain the gap: Future-Built companies existed in all 20 industries surveyed.

Rethink Workflows Before Adding Tools#

Future-Built companies expect 70% of the value they gain from AI to come from redesigning existing processes and creating new ones. This means looking beyond the accuracy of AI responses to reconsider who receives which information, what decisions they make, and what happens next.

For customer inquiries, for example, one approach is to have AI draft replies. Going a step further, companies can redesign the entire workflow, from classifying inquiries and surfacing relevant internal information to routing them to staff and recording the outcome. Distinguishing tasks that require human judgment from those AI can handle also makes it easier to track changes in time and quality after deployment.

There is no need to change the entire organization at once. Start with work that places a heavy operational burden on staff and produces easily measurable results. Recording baseline measures such as processing time, rework rates, and inquiry resolution rates allows comparisons after deployment. If results fall short, revisit the procedures and data as well as the AI model.

Focus Investment on a Few Use Cases First#

BCG’s survey found that core business functions accounted for 54–78% of the value generated by AI. Technology/IT and R&D ranked first or second as drivers of AI value across every industry, while marketing, sales, and customer service also delivered measurable value.

Choosing a few important workflows and working on them in depth makes results easier to assess than spreading adoption thinly across many departments. The approach can be outlined as follows.

• Choose work tied to business goals. Decide first whether the aim is to reduce costs, respond faster, or increase sales opportunities.
• Measure the current state. Record predeployment baselines such as time spent, processing volumes, and frequency of corrections.
• Run a small trial and assess the entire workflow. Track the time people spend reviewing outputs and handling exceptions, as well as the AI’s output.
• Extend proven use cases to other teams, together with shared rules and infrastructure.

Choose use cases based on your company’s priorities rather than their popularity. When departments run similar experiments independently, rules for data handling and safety can diverge, making results difficult to compare. Narrowing the initial focus and aligning ownership and evaluation criteria lays the groundwork for expansion.

Deploying Agents Requires Data and Clear Accountability#

AI agents are expected to perform more work autonomously. In the survey, about 42% of companies predicted that agents would act autonomously by 2030. Agentic AI’s share of AI-generated value rose from 17% in 2025 to 22% in 2026, and BCG forecasts that it will reach 39% in 2030.

As agents take on more work, however, the quality of the data they access and controls on their actions become more important. Preventing actions based on incorrect information or unexpected operations requires defining permitted data and permissions, along with conditions for human review of results.

Among Future-Built companies, 95% were pursuing data transformation, and more than 66% were building enterprise-wide AI platforms. This foundation does not necessarily mean standardizing on one vendor’s products. It means establishing shared architecture and controls that make AI use easier to manage across departments.

Workforce and role design also deserve attention. Among Future-Built companies, 55% were planning for humans and autonomous AI agents to work together. Only 11% of surveyed companies expected AI to replace existing jobs, while 89% expected it to create new ones. These are forecasts and expectations; the roles that actually grow or shrink will vary by company and workflow. Involving frontline staff early and clarifying responsibility for decisions and exception handling are essential.

Start Tomorrow with One Workflow#

According to the survey, the share of stagnating companies fell from 14% in 2025 to 4.5% in 2026. More companies are moving beyond simply testing AI to gaining value across multiple business units. That does not guarantee the same results for your company. You need to assess your workflows and data, then measure outcomes.

Start by choosing one time-consuming workflow and writing down its current processing time and problems. Next, decide which tasks AI will handle, which require human judgment, and which metrics to track after deployment. Review the results of a small trial before deciding whether to continue, change the process, or expand to another department.

Source: https://japan.zdnet.com/article/35253550/