The 10-20-70 rule: why AI projects fail before the tech.

The 10-20-70 rule is easy to quote and harder to put into a budget. It assigns 10 percent of an AI effort to the algorithm, 20 percent to data and technology, and 70 percent to people, process, and changed ways of working. Small business plans often lean the other way because software is the part with a price tag. The overlooked work is deciding what changes, who owns it, and whether it helped.

What the 10-20-70 rule actually says

The rule belongs to Boston Consulting Group. BCG was using it publicly by November 2020, and its 2025 AI Radar, a survey of more than 1,800 executives, describes the split as 10 percent algorithms, 20 percent data and technology, and 70 percent people, process, and cultural transformation. BCG’s current framing similarly puts most AI-transformation value in workflow redesign, governance, and how people and AI work together.

In BCG’s 2024 survey of 1,000 executives, 26 percent of the surveyed companies had moved AI beyond proof of concept and were generating value; 74 percent were still struggling. Its 2025 survey also found that fewer than a third had trained even a quarter of their workforce on AI. Those findings are about BCG’s surveyed companies, not a measure of every small business. They do point to the same practical gap: buying a tool is easier than changing the job around it.

Why the budget is the mirror image

The 10 percent is easy to buy. A licence has a price, a demo has a date, and a vendor has a proposal. The 70 percent is a harder set of decisions: which workflow matters, who owns it, what changes this week, and how you will know it worked. RAND’s 2024 study, based on interviews with 65 experienced practitioners, named misunderstanding or miscommunicating the problem as the leading cause of AI project failure. That is a process problem before it is a technology problem.

The MIT study of enterprise pilots behind the widely quoted 95 percent figure described a learning gap: tools that did not learn from or fit the workflow where they were placed. Gartner’s abandonment prediction cites poor data quality, unclear business value, and rising costs. Taken together, these sources are a reason to examine the workflow and data as closely as the model choice.

10 percent

The algorithm

The model is the smallest part and the easiest to change.

Which model drafts your replies matters far less than whether anyone reads the draft before it goes out. Models get swapped every few months. The workflow around them lasts years. Spend the least time here and keep the choice reversible: bring your own keys, avoid platforms that lock the model to the workflow, and expect to change it.

At ten peopleI would choose one tool for one step and keep an easy way out if it proves unsuitable.

Where it goes wrongThree months comparing vendors while nobody has written down the workflow the vendor is supposed to fit.

20 percent

Data and technology

Clean records and connected tools, before any model sees them.

At small scale this does not need a data warehouse. It means deciding where the customer record lives, making the intake form feed it, and connecting the two or three tools that touch the same job. Conflicting records can make any automation unreliable, so this is work I would do before adding an AI step.

At ten peopleOne source of truth per kind of record, and the tools that need it can read it without anyone copying.

Where it goes wrongBuying the AI first and discovering the data is in seven inboxes and a spreadsheet that only one person understands.

70 percent

People and process

One workflow, one owner, one number.

Map the workflow as it actually runs, including the workarounds. Name the person who owns it. Write the rule for what the AI may do alone and what needs a check. Measure how long the workflow takes today, then measure it again a month after. McKinsey’s 2025 State of AI found the companies getting real earnings impact from AI were nearly three times as likely to have fundamentally redesigned the workflow, 55 percent against 20 percent. Redesign is the 70 percent.

At ten peopleStart with a whiteboard session, a written rule, a named owner, and a before-and-after number.

Where it goes wrongTreating training as a lunch-and-learn and adoption as an email. If nothing changed on Monday, the 70 percent did not happen.

The 30 percent rule and the 10-20-70 rule are not the same thing

People search for both together, so it is worth separating them. The 30 percent rule is Gartner’s July 2024 prediction that at least 30 percent of generative AI projects would be abandoned after proof of concept by the end of 2025. That was a forecast of abandonment, not a measured outcome. The 10-20-70 rule describes effort. Both are useful reminders to check data quality, business value, and costs before expanding a project.

A third number floats around the same conversations: the claim that 70 percent of change programs fail. McKinsey has repeated it, and it traces back to John Kotter’s work in the 1990s, but a 2011 review in the Journal of Change Management found no valid empirical basis for the figure. Use it as folklore, not evidence. The other figures need care too: BCG reports survey findings, RAND cites a failure estimate, MIT NANDA presents preliminary research, and Gartner made a forecast. They cannot be combined into one failure rate.

The 70 percent, translated for a ten-person business

Enterprise versions of the 70 percent involve change offices and fluency ladders. At small scale the same work is smaller and blunter, and it is where Canadian businesses are visibly thin. Statistics Canada’s 2026 survey found 19 percent of businesses using AI, and of those, only 32 percent had trained employees on it. The CFIB reports 45 percent of small firms using generative AI, mostly ad hoc. I would use those findings as a reason to ask who needs training and support, rather than assume a tool purchase has finished the work.

• Pick one workflow. The one that costs the most hours per week, or the one where mistakes cost real money. Not "the business."
• Draw it on a whiteboard as it actually runs, with the person who runs it in the room. The gap between the documented process and the real one is where the value is.
• Name an owner. One person who can say yes to a change within a week. Without this, the consultant’s clock runs while the decision waits.
• Write the rule before the tool: which steps get automated, which get AI, which keep a person. Our automation vs AI guide is the decision method.
• Measure it today. Minutes per job, days to invoice, calls missed per week. Then measure again in thirty days. Without a baseline, it is harder to tell whether the change helped.

For a small team, write the workflow owner’s name before you write the vendor’s. If you cannot, pause the buying conversation.

What to do Monday

Look back at your last AI conversation, whether it was internal or with a vendor. How much time went to the tool, the data it needs, and what people would do differently after it arrives? If the tool took most of the conversation, that is useful information. The free AI Readiness Check looks at the people-and-process side across six areas and points to the next thing to fix. You can also use the full checklist on paper.

Ten percent is the tool.Seventy percent is Monday.

Common questions

What is the 10/20-70 rule for AI?

It is Boston Consulting Group’s rule of thumb for where the value of an AI effort comes from: about 10 percent from the algorithms, 20 percent from data and technology, and 70 percent from people, processes, and the way work changes. BCG has used it since at least 2020 and restated it in its 2025 AI Radar survey of more than 1,800 executives. It describes effort, not a success rate.

What is the 30% rule in AI?

The 30 percent rule usually refers to Gartner’s July 2024 prediction that at least 30 percent of generative AI projects would be abandoned after proof of concept by the end of 2025, because of poor data quality, inadequate risk controls, escalating costs, or unclear business value. It is a failure prediction, distinct from the 10-20-70 allocation rule, though the causes it names sit in the 20 and 70 percent.

Does the 95 percent figure mean almost every AI project fails?

No. MIT NANDA’s preliminary 2025 report discusses organizations seeing no measurable profit impact from generative AI. That is different from counting failed projects. Its discussion of a learning gap is useful when planning feedback and workflow fit, but the headline should not be treated as the odds for your own project.

What is a common reason for AI project failure?

RAND’s 2024 study, based on interviews with 65 experienced data scientists and engineers, found the most common cause was stakeholders misunderstanding or miscommunicating what problem the AI was supposed to solve. Poor data quality, chasing technology rather than a problem, and underinvestment in the surrounding infrastructure followed. All four are process failures, not model failures.

Is it true that 80% of AI projects fail?

RAND’s 2024 report cites an estimate that more than 80 percent of AI projects fail; its own interviews investigate causes. That does not establish a failure rate for small businesses. BCG’s survey findings and MIT NANDA’s preliminary return-on-investment findings also use different samples and definitions. Read the method before applying a percentage to your plans.

Explore our AI consulting approach, starting with the process and the people doing the work.