{"id":77366,"date":"2026-11-05T16:33:44","date_gmt":"2026-11-05T19:33:44","guid":{"rendered":"https:\/\/actiosoftware.com\/?p=77366"},"modified":"2026-11-05T16:33:45","modified_gmt":"2026-11-05T19:33:45","slug":"workflow-was","status":"publish","type":"post","link":"https:\/\/actiosoftware.com\/en\/blog\/workflow-ia\/","title":{"rendered":"Workflow IA: how to apply AI to strategy execution\u00a0"},"content":{"rendered":"

The IA workflow no longer was an investment in productivity and became a matter of governance. In the 2025 research of the McKinsey<\/a>, 39% of respondents attribute some impact of AI to EBIT, and the majority place it below 5%.  <\/p>\n\n\n\n

At the same time, the pressure for efficiency increases, and the topic begins to take center stage on the agendas of the executive committees. <\/p>\n\n\n\n

If you already master the concept, the relevant question is another: what separates an isolated pilot from an operation that delivers results on a scale? In practice, the challenge has ceased to be technological and has become one of design, responsibility, and measurement. <\/p>\n\n\n\n

Throughout the text, you will see what differentiates an AI workflow from traditional automation, why the return is often disappointing, how to structure and prioritize the flows, how to implement them with governance, which metrics to monitor, and what the role of Actio IA is. <\/p>\n\n\n\n

What distinguishes an AI workflow from traditional automation?<\/strong> <\/h2>\n\n\n\n

An AI workflow adds context interpretation to the task flow: in addition to executing predefined steps, it analyzes data, detects deviations, prioritizes actions, and recommends decisions. Traditional automation follows fixed rules, while the AI-powered flow connects to the expected outcome and learns from execution. <\/p>\n\n\n\n

This distinction changes the starting point of the project. In a classic article from the Harvard Business Review<\/a>, Michael Hammer argued that investing in technology just to automate existing processes is disappointing. Work needs to be rethought before it is accelerated. <\/p>\n\n\n\n

Dimension<\/strong> <\/td>Traditional automation<\/strong> <\/td>Workflow IA<\/strong> <\/td><\/tr>
Decision logic <\/td>Fixed and conditional rules <\/td>Contextual analysis and recommendation <\/td><\/tr>
Exceptions <\/td>Sent to a person <\/td>Classified, prioritized, and explained <\/td><\/tr>
Link with the strategy <\/td>Indirect <\/td>Direct to indicators and objectives <\/td><\/tr>
Evolution <\/td>Manual maintenance of the rules <\/td>Improves with the execution history <\/td><\/tr>
Role of the manager <\/td>Configure and monitor <\/td>Validate, decide and correct the route <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The Intelligent automation<\/a> It acts on data, indicators and responsible parties, and not just on isolated tasks, which explains why it depends on a consistent management model. <\/p>\n\n\n\n

Why workflow automation with AI does not scale in most companies?<\/strong> <\/h2>\n\n\n\n

Because technology is usually deployed on processes that have not been redesigned. The 2025 studies show that deep process review, engaged leadership, and value measurement distinguish companies that scale from those that remain in pilots without clear financial returns. <\/p>\n\n\n\n

Source<\/strong> <\/td>Evidence<\/strong> <\/td>Implications for the manager<\/strong> <\/td><\/tr>
McKinsey (Nov\/2025) <\/td>Only about a third started the scale at the company; female leaders are almost three times more likely to have fundamentally redesigned their workflows. <\/td>Adoption is not a result. <\/td><\/tr>
BCG<\/a> (set\/2025) <\/td>Only 5% capture AI value on a scale, and 60% report minimal gains. <\/td>Maturity has become a competitive differentiator <\/td><\/tr>
Gartner<\/a> (June\/2025) <\/td>More than 40% of AI agent projects will be canceled by the end of 2027 <\/td>Choose use cases with measurable value <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The three findings converge on one point: the bottleneck is rarely in the AI model. It lies in the design of the flow, the quality of the data, and the absence of responsible parties and metrics. <\/p>\n\n\n\n

Execution: the link that AI does not replace<\/strong> <\/h3>\n\n\n\n

Kaplan and Norton<\/a> They argue that organizations fail less when formulating the strategy than when translating it into operation.  <\/p>\n\n\n\n

Logic applies to AI: without one Enterprise resource planning<\/a> that with objectives, indicators and initiatives, it produces analysis without action. <\/p>\n\n\n\n

If execution is the weak link, AI needs an organized foundation. Therefore, the next step is to define which layers this model relies on. <\/p>\n\n\n\n

How to structure the architecture of an IA workflow in layers?<\/strong> <\/h2>\n\n\n\n

It is structured in five layers: strategy, data and indicators, execution with responsible parties, AI for analysis and recommendation, and governance with an audit trail. AI only delivers value when it operates on consistent and connected, truly integrated lower layers within the company\u2019s management, rather than as a standalone tool. <\/p>\n\n\n\n

Layer<\/strong> <\/td>Function<\/strong> <\/td>Control question<\/strong> <\/td><\/tr>
1. Strategy <\/td>Objectives, OKRs, and BSC <\/td>What objective does this flow respond to? <\/td><\/tr>
2. Data and indicators <\/td>Reliable and integrated KPIs <\/td>Who owns each piece of data? <\/td><\/tr>
3. Execution <\/td>Stages, deadlines and responsible parties <\/td>Is adherence traceable? <\/td><\/tr>
4. AI <\/td>Analyze, explain, and recommend <\/td>Which decision does she support? <\/td><\/tr>
5. Governance <\/td>Human validation and auditing <\/td>Who approves and registers? <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

This consistency transforms the Process flow<\/a> in a living system, and not on a static map. In complex structures, a Corporate governance system<\/a> define jurisdictions, prioritization criteria, and responsibilities. <\/p>\n\n\n\n

From a technical standpoint, the most robust workflow automation is one where each AI recommendation leaves a record of origin, the person responsible for validation, and the result obtained. <\/p>\n\n\n\n

With the layers defined, the next step is to choose which flows receive the AI first. <\/p>\n\n\n\n

Where to start the AI workflow: which flows to prioritize?<\/strong> <\/h2>\n\n\n\n

Prioritize recurring workflows linked to strategic indicators, with accessible data and assigned owners. Start where deviation is frequent and the cost of delay is high, such as results analysis, action plans, and risk management, and only then expand the scope to other areas. <\/p>\n\n\n\n

To decide, evaluate each candidate on five criteria: <\/p>\n\n\n\n