AI in SMEs: technology is moving ahead, but who is really steering it?
- Apr 10
- 9 min read
Updated: Apr 27
Your consultant has sold you an AI project. Your CIO is already overloaded. And your teams are watching the train leave the station. The real issue may no longer be the technology. It may be the lack of a clear owner.
What business leaders are facing right now
For many SMEs and mid-sized companies, artificial intelligence has entered the business in small, scattered ways: a content generation tool within the marketing team, an assistant used by sales, an automation tested by finance, a data project led by IT. Adoption is accelerating, sometimes faster than governance.
That is where the risk starts to appear.
AI can certainly improve productivity, streamline processes, accelerate data analysis and transform customer relationships. Without clear leadership, however, it can also create dispersion, unrealistic expectations, poorly framed spending and internal disappointment.
For the CEO or managing director of an SME or mid-sized company, the central challenge is broader than identifying the right tools. The real task is to install a governance model capable of turning isolated experiments into tangible results.
This is precisely where an interim manager specialising in transformation can play a decisive role.
AI is taking hold in SMEsArtificial intelligence is no longer reserved for large corporations. French SMEs and mid-sized companies are now directly concerned.
According to the study published by Bpifrance Le Lab in June 2025, based on a survey of 1,209 SME and mid-sized company leaders, 58% of respondents believe that AI represents a medium-term survival issue for their business. By the end of 2025, 55% of very small businesses and SMEs said they were using generative AI tools, compared with 31% a year earlier, a shift Bpifrance described as historic.
This acceleration reflects genuine awareness. It also reveals a deeper difficulty: many companies are already using AI, yet only a limited number have a sufficiently structured framework to generate measurable value from it.
In the same Bpifrance study, 57% of business leaders said they had no formalised AI strategy. The white paper published by Siparex and Bpifrance in April 2026 also cites a McKinsey study showing that more than 80% of organisations that invested in generative AI had seen no tangible financial impact, mainly because use cases had not been properly prioritised and user adoption remained insufficient.
This gap matters. Business leaders do not need another presentation on AI’s potential. They need an operating model to select the right use cases, engage teams, steer decisions and measure outcomes.
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The main weakness: fragmented leadership
In many companies, the first AI initiatives are launched through enthusiasm or pressure. One department tests a solution. A provider offers a diagnostic. The IT team explores a possible architecture. Senior management asks for a roadmap. Business teams expect quick wins.
Each party has a legitimate intention, yet the overall picture often remains fragmented.
The problem emerges when no one connects strategy, processes, data, tools, skills and adoption on the ground. The company can then accumulate initiatives without building a coherent trajectory.
A poorly governed AI project tends to create familiar problems: vague scopes, different expectations across functions, teams that test without transforming, providers delivering recommendations that are difficult to operationalise, and senior management forced to arbitrate technical or organisational issues that should have been framed earlier.
The issue becomes primarily managerial. The company needs someone able to connect senior leadership, IT, business functions, HR, finance and external partners. This role requires cross-functional legitimacy, execution capability and a concrete understanding of operational constraints.
Why the CIO cannot carry the AI transformation alone
Assigning AI to the CIO or IT Director may seem logical at first. Information systems, data, cybersecurity and technical integration all fall within their remit.
However, an AI transformation reaches far beyond IT.
It affects business processes, working methods, the quality of data used every day, expected skills, decision-making practices and sometimes the commercial or industrial organisation itself. It can also challenge customer relationships, administrative productivity, knowledge management, sales performance, financial management and HR practices.
The SME and mid-sized company barometer published by implid and L’Entreprise du Futur in the first quarter of 2025, based on 1,000 business leaders, illustrates this clearly: for 53% of respondents, the main challenge in integrating AI is training and adapting skills. For 49%, the issue is integrating new tools with existing systems.
These two dimensions show that IT is essential, yet cannot carry the whole project alone.
A successful AI project requires leadership that goes beyond information systems. It means aligning business teams, prioritising use cases, clarifying responsibilities, addressing internal resistance and linking initiatives to the company’s economic objectives.
The CIO can act as the technical guardian. They cannot always become the sole conductor of a transformation affecting every function.
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What an interim manager brings to an AI transformation
An interim manager specialising in transformation intervenes with a different remit from that of a consultant.
Their role extends beyond producing an analysis, a mapping exercise or a roadmap. They step into the organisation to frame, arbitrate, coordinate and move projects forward within a defined timeframe.
Their first contribution is clarity. In the opening weeks, they may assess the company’s real maturity: data quality, level of process digitalisation, existing uses, business expectations, IT capacity, legal or compliance risks, available skills, management barriers and economic priorities.
This phase usually prevents AI from becoming a catalogue of tools. It brings the focus back to the company’s business priorities: reducing delays, improving productivity, making forecasts more reliable, accelerating content production, automating selected tasks, improving customer support, strengthening financial management or simplifying internal processes.
The Siparex-Bpifrance white paper also stresses the importance of a progressive method: aligning senior leadership, launching initial initiatives, prioritising use cases and then industrialising them.
This is exactly the kind of approach an interim manager can implement when joining an organisation that wants to move quickly while keeping its resources focused.
Choosing the right use cases before multiplying tools
Every company now wants to “do AI”. Yet not every idea deserves the same level of investment.
An effective AI transformation starts with rigorous selection of use cases. Some projects can deliver quick gains: automation of administrative tasks, AI-assisted document generation, automated meeting notes, support for sales teams, use of internal knowledge bases, improved reporting or recurring data analysis.
Other projects are more complex. They require reliable data, deeper integration with existing systems, more intensive change management or an evolution of core business processes.
The interim manager’s role is to establish this hierarchy. They distinguish short-term useful initiatives, projects to structure in a second phase and attractive ideas that remain premature.
This prioritisation protects the company from two common pitfalls: showcase projects that produce impressive demonstrations with little operational value, and over-ambitious programmes that absorb significant energy despite being too broad for the organisation’s actual maturity level.
For an SME or mid-sized company, the right AI trajectory must remain pragmatic. It must produce visible results, strengthen team confidence and gradually prepare for a wider transformation.
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Installing clear governance between leadership, business teams and IT
A successful AI transformation depends on simple, robust governance.
The executive committee must be able to decide priorities. Business teams must express their needs, test uses and take ownership of changes. IT must secure the architecture, data, access and integration. HR must anticipate training needs and skills impact. Finance must monitor investments and expected gains.
Without coordination, each function advances according to its own logic. With clear governance, the company turns dispersed initiatives into a shared trajectory.
The interim manager brings cross-functional authority. They can organise arbitrations, clarify roles, structure steering committees, monitor decisions and prevent AI from becoming the protected territory of a single function.
This position is especially valuable in SMEs and mid-sized companies, where teams are often already mobilised on multiple priorities: growth, recruitment, digital transformation, commercial pressure, cash-flow tensions, regulatory change or internal reorganisation.
AI requires time, method and decision-making capacity. An interim manager provides that capacity at a point when the company cannot afford to slow down.
Turning AI into operational results
Business leaders expect concrete effects. They want to know where AI can genuinely improve performance, reduce costs, speed up certain tasks or strengthen service quality.
That expectation is healthy. It pushes the organisation away from broad innovation rhetoric and back towards operational reality.
In a well-framed assignment, the first results can take several forms: a map of priority use cases, a ranked project portfolio, a pilot launched on a limited scope, a business team trained, a simplified process, a more reliable dashboard or a reduction in time spent on repetitive tasks.
These results do not transform the company by themselves. They do, however, create a crucial effect: they rebuild confidence. Teams see that AI can respond to real irritants. Senior management gains a clearer view of which investments to continue. Business teams better understand what is expected of them.
The interim manager ensures that every initiative is tied to a specific objective: time saving, quality, reliability, productivity, customer experience, analytical capability or risk reduction.
This discipline prevents success from being measured by the number of tools deployed. The true measure remains the impact produced on the organisation.
Preparing the organisation before recruiting a permanent profile
Some companies consider recruiting a Chief Data Officer, Digital Director, Transformation Director or AI lead. This may be the right choice, although timing matters when priorities remain unclear.
Recruiting too early can create a classic difficulty: appointing a permanent profile to an unclear remit, without established governance, without prioritised use cases and without genuine alignment between business functions.
An interim manager can step in upstream to prepare the ground. They help senior management specify the need, structure the roadmap, identify the required skills, frame budgets, organise governance and define the right profile for the next stage.
This approach reduces recruitment mistakes. It also avoids creating a theoretical role disconnected from the company’s actual uses.
In some SMEs and mid-sized companies, the interim assignment may even show that a full-time recruitment is not immediately necessary. A lighter organisation, supported by clear governance and well-identified business relays, may be sufficient in the first phase.
Signals that should alert a business leader
Certain signs indicate that an AI project needs more structured leadership.
A first project has been running for several months, yet its results remain difficult to measure. IT is carrying the subject without enough time or authority to bring business teams on board. External providers have delivered recommendations, but implementation remains limited.
Teams are already using AI tools individually, without a common framework. Decisions are scattered across several departments. The CEO or managing director is spending more and more time arbitrating topics that should have been framed earlier.
Taken separately, these signs can appear manageable. Together, they reveal a transformation moving forward without a sufficiently identified owner.
In this kind of situation, time rarely works in the company’s favour. The more initiatives multiply without governance, the harder it becomes to align them, secure uses and create a collective momentum.
An interim manager can help regain control without stopping projects already under way. The aim is to restore order, prioritise and concentrate effort where the impact will be strongest.
Why interim management is well suited to this type of challenge
Interim management has long been associated with crisis situations: urgent replacement, restructuring, turnaround, management breakdown or sensitive transformation.
Its use has broadened. Business leaders in SMEs and mid-sized companies increasingly turn to interim managers to accelerate complex projects, secure a period of change or test a direction before committing to a permanent hire.
AI transformation fits this framework particularly well.
It requires scarce expertise, immediate execution capability, a leadership posture and enough authority to coordinate several functions. It also demands a high degree of judgement: companies do not all need the same level of investment, the same pace or the same organisational model.
The interim manager brings this combination: strategic perspective, operational experience, ability to arbitrate and commitment to concrete results.
For an SME or mid-sized company, this solution also provides flexibility. The business benefits from a senior profile for a defined period, without waiting for the timelines of a permanent recruitment and without immediately creating a new function whose scope still needs to be stabilised.
AI in SMEs and mid-sized companies: regain control before accelerating
AI will continue to spread across businesses. Tools will improve, uses will expand, and expectations from clients, employees and shareholders will increase.
Competitive advantage will come from the ability to choose the right uses, integrate them into processes, engage teams and measure results.
For SMEs and mid-sized companies, the challenge is clear: move forward without being carried along by the trend, test without dispersing effort, invest without giving in to showcase effects.
An interim manager specialising in transformation can help business leaders cross this threshold. They bring method, operational authority and the ability to move AI from strategic discussion to practical execution.
TOPS Ressources: mobilise an interim manager to lead your AI transformation
TOPS Ressources supports SME and mid-sized company leaders through sensitive phases of transformation, structuring and change.
When an AI project struggles to take off, when initiatives become scattered or when the company wants to frame its transformation before making a permanent hire, an interim manager can provide the level of leadership required.
Their mission: secure priorities, align stakeholders, accelerate first results and prepare the organisation for the next stage.
Do you want to frame your AI transformation, regain control of an existing project or identify the right profile to structure your approach?
TOPS Ressources can help you quickly mobilise an interim manager suited to your context.
Sources
TOPS Resources, Interim Management in Executive Committee roles: CEO, CFO, HR Director, Supply Chain Manager, Transformation Director, Sales Director...
