AI Leadership Index 2026
39 out of 100

Executive summary

The AI Leadership Index 2026 is 39 out of 100, placing the companies in the survey in the AI Adopters category. AI is being used in parts of their operations, but it is still rarely integrated into core processes and business-critical workflows. Almost two-thirds remain at the starting, experimentation or pilot stage, while only 9% say their AI initiatives have collectively reached business-critical production.

The results reveal a clear progression. Companies have advanced furthest in building the technical and data foundation for AI, scoring 57 out of 100 for data and technology readiness. This is followed by operational integration at 41. Strategic governance and control scores 31, while business impact and follow-up is the largest challenge at 28. Overall, the companies have progressed further in creating the conditions for AI than in turning their investments into governance, measurable business value and long-term organisational development.

Six gaps explain where progress is being lost between experimentation and business-critical use:

Important context: The survey provides an indicative view of AI maturity and AI leadership among the organisations in the sample. It is not statistically representative of Swedish business as a whole.

Time to move from AI Adopters to AI leaders

In February 2026, the Swedish government presented a new national AI strategy with the goal of placing Sweden among the world's ten leading nations in artificial intelligence. AI is to be used and developed to drive societal benefit, sustainable development, competitiveness and innovation. This is an important ambition.

But national AI leadership is not created through strategies and targets at the national level. It is built every day in Swedish companies through their ability to turn AI opportunities into better decisions, more efficient operations, new innovations and real business value.

The potential is significant. AI can help companies free up time, strengthen decision-making, reduce wasted resources, develop new offerings and create entirely new ways of working. But having access to the technology is not the same as creating value from it.

At Elvenite, we meet companies every day that are investing in AI and see major opportunities ahead. At the same time, we often read and hear that many initiatives struggle to move from experimentation to real operational change. Projects become stuck in the pilot phase, use remains limited or the expected impact fails to materialise.

To gain a better understanding of how Swedish companies actually work with AI in practice, we developed the AI Leadership Index 2026. The index captures how far Swedish companies have come in putting AI into practice, based on operational integration, business impact and follow-up, strategic governance and control, and data and technology readiness.

The results show that many companies in the survey have started using AI. Adoption is increasing, investment continues to grow and many are already seeing positive effects. At the same time, the survey shows that there is still a clear gap between using AI and making it an integrated part of the organisation.

What becomes clear is that AI leadership is not primarily about access to technology. For most companies, the technology is already available. What distinguishes AI leaders from AI Adopters is the ability to put the opportunities created by the technology into practice, integrate AI into processes and ways of working, and create measurable results over time.

This report does more than describe where Swedish companies stand today. Through the AI Leadership Index, supplemented by perspectives from two business experts, it also identifies the capabilities, priorities and ways of working needed to take the next step and turn AI's potential into real business value. The more companies succeed in moving from AI Adopters to AI leaders, the stronger Sweden's prospects of becoming a leading AI nation.

What is Elvenite's AI Leadership Index?

A measure of how far Swedish companies have come in putting AI into practice

To understand the results in this report, we first need to look at how AI leadership has been measured. The AI Leadership Index is designed to capture how far Swedish companies have actually come in putting AI into practice. It uses a scale from 0 to 100 and measures organisations' operational AI maturity, from early tests and pilot projects to integrated, business-critical use.

Each response option in the questions included in the index was coded as a score between 0 and 100, where a higher score corresponds to higher operational AI maturity. The questions were combined within each dimension, and the total index was calculated as a weighted average. The purpose is both to provide an overall picture of how far Swedish companies have progressed in their AI development and to identify the capabilities that separate AI beginners from AI leaders.

The AI Leadership Index is based on four dimensions: operational integration, business impact and follow-up, strategic governance and control, and data and technology readiness. The dimensions are weighted to give the greatest influence to actual AI integration and business impact. Strategic governance and control, and data and technology readiness, are weighted as essential conditions for scaling AI sustainably. Together, they provide an overall view of companies' AI maturity and the capabilities needed to take the next step.

Four dimensions of AI leadership

• Operational integration (35%): Whether AI is actually integrated into operations and used by employees.
• Business impact and follow-up (25%): Whether AI creates value and whether its effects are followed up systematically.
• Strategic governance and control (20%): The governance, responsibility and control required for sustainable and secure AI work.
• Data and technology readiness (20%): Whether the data foundation and technical conditions are in place to scale AI.

Four maturity levels in the AI Leadership Index

• 0-25 - Explorers: AI is primarily tested in pilot projects.
• 26-50 - AI Adopters: AI is used in parts of the organisation.
• 51-75 - Scalers: AI is used in several processes and is beginning to create business value.
• 76-100 - Leaders: AI is integrated into the organisation's central workflows and business-critical processes.

AI Leadership Index 2026: 39 out of 100

The AI Leadership Index 2026 is 39 out of 100, placing Swedish companies in the AI Adopters category. This means that AI is used in parts of the organisation, but the technology is still rarely integrated into core processes and business-critical workflows. Almost two-thirds are still at the starting, experimentation or pilot stage. Just under one in ten say their AI initiatives have collectively reached business-critical production.

The results show a clear progression. Companies have advanced furthest in building the technical and data foundation for AI, scoring 57 out of 100. This is followed by operational integration at 41, showing that many have begun to use AI in their operations but have not yet fully integrated the technology into core processes. Strategic governance and control trails further behind at 31, while business impact and follow-up, at 28, is the greatest challenge. Overall, the results show that Swedish companies have generally come further in creating the conditions for AI than in translating their investments into governance, measurable business value and long-term organisational development.

At the same time, results vary clearly between different types of companies. Smaller companies are more likely to have no AI initiatives at all, while larger companies are more likely to have begun through tests and pilot projects and to have more developed governance structures. Industry type also matters. Operational industries, including industry, manufacturing, retail, food, transport and construction, have often started using AI but find it more difficult to move from testing to business-critical use and to integrate the technology into production, workflows and core processes. This is in comparison with technology and software, finance and insurance, the public sector and other service industries.

What, then, distinguishes those that test AI from those that succeed in making the technology a natural and genuinely value-creating part of the organisation?

Our analysis reveals six clear gaps. These are the points where many organisations lose momentum on the journey from experimentation to business-critical use. They are also where the greatest opportunities lie. The following chapters explore each of these gaps and identify the capabilities, ways of working and priorities needed to close them.

AI Leadership Index dimensions: weight and score

• Operational integration: weight 35%, score 41, weighted contribution 14.4
• Business impact and follow-up: weight 25%, score 28, weighted contribution 7.0
• Strategic governance and control: weight 20%, score 31, weighted contribution 6.2
• Data and technology readiness: weight 20%, score 57, weighted contribution 11.4
• AI Leadership Index: total weight 100%, overall score 39.0

How far have companies' AI initiatives progressed?

• Not started: 28%
• Experimentation and pilot: 36%
• Limited production: 21%
• Business-critical production: 9%
• Don't know: 7%

Rounded percentages may mean that the total differs from 100%.

Gap 1: The competitive risk gap

Many invest in AI, but few see the strategic urgency

It is clear that AI is now a priority for Swedish companies. Despite a challenging economic environment marked by geopolitical uncertainty, rising costs and stricter investment requirements, one-third say they have increased their AI investments over the past year. Only 4% say their investments have decreased.

At the same time, relatively few see a clear risk if their AI development proceeds more slowly than that of their competitors. Almost half of respondents say the risk is low or non-existent. Just under one-fifth see a high risk of losing competitiveness if their own organisation does not succeed in scaling AI faster.

There is therefore a clear gap between willingness to invest and perception of risk. Many companies choose to invest in AI, but significantly fewer appear to see the technology as critical to future competitiveness. AI is prioritised, but not always with the strategic urgency required to move from initiatives to real change.

At the same time, the increased willingness to invest does not yet appear to be matched by equal urgency in the move towards business-critical AI. Only 14% say that AI is already integrated, or is expected within 12 months to be integrated, into business-critical operations across larger parts of the organisation. This reinforces the picture of a competitive risk gap: AI is a priority, but broader business-critical integration still lies further ahead for many companies.

Key figures

• 33% have increased their AI investments during the past 12 months.
• 45% see a low risk or no risk of losing competitiveness if AI is not scaled faster.
• 14% say AI is already, or is expected within 12 months to become, integrated into business-critical operations.

How have investments in AI changed over the past 12 months?

• Increased: 33%
• Unchanged: 39%
• Decreased: 4%
• Don't know: 24%

How great is the risk of losing competitiveness if AI is not scaled faster?

• High or fairly high risk: 17%
• Some risk: 28%
• No risk or low risk: 45%
• Don't know: 10%

Gap 2: The integration gap

AI is used in the business - but not in working methods and processes

Although AI use is widespread, few Swedish companies have integrated the technology into their core processes. The results point to a clear integration gap: AI is more often used alongside the business than as a natural part of the processes, ways of working and decisions where value is created.

Use is highest in IT and development

AI is used most in IT and development, analysis and decision support, and sales and marketing. Here, around six in ten say AI is used in some part of the organisation. Use is significantly lower in functions closer to core operations. Around three in ten say AI is used in areas such as production, logistics, planning or procurement.

Using AI to analyse information, support decisions or create content is often easier than embedding the technology in production flows, warehouse processes or planning systems. At the same time, the greatest potential is often found in these operational processes, because this is where much of a company's efficiency, productivity and value creation is determined.

AI has not yet reached the core of the organisation

The difference between use and real integration becomes clearer in business-critical production. More than half say they have no AI solutions there, while only just over one in twenty use AI in several central parts of the organisation. More than one in ten say they do not know whether the company has AI in production.

This shows that many companies are still at an early stage. AI is present in many parts of the organisation, but it has not yet made an impact on the processes closest to the core of the business. For most, the step from tests and limited initiatives to broad organisational integration remains.

For many, AI remains a tool

The fact that AI is not yet integrated into core operations is also evident in how the technology is used in everyday work. When asked how integrated AI is in employees' tasks and decisions, more than four in ten say AI primarily functions as a standalone tool. Significantly fewer say AI is integrated into employees' tasks and decision-making, and only 4% describe AI as fully integrated.

This suggests that use often still takes place outside ordinary processes. AI is available and used in different parts of the organisation, but it has not yet become an integrated part of how work is performed.

Broad adoption is still to come

The picture is confirmed by how frequently employees use AI. In just under three in ten companies, AI is used daily by a significant proportion or a majority of employees. At the same time, almost four in ten say that almost no employees use AI daily.

This indicates that AI initiatives are often driven by individual teams, functions or committed individuals. The technology has not yet achieved the broad adoption required to change how the organisation works or create impact at scale.

Key figures

• 63% use AI in at least one support or knowledge function.
• 33% use AI in business-critical production to some extent.

In which functions is AI used?

• IT and development: 30%
• Analysis and decision support: 28%
• Sales and marketing: 25%
• AI is not used in the organisation: 22%
• Production and operations: 19%
• Customer service: 18%
• HR and internal support functions: 15%
• Product development: 15%
• Warehouse and planning: 9%
• Supply chain and logistics: 9%
• Procurement: 6%
• Don't know: 8%

The question allowed multiple responses, so the percentages do not add up to 100%.

To what extent is AI used in business-critical production?

• Several core processes: 6%
• Individual core processes: 12%
• Limited production: 15%
• No AI in production: 54%
• Don't know: 13%

How integrated is AI into employees' daily tasks and decisions?

• Not used: 33%
• Used as a standalone tool: 43%
• Partially integrated: 16%
• Fully integrated: 4%
• Don't know: 5%

Rounded percentages may mean that the total differs from 100%.

What proportion of employees use AI-based tools daily?

• Almost none: 37%
• A smaller proportion: 22%
• A significant proportion: 15%
• A majority: 13%
• Don't know: 12%

Rounded percentages may mean that the total differs from 100%.

Gap 3: The leadership gap

Many have an AI manager – few have made AI a leadership issue

Achieving AI leadership requires exactly that: leadership. The results show that many companies have appointed someone responsible for AI, but significantly fewer have made AI a strategic management issue. Formal responsibility is often in place, but this does not always amount to clear ownership with a mandate to drive change throughout the organisation.

Many have appointed someone responsible, but responsibility is less clear in practice

Most companies now have formal responsibility for AI matters. Responsibility most commonly sits with management, followed by the IT or technology function. In approximately one in seven companies, responsibility is shared among several functions, while almost one in ten say the allocation of responsibility is unclear.

When respondents are instead asked who actually drives AI work in practice, a similar picture emerges. Management remains the most common driver, followed by the IT or technology function. At the same time, the share describing AI work as unclearly organised rises from 9% to 16%, while slightly fewer say the work is led by management or shared among several functions.

This suggests that many companies have assigned formal responsibility, but that leadership becomes less clear when AI is put into everyday practice. This makes the next question crucial: how do companies actually view AI?

Few see AI as a clear leadership issue

Here the picture becomes even clearer. Just under one in ten describe AI as a strategic leadership responsibility. Significantly more see it as an IT or technology initiative, a shared organisational responsibility or an area that is still unclearly organised. In fact, almost half feel that the organisation of AI remains unclear.

This shows that many companies have made some progress in allocating responsibility, but that AI has not yet fully moved from a technology issue to a leadership issue. As long as AI lacks clear business ownership, it also becomes more difficult to prioritise, coordinate and drive the change needed to create real business value.

Key figures

• 42% say the board or executive management has formal responsibility for AI.
• 7% primarily see AI as a strategic leadership responsibility.

Who has formal responsibility for AI, and who drives the initiatives in practice?

• Management: formal primary responsibility 42%, drives in practice 31%
• IT and technology: formal primary responsibility 22%, drives in practice 20%
• Shared among several functions: formal primary responsibility 14%, drives in practice 12%
• Unclear: formal primary responsibility 9%, drives in practice 16%
• Business operations: formal primary responsibility 7%, drives in practice 8%
• External or specialist-led: formal primary responsibility Not reported, drives in practice 4%
• Product organisation: formal primary responsibility 1%, drives in practice 1%
• Don't know: formal primary responsibility 4%, drives in practice 7%

Rounded percentages may mean that the total differs from 100%.

How is AI viewed in the organisation?

• Strategic leadership responsibility: 7%
• Shared organisational responsibility: 17%
• IT or technology initiative: 23%
• An area that is unclearly organised: 44%
• Don't know: 9%

Gap 4: The business value gap

Many see positive effects from AI, but few know how great the value is

More and more companies are using AI to make work more efficient, improve customer experiences and create new opportunities. But as AI investments grow, so do the demands to demonstrate results. The survey shows that many Swedish companies are already seeing positive effects from AI, but few work systematically to measure and understand the value that is actually created.

Increased productivity is the most common positive effect of AI

When asked whether AI initiatives have improved their organisation so far, the majority say they see positive effects. The most common is increased productivity. Almost one-third say AI has contributed to more efficient ways of working, while around one in six highlight improved customer experiences or cost savings. At the same time, approximately one-quarter say they do not yet see any measurable effect from their AI initiatives.

The results show that AI is already creating value in many organisations. But the size of that value, which initiatives drive it and where the effects are greatest are considerably more difficult questions to answer.

Few follow up on the value AI actually creates

One explanation may be that relatively few companies systematically follow up on the effects of their AI investments. Only around one in twenty say they systematically measure ROI or other business effects of AI, while six in ten do no structured follow-up at all. In addition, almost one-quarter say they do not know whether the effects are followed up.

This means that many know AI creates benefits, but significantly fewer know how large those benefits are or which initiatives actually deliver the best results. Without that knowledge, it becomes difficult to prioritise investments and scale what works.

Objectives are often missing from the outset

The lack of follow-up often begins before the projects have even started. Only just over one in ten say they use clear objectives or KPIs for their AI initiatives to a high extent. Six in ten have no or almost no such objectives, and a further one in ten do not know whether any objectives exist.

When it is unclear from the outset what is to be achieved, it also becomes difficult to determine what has actually been achieved. This makes it harder to prioritise, compare and decide which initiatives should be developed further.

Business value is both the obstacle and the goal

The consequence becomes clear when companies are asked what is slowing down their AI work. The most common obstacle is unclear business value. At the same time, few work with clear objectives, KPIs or systematic follow-up.

Many therefore ask for proof of AI's value but lack the ways of working required to produce it. As long as the effects are not measured, it also becomes harder to justify further investments, prioritise the right initiatives and scale use across the organisation.

Key figures

• 31% say AI initiatives have improved productivity.
• 60% do not measure ROI or other business impact in a structured way.

What positive effects have AI initiatives had in the organisation?

• Productivity: 31%
• Customer experience: 16%
• Cost savings: 15%
• No measurable effect: 26%
• Don't know: 19%

The question allowed multiple responses, so the percentages do not add up to 100%.

Is ROI and other business impact from AI initiatives measured in a structured way?

• Yes, systematically: 6%
• Partly: 12%
• No: 60%
• Don't know: 23%

Rounded percentages may mean that the total differs from 100%.

To what extent are clear objectives and KPIs in place for AI initiatives?

• High or fairly high extent: 12%
• Some extent: 16%
• Low extent or not at all: 60%
• Don't know: 12%

What is the greatest obstacle to scaling AI in the organisation?

• Unclear business value: 18%
• Limited organisational buy-in: 13%
• Lack of resources or skills: 13%
• Budget or level of investment: 11%
• Risk, legal issues or compliance: 7%
• Poor data quality or data access: 6%
• Unclear ownership or governance: 4%
• Inadequate system support or technical platform: 3%
• Other: 9%
• Don't know: 14%

Rounded percentages may mean that the total differs from 100%.

Gap 5: The structure gap

AI is widely used, but often without the governance structures required

The greater the role AI plays in companies, the more important the structures governing it become. Shared guidelines, clear processes and a functioning data platform are essential if AI is to be used safely, efficiently and at scale. The results show that many companies still have some way to go. AI use simply appears to be growing faster than the governance structures needed to support it.

Guidelines are often still under development

This becomes clear when companies are asked about guidelines and policies for AI use. Only around one in ten say the guidelines are fully implemented, while almost four in ten have none at all. For approximately half, the work is still under development. This shows that many companies are still finding ways to use, govern and follow up on AI in practice.

Few have processes for follow-up and control

Developing guidelines is only a first step. As AI is used in more decisions and workflows, the need to understand, review and explain how the technology is used also increases. Despite this, just under one in ten say they have documented processes for tracing, reviewing or explaining AI-based decisions. Almost six in ten have no such processes at all, and more than one in ten do not know whether they exist.

The same pattern is visible in model follow-up and monitoring. Only a small group systematically follows AI models' performance, quality or risk over time. A majority still have no such processes at all, and here too, more than one in ten say they do not know whether any follow-up takes place.

A shared data environment is often missing

For AI to be used broadly across the organisation, data must be shareable, combinable and usable in a coherent way. Here too, many companies lack important conditions. Only just over one in ten say they have an established shared data environment that is used widely, while four in ten have none. Around one-third have a solution under development or use it to a limited extent. A further 14% do not know whether a shared data environment exists.

Data sharing between different systems is also often limited. For almost half, data is shared only between certain systems, while only just over one in ten companies have fully automated data flows. When data is dispersed across different systems, it becomes harder to create connected processes, shared ways of working and AI solutions that function across several parts of the organisation.

Key figures

• 39% lack clear guidelines or policies for AI use.
• 41% lack a shared data environment for analysis, reporting or AI initiatives.

Are there clear guidelines or policies for how AI may be used?

• Fully implemented: 10%
• Partly implemented or under development: 47%
• No: 39%
• Don't know: 4%

Are there processes for following up on AI models' performance, quality and risk?

• Yes: 6%
• Partly: 26%
• No: 57%
• Don't know: 11%

Are there documented processes for tracing, reviewing and explaining AI-based decisions?

• Yes: 8%
• Partly: 21%
• No: 58%
• Don't know: 13%

Is there a shared data environment for analysis, reporting and AI initiatives?

• No: 41%
• Under development: 13%
• Used to a limited extent: 20%
• Established and widely used: 12%
• Don't know: 14%

How easy is it to share data between different business systems?

• Data is shared between certain systems: 45%
• Data is mostly shared manually: 17%
• Systems are largely separate: 13%
• Automated sharing: 11%
• Don't know: 13%

Rounded percentages may mean that the total differs from 100%.

Gap 6: The skills and trust gap

The technology is available; knowledge and trust now determine progress

Throughout the report, we have seen that many of the obstacles slowing AI development are about more than technology. AI is now available to more companies than ever before. But having access to the technology is not the same as using it fully. As AI takes on a greater role in the organisation, knowledge, experience and trust become decisive in determining how far its use can develop.

A lack of skills holds back AI scaling

This becomes clear when respondents are asked what the greatest obstacle is to scaling AI further in the organisation and what is slowing down AI work. A lack of resources or skills is tied for second place among the greatest obstacles to scaling AI further. Around one in eight identify it as the main obstacle. At the same time, approximately the same number say they do not know what is slowing AI development.

This shows that many companies are still developing a clearer understanding of how AI is best used, which obstacles need to be addressed and where the technology can create the greatest value. AI is being used to an increasing extent, but for many, learning is still taking place in parallel with implementation.

More feel uncertain than confident

Skills are only part of the picture. When AI is used to support important decisions and processes, trust also becomes crucial. Here, the picture is far from clear-cut. Just over one-third say they feel uncertain about using AI in important decisions and processes. Only around one-quarter feel confident, while many place themselves somewhere in between.

This may not be particularly surprising. The greater the impact AI has on the organisation, the more important it becomes to understand how the results are produced, what limitations exist and when the technology can be trusted.

Trust begins with the data

Trust is not only about the AI model itself, but also about the quality of the information on which analyses, recommendations and decisions are based. Only around one-quarter say they have fairly high or high trust in the quality of the data used for decisions and AI initiatives. For many, trust is more cautious, and almost one-quarter have low or no trust at all. When uncertainty about the data is high, it also becomes more difficult to trust the results. Trust in AI therefore often begins with trust in the data on which AI is built.

Key figures

• 13% identify a lack of resources or skills as the greatest obstacle to scaling AI.
• 35% assess their organisation as uncertain about using AI in important decisions or processes.

What is the greatest obstacle to scaling AI in the organisation?

• Unclear business value: 18%
• Limited organisational buy-in: 13%
• Lack of resources or skills: 13%
• Budget or level of investment: 11%
• Risk, legal issues or compliance: 7%
• Poor data quality or data access: 6%
• Unclear ownership or governance: 4%
• Inadequate system support or technical platform: 3%
• Other: 9%
• Don't know: 14%

Rounded percentages may mean that the total differs from 100%.

To what extent do companies trust the quality of the data used for decisions and AI initiatives?

• Fairly high or high trust: 25%
• Some trust: 42%
• Low or no trust: 23%
• Don't know: 10%

How confident is the organisation about using AI in important decisions or processes?

• Confident: 25%
• Neither confident nor uncertain: 29%
• Uncertain: 35%
• Don't know: 12%

Rounded percentages may mean that the total differs from 100%.

Business experts comment

To place the report's findings in a broader context, we invited two experts from the Swedish business community to review the study.

The report shows that many Swedish companies have started using AI, but few have integrated the technology into their operations. Do you recognise that picture?

Yes, I do. Sweden has long been known as a country that is keen to test and adopt new technology. We have strong digital infrastructure, high technological maturity and a culture in which many want to be early in trying new solutions. This is also evident with AI. Many companies already use the technology in their everyday work, but often for simpler tasks and at an individual level. Integrating AI into the organisation's processes and ways of working is a significantly bigger step.

Why is it important for Swedish companies to succeed in that transition?

At its core, AI is about Sweden's future competitiveness. Companies that succeed in turning AI into increased productivity and better business decisions will be in a stronger position as competition intensifies. If more Swedish companies succeed in creating real business value with AI, this strengthens not only their own development, but also Sweden's growth, innovation capacity and long-term competitiveness.

What are the greatest obstacles to taking the next step?

For many companies, it is not about unwillingness, but about not quite having enough time, expertise and resources. This is particularly true for smaller companies. Many simply do not know where to start or how AI can create value in their own organisation. As a result, AI often becomes a collection of isolated experiments instead of a natural part of the business.

What opportunities do you see AI creating for Swedish companies in the future?

AI can give companies access to analysis, automation and decision support in ways that previously often required significantly greater resources. This makes it possible to make processes more efficient, make better decisions and free up time for more value-creating work. For many smaller companies, it also provides access to opportunities that were previously primarily a strength of larger companies.

What do companies need to do now to succeed better?

The most important thing is to start working more systematically. Management needs to allocate time to explore how AI can be used in the organisation and encourage people to test new ways of working. At the same time, the business community needs to become better at highlighting concrete examples of how AI is used in practice. When companies can see how others in the same industry have streamlined processes, automated tasks or created business value, it becomes significantly easier to turn AI from an idea into reality.

The results show that many people still feel uncertain about AI. Why do you think that is?

Most companies have probably understood that AI will change their operations. What they are still trying to understand is more specifically how. Where exactly is the boundary of what AI can do? What still needs to be decided by a person? How do we know when the technology gets it right? I think that uncertainty is linked to a gap between the broad discussion about AI and companies' everyday reality. We talk a great deal about competitiveness, productivity and national strategies, but often at a fairly abstract level. Far less is said about what AI actually means for a buyer, a production manager or a salesperson on Monday morning. It is only when AI is connected to specific tasks and solves real everyday problems that the technology begins to create value. That is also when trust grows.

So what is required for Swedish companies to take the next step?

We need to stop trying to understand AI from a distance and start learning by using it. Trust is built through experience. Start small, test, evaluate and learn from the results. Only then does it become clear when AI is the right tool, and when a person needs to make the final decision.

Fundamentally, however, this is not about technology, but about change management. If you look at companies that have been successful over time, regardless of the area, they often look completely different today than they did five years ago. They have not found a perfect model; they have built a culture in which people continuously test, learn and develop new ways of working. I believe the Swedish business community needs to accept that AI is not a project that will be completed, but a capability that must be continuously developed.

How can Sweden position itself in the development of AI?

We are unlikely to compete by building the largest AI models. Our opportunity lies in becoming one of the world's leading countries in putting AI into practice: using the technology responsibly and close to the business to solve real problems and create business value.

Sweden has a long tradition of innovation, digitalisation, creativity and rapid adoption of new technology. If we build on those strengths, we can create a business sector in which AI becomes a natural part of how companies develop products, make decisions and strengthen their competitiveness. This strengthens not only the competitiveness of Swedish companies. It also makes Sweden more attractive to AI talent and other specialist expertise in both technology and the creative industries. They are drawn to environments characterised by curiosity, the courage to try new things and a culture in which innovation goes hand in hand with responsibility. I believe Sweden has a clear niche here: demonstrating how AI can be used broadly, responsibly and in a value-creating way in a modern business sector.

What does this mean for business leaders and decision-makers?

Development is moving so quickly that both companies and national strategies need to become more agile. For business leaders, it is no longer about waiting for the perfect AI strategy. It is about creating space for employees to test, learn and develop new ways of working, and then scaling what actually works.

Six steps to AI leadership

Six concrete steps showing how we as Swedish companies can close the gaps between AI adoption and AI leadership

AI has quickly become an obvious part of many companies' everyday life. But the results in the AI leadership index show that it is no longer access to the technology that determines which companies succeed. Investments are increasing and AI is being used in more parts of the business, but the technology is still rarely integrated into core processes and business-critical workflows. Responsibility, goals, follow-up, data environments, skills and trust have not developed at the same pace. The real challenge is therefore to translate AI into changed ways of working, measurable business value and long-term competitiveness.

The six gaps identified in the report also show the way forward. The companies that manage to close these gaps will not only use AI more, but also better. They will have better conditions to make well-founded decisions, work more efficiently and create greater business value over time.

It is a picture we recognise from our work at Elvenite. Every day we help companies in industry, trade and food to turn data, digitalisation and AI into better decisions, more efficient processes and measurable results. The companies that come furthest are rarely those that test the most tools. They are the ones who integrate AI into the organisation’s ways of working, processes and decisions – and who develop leadership, structure, data and people together.

Here we summarise the report's most important conclusions in six concrete steps that show how we as Swedish companies can close the gaps and move from AI adoption to AI leadership.

1. Make AI a strategic competitive issue

Many companies invest in AI, but fewer see the competitive risk of not scaling faster. The companies that progress furthest view AI as a strategic issue for future competitiveness, not as an isolated innovation project.

2. Integrate AI into core business processes

AI is used in many companies today, but often alongside the business. The greatest effects arise when the technology becomes an integrated part of processes, decisions and workflows.

3. Establish clear business ownership

Many companies have appointed people responsible for AI, but few have made AI a clear management and business issue. When responsibility, mandate and direction are unclear, it becomes difficult to prioritise, coordinate and scale initiatives.

4. Measure and develop the value created by AI

The majority of companies see positive effects from AI, but few follow them up systematically. Without clear objectives and follow-up, it becomes difficult to know which initiatives create the greatest benefit.

5. Build the governance structures that make AI scalable

AI is developing faster than the governance structures around it. Guidelines, follow-up processes, data platforms and data sharing are essential if AI is to be used safely, efficiently and at scale.

6. Strengthen the capability and trust to use AI

The technology is now available to most companies. What increasingly determines progress is people's knowledge, experience and trust in AI. Only when employees and leaders feel confident with the technology can its full potential be realised.

About Elvenite

Elvenite is the leading Infor CloudSuite M3 partner in the Nordics and specialises in helping companies move from ERP to ERX, where ERP, data and AI together redefine how companies make decisions, evolve and create value.. Since its foundation in 2007, Elvenite has grown to approximately 220 employees, with offices in Sweden, Norway, Finland and Romania.

As the leading Infor CloudSuite M3 partner in the Nordics, Elvenite works closely with companies in sectors including manufacturing, food, retail and distribution to streamline processes, strengthen decision-making and create the conditions for long-term competitiveness.

By combining technology, data and business development, Elvenite helps companies move from insight to action, and from AI potential to real business value.

Contact

For questions about the report:

• Email: hello@elvenite.com
• Phone: +46 54 150 115

Elvenite (2026). AI Leadership Index 2026: Six gaps to close to move from AI Adopters to AI leaders. Survey conducted by Prime on behalf of Elvenite.

Copyright 2026 Elvenite AB.