What is an augmented enterprise?
The concept starts from what Gartner calls augmented intelligence: a people-centred model of collaboration in which humans and artificial intelligence work together to enhance cognitive performance, that is, learning, decision-making and the creation of new experiences (as defined in Gartner's own glossary). The key lies in the word collaboration. AI is not here to replace human intelligence, but to enhance it.
An augmented enterprise, therefore, is one that has built a layer of artificial intelligence connected to its reality: its documents, its processes, its data and its limits. It is not about each employee using a generic assistant on their own, but about the organisation having specialised agents, redesigned workflows and clear rules for human oversight.
The difference from the traditional company is not technological, it is organisational. Two companies can use the same AI models and get opposite results. The one that wins is the one that has decided which tasks the machine takes on, which decisions are reserved for people and how both are audited.
How does an augmented enterprise differ from an automated one?
It is worth distinguishing between two concepts that are often confused. Classic automation carries out predefined tasks following fixed rules: moving data from one system to another, generating an invoice, sending a reminder. It is valuable, but rigid. Augmentation goes a step further: AI processes large volumes of information, detects patterns and proposes options, while the person contributes context, intuition and responsibility for the decision.
In practice, the two coexist. An augmented enterprise combines intelligent automation (for example, RPA with AI capabilities for administrative processes) with cognitive assistance (agents that answer questions about internal documentation using RAG technology, which retrieves verified information from the company's own sources before generating an answer).
The nuance matters because it defines where the value lies. Automating reduces costs in repetitive tasks. Augmenting improves the quality of decisions and the speed of access to knowledge. According to McKinsey's The State of AI survey (2025), 88% of organisations already use AI in at least one business function, but almost two thirds have not yet begun to scale it across the company. Using AI is easy. Becoming an augmented enterprise, not so much.
How processes change: redesign, don't patch
The first major transformation is organisational. McKinsey analysed what distinguishes companies that achieve real economic results with generative AI from those that do not, and the conclusion was clear: what matters most is not the tool chosen, but having redesigned work processes to incorporate it. However, according to that same study, only 21% of organisations using generative AI have done so. Most add the tool on top of their usual way of working, and that is why they notice no difference in results.
The consequences of that patching can be seen in another widely cited study: MIT's The GenAI Divide report (NANDA project, 2025) concluded that around 95% of generative AI pilots in companies generate no measurable return. The problem was not the quality of the models, but integration: generic tools that impress in the demo but do not retain context, do not learn from use and do not fit into real workflows.
The lesson for any executive is clear. Before asking which tool to buy, you need to ask which process deserves to be rethought. In our experience supporting companies along this path, the projects that work start with a specific, measurable process (handling internal queries, document management, preparing proposals) and redesign it from start to finish, defining what the AI does, what the person validates and how the result is measured.
How people change: new roles and new skills
The second transformation affects teams. In the augmented enterprise, part of the work shifts from carrying out tasks to supervising systems that carry them out. Gartner predicts that 40% of enterprise applications will incorporate task-specific AI agents by the end of 2026, compared with less than 5% in 2025. This means many professionals will work every day alongside AI agents that prepare drafts, classify information or propose actions, and their value will lie in guiding them, correcting them and validating their results.
This change creates an obvious demand for training. According to an ONTSI report cited by the España Digital programme, 78% of Spanish workers want training in digital technologies and artificial intelligence. The gap is not one of attitude, but of support: workforces want to learn and many organisations have not yet structured how to teach them.
The culture changes too. An augmented enterprise needs tolerance of controlled error (pilots are tested, measured and sometimes discarded), transparency about which decisions AI assists with and shared criteria on when a machine-generated result can be used as is and when it requires review. None of this is solved with an internal memo. It requires hands-on training by team and internal champions who act as a bridge between business and technology. That is why at LurNova AI training is a core service and not an add-on: without trained people, the best technical architecture remains a pretty demonstration.
It is worth adding a warning that the analysts themselves underline. Gartner estimates that more than 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value or insufficient risk controls. In other words: launching agents without having prepared the people who must supervise them and without having defined what problem they solve is the quickest recipe for swelling that statistic. The augmented enterprise is not measured by how many agents it deploys, but by how many are still adding value a year later.
Governance, security and sovereignty: the condition for it to work
The third transformation is the most underestimated and the one with the greatest consequences. When AI touches customer data, financial information or strategic knowledge, the question is no longer just what it can do, but under what conditions it does it: where the data is hosted, who accesses the models, under which jurisdiction they operate and what traceability exists for each answer.
The regulatory framework pushes in the same direction. The EU AI Act has applied its general regime since 2 August 2026, including transparency obligations on AI systems and generated content. Failure to meet those transparency obligations can be penalised with up to 15 million euros or 3% of worldwide turnover, while directly prohibited practices are reserved for the most severe tier, of up to 35 million or 7%. For an augmented enterprise this is not a formality: it is proof that AI governance is no longer optional.
This is where technological sovereignty comes in, understood as the organisation's ability to decide autonomously on the AI systems it uses. Generic public cloud solutions solve simple use cases, but they have limits when the knowledge they process is sensitive. Private AI models, deployed on infrastructure controlled by the company, make it possible to harness the power of generative AI while keeping data within the perimeter, with traceability and auditability. At LurNova we have made this approach our differentiator, with products such as MIA (Mi Inteligencia Artificial) and LurNova AI Model Hub, precisely because we have seen that trust is the precondition for adoption: teams use AI when they know they can do so without putting the company's information at risk.
Where to start: the realistic path to the augmented enterprise
There is no switch that turns an organisation into an augmented enterprise. There is a path that, in most cases, follows a recognisable logic. First, an honest diagnosis: which processes concentrate the most time on low-value tasks, what knowledge is scattered and what data really exists. Second, one or two pilot use cases with measurable impact, redesigning the whole process instead of adding a tool on top. Third, training for the teams involved, not as a one-off event but as ongoing support. And fourth, a governance framework that defines responsibilities, human oversight and regulatory compliance before scaling up.
The context data invites you to move with judgement, not in blind haste. In Spain, according to ONTSI indicators based on INE data, 21.1% of companies already use artificial intelligence, an increase of 8.8 points in a single year. Adoption is accelerating, but the McKinsey and MIT studies we have cited show that adopting is not the same as transforming. The competitive advantage will not lie in being first to the tool, but in being one of the few who integrate it well.
There is also a timing reason not to postpone the diagnosis. The transparency obligations of the EU AI Act are already applicable and the systems a company deploys today will have to comply with them from day one. Designing with governance built in from the outset is much cheaper than correcting afterwards. In most of the cases we have supported, organisations that start with a well-defined use case, with their own data and defined oversight, tend to scale sooner and with less friction than those that begin with large-scale deployments of generic tools.
Conclusion
The augmented enterprise is not a company with more technology, it is a company with a better relationship between its technology and its people. Processes change, because they are redesigned instead of patched. Roles change, because supervising and guiding AI becomes part of the job. And governance changes, because security, traceability and data sovereignty go from being technical concerns to business conditions.
At LurNova we support organisations along that entire journey, from initial consultancy to the deployment of AI agents and private models, with team training as the common thread. If your organisation is considering how to take the step with judgement and security, let's talk: the first diagnosis usually clarifies more than months of aimless testing.
CEO of LurNova, with extensive experience in technology and digital transformation. Involved in developing technology projects and applying artificial intelligence in organisations, he combines technical knowledge with business vision.
