Industrial strategy: From policy framework to effective innovation architecture

At the beginning of March, the Office of the Productivity Board published, together with co-authors, a short analysis¹ of the “Industriestrategie Österreich 2035”². Its central message is that the strategy is an important step, but in its current form still too programmatic and not yet ready for implementation. The short analysis therefore acknowledges the strategic direction but criticises the lack of clear operationalisation, responsibilities, financing logic and a robust governance architecture. In the authors’ view, precisely these elements need to be added if a political framework document is to become an effective instrument of industrial policy.

What this can mean in concrete implementation terms is illustrated in this article using historical and current evidence: historical evidence is provided by a publication by Hartog et al.³, current evidence by Lin, Frey and Wu⁴.

Historical evidence as a lens on structural change in innovation

Why is it useful to look more closely at the publication by Hartog et al. in connection with the short analysis by the Office of the Productivity Council, even though the historical data covers the period from 1856 to 1945? If one wants to make structural change in the organisation of innovation empirically visible, one needs an observation period in which different historical data sources can be reliably linked with one another. Hartog et al. show that this is possible in the US case precisely because patent, census and laboratory data overlap in this period. This makes it possible to demonstrate empirically when, and under which organisational conditions, the US innovation system underwent a lasting structural transformation. The fact that organisational questions are not only historically relevant is also underlined by the current evidence in the paper by Lin, Frey and Wu. The authors analyse a total of 20 million scientific articles and 4 million patent applications over a period of around half a century.

Key statements and findings of the short analysis

The “Industriestrategie Österreich 2035” sets the right goals, but is not yet a robust implementation plan. For the first time, Austria has presented a cross-ministerial industrial strategy with six strategic goals, 117 measures and nine key technology fields. The main criticism in the short analysis is that many measures have not yet been designed as clearly steerable interventions with roadmaps, responsibilities and a coherent budgeting logic.

According to Hartog et al., the transformation towards an innovation-strong economic system does not succeed through abstract priority setting alone, but above all where new technological search processes are supported organisationally – by teams, firms and research laboratories. The current evidence from Lin, Frey and Wu complements this finding with an important contemporary dimension: even in a digitally networked knowledge economy, the way teams collaborate, and whether they can draw on physical proximity, shared infrastructures and direct interaction, remains relevant. This makes organisational questions even more pressing for the Austrian industrial strategy.

The core problem of Austrian industry is not clearly enough spelt out. The short analysis emphasises that Austria’s traditional industrial specialisation and its export-driven growth model are under pressure – a development that is also clearly visible in the FORWIT FTI Monitor since 2022. What is needed, according to the short analysis, is therefore not only adjustment but an accelerated structural shift towards new growth fields.

Hartog et al. do not directly prove this macroeconomic finding, but they do provide an important contribution to the question under which conditions such structural change can succeed from an innovation perspective: historical evidence from the US shows that the transition to a new innovation regime was heavily dependent on organisational carriers such as teams, engineers and industrial research laboratories, and on how this knowledge was brought together. Consequently, a shift into new, more complex growth fields requires not only technology but also appropriate institutional and organisational carriers. Lin, Frey and Wu reach a similar conclusion. The authors show that geographically dispersed teams produce breakthrough-oriented ideas less frequently on average than teams working together on site, because early conceptual work that relies heavily on tacit knowledge is more difficult to integrate digitally. This implies that the transition into new growth fields requires suitable innovation spaces.

The focus on key technologies is still too imprecise. The strategy identifies nine key technologies and provides for a budget of around €2.6 billion up to 2029. According to the short analysis, however, it remains unclear whether this in fact represents a new prioritisation, as a large share of the funds apparently is already committed in these areas. What is therefore needed is not merely a list of strategic technology fields, but a more precise delineation of areas of strength and funding priorities within these technologies.

Yet even if technology priorities are chosen sensibly in political terms, this still does not answer the question of how new industrial dynamics, market-ready innovation and scalable value creation are actually to emerge in these fields. Historically, Hartog et al. show that technological breakthroughs gained particular momentum where they were tied to robust organisational forms – such as permanent R&D structures, close linkages between science and industry, capable teams and systematically developed transfer capacities. Lin, Frey and Wu become even more concrete: key technologies need not only funding budgets, but above all physical and organisational carriers as translators – such as innovation hubs, clusters, shared lab spaces, pilot plants, testbeds and other application-oriented research infrastructures. It is often at these interfaces that the decision is made as to whether technological potential becomes marketable and scalable innovation. This means that if genuine areas of strength are to be defined within the nine key technologies, it must also be specified through which innovation ecosystems they are to be realised. The industrial strategy already refers to such elements; the additional insight from Hartog et al. and Lin, Frey and Wu is to understand these systems not as merely accompanying measures, but as central carriers of an effective key technology policy.

Governance is a weak point. The short analysis makes it clear that the strategy remains vague on who is politically, administratively and operationally responsible for which measures, how ministries are to cooperate and how the federal government, Länder, municipalities and the EU are to be involved. What is therefore needed is a genuine “whole-of-government” approach with clear roles, responsibilities and escalation mechanisms.

Hartog et al. also provide strong argumentative support for this observation, as they describe organisational innovations themselves as drivers of technological progress. Governance is therefore not just a matter of administrative detail, but an integral part of the innovation strategy: if teams, research, transfer and industrial application are not institutionally organised, the innovation impact of the strategy is weakened. The findings of Lin, Frey and Wu reinforce this argument: if spatial proximity, co-presence and the quality of collaborative settings remain relevant for disruptive innovation, then governance and strategy must also define where and how such innovation spaces are created, operated and interconnected.

Many measures are still insufficiently concrete and the financing logic remains unclear. According to the short analysis, around 60% of the measures contain, at least in part, declarations of intent or problem descriptions rather than clearly operationalisable interventions; for more than half, it remains open whether additional funds are needed and how they are to be financed.

As Hartog et al. show, industrial research was historically successful above all because of long-term planning horizons, stable resources and a close link to concrete industrial missions. This supports the short analysis’s call for multi-annual budget paths, clear prioritisation and a robust financing architecture. The current evidence from Lin, Frey and Wu on remote versus on-site collaboration also suggests that investments in shared infrastructures, pilot environments and physical innovation spaces should not be treated as a side issue, but as productivity-relevant components of an innovation-oriented industrial policy.

KPI monitoring is not enough. A central point in the short analysis is that macroeconomic indicators alone do not provide a robust basis for assessing the effectiveness of individual measures. This requires explicit impact logics, process goals, milestones, evaluations and in-depth analyses.

Hartog et al. are even more specific here: innovation systems change through teamwork, skills profiles, links to science, spatial concentration and participation. Lin, Frey and Wu expand this perspective by adding the quality of collaboration itself. Good monitoring should therefore capture not only high-level KPIs, but also cooperation structures, transfer activities, skills profiles, regional concentration and inclusion – and, in addition, track whether physical innovation infrastructures are actually used, the extent to which research and firms work together in co-presence, and where co-location or shared research environments contribute to the emergence of new ideas.

The recommended solution is field-specific roadmaps. The authors of the short analysis propose translating the respective fields of action in the industrial strategy into roadmaps that systematically link objectives, measures, resources, responsibilities, timelines and milestones.

On the basis of their historical analysis, Hartog et al. further suggest that roadmaps should not only list technologies or funding instruments, but also identify the organisational carriers of innovation: where will application-oriented R&D capacities emerge? Who coordinates science–industry linkages? How are teams, engineering capabilities and repeated collaboration built up? Lin, Frey and Wu suggest taking this roadmapping approach even further: roadmaps should specify which physical innovation spaces in each field are to be built up or strengthened and used, and which role they play in transferring research results into industrial application.

Monitoring, evaluation and policy learning should be set up as a learning system. The industrial strategy envisages that the Productivity Council will take on the monitoring function and that specialised research institutions will carry out evaluations. The short analysis proposes ex-ante impact assessments, accompanying evaluations, ex-post, sectoral and regional analyses, as well as the use of microdata sets, patent and project information and horizon scanning.

Hartog et al. also underline the value of evidence-based steering. They show that the innovation impact of particular organisational forms can change over time – for example, firm-based team research lost some of its radical novelty dynamics after 1950. This is precisely why a learning, adaptive steering system is more appropriate than a static instrument logic.

It is also important to link the industrial strategy with higher education and skills policy. Hartog et al. demonstrate that the rise of industrial research was historically closely linked to higher education, the emergence of engineers, increasing engagement with science and new forms of university involvement. This supports the short analysis’s conclusion that a technology strategy without a coordinated higher education, transfer and skills policy remains incomplete.

Start-ups, scale-ups and spin-offs

The “Industriestrategie Österreich 2035” refers in several places to start-ups, scale-ups and spin-offs. This is particularly explicit in the field of action “Research, Technology & Innovation”, where the “scaling of start-ups into future industrial companies” is highlighted, and where start-ups, scale-ups and spin-offs are explicitly described as important drivers of technology transfer and as potential industrial enterprises of tomorrow. In addition, performance agreements with universities are to place greater emphasis on technology transfer and spin-off measures, as well as entrepreneurship education. Furthermore, support and advisory services for business creation and scaling – such as spin-off fellowships, pre-seed and seed financing, and shared lab spaces – are mentioned.

In the short analysis, the topic is not developed as a separate priority and is only addressed indirectly. Nevertheless, it is criticised that the industrial strategy does not systematically examine whether the existing RTI funding system produces a “robust pipeline of young, high-growth firms” and thus supports the emergence of future flagship companies. The topic remains scattered across various fields of action without a clearly defined, integrated logic for transfer and scaling.

This is precisely where there is an important point of connection for start-ups and spin-offs. Lin, Frey and Wu argue that early, conceptual phases of innovation benefit particularly strongly from spatial proximity and direct collaboration. Shared lab spaces, clusters, pilot environments and application-oriented innovation ecosystems are therefore not a marginal issue for research-intensive start-ups and spin-offs, but a core element of a functioning transfer architecture. Their role as carriers of the translation of research results into value creation and markets should therefore be anchored explicitly, systematically and with a dedicated impact logic.

Conclusion

The overall direction of the industrial strategy is right, but without a precise impact logic, clear responsibilities, multi-annual budget paths, roadmaps and robust monitoring, it remains too weak in practice. Hartog et al. show historically that technological dynamism was particularly strong where teams, engineers and industrial research laboratories were brought together organisationally. Lin, Frey and Wu confirm this historical perspective with current evidence: even today, spatial proximity remains relevant for breakthrough-oriented innovation.

For Austria, this leads to a clear guiding principle: industrial policy cannot stop at naming key technologies and allocating funding. It must also actively shape the organisational and physical conditions for innovation – that is, governance, learning roadmaps, application-oriented R&D structures, clusters, shared lab spaces, pilot and test infrastructures, as well as a coherent transfer and scaling logic for start-ups, scale-ups and spin-offs. Only then can the “Industriestrategie Österreich 2035” evolve from a programmatic guideline into an effective instrument of industrial renewal.

1. Office of the Productivity Board and co-authors, Industriestrategie Österreich 2035: Von der programmatischen Leitlinie zur wirksamen Umsetzungs- und Steuerungsarchitektur↩
2. BMWET, Industriestrategie Österreich 2035 – für einen wettbewerbsfähigen Industriestandort und wirtschaftliche Resilienz.↩
3. Matte Hartog, Andres Gomez-Lievano, Ricardo Hausmann, Frank Neffke: „Inventing modern invention: The professionalization of technological progress in the US“, Research Policy, Elsevier, Volume 55, Issue 3, 2026↩
4. Lin, Y., Frey, C.B. & Wu, L.: „Remote collaboration fuses fewer breakthrough ideas“, Nature 623, 987–991, 2023↩