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π Science and technology

Edge innovation: a creative approach to revealing the unexpected?

Didier Bazalgette
Didier Bazalgette
Senior Officer in the French Army and Economics Lecturer at Sciences Po Paris
Jean LANGLOIS-BERTHELOT
Jean Langlois-Berthelot
Specialist in innovation and complex risk management
Christophe Gaie
Christophe Gaie
Head of the Engineering and Digital Innovation Division at the Prime Minister's Office
Key takeaways
  • Edge innovation explores atypical, often neglected research that can lead to major discoveries such as X-rays or the theory of relativity.
  • This approach encourages interdisciplinarity, fostering innovative synergies between different areas of research.
  • Redirecting resources towards exploratory projects can diversify scientific approaches and reduce the risk of dead ends.
  • Valuing productive failures in such research is crucial, as they can open up unexpected new avenues.
  • Citizen participation enriches edge innovation by providing new perspectives and facilitating the social acceptance of discoveries.

Edgein­no­va­tion illus­trates how invest­ment in orig­i­nal or atyp­i­cal research can unlock unex­pect­ed research avenues and results, open­ing up new per­spec­tives for tech­no­log­i­cal and sci­en­tif­ic development.

The challenges of edge innovation

Edge inno­va­tion refers to the strat­e­gy of invest­ing in research projects that are off the beat­en track and out­side the imme­di­ate pri­or­i­ties of sci­ence and tech­nol­o­gy. Unlike tra­di­tion­al research focused on clear­ly defined objec­tives and direct appli­ca­tions, these projects explore periph­er­al ques­tions, often spec­u­la­tive, but which can lead to rev­o­lu­tion­ary dis­cov­er­ies. The impor­tance of this approach lies in its abil­i­ty to decom­part­men­talise sci­en­tif­ic research and open up new per­spec­tives. By focus­ing on more unusu­al areas, researchers can iden­ti­fy nov­el con­nec­tions and inno­v­a­tive solu­tions to com­plex problems.

The his­to­ry of sci­ence is rich in exam­ples of major dis­cov­er­ies result­ing from seem­ing­ly mar­gin­al research. One of the most famous exam­ples is that of X‑rays, dis­cov­ered by Wil­helm Con­rad Rönt­gen in 1895 while study­ing the prop­er­ties of cath­ode rays1. This chance dis­cov­ery rev­o­lu­tionised med­i­cine and physics, demon­strat­ing the enor­mous poten­tial of explorato­ry research. Sim­i­lar­ly, Albert Einstein’s the­o­ry of rel­a­tiv­i­ty emerged from his reflec­tions on prob­lems of mechan­ics and elec­tro­mag­net­ism that were not cen­tral to the sci­en­tif­ic con­cerns of his time. These exam­ples illus­trate how edge inno­va­tion can lead to major sci­en­tif­ic and tech­no­log­i­cal breakthroughs.

Unconventional fields of research: fertile ground for innovation

An emblem­at­ic exam­ple of edge inno­va­tion is the search for extrater­res­tri­al sig­nals, notably through ini­tia­tives such as Berke­ley and NASA’s SETI (Search for Extrater­res­tri­al Intel­li­gence) pro­gramme2. Although the prob­a­bil­i­ty of detect­ing extrater­res­tri­al sig­nals is uncer­tain, the tech­nolo­gies and meth­ods devel­oped for this type of research have poten­tial appli­ca­tions in oth­er fields of sci­ence and tech­nol­o­gy. The data pro­cess­ing algo­rithms and sig­nal detec­tion tech­niques used by the SETI pro­gramme have been adapt­ed to analyse large datasets in the life sci­ences, cli­ma­tol­ogy and even finance. This cross-dis­ci­pli­nary use of tools and meth­ods is a per­fect illus­tra­tion of the con­cept of edge innovation.

Invest­ing in uncon­ven­tion­al research can also have a sig­nif­i­cant impact on the devel­op­ment of arti­fi­cial intel­li­gence (AI). Advances in AI often ben­e­fit from dis­cov­er­ies made in seem­ing­ly unre­lat­ed fields. For exam­ple, research in neu­ro­science has pro­found­ly influ­enced the neur­al net­work mod­els used in AI.

What strategies are needed to facilitate edge innovation?

To max­imise the ben­e­fits of edge inno­va­tion, it is vital to have research man­agers who are will­ing to sup­port this approach and to put in place spe­cif­ic strate­gies to opti­mise exist­ing resources.

Proposal #1: Encourage interdisciplinary research

Col­lab­o­ra­tion between dif­fer­ent dis­ci­plines is essen­tial to fos­ter edge inno­va­tion. By bring­ing togeth­er experts from dif­fer­ent fields, it is pos­si­ble to cre­ate syn­er­gies that enable the dis­cov­ery of inno­v­a­tive solu­tions to com­plex prob­lems3. Col­lec­tive intel­li­gence feeds on the diver­si­ty of view­points so that “1 ⊕ 1 = 3” since the intel­li­gence of two peo­ple exceeds the sum of their sep­a­rate intelligences.

Research insti­tu­tions and uni­ver­si­ties should pro­mote inter­dis­ci­pli­nary projects and pro­vide plat­forms to facil­i­tate these col­lab­o­ra­tions. High­light­ing proven method­olo­gies for inter­dis­ci­pli­nar­i­ty can act as a cat­a­lyst in the process, improv­ing effi­cien­cy and hence results4. Par­tic­u­lar atten­tion needs to be paid to the oth­er com­po­nents of research to involve them in the process and to ensure the community’s sup­port. “Inter­dis­ci­pli­nary research is not car­ried out at the expense of basic research but sup­ports it to reveal its full potential.”

Proposal #2: redeploy existing resources

Rather than hav­ing to jus­ti­fy bud­gets with exces­sive fre­quen­cy, it is pos­si­ble to ratio­nalise and real­lo­cate exist­ing resources to more explorato­ry projects. For exam­ple, by real­lo­cat­ing some of the funds allo­cat­ed to tra­di­tion­al projects to more uncon­ven­tion­al research, it is pos­si­ble to diver­si­fy research port­fo­lios and encour­age inno­va­tion. This real­lo­ca­tion can be achieved by pre­sent­ing the results and the per­for­mance achieved. In fact, these are often more effec­tive in the long term56, par­tic­u­lar­ly when they enable us to respond to a new field such as the use of arti­fi­cial intel­li­gence for agri­cul­ture, the eco­log­i­cal tran­si­tion, med­i­cine, etc.

To select projects for fund­ing, it is there­fore rec­om­mend­ed that a rig­or­ous eval­u­a­tion grid be put in place, pri­ori­tis­ing ini­tia­tives with strong poten­tial for dis­rup­tive inno­va­tion, close align­ment with the organisation’s strate­gic objec­tives and suf­fi­cient tech­no­log­i­cal matu­ri­ty to ensure their fea­si­bil­i­ty. This strate­gic real­lo­ca­tion, by favour­ing explorato­ry research projects, offers a for­mi­da­ble lever for stim­u­lat­ing inno­va­tion. It will allow for the devel­op­ment of new exper­tise, push­ing back the fron­tiers of our field and acquir­ing a sus­tain­able com­pet­i­tive advan­tage. Although this approach involves the uncer­tain­ties inher­ent in research, the poten­tial ben­e­fits in terms of major dis­cov­er­ies and new appli­ca­tions are considerable.

Proposal #3: Make the most of productive failures

Edge inno­va­tion often involves a degree of risk, and it is cru­cial to make the most of pro­duc­tive fail­ures. Fail­ures in these areas can pro­vide valu­able infor­ma­tion and open up new avenues of research. Cre­at­ing a cul­ture that val­ues exper­i­men­ta­tion and accepts fail­ure as an inte­gral part of the sci­en­tif­ic process is essen­tial to encour­age innovation. 

Proposal #4: Rely on citizen participation

Edge inno­va­tion can also emerge spon­ta­neous­ly in the event of a major cri­sis. The Covid-19 pan­dem­ic7, major mil­i­tary events or cli­mate change are obvi­ous exam­ples. When the com­mon inter­ests of human­i­ty are at stake, cit­i­zens from all walks of life come togeth­er to con­tribute their knowl­edge, skills and exper­tise in the ser­vice of the gen­er­al interest.

Edge inno­va­tion often involves a degree of risk, and it is cru­cial to make the most of pro­duc­tive failures.

There are sev­er­al mech­a­nisms that can be used to encour­age cit­i­zen par­tic­i­pa­tion. For exam­ple, co-cre­ation plat­forms offer a space for dia­logue where cit­i­zens, researchers and com­pa­nies can work togeth­er to define research issues, co-con­struct projects and share the results. Anoth­er pos­si­bil­i­ty is to organ­ise open ideas com­pe­ti­tions. This enables inno­v­a­tive pro­pos­als to be col­lect­ed and emerg­ing tal­ent to be iden­ti­fied. Final­ly, open­ing up data is a major way of facil­i­tat­ing the emer­gence of these col­lab­o­ra­tive ven­tures89.

Cit­i­zen par­tic­i­pa­tion offers major prospects for edge inno­va­tion. It brings new research ques­tions to the fore by bring­ing togeth­er the whole of civ­il soci­ety, of which researchers are an active stake­hold­er. It fos­ters the cross-fer­til­i­sa­tion of ideas and their com­bi­na­tion to meet the high expec­ta­tions of the pop­u­la­tion and accel­er­ates the adop­tion of ideas and inno­va­tions, facil­i­tat­ing the dis­sem­i­na­tion of knowledge.

Future prospects

Edge inno­va­tion offers an essen­tial per­spec­tive for broad­en­ing the scope of sci­en­tif­ic and tech­no­log­i­cal research. This approach, which involves explor­ing uncon­ven­tion­al or periph­er­al areas, is prov­ing its worth by open­ing up avenues of dis­cov­ery often over­looked by tra­di­tion­al meth­ods. His­tor­i­cal exam­ples show that major break­throughs, such as the dis­cov­ery of X‑rays or the the­o­ry of rel­a­tiv­i­ty, emerged from seem­ing­ly mar­gin­al research, under­lin­ing the impor­tance of sup­port­ing such initiatives.

In an increas­ing­ly com­pet­i­tive and spe­cialised research envi­ron­ment, edge inno­va­tion is prov­ing nec­es­sary to stim­u­late dis­rup­tive advances. By encour­ag­ing inter­dis­ci­pli­nary col­lab­o­ra­tion, real­lo­cat­ing resources to explorato­ry projects, and mak­ing the most of pro­duc­tive fail­ures, this method makes it pos­si­ble to diver­si­fy approach­es and reduce the risk of sci­en­tif­ic dead-ends. What’s more, cit­i­zen par­tic­i­pa­tion can enrich this dynam­ic, by pro­vid­ing new per­spec­tives and pro­mot­ing the social accep­tance of innovations.

In con­clu­sion, edge inno­va­tion is not just an option, but a neces­si­ty for research insti­tu­tions wish­ing to remain at the cut­ting edge of sci­en­tif­ic and tech­no­log­i­cal dis­cov­ery. It is an essen­tial com­ple­ment to tra­di­tion­al approach­es, enabling the explo­ration of unchart­ed ter­ri­to­ry and the antic­i­pa­tion of future challenges.

1Nüsslin, Fridtjof. “Wil­helm Con­rad Rönt­gen: The sci­en­tist and his dis­cov­ery.” Phys­i­ca Med­ica 79 (Novem­ber 1, 2020): 65–68. https://​doi​.org/​1​0​.​1​0​1​6​/​j​.​e​j​m​p​.​2​0​2​0​.​1​0.010
2Mor­ri­son, Philip, John Billing­ham, and John Wolfe. “The Search for Extrater­res­tri­al Intel­li­gence (SETI).” NASA Tech­ni­cal Reports Serv­er (NTRS), Jan­u­ary 1, 1977. https://​ntrs​.nasa​.gov/​c​i​t​a​t​i​o​n​s​/​1​9​7​8​0​0​10828
3Tobi, H., Kam­p­en, J.K. Research design: the method­ol­o­gy for inter­dis­ci­pli­nary research frame­work. Qual Quant 52, 1209–1225 (2018). https://doi.org/10.1007/s11135-017‑0513‑8
4Van Noor­den, R. Inter­dis­ci­pli­nary research by the num­bers. Nature 525, 306–307 (2015). https://​doi​.org/​1​0​.​1​0​3​8​/​5​2​5306a
5Sun, Y., Livan, G., Ma, A. et al. Inter­dis­ci­pli­nary researchers attain bet­ter long-term fund­ing per­for­mance. Com­mun Phys 4, 263 (2021). https://doi.org/10.1038/s42005-021–00769‑z
6Rons, Nadine. “Inter­dis­ci­pli­nary Research Col­lab­o­ra­tions: Eval­u­a­tion of a Fund­ing Pro­gram.” Coll­net Jour­nal of Sci­en­to­met­rics and Infor­ma­tion Man­age­ment 5, no. 1 (June 1, 2011): 17–32. https://​doi​.org/​1​0​.​1​0​8​0​/​0​9​7​3​7​7​6​6​.​2​0​1​1​.​1​0​7​00900
7Sharachchan­dra Lélé, Richard B. Nor­gaard, Prac­tic­ing Inter­dis­ci­pli­nar­i­ty, Bio­Science, Vol­ume 55, Issue 11, Novem­ber 2005, Pages 967–975, https://doi.org/10.1641/0006–3568(2005)055[0967:PI]2.0.CO;2
8Gaie, Mueck “Pub­lic ser­vices data ana­lyt­ics using arti­fi­cial intel­li­gence solu­tions derived from telecom­mu­ni­ca­tions sys­tems.” Inter­na­tion­al Jour­nal of Busi­ness Intel­li­gence and Sys­tems Engi­neer­ing 1, no. 4 (Jan­u­ary 1, 2021): 283. https://​doi​.org/​1​0​.​1​5​0​4​/​i​j​b​i​s​e​.​2​0​2​1​.​1​22747
9Evans, A. M., & Cam­pos, A. (2013). OPEN GOVERNMENT INITIATIVES: CHALLENGES OF CITIZEN PARTICIPATION. Jour­nal of Pol­i­cy Analy­sis and Man­age­ment, 32(1), 172–185. http://​www​.jstor​.org/​s​t​a​b​l​e​/​4​2​0​01520

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