Global Lighthouse Network 2026

Lighthouse blueprint: six core enablers for high-impact and scalable transformations FIGURE 29 Notes: DOE = design of experiments, MVP = minimum viable product, PLM = product lifecycle management, UI = user interface, UX = user experience. DevOps combines software development (Dev) and IT operations (Ops) to shorten the software development lifecycle and deliver applications and services at a high velocity. Source: Global Lighthouse Network.1 2 3 4 lanoitazinagro eht semoceb eligA DNA; networked teams-of-teams link central data labs with site operations snoipmahc egnahc dna sresu-repuS drive adoption at scale. tuollor dezidradnats yb deniatsuS templates, gover nance and “train- the-trainer” models for parallel deployments. sisongaid-eulav dipar yb nevirD sprints, clear business sponsorship & prioritization frameworks to quickly align IT/OT teams on use cases. /TI ,srotalsnart latigid ,renwo tcudorP data engineers and OT specialists. sdauqs lanoitcnuf-ssorc llamS identify bottlenecks, frame use cases and pr ototype quick wins with 2- week sprints. ,retsam murcs ,renwo tcudorP translators, softwar e developers, data scientists, algorithm and OT engineers. lellarap ni etarepo sdop eliga elpitluM under shar ed gover nance; sprint-to- release cadence delivers pr ototypes. .tuollor ytilibapac ediw-esirpretnE Train-the-trainer and e-lear ning models embed agile, design thinking, and data literacy . seimedaca cfiiceps-noitcnuF formalize continuous lear ning. Early capability focus on digital literacy and translator skills: r ole- specific and need-based training on data, pr ocess mapping, and algorithm basics. gnipytotorp no gnillikspu detegraT tools, UI/UX, cloud and DevOps, and advanced data analytics. ledom ,sdohtem EOD fo noitcudortnI training skills, and cr oss-functional “lear ning loops.” ,erutcetihcra esirpretne evitan-duolC real-time AI/ML and GenAI copilots, IoT & edge data str eaming, optimization algorithms. evitciderp rof noitargetni E2E lluF operations and self-lear ning systems. ssecorp ,ytilibisiv atad no sisahpmE mapping, data cleansing, and basic analytics platforms. ,ytivitcennoc ToII ,kcats nommoC data lakes, basic dashboar ds, and early AI/ML experiments. ,sekal-atad elbalacs fo tnemyolpeD AI/ML and GenAI models, digital twins, and edge integration. D3 ,noitareti ledom dipar no sucoF simulation, and system interoperability (SAP/MES/PLM). knil skrowten noitavonni-oc erutaM ecosystem partners and global academia for sustained model impr ovement and r eplication. ,detargetni sremotsuc dna sreilppuS extending digital twins to the entir e value chain. ,TI ,ssecorp( tnemngila lanretnI quality) supported by early academic or vendor partnerships for data acquisition, benchmarking, and concept validation. seitisrevinu htiw spihsrentrap lamroF and tech vendors to co-develop AI/IoT solutions. mroftalp derahs dna D&R tnioJ gover nance models emerge. egarevA months, top levers tnemyolpeD phase atad raludom yb delbanE architectur es, co-located agile squads, early user testing that accelerates iteration and validation of MVPs.ezitiroirp & yfitned I epytotorp dliub & ngise D niatsus & tnemelpmI pamdaor nevird-eulaV eroc rep sesac esu fo noitazitiroirp desab-eulaV process, high ROI network-wide deployment roadmap, finance & senior leaders' endorsementnoitanigamier ssecorp ssenisuB ,pu-pmar & hcnual ni ngiseder ssecorp eroc dne-ot-dnE manufacturing, quality, maintenance, utilities and energy g-upnilacs dna tfihs larutlu C erutluc IA ,sevitnecni ,gnilledom elor ,evitarraN semmargorp noitpodA deddebme ,reniart-eht-niart ,spmactoob latigiD super-users, feedback loopsStrategy Change managementCapabilities~3 ~5 ~7 ~10 FTE 6 core / 4 extended~18 FTE 10 core / 8 extended~30-40 FTE ~50-100 FTE ~200 FTE ~500 FTE12–15 core / 20–25 extended Typical reach: Typical reach: Typical reach:Strategic roadmap Agile operating model Talent and training Technology and data 5 Ecosystem collaboration 6 Adoption and scaling Global Lighthouse Network: Rewiring Operations for Resilience and Impact at Scale 45