A decade in the past, Picnic got down to reinvent grocery buying with a tech-first, customer-centric strategy. What started as a daring experiment rapidly grew right into a high-scale operation, powered by steady innovation and a willingness to problem conventions.
Alongside the best way, we’ve learnt invaluable classes about scaling know-how, fostering tradition, and driving innovation. Some had been anticipated, others had been hard-earned, and some utterly reshaped our pondering.
Listed here are 10 key classes we’ve learnt—together with how the trade is tackling related challenges.
1. Constructing a tradition of innovation
Early on, we recognized that tradition and organisation are key enablers for innovation. Therefore, we emphasised autonomy by forming small groups that take end-to-end possession. We had been impressed by Amazon’s two-pizza teams, the place small, self-sufficient groups are inspired to innovate with out paperwork.
Nevertheless, we took it a step additional and constructed an “Everyone is an Innovator” tradition and mixed it with our current “Everyone is an Engineer” tradition. Enterprise operators outdoors of the tech organisation can now actively contribute to the characteristic design, specification, growth and configuration. As an illustration, we developed frameworks like Define, Extract, Transform, Present (DETP), the place analysts can use SQL to construct personalised suggestions with out the involvement of engineering groups. Moreover, we developed the Picnic Page Platform that enables enterprise operators and analysts to ship app options independently from the software program growth cycle.
The important thing metrics we optimise for are the idea-to-impact lead time and the time share of innovating and constructing vs sustaining and working. We haven’t reached our aim but, however we’re on path in the direction of everyone spending no less than 80% of their time on innovation and growth.
2. Developer expertise as a progress catalyst
Within the early days, developer expertise was a shared duty throughout all the tech staff. Nevertheless, shared duty led over time to declining accountability, unclear possession, and inefficiency in decision-making. Moreover, whereas we grew as an organisation, expectations rose and complexity elevated. Thus, we rethought our strategy and made Developer Expertise a first-class citizen.
To handle this, we developed a collection of instruments and processes and fashioned a tradition that empowers builders to work effectively, creatively, and fortunately. As an illustration, we constructed self-service instruments for all our engineers that permit them to deal with duties like surroundings setup, database administration, or characteristic deployment successfully.
Subsequent, we embraced a feedback-driven tradition, gathering common enter from builders to determine ache factors of their workflows and proactively deal with them. One key perception from these retrospectives was that sluggish construct instances had been a serious blocker, so we optimised our platform surroundings, decreasing construct instances from 13 minutes to only 1 minute.
Past that, we monitor DevEx metrics like developer satisfaction, cycle instances, and deployment frequency to measure the impression of DevEx initiatives and rejoice milestones to strengthen the significance of DevEx.
Related initiatives have been undertaken by different massive tech organisations, e.g. Google’s Bazel construct instrument that ensures high-efficiency builds and testing at scale, Netflix’s Paved Highway Philosophy, Meta’s Glean, and Stripe’s Developer-First Tradition and Working Rules. Going ahead, we’ll monitor intently how AI-assisted coding instruments reshape the developer expertise and produce us nearer to the last word engineering expertise with high-quality code strategies, real-time concern resolutions, and automatic code refactoring.
3. The facility of platforms
Within the first few years, we centered on constructing core performance to cowl a baseline of options throughout our merchandise. Nevertheless, by 2020, we started noticing commonalities between merchandise and duplication inside them. This led us to issue out these redundancies, outline reusable patterns, and package deal them into the Web page Platform and Picnic Assist Modules.
This platform strategy promotes consistency, reduces code duplication, simplifies upkeep, and lowers complexity, in the end bettering the general high quality of our codebase. Moreover, we built-in instruments like Error Susceptible (which we additionally open-sourced) to catch programming errors early.
Past bettering code high quality, these assist modules play a vital function in main framework migrations, resembling our transition to Spring 5 (by now we’re utilizing even Spring 6), making certain easy upgrades and long-term compatibility. By offering a strong basis, these modules speed up growth, implement finest practices, and preserve a unified tech stack.
Moreover, our platform groups have been instrumental in standardising provisioning and deployments by Terraform, Helm and Spacelift, and accelerating our transition in the direction of CI/CD by Spinnaker, Argo CD and TeamCity.
Wanting past Picnic, related platform approaches have pushed effectivity at Fb (React) and Stripe (Funds), providing highly effective abstractions for complicated flows and simplified integrations at scale.
However that is only the start. Platform pondering at Picnic will proceed evolving, with superior ML performance not solely powering the platform but in addition changing into a core constructing block for the following technology of our know-how.
4. Scaling infrastructure for innovation
Infrastructure is a key enabler and accelerator of innovation, driving developer autonomy, automation, and scalability. By making use of Infrastructure as Code and Steady Integration throughout our stack, we automate and standardise service configuration, making certain quick, dependable, safe, and cost-efficient deployments whereas sustaining agility.
Integrating steady integration into infrastructure administration has simplified the provisioning and modification of assets, decreasing guide effort and growing effectivity. To take this a step additional, we are actually decentralising infrastructure possession throughout product groups utilizing Spacelift. This allows builders to independently handle infrastructure wants whereas making certain compliance with organisational requirements.
This mixture of IaC, automation, and developer empowerment permits us to scale infrastructure seamlessly, fostering steady innovation and operational flexibility, as demonstrated in the course of the COVID-19 pandemic. Related methods have been adopted by Netflix, which has gone even additional with chaos engineering practices to stress-test resilience.
Wanting forward, we purpose to additional improve our infrastructure by working in the direction of predictive infrastructure administration, proactively anticipating and dynamically allocating assets to enhance efficiency and effectivity.
5. Information-driven determination making
Information is for us the strategic asset to innovate, determine and function. Within the early days, knowledge was the inspiration to assist fundamental operations and learn to obtain operational excellence. Over time, knowledge grew to become the motive force for strategic decision-making and innovation.
Therefore, we constructed a lakeless Information Warehouse integrating knowledge from over 300+ microservices and operating hundreds of ETL/ELT jobs and ingesting thousands and thousands of occasions each day for environment friendly evaluation. This enabled us not solely to analyse knowledge, construct experiences, and get insights, but in addition to unlock our journey to construct highly effective machine studying merchandise, with out spending weeks on knowledge cleansing. As an illustration, we constructed a collection of demand forecasting instruments leveraging Temporal Fusion Transformers (TFTS), a collection of customer support agent assist instruments to allow service excellence, environment friendly route planning, sensible buying lists, and a lot extra.
All of that’s solely doable by leveraging our personal ML Information Platform that permits not solely ML engineers however everyone in Picnic to construct, practice and deploy fashions in manufacturing. Efficient studying requires suggestions and experimentation, so we constructed a scalable experimentation framework to run many A/B exams concurrently to study what works and what doesn’t. By combining democratised entry to knowledge with scalable ML infrastructure and a data-first strategy to software program growth, we constructed a powerful and sustainable tradition of organisational data-driven studying.
Related examples within the trade embody Netflix’s use of information analytics, enabling data-driven content material creation and personalised suggestions, and Uber’s evaluation of journey knowledge, demonstrating methods to optimise each buyer expertise and operational effectivity.
Going ahead, we’ll see many extra AI-native product options (e.g. superior steerage and automation of key flows) and AI-native enterprise models (operating enterprise operations that natively rely upon AI know-how).
6. Folks-centric tech
Our individuals strategy is deeply rooted in our conviction that “Tech is a individuals enterprise.” Which means that we prioritise expertise growth, collaboration, and steady studying over inflexible constructions and conventional hierarchies. Engineers are inspired to develop, join, and contribute from day one, creating an surroundings the place innovation is pushed by a shared mission moderately than top-down directives.
We attempt to rent high-calibre expertise (who doesn’t? ;)) however moreover, we additionally look fastidiously into alignment with our imaginative and prescient. Therefore, we prioritise cultural match over simply expertise and have a powerful bias in the direction of missionaries instead of mercenaries. Studying and progress are a steady course of; for that, we’ve constructed the Tech Academy that gives tailor-made coaching for each expertise degree. Our onboarding is designed as a transformative journey moderately than simply getting began on the job.
Not solely can we nurture engineering expertise, however we additionally present intensive management coaching. We’ve got put intensive thought into the precise strategy. Our formulation: management permits moderately than controls. Leaders act as mentors, coaches and enablers, moderately than micromanagers. They foster bottom-up innovation, the place concepts can emerge from any degree, and domesticate a clear, trust-based tradition that ensures staff really feel valued. It’s the rationale why a lot of our tech leads are engineers who’ve naturally grown into this function!
On the identical time, we prioritise well-being initiatives that assist a wholesome and balanced work surroundings, recognising that sustained efficiency stems from each skilled progress and private well-being.
Our strategy combines insights from main corporations: Valve’s flat org construction, permitting staff to decide on tasks and type groups organically; Canva’s deal with function over perks, aligning work with staff’ values; and NVIDIA’s tradition of mental honesty, selling steady studying and transparency.
Going ahead, we’ll work in the direction of a extremely adaptive organisational construction with fluid groups that repeatedly (re-)align with strategic targets, enterprise goals, and tech priorities.
7. Steady enchancment by suggestions
Suggestions is the last word superpower for studying and progress. From day one, we embedded this philosophy into our product and tech tradition, making certain that suggestions drives innovation, high quality, and steady enchancment.
On the product facet, buyer suggestions performs a central function. We actively collect insights by in-depth interviews, app evaluations, customer support retrospectives, and social media interactions, addressing each explicitly expressed and inferred wants.
Internally, we foster a innocent incident administration tradition, the place accountability is inspired with out worry. This strategy turns failures into alternatives for course of refinement and systematic enhancements. Nevertheless, what makes our strategy really particular is the shut relationship between our tech groups and their inner clients. Engineers, analysts, and enterprise groups work hand in hand, making certain direct and rapid suggestions, which creates an unparalleled sense of possession and alignment.
In software program engineering, we depend on structured suggestions loops, together with peer code evaluations and instruments like Error Susceptible, to keep up code high quality. Past formal processes, we encourage staff studying and data sharing by Lunch & Study periods and our Tech Safari programme, which provides new joiners a 360° view of enterprise and tech. Their progress continues by our Tech Academy, providing structured studying paths tailor-made to every expertise degree.
We love pace, however much more, we worth feedback-driven decision-making. Therefore, we foster a tradition wherein builders and enterprise work collectively to realize pace not by slicing corners, however by delivering high-quality MVPS and utilizing future-proof structure.
Wanting forward, suggestions loops will turn out to be much more autonomous and predictive. AI will monitor person interactions, detect inefficiencies, and dynamically optimise merchandise in actual time, blurring the road between iteration and innovation.
8. Balancing agility and stability
Over time, we advanced from a fast-moving startup (don’t choose, however sure, there was even a time after we ran our growth course of with out PR evaluations ;)) to an efficient scale-up the place we discovered a brand new candy spot between agility and stability to reliably serve our greater than 1 million clients. The early years had been characterised by a easy growth course of, light-weight infrastructure, and excessive autonomy with ultra-short determination traces (in different phrases: thrilling, but in addition chaotic and unsustainable). Retrospectively, there’s a temptation to romanticise this time (pure innovation, infinite startup power, David vs Goliath narrative, false reminiscence of complete freedom), however in actuality, it’s a part that may’t – and shouldn’t – be maintained. As a substitute, it must be thought-about a stepping stone in the direction of the scaling and maturity part.
That is the part we are actually in, with formalised growth processes, efficient governance fashions for knowledge and infrastructure, and platforms that allow and empower fast growth of next-generation concepts. Our most important learnings are that agility have to be structured to scale, tradition evolves (and that’s OK!), technical debt can’t be ignored, hiring shifts from generalists to specialists, autonomy wants guardrails, and scaling a staff additionally requires scaling the management. The perfect corporations by no means lose their means to maneuver quick—they simply get higher at scaling responsibly.
A key realisation on this journey has been the significance of “shift left” pondering—embedding high quality, safety, and reliability earlier within the growth course of moderately than addressing them reactively. By frontloading vital checks and automating governance, we’ve considerably decreased friction down the road, permitting us to keep up pace with out sacrificing stability. Equally, we’ve embraced the precept of “decelerate to hurry up”, recognising that investing time in structured foundations—be it well-designed structure, sturdy CI/CD, or clear decision-making frameworks—pays off in execution pace and long-term agility.
Nice examples from different tech corporations embody Spotify’s mannequin of autonomous squads to foster innovation and agility, Facebook‘s evolution from “Transfer quick and break issues” to “Transfer quick with steady infrastructure”, and Netflix’s migration in the direction of microservices. Going ahead, we’ll transfer from merely balancing agility and stability to scaling innovation intelligently, making certain that autonomy, effectivity, and impression develop in tandem. One other fascinating route is Autonomous Innovation Networks, the place fluid, mission-driven groups dynamically type round high-impact issues, making certain agility and engagement.
9. Embracing AI-driven automation
Everyone in tech loves automation, however the actuality is that automation is the ultimate step in an extended journey. Our journey started by constructing a powerful Grasp Information Basis, which laid the groundwork for our first technology of programs. These programs adopted easy rule-based determination logic, both as hard-coded Java enterprise logic or determination tree logic encoded in configuration. Whereas this strategy labored properly for the primary few thousand clients, we rapidly realised it wouldn’t scale successfully.
The lacking piece was a real-time efficiency suggestions loop that would analyse system efficiency, determine enchancment alternatives, and set off modifications in actual time. To handle this, we constructed a close to real-time Information Vault alongside our Lakeless Information Warehouse to offer a unified, fine-grained view of enterprise efficiency throughout all programs. Moreover, we ensured that each one programs had API endpoints to configure and management their logic, permitting us to develop instruments that repeatedly monitor and enhance efficiency.
For instance, we now have edge instruments that optimise inbound, fulfilment, and last-mile operations—from dynamically slotting merchandise in warehouses to demand forecasting and route planning. The primary technology of those instruments was constructed utilizing Google Scripts, enabling ultra-fast growth but in addition resulting in a shadow IT ecosystem that was troublesome to keep up.
To resolve this, we constructed a Platform for Edge Methods, the place this logic could possibly be carried out in Python, the lingua franca of all analysts at Picnic. This shift permits not solely tech groups however everybody at Picnic to contribute actively to the system panorama.
The most recent evolution in our automation journey is the transition from rule-based enterprise logic to AI-based determination logic. Our aim is to maneuver each data-driven determination into ML fashions whereas preserving all remaining rule-based logic in Java, Python, or TypeScript. For instance, in route planning, the cease time calculation is AI-based, whereas the shortest path calculation makes use of a heuristic algorithm in Java.
In abstract, automation isn’t nearly robots—it’s about clever, data-driven decision-making. The important thing to success is scalability, enabling a self-learning, real-time adaptive ecosystem.
10. Prioritising long-term imaginative and prescient
We’ve got constructed each our enterprise and know-how with a built-to-last mindset, aligning technique with tradition and operational decision-making with sustainable long-term targets.
From the beginning, we embraced data-first pondering, customer-centric reasoning, and scalable infrastructure tailor-made for agility. This strategy permits us to attach short-term wants with long-term imaginative and prescient, making certain that our options stay adaptable as we develop.
On the tech facet, we centered on modular and scalable structure, developer autonomy, and shut collaboration between tech and enterprise groups. This ensures that our merchandise not solely meet right this moment’s necessities however can be prolonged and modified with ease.
One among our core ideas is to optimise not for the quickest construct to the primary launch however for the simplest path to the a hundredth iteration. We all know that true excellence comes from steady enchancment, so we optimise for long-term outcomes moderately than short-term pace.
After all, our journey hasn’t been with out challenges. We needed to study the laborious approach that over-indexing on pace results in important technical debt, scaling with out clear enterprise priorities results in costly overgeneralisation, and fast hiring whereas ignoring tradition results in misalignment and requires painful correction.
Wanting forward, we’re exploring promising approaches resembling AI-driven operational effectivity (e.g., predictive scaling and AI for engineering), hyper-personalised expertise retention and tradition scaling, and additional decentralisation, the place engineers collaborate with AI brokers because the norm.
That stated, we’re totally conscious that AI just isn’t a silver bullet. It comes with limitations, dangers, and moral challenges. Not each breakthrough must be AI-driven—there are lots of different paths to innovation, and we consider AI must be used the place it provides really distinct worth.
The teachings learnt over the previous decade should not simply milestones—they’re the inspiration for what comes subsequent. And one factor is obvious: the journey was, is, and can by no means be linear. From embracing uncertainty to scaling with agility, the previous ten years have strengthened our robust perception that buyer obsession, relentless innovation, and adaptive management are key to long-term success. As we glance forward, the following wave of grocery tech innovation will demand even larger pace, resilience, and collaboration, reminding us that the best breakthroughs are nonetheless to come back.
