Insights

The AI Path to Purchase: How Agentic Commerce Works

Elisabetta Ceriani — Wed, 13 May 2026 12:00:33 GMT

What is agentic commerce? Agentic commerce is when AI handles the shopping process for you, from figuring out what you need to comparing options and making the purchase. That means an AI assistant finding and ordering products on their behalf, whether through a brand‑owned direct‑to‑consumer (DTC) experience, a marketplace, or a third‑party AI interface.

Hidden Revenue Loss in CPG Sales Execution

Gözde Sakarkaya — Wed, 13 May 2026 07:14:00 GMT

In CPG (Consumer Packaged Goods) sales execution, even a small 1–2% gap at the outlet level can cost millions each year. Yet most organizations still struggle to identify where this revenue loss occurs.

Sales reps spend hours preparing for store visits, but often enter those visits without reliable, outlet‑specific insights. As a result, they make high-stakes fulfillment decisions with incomplete information. This directly affects what retailers order, what suppliers deliver to the shelf, and what customers buy.

Why traditional sales execution falls short

During CPG sales execution, field sales staff decide which products, assortment and quantities to order for each store. This includes pre‑visit preparation, order building, and assortment compliance. When teams rely on manual preparation or average‑based planning, execution can break down. Stockouts increase, assortment gaps appear, and revenue slips away at the shelf.

This is not an organizational performance issue; it is a data issue. The tools sales reps use today rely on stale, outdated data from legacy systems and historical databases.

This is where AI adds clear value. By embedding AI into daily sales execution workflows, CPG organizations enable data‑driven decisions, reduce guesswork, and unlock incremental revenue across markets.

How revenue is lost in CPG sales execution

The average CPG field sales rep spends up to 40% of pre‑call preparation time manually estimating orders. Too often, these estimates rely on spreadsheets, memory, and gut feeling rather than reliable, real-time outlet‑level data.

This isn’t a productivity issue — it's a revenue issue.

When reps visit a store without outlet insights, they mainly rely on intuition, not data, to make sales decisions. This often leads to suboptimal order quantities, gaps in core assortment, and misaligned SKU mixes.

The result is poor on-shelf availability, hence lost sales. It also leads to decreased customer satisfaction, lower return per visit, and less impactful and less credible retailer conversations. All these ultimately limit both distribution quality and revenue growth.

Preventable revenue leakage

Stockouts cause immediate revenue loss and are the most visible source of revenue leakage in CPG sales execution. Effective stockout reduction is one of the fastest ways to protect topline revenue. They cost the CPG industry billions each year.

Assortment gaps happen when an outlet’s current products do not match what customers want. They appear as:

SKUs that were ordered, but are missing from the shelf
SKUs that were ordered in the wrong proportions
SKUs that were never ordered in the first place

This is where assortment compliance becomes critical. If your golden SKUs are not present in the right outlets, you will quietly lose sales, even when the demand exists.

These gaps are where revenue quietly disappears. When organizations only measure what they sell – not what they should be selling – assortment gaps remain largely invisible.

Reviewing order history, checking stock levels, and preparing recommendations all consume time. As a result, sales reps can spend up to 30-45 minutes preparing for a single visit.

Too much preparation time keeps sales reps from other important work. It reduces time for meaningful conversations and can also weaken relationships with retailers. The result isn’t just lost time, but lost opportunities that directly impact revenue and sales rep productivity.

What is Suggested Order?

Suggested Order by Lingaro is an AI‑driven approach to sales execution. It shifts ordering decisions from averages and intuition to outlet‑level facts. Instead of asking sales reps to figure out the “right” order themselves, it provides visit‑ready recommendations. These recommendations reflect how each outlet actually sells.

Sales teams can shift from reactive ordering to steady, data-driven sales execution without adding workflow complexity.

Fixing revenue loss at outlet level

Stockouts and assortment gaps rarely stem from poor sales rep preparation. More often, they happen because order decisions are not real-time and don’t reflect what each outlet actually needs. Suggested Order provides outlet‑level recommendations that match stock and assortment to how each store sells.

Here is what it changes in practice:

Outlet-level recommendations, not averages

Using machine learning and advanced store profiling, predictive models analyze sell‑out patterns, local demand drivers, promotions, and seasonality. The system uses data and AI-generated insights to recommend the right products and quantities for each outlet.

Immediate revenue impact and revenue uplift

CPG organizations using Lingaro’s Suggested Order see up to a 20% reduction in underselling. This directly drives revenue uplift while also creating a sustained reduction in stockouts over time.

Peer benchmarking and store profiling

The system compares each outlet’s performance against similar stores with comparable profiles (e.g., size, location, shopper mix). When it detects a meaningful performance gap on a product and assortment, it flags the opportunity. This enables reps to take targeted action to close the gap.

Ensure assortment compliance and Must Stock List (MSL) checks

The system embeds the Must Stock List (MSL) and assortment rules into its data and AI-driven recommendation logic. This ensures consistent prioritization of core products while still optimizing the total order for maximum impact.

Strong support during new product launches

Suggested Order pushes new SKU recommendations to every relevant outlet from day one. This enables a faster and more consistent rollout.

Figure 1:An overview of the Suggested Order solution.

When reps spend much of their day manually preparing orders, they have less time to sell. This means they have less time for strengthening retailer relationships and growing their accounts. AI order planning reduces that burden. With machine learning order planning, reps can make faster, more consistent decisions while still applying local context.

What this looks like in reality:

Order optimization across scenarios

AI models assess outlet potential, past order trends, promotions, seasonality, and growth. This guides tailored order recommendations and strategic planning for each outlet.

Less preparation time = more selling time

AI powered order planning handles time-consuming prep work. This lets reps focus on revenue-focused work, like making more sales visits and having high-value talks with retailers.

Smart allocation of sales effort

The Suggested Order platform shows where effort will have the most impact. It recommends priorities based on outlet potential, not habit or guesswork.

Productivity gains without added cost

Implementing Suggested Order achieves improvements without the need to hire more reps or increase promotional spending. By simply making every rep more efficient at every visit, overall productivity increases.

Proven financial impact

Organizations using Suggested Order typically see a 2x-6x ROI within 12-24 months of deployment. Successful adoption, higher sales productivity, and better CPG sales execution quality drive this impact.

Figure 2: The Suggested Order dashboard.

Why Databricks matters

One of the biggest risks with legacy shelf forecasting tools is the lack of real-time information. Most systems depend on latent, historical data instead of real‑time OLTP data or data lakes.

In addition, legacy systems can’t share enterprise data from the supply chain, POS, or loyalty programs. It introduces further issues by reusing stale legacy data across use cases.

That is where architecture matters.

Suggested Order runs on the Databricks Data Intelligence Platform. It provides a unified data and AI foundation that runs at the speed of commerce without added complexity.

Built for cost-efficient scale

Databricks is optimized for large-scale data and AI workloads. Compared to legacy systems and traditional data warehouses, processing costs drop by up to 50%. The savings grow as the solution scales.

Enterprise-ready by design

Lingaro’s Suggested Order is Databricks‑native. You benefit from the full Data Intelligence Platform, now and in the future. This includes serverless data pipelines, near‑instant data latency with Lakebase, agentic decisions powered by AgentBricks, and natural language capabilities through Genie.

Real-world proof: scaling Suggested Order globally

The scalability of Suggested Order is proven in practice. Built on Databricks, the solution scaled across 28 markets and more than 300,000 outlets for a global CPG beverage bottler. This delivered a 3.2% uplift in sales volume and a 10× faster rollout, without added cost or complexity.

The balance of scale, speed, and control makes Databricks a strong foundation for long‑term, AI‑driven CPG sales execution.

Figure 3: Success metrics from a client using Suggested Order by Lingaro.

A model that improves over time

Most ordering tools rely on historical data and fixed rules that quickly become outdated. In contrast, Suggested Order learns continuously from real outcomes and improves with every sales cycle.

Learns from real outcomes, not just historical data. The model adapts using actual sell-out data, not just historical averages.
Improves through feedback. When a rep overrides a recommendation and sell‑out data confirms the choice, the model learns from it. The model then uses this signal to refine future sales decisions.
Accuracy improves over time. The platform becomes more accurate with continued use.

From hidden losses to real returns

Lack of effort is rarely the cause of revenue loss in CPG sales execution. It happens when teams make everyday CPG field sales decisions without the outlet-level insight they need to get them right.

Suggested Order by Lingaro applies outlet‑level recommendations and AI order planning. This improves assortment compliance, reduces stockouts, and increases sales rep productivity. The result is stronger commercial excellence, with clearer returns and better execution where it matters most.

Want to explore outlet-level ordering in practice? Contact us and talk to our experts.

Advancing LCA through process scale-up and automation

Dominik Jasiński — Thu, 16 Apr 2026 10:21:13 GMT

Life Cycle Assessment (LCA) is a powerful tool for enhancing sustainability, driving innovation, and enabling data-driven decision-making. It can guide how to improve operations, reduce costs, and strengthen your business. Nevertheless, embedding LCA in strategic business decisions is challenging due to high data intensity, methodological complexity, and resource constraints.

A single LCA can cost up to $100,000 or even more, depending on the complexity of the product, the depth of required data collection, and scope (e.g., environmental metrics included in the assessment). Furthermore, navigating the complexities of LCA often requires expert guidance and specialized software tools like SimaPro, Sphera (GaBi), and openLCA.

Organizations with limited experience in LCA typically reach out to external services for support when conducting their first LCA. However, as described in Sphera’s 2024 overview of LCA automation, when the number of LCAs increases, organizations move towards building up capacity by training in-house experts and licensing LCA software and databases to manually perform all relevant LCA steps, from data collection to LCA report production.

Over time, companies start to define clear processes and procedures, which act as the "connective tissue" for delivering systematic, efficient, and consistent LCAs, which is also a prerequisite for LCA process automation and scale-up.

What is LCA automation and scale-up?

The static nature of LCA results brings limitations to companies’ ability to adapt to continuously changing product designs, product portfolios, and supply chains. This is especially problematic for companies managing a broad range of products — sometimes thousands of SKUs — for which conducting the traditional LCAs is expensive and inefficient.

LCA automation enables organizations to perform LCAs across a full product portfolio with minimal manual input at high speed and high volume, moving beyond slow, manual, one-off studies. It allows organizations to automatically calculate the environmental impact of thousands of products across their entire life cycle in real time, enabling continuous, scalable, and data-driven insights for faster, evidence-based decision-making.

LCA scaling and automation with Lingaro

At Lingaro, we use technology to support clients in moving away from traditional manual LCAs towards LCA innovative automation platforms. We provide a scalable, end-to-end LCA infrastructure solution based on a cloud-agnostic architecture and dynamic visualization platforms that can be tailored to the specific product portfolio and company demand.

Key features of an LCA automation platform with Lingaro:

Seamless Integration: dynamic data integration with the company’s existing ERP/PLM (Enterprise Resource Planning and/or Product Lifecycle Management) solutions, establishing an always up-to-date and single source of truth for data. Possibility to connect with other Ecodesign tools in the company ecosystem and external databases to ensure data accuracy and efficiency.
Scalability: the ability to accommodate processes and combine large data volumes and requirements without sacrificing performance or accuracy and transform it into actionable insights for sustainable decision-making.
AI-empowered: a platform supported by AI accelerators and algorithms (e.g., SVMs, ANNs, decision-tree based models, etc.) to handle LCA data gaps (e.g., missing characterization factors, material or process data), and to automatically match background databases (e.g., bill of materials inputs) to the most accurate LCA datasets and emission factors (e.g., Ecoinvent).
Adaptive Methodologies: a flexible solution that can be adapted to multiple LCA methodologies (including specific LCA impact assessment methods) and different legal requirements and standards regardless of geographic location or industry-specific regulations.
Accessibility and interpretation: an intuitive, user-friendly, and dynamic interface that displays complex scientific LCA results in an understandable manner so that even non-experts can understand and derive actionable insights from the analysis.
Dynamic insights: a platform that provides dynamic and up-to-date information about environmental impacts of product portfolio, as well as any potential changes (e.g., what-if simulations) in product formulations, supply chain practices, end-userbehaviors and others. It is a powerful tool, deeply embedded in LCA science, for shaping the decarbonization, water reduction, and biodiversity conservation strategies at the organizational level and along the broader industry value chain.

How does the process work?

The process of developing an automated and scalable LCA platform is based on close collaboration with our customers, which is essential for delivering the product, service, and experience tailored to their individual needs and technical requirements. The exemplary process and technologies used for LCA automation platform development at Lingaro is presented in Figure 1.

Figure 1 exemplary process and technologies used for LCA automation platform development at Lingaro

Extracting relevant product data from company’s records

Lingaro’s LCA automated platform connects with the company’s existing ERP/PLM systems, which allows for bulk importing of relevant product data and specifications. Data lake extraction from ERP/PLM systems involves pulling structured, transactional data (e.g., BoMs, shipment data, energy and other production inventory data) into a scalable repository for analytics. This eliminates the need for manual input, saving time, and enabling organizations to reach actionable insights more quickly.

Developing LCA models

An integral and foundational part of LCA is modeling, which requires a combination of deep methodological expertise and, increasingly, technical skills to leverage automation. In the 2018 article How can we scale up LCA studies without compromising on quality?, PRé Sustainability emphasizes that a qualified LCA expert remains essential to the process, responsible for building models, selecting appropriate background data, and making sound methodological choices.

LCA is deeply embedded in science, and a knowledgeable person with ample experience in LCA who knows how to handle data gaps and quality issues and how to integrate, validate and interpret data will be an integral part of this process. Other, more time-consuming tasks (e.g., collecting some relevant data, minor adjustments in LCA model, creating individual reports) can be supported via technology (e.g., APIs, cloud platforms, AI).

At Lingaro we combine LCA expert knowledge relevant for building LCA models with technical automation skills to deliver an end-to-end solution to the client. Our LCA experts work closely with the client to collect relevant inventory data for building LCA models, handle data quality issues, and provide relevant recommendations to the technical team responsible for the process automation. 

Building LCA libraries for enhancing scalability

In a conventional LCA, practitioners frequently encounter situations where they must build "one-off" or non-reusable models, often for bespoke products, specific processes, or regions, which cannot be easily adapted or reused by other LCA practitioners. Even if these models could be reused for future LCAs, other LCA practitioners may not be aware that they even exist. This is especially a major limitation in large organizations, where LCA practitioners are increasingly spread across diverse, specialized departments, transitioning from a solely environmental function to cross-functional teams integrated into product development, engineering, marketing, and strategy.

LCA automation and scale-up shifts this paradigm by moving from individual, project-specific LCA models to library-based, modular, or configurable LCA models. Instead of building a new individual LCA model for every single product or project – which is time- and resource-consuming – LCA libraries provide standardized pre-built databases that can be reused for multiple projects, product categories, or systems. Once connected to configurable models, it is possible to make adjustments in materials, componentweights, and other variables (depending on the library structure and model parametrization) with minimum effort and cost, and thus enabling scalability, consistency, and faster, more efficient assessments.

There are multiple software solutions (e.g., SimaPro Synergy or Sphera Cloud) already available on the market, that allow not only on building individual LCA models, but also offer solutions for shared LCA libraries, configurable models, and APIs to scale from asingle product LCA to entire portfolio assessment.

Analyzing and mapping data

Lingaro scales the project by analyzing, mapping, and aggregating the data. The process involves the automated connection of product input data (such as BoMs, production inventories, and shipment data) from the ERP/PLM system with the appropriate LCAlibraries and models via API. This enables organizations to speed up the LCA process significantly (from months to even days), eliminates manual data entry, and reduces the risk of errors occurring.

Exploring the data and creating LCA reports

In the final step, our skilled developers connect to relevant data sources and software solutions and create a user-friendly interface (dashboard) that translates the complex LCA modeling and data into interactive visualizations, such as tables and graphs (e.g., bar, pie charts, and spider charts) with several sorting and filtering options (e.g., presenting impacts per specific life cycle stages, impact category, product, material, country, region, and so on).

The analytics dashboard (e.g., Power BI 2-in-1) is increasingly used to provide standardized, interactive, and actionable LCA reports, transforming complex environmental data into clear visualizations for sustainability reporting. These reports enable organizations to track key performance indicators related to carbon, water, and waste, ensuring compliance with international standards and facilitating informed, data-driven decisions. The analytics dashboard is an intuitive interface, requiring minimal learning, that enables organizations to explore the data by:

Reporting LCA results - presenting the total LCA impacts across all life cycle stages for product portfolios;
Identifying portfolio hotspots - pinpointing the most significant contributors to environmental impact within a product's life cycle, and;
Running WHAT-IF simulations - exploring potential outcomes by changing variables, e.g., BoM and volume mix.

An example of such a dashboard for packaging for a Fast-Moving Consumer Goods company is presented in Figure 2.

Are You AI-Ready? Lingaro at Reuters Events Pharma 2026

Anastasiia — Thu, 09 Apr 2026 15:31:46 GMT

Pharma does not need more AI ideas. It needs technology that actually works in the real world.
For businesses, real impact happens when you have a perfect mix of strategy, data, and adoption. That means you have a clear strategy with trusted and connected data. You also implement AI solutions for genuine business use, and your teams adopt these tools into their daily workflows.

That is what AI-readiness looks like in pharma and life sciences. And that is exactly what we will be bringing to Reuters Events Pharma 2026 in Barcelona.

Here is everything you need to know, whether you are attending in Barcelona or just keeping tabs on the latest insights in pharma.

Predict, Prevent, and Optimize Scrap with an AI Response Agent

Yassin Ibrahim — Tue, 07 Apr 2026 15:05:58 GMT

Scrap has long been one of the most persistent and expensive challenges in manufacturing resulting in an estimated $250B-$300B in lost profit. Whether caused by excess inventory, product obsolescence, quality defects, or shifting demand, scrap erodes margins and disrupts supply chains in ways that are often difficult to detect early.

Self-healing Data

Priya Yadav — Fri, 27 Mar 2026 05:22:48 GMT

Retail and consumer goods companies are experiencing one of the most dramatic shifts in customer behavior in decades. Consumers are no longer following linear buying journeys, but moving fluidly across physical stores, digital marketplaces, social commerce channels, and mobile experiences. They discover products through influencers, compare prices instantly, expect personalized promotions, and demand near-real-time fulfillment.

Case Study: Sustainable Packaging Through LCA Automation

Maciej Drwal, MPA — Wed, 25 Mar 2026 15:26:57 GMT

A Fortune 500 company scaled packaging sustainability analysis with Lingaro’s Automated LCA Platform, a governed data lake and analytics layer that enabled real-time environmental and cost insights across a large SKU portfolio.

Client Profile:

Lingaro partnered with a Fortune 500 company focused on reducing carbon footprint and advancing a circular economy through sustainable packaging across a complex, high-volume product portfolio.

Challenge:

The client managed thousands of packaging combinations and lacked visibility into their environmental impact. Life Cycle Assessment served as the standard methodology, but scaling it across the portfolio proved difficult.

High LCA costs limited coverage. Assessments exceeded 10,000 USD per SKU, restricting analysis to a small portion of the portfolio.
Slow turnaround times blocked decision-making. Manual LCAs took months, which did not support R&D workflows.
Disconnected cost and sustainability data created inefficiencies. Teams merged LCA and Life Cycle Costing data manually, which introduced inconsistencies and delayed insights.
The organization needed a scalable, automated approach to evaluate sustainability and cost together across the full portfolio.

Solution:

The Automated LCA Platform is a unified data platform for sustainability and cost analytics. Lingaro replaced manual LCA processes with a governed data lake and automated calculations, enabling real-time analysis across nearly the entire packaging portfolio.

Centralized data lake integrating packaging bill of materials, LCA datasets, and cost data.
Automated LCA calculations aligned with PEF and ISO standards.
Integrated Life Cycle Costing data for combined environmental and financial analysis.
Real-time scenario modeling for packaging design and portfolio changes.
User-facing dashboards for hotspot detection, scenario analysis, and decision support.

Impact:

The solution replaced manual, limited-scope LCA studies with automated, portfolio-wide analytics, improving speed, coverage, and decision quality.

Near full portfolio LCA coverage enabling hotspot identification across environmental impacts and recyclability criteria.

Real-time updates reflecting product and volume changes.
Faster scenario analysis from months to near-instant insights.
Combined environmental and cost analytics in one platform to guide decision-making.
Secure deployment within the client environment protecting sensitive data.
98 percent portfolio coverage, 50 USD per SKU or less, 60 percent packaging costs modeled or higher.

Lingaro built an Automated LCA Platform for a Fortune 500 company to replace slow manual studies. The secure data lake scaled environmental and financial analytics to 98 percent of their packaging portfolio. Analysis costs dropped to 50 USD per SKU or less. Turnaround times shrank from months to near-instant simulations. The client now makes fast, data-driven sustainability decisions.

Case Study: The Hidden Cost of Fragmented Sustainability Reporting

Oliwia Sobczyk — Mon, 23 Mar 2026 15:22:27 GMT

A Fortune 500 CPG company strengthened sustainability reporting with Lingaro’s Sustainability Reporting Hub, a governed data lake and dashboard layer that unified fragmented data and enabled audit-ready disclosures.

Client Profile:

Lingaro partnered with a global CPG leader and Fortune 500 company operating across multiple regions under complex, evolving regulatory requirements.

Why Net Zero Depends on Automated Life Cycle Assessments

Maciej Drwal, MPA — Mon, 16 Mar 2026 11:44:33 GMT

Understanding environmental impacts is imperative for managing them and communicating progress to consumers. Consumer Packaged Goods (CPG) companies have so far focused on company-wide reporting obligations, not product sustainability.

With only a company-wide understanding, CPG leaders can’t pinpoint which products drive impacts across thousands of SKUs. You must understand product impacts to tailor initiatives and communicate product sustainability information to stakeholders.

Life Cycle Assessment, the gold standard for product sustainability measurement is slow and costly to deploy across large SKU portfolios. Automating LCAs reduces costs by at least 90%, enabling large-scale deployment of LCAs across even the largest portfolios.

Corporate sustainability reporting is how corporate environmental impacts are measured today

Corporate sustainability reporting has expanded over the past two decades. The UN's 2015 Paris Agreement sped up this shift. It culminated in the early 2020s era of emerging Net Zero 2050 commitments and pledges. To reach Net Zero, companies needed to first understand how much net-negative they were. What was their carbon footprint?

The GHG Protocol’s standard, published in 2001, gave companies a clear way to measure their climate impact, covering both direct operations and the full value chain. It underpins Corporate Carbon Footprints (CCF) which became a priority for corporations.

CCFs answer key questions for external stakeholders such as regulators, investors and business partners on corporate climate impacts. They became integrated into larger reporting frameworks and standards such as SASB, GRI, and CSRD. These frameworks gave companies tools to report their carbon footprint and other environmental impacts, such as water use.

Corporate sustainability reporting is necessary but insufficient

Sustainability reporting utilizing CCFs and equal methods for other environmental impacts has one critical limitation. It aggregates impacts to company level data. Such aggregates are good for many uses and stakeholders but insufficient for important uses such as:

Communicating product sustainability information to customers

In both B2C and B2B, buyers want to understand the sustainability of the specific product they buy. They do not want to rely only on how sustainable the seller appears in total. To check product sustainability, companies need product level data. They also need it to benchmark alternatives.

Realizing sustainability ambitions through impact reductions

For initiatives aimed at improving sustainability performance to be effective both the before and after need to be measurable. Without enough detailed data it’s impossible to identify hotspots and target initiatives. Sustainability reporting shows that energy use at a specific factory may be a problem. It does not show which products drive that energy consumption.

With a strong focus on products, Life Cycle Assessments were the answer all along

Life Cycle Assessment is a standardized method for product sustainability characterized by:

Evaluating the entire product lifecycle and breaking it down by life-cycle stage (e.g. manufacturing, use etc.)
Covering multiple environmental impacts, not being limited to “carbon only” (e.g. water, land use etc.)

LCAs are perfect for evaluating product sustainability, identifying hotspots and improvement areas and communicating it to stakeholders. LCA predates corporate carbon accounting, with the first ISO standard in 1997. Since then, it has become the go-to method for product sustainability.

See the graph below to understand how LCAs map against other methods and standards. LCAs serve products in the same way sustainability reporting serves companies.

Environmental impact data coverage by method:

Despite its advantages, LCA adoption remains limited

LCAs face three key challenges that limit their widespread adoption. These challenges restrict both which companies use them and how many products they apply them to.

High Costs

The cost of a proper LCA study starts at $10,000. For large corporations and complex products, it can exceed $50,000 per product. This approach works in some industries. It fits companies with small, standardized, and stable product portfolios.
Most consumer companies, however, manage large amounts of products and change their portfolio regularly. To assess over 80% of those products with traditional LCA studies, our clients would need to spend over $60 million annually. Those studies would become outdated within one to two years.

Long Turnaround

LCA studies require approximately three to six months. Manual data collection and modeling slow the process. At best, teams complete one LCA study per quarter.
Organizations with in-house LCA experts and ready data can shorten this timeline to two to three months, assuming no backlogs exist. In practice, that situation is rare.
This process creates uncertainty for decision makers because results often arrive after they have already made the relevant decision. Iteration of initiatives and experimentation becomes impractical, as a full cycle can take years.

Scarce Primary Data

LCA standards require a share of primary data in their calculations, not only data from databases with industry average numbers.
Gathering this data for internal operations is tiresome but workable. The challenge begins when companies must collect information from suppliers about purchased components.

This is particularly challenging for the consumer goods industry. Companies in this sector operate large and complex supply chains. Suppliers beyond Tier 1 (direct suppliers) may be unknown, making it difficult to receive data from them. In addition, suppliers need their own tools and LCA expertise to produce qualifying data. For these reasons, most industries have not yet fully utilized the potential of LCAs.

Some industries stand apart. Certain heavy industries have led with intensive use of LCAs. Parts of the construction sector, especially in the EU, have done the same. They have also adopted Electronic Product Declarations based on LCA methods.

These industries and companies are the least affected by these three challenges. Cement manufacturers change processes less often and operate shorter supply chains. They also tend to manage fewer products than a typical consumer company has brands.

For CPG, demand for LCA studies increasingly exceeds supply

As outlined, being able to understand an entire product portfolio through LCAs already brings immense benefits. This trend will only increase as pressure mounts. Digital Product Passports in the EU are approaching, and each new generation is more sustainability conscious. The leaders of tomorrow will be those who embrace LCAs today.

Looking at past trends and our discussions with clients, we see a clear pattern. This is how environmental impact measurement maturity is evolving for consumer companies.

Lingaro analyzes measurements by data type as per our Sustainability Data Matrix. Our assessment suggests that product sustainability data will only become more notable. This poses a question, how to improve adoption of LCAs given their challenges?"

For most of the consumer industry, automation is the only way forward

As many times, in human history, to improve productivity, we had to resort to automation. From simple watermills, through complex industrial machines and recently through digital solutions. LCAs must undergo an industrial revolution of their own: going from an artisanal product to a mass produced good.

The digital age gives us the tools to achieve this. Digital data integration allows us to automate most of an LCA study and conduct it at scale, almost instantly. From an automation point of view, we divide an LCA study into four key components.

LCA Study Component	Traditional Manual LCA Study	Automated LCA Solution
Modeling Determines scope and types of data required	Done by LCA practitioner in dedicated software for a specific study	Large LCI library models builtby LCA practitioners on a per product group basis
Product Data Contains details on components and material information such as product use	Gathered manually from company records for a specific study	Data pipeline from ERPs for data such as BoMs into a central LCA data lake
Environmental Data Consists of impact factors translating product into environmental data	Gathered manually or through software by LCA practitioner for a specific study	Data pipeline from LCA software, company databases, or direct supplier data into a central LCA data lake
Report Creation Final report detailing the study results, may be for external declarations or verification	LCA practitioner manually compiles study results for internal analysis or external declarations	Dedicated application automatically compiles reports ready for review

How Lingaro scales automated LCA across your portfolio

Traditional LCA studies are expensive, slow, and difficult to scale across large product portfolios. Lingaro helps enterprises deploy Automated Life Cycle Assessment across their entire product range.

We combine two core capabilities:

Advanced data integration expertise to automate the collection and management of any data source.
A dedicated Sustainability Practice with LCA consultants experienced in automation-ready modeling.

Lingaro stays technology agnostic and builds a centralized LCA data lake within your existing infrastructure in Azure, AWS, or Databricks.

Your product data remains in-house and under your control. This architecture removes the two main adoption bottlenecks: excessive costs and turnaround. It also helps alleviate the third: scarce primary data.

Cost

Automation can bring down the cost of LCAs by over 90%
Less than $50 per SKU for partial or simple products (such as packaging only)
Under $200 per SKU for holistic LCA for typical consumer goods

Time

Instant calculations with LCAs available in real time as portfolios change.
Analytics modules can effectively eliminate wait times: with simulations available instantly in dedicated solutions
Near-zero cost of simulations enabling efficient R&D

Primary data

Streamlining data collection and processing through automated pipelines enables smooth ingestion into LCA tools
Dedicated optional supplier data collection platforms help collect and structure supplier data

With this approach, full portfolio LCA coverage becomes practical and repeatable. See how Lingaro can help your organization move from isolated studies to continuous product level insight.

The Hidden Cost of Fragmented Sustainability Reporting

Oliwia Sobczyk — Tue, 10 Mar 2026 10:23:05 GMT

Why does fragmented sustainability data matter? It raises audit costs, increases compliance risks, and weakens decision-making. For leaders, this hidden cost appears when reporting stalls, audits flag risks, or compliance penalties hit.

The issue of fragmented sustainability data is urgent in 2026. A perfect storm is forming. Strict EU CSRD mandates have arrived. Sustainability reporting demands unified, audit-ready datasets. The standard matches the rigor of a financial audit, yet many still use spreadsheets and disconnected systems.