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AI-Driven Robotic Systems: Fueling Fulfillment's Future
CIO Review
Steve Johnson, President and COO, Berkshire Grey
2020 has been an unorthodox year for businesses in nearly every industry on the planet — with retail fulfillment being among the most affected.
• Health concerns surrounding COVID-19 have had wide-ranging effects felt not just inside warehouses and distribution centers, but all the way to our neighborhood grocery stores.
• eCommerce sales skyrocketed as many consumers entered lockdown and as non-essential brick-and-mortar stores temporarily shut down (some never to reopen). In late September, Walmart, Target, and Kohl's announced that they would hire an estimated 240,000 employees this holiday season—many of which would be dedicated to fulfilling online orders.
• Unexpected shifts in consumer consumption patterns stress-tested existing fulfillment systems and forced acceleration of omni-channel strategies. For example, Bain has estimated that, as a result of the pandemic, online grocery adoption will speed up by anywhere from two to five years in the U.S. and Western Europe, with big ramifications for industry capacity.
Check Out This: Top E Commerce Fulfillment Service Companies
While dealing with all this change, businesses were also challenged to improve workplace safety and efficiencies.
Throughout, Berkshire Grey’s AI-driven robotic systems for automating fulfillment have proved uniquely suited to solving these challenges. For example: due to volume increases that were 2 to 3 times normal, retail customers who provide essential goods to consumers have doubled their reliance on Berkshire Grey systems in 2020. This has included moving to 24/7 operations and increasing the number of product categories handled by Berkshire Grey systems by more than 40%.
Fulfillment’s Future is Here Now
Berkshire Grey systems tackle the most difficult, time-consuming problems in fulfillment for retailers, ecommerce, grocery and logistics companies. Our intelligent robotic systems can execute tasks that our competitors can’t: efficient order picking and processing for the tens of millions of different items (SKUs) consumers buy daily.
Today, fulfillment workers remain scarce and turnover high, and public health demands social distancing and fewer people touching products. Berkshire Grey's intelligent robotic systems can reduce the need for manual labor by as much as 70%, reducing dependence on non-existent labor and avoiding the need to add second (or third) shifts. This also allows warehouse operators to re-assign their skilled workers to more value-added tasks and processes.
Even pre-COVID, Berkshire Grey was leading the way in solutions for omni-channel fulfillment operations, which faced significant challenges meeting demand as distribution centers had to package simultaneously for retail store shelf replenishment, store pickup, and direct-to-consumer deliveries.
Today’s consumers can buy any product we want online and receive it the next day (or sooner). But this is labor-intensive and inefficient for fulfillment operations, which must “pick,” pack, and ship millions of orders every day. Berkshire Grey’s systems use fewer people and process steps, while providing more speed, accuracy and less possibility of item damage. Our “full-stack” systems use a battery of patented hardware, and trade-secret algorithms in computer vision, machine learning, novel sensing, motion planning, grasp planning, and task execution, all running on an AI platform.
As many of the retail consumption trends that changed so quickly during COVID 19 won’t be reverting, retailers should rethink fulfillment automation now to be ready for peak season 2021— and beyond.
A Full-Stack Solution for Fulfillment
Berkshire Grey’s robotic picking systems for eCommerce are the first to bring a holistic (vs. piece parts) robotics approach to automating product fulfillment. We deliver turnkey solutions that complement customers’ existing operations and workflows.
Berkshire Grey systems automate picking, packing, and sorting for the widest range of item types in diverse settings at speeds with accuracy levels that bring real results to customers including the ability to:
• Handle fragile items without crushing them (tactile sensors combined with AI algorithms).
• Efficiently handle tens of millions of SKUs in multiple form factors.
• Continue operating if the systems come across an unfamiliar item. Instead of requiring human intervention like other systems, Berkshire Grey systems use prior knowledge gained from related product types to determine how to pick the new items. For example, when a new sauce bottle is presented the system adapts prior knowledge about a shampoo bottle to pick-up the sauce bottle.
• Identify products and packages with our patented HyperScanner™ technology. It incorporates 360-degree vertical scans to detect barcodes and labels on hard-to-read items and packaging. In contrast, other solutions require a human or robot to wave an item by a barcode scanner, which slows down the entire process.
• Continuously self-learn and get more efficient over time while also optimizing where objects are placed in packages going directly to consumers where space is at a premium.
• Transport picked goods to a wide range of locations without dropping anything or colliding with other objects over everything from shuttle systems to mobile robots, depending on the customer’s specific requirements.
Introduced in early 2020, a new Robotics-as-a-Service (RaaS) option reduces upfront capital costs for customers, making AI-driven robotic fulfillment both more affordable and faster to deploy. New customers in 2020 included 3 of the Fortune 50 and a large retailer in Japan. In January, VCs invested $263 million in the company, validation of our mission and progress.
Before founding Berkshire Grey in 2013, veteran robotics/AI expert Tom Wagner spent two years looking for a big problem to solve where the technology could make a big difference in industry. Clearly, he saw the future. Today, we— and our customers—are well on our way, with a modern robotics approach to fulfillment automation.
To learn more about Berkshire Grey, visit us at www.berkshiregrey.com.
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As software development becomes more reliant on AI, companies have started to focus more on the process of coding, changing, and attributing. Code attribution systems for AI are becoming common for helping engineering professionals know the source of code, differentiate human contributions from that done by AI, and remain visible in the development environment.
Their importance goes beyond that of mere tracking since attribution can affect intellectual property management, security assessments, compliance procedures, and engineering performance. However, the implementation of these platforms poses some problems that organizations need to solve before they can effectively use attribution.
How Can Organizations Maintain Accurate Code Attribution?
An important issue here is the question of establishing accurate attribution in a complicated process of software development. Nowadays, software development includes many repositories, development environments, libraries, automated systems, and cooperation models. The suggestions generated by the artificial intelligence can be accepted, modified, mixed with the existing code, or completely rewritten by the developer. As a result, it becomes difficult to distinguish what part was done with the help of AI and what was developed independently by humans.
Data quality also poses a challenge. Attributing authorship requires the availability of development history, history of code changes, prompts, suggestions, and modification patterns. Poor data quality can lead to erroneous findings, especially where organizations employ different methods for software development or use a different set of tools for development. It is imperative for businesses to come up with data standards for the consistency of findings.
Issues related to privacy and intellectual property rights make the scenario even more complicated. The source code may include business logic, proprietary processes, and customer data. Companies that choose to implement the attribution platforms need to pay special attention to the way the development data will be gathered, analyzed, stored, and made available.
What Makes AI Attribution Difficult Across Enterprise Development?
The other challenge is that of incorporating the attribution process into the current engineering process. Companies typically have heterogeneous technology stacks and development processes. This implies that any attribution solution has to be compatible with the source code repositories, issue tracking platforms, code reviews, security mechanisms and so forth. If not, fragmentation and extra manual processes arise.
Interpretation is just as crucial. Metrics related to attribution should not be automatically assumed to reflect developer productivity or the quality of code written. While AI can alter how engineers spend their time, more generated code does not imply a better result. Business context regarding maintainability, reliability, reviews, security, and business needs should also be factored in, along with attribution.
Attributing AI code is going to be contingent upon transparency, interoperability, and good governance. Enterprises are going to require established processes for attributing contributions made by AI and also explaining how the information is to be utilized. Software systems capable of creating traceable evidence, easy integration, and clear reporting can enable enterprises to create more trust when it comes to AI-enabled development efforts. Taking a good approach towards these considerations will enable enterprises to get visibility regarding the use of AI in coding without having to risk their intellectual property rights and engineering accountability. ...Read more
Customer outreach is moving faster than many compliance programs were designed to govern. A campaign assembled in hours can pass through several applications before a call, text, email or prerecorded message reaches a customer, while autonomous agents compress that cycle further. The exposure is no longer limited to whether a record appeared on a suppression list. Consent status, channel permissions, time-of-day rules and state or federal restrictions can change the answer at the moment of contact. A platform that checks too late leaves legal teams reconstructing decisions after the communication has already occurred.
Static controls also create a quieter commercial problem. Large enterprises often carry separate customer records across business units, and a broad opt-out can be applied far beyond the product or channel the customer intended. Conservative suppression may reduce legal exposure, yet it can also remove legitimate audiences from campaigns and weaken the return on CRM or marketing technology investments. Effective governance needs enough context to distinguish a prohibited contact from an allowable one without forcing every business unit to maintain its own interpretation of the rules. The harder test is whether those distinctions survive as consent records move between systems and outreach programs change.
“Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made.”
Speed matters at the decision point, not merely during campaign preparation. List scrubbing and periodic audits remain useful for certain tasks, but neither is designed to govern communications that originate across contact centers, individual employees, enterprise applications and autonomous agents. Decisioning should sit inside the existing workflow and evaluate the applicable permissions before outreach proceeds. The answer also needs to return quickly enough that compliance does not become a queue. Enterprise scale is equally important. A control layer that works only for one channel or one application recreates the same fragmentation it was purchased to remove. Policy changes also need to propagate without campaign teams waiting for separate rule updates in each downstream application, especially when restrictions take effect quickly.
Defensibility separates governance from simple blocking. Executives should expect a clear record of the rule applied, the evidence used, the policy version and the reason a communication was allowed or stopped. Those records need to remain searchable as regulations and internal policies change. Deterministic decisioning has particular value where an organization must later explain exactly why a contact was permitted. The same discipline helps compliance teams identify oversuppression rather than treating every uncertain record as unusable. Buyers should also examine how readily the platform connects to existing CRM, contact-center, marketing automation and governance systems, since a long replacement project can undermine the speed advantage that real-time controls are meant to provide.
Gryphon is the premier choice for enterprises that need contact governance embedded directly into customer engagement rather than added as a later review. Its platform applies real-time controls across voice, SMS, email and interactions generated by AI agents while integrating with existing enterprise applications. Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made. Compliance Hub extends that visibility into audit research and reporting. The platform also identifies contacts suppressed too broadly, helping organizations preserve legitimate reach without relaxing policy enforcement. For buyers balancing regulatory exposure against legitimate customer contact, point-of-contact enforcement paired with documented decision logic makes Gryphon a practical recommendation. ...Read more
Disconnected data work rarely begins at the pipeline itself. The delay often appears earlier, when a proposed data product moves from a business idea into requirements, architecture decisions, access controls and a development environment. Each handoff can introduce another tool or approval path, while product context becomes harder to preserve. By the time engineering begins, teams may already be reconciling mismatched project names, duplicated documentation, fragmented ownership and inconsistent setup across systems.
Portfolio-level visibility also matters before engineering starts. A platform that preserves business cases alongside product definitions can help leadership compare proposed work, assign teams and select technology stacks without separating prioritization from the delivery path that eventually executes those decisions.
That fragmentation becomes expensive when orchestration is purchased as another isolated layer. Data teams commonly work across cloud infrastructure, code repositories, ticketing systems and specialist data platforms, while product managers and architects need continuity across the same work. Replacing that estate is rarely the practical objective. A stronger platform coordinates existing environments while preserving product identity and approved technology choices throughout delivery. Integration depth matters less as a feature count than as a way to remove repeated setup and cross-tool reconciliation.
“Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments.”
Self-service also needs boundaries. Provisioning development environments, granting access, creating repositories and triggering infrastructure changes can remove substantial waiting time, but only when those actions follow established controls. The useful distinction is whether routine requests can execute from approved templates and policies rather than pass through manual service tickets. That changes the role of platform and architecture teams. Instead of completing repetitive setup on demand, they can establish guardrails that engineering teams use independently.
Traceability becomes harder once a project leaves experimentation and enters controlled delivery. Changes to requirements can alter pipeline work, while release movement creates dependencies across development, test, staging and production. Executives need a clear line from the original business case to the technical work that follows, particularly when multiple data products compete for budget or shared engineering capacity. Visibility into status, resource use, dependencies and release progress helps management identify where work is waiting without rebuilding the picture from separate tools. It can also expose queueing between teams before delayed approvals become late-stage release problems.
Release control should be treated as part of orchestration rather than an adjacent DevOps concern. Creating a pipeline is only part of the purchase decision. The harder question is whether code and data products can move through governed environments without custom coordination each time. Automated CI/CD setup, reusable templates, policy-based promotion and dependency visibility can make that movement repeatable. This becomes more important for AI-related data work, where experiments can appear quickly but production use depends on controlled access, governed data movement, documented lineage and consistent release practices.
Calibo merits recommendation for enterprises that want data orchestration tied directly to the broader delivery lifecycle. Its Data Fabric Studio supports reusable data pipelines. The wider platform carries product context into the development toolchain while automating environment setup. Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments. Its Release Orchestration capability extends that model into deployment governance and dependency management. This gives data teams a self-service framework that reduces manual handoffs while keeping technical work connected to the product context and enterprise controls that initiated it. ...Read more
The rapid growth of genomic data is transforming modern healthcare, biotechnology, and life sciences. AI-powered genomic interpretation platforms are emerging as critical tools for converting complex genomic datasets into actionable intelligence, enabling faster discoveries and more precise clinical decision-making. The platforms combine artificial intelligence, machine learning, and advanced analytics to improve the speed, accuracy, and scalability of genomic interpretation.
The increasing adoption of precision medicine is driving demand for technologies capable of identifying genetic patterns linked to diseases, treatment responses, and biological risks. AI-based genomic interpretation helps researchers, clinicians, and healthcare organizations unlock deeper insights from genetic data while reducing analytical complexity.
What Technologies Are Powering Genomic Interpretation Platforms?
Advanced algorithms can analyze extensive genomic datasets, identify meaningful patterns and detect genetic variants that may be linked to specific health conditions or biological traits. These capabilities improve analytical speed while reducing the manual effort required for large-scale interpretation. Cloud at Work supports organizations with cloud hosting and managed technology services that help businesses maintain secure and scalable digital environments for complex workloads. Genomic platforms are increasingly using language models to interpret scientific literature, clinical reports and research databases, helping connect genetic findings with broader biological and clinical knowledge.
Genomic analysis requires significant computational resources, and cloud-enabled platforms allow organizations to process large datasets efficiently while supporting collaboration across research and healthcare environments. Automated variant classification, annotation pipelines, and reporting systems help streamline genomic analysis processes and reduce turnaround times for clinical and research applications. Modern platforms can combine genomic data with clinical, molecular, and population-level datasets to generate more comprehensive insights and support more personalized decision-making.
Quasi Robotics supports automation initiatives through robotics solutions, intelligent systems and advanced technology integration.
What Challenges Are Shaping the Future of AI-Driven Genomics?
Genomic datasets are highly complex, and interpreting biological significance requires sophisticated analytical models capable of distinguishing meaningful signals from large amounts of variation. Genetic information is highly sensitive, making secure data management, access control, and ethical governance essential for organizations working with genomic datasets. Healthcare professionals often require transparent explanations of AI-generated insights before integrating them into clinical decision-making.
AI models trained on limited or non-representative genomic datasets may produce less reliable results across diverse populations. Expanding dataset diversity is essential for improving fairness and accuracy. Regulatory and validation requirements continue to influence platform adoption. AI-powered genomic tools used in clinical environments must demonstrate reliability, accuracy, and reproducibility to support responsible implementation.
The future of genomic interpretation is driven by stronger AI models, improved data integration, and more sophisticated analytical frameworks. Their ability to accelerate analysis, improve diagnostic insights, and support personalized treatment strategies is reshaping how genomic information is utilized. By overcoming current challenges and advancing intelligent analysis capabilities, these platforms will play a foundational role in the next generation of precision healthcare and biomedical innovation. ...Read more
