The state of sustainability in the supply chain

The MIT Center for Transportation & Logistics and the Council of Supply Chain Management Professionals, in their 2023 State of Supply Chain Sustainability report, highlighted key trends and challenges in this area.

One of the notable findings was that, despite economic uncertainties and global crises such as the COVID-19 pandemic and geopolitical tensions, commitment to supply chain sustainability has remained resilient. However, it’s also evident that this commitment is not uniform across the globe, with net-zero carbon emissions goals more prevalent in wealthier countries compared to lower-income regions.

The pressure to enhance supply chain sustainability continues to grow. This is evident from the increasing focus on improving sustainability profiles, with a particular emphasis on Scope 3 emissions — the indirect emissions in a company’s value chain which are often the most challenging to measure and reduce.

Advancements and trends in supply chain sustainability

Advancements in technology, particularly in measuring carbon footprints, have played a crucial role in this evolution. Enhanced data analytics and emerging technologies have enabled businesses to measure their greenhouse gas emissions with greater accuracy, facilitating more effective carbon reduction strategies.

We’re also now seeing a significant push towards greater transparency in supply chains. These are being driven by consumer demand, investor expectations and regulatory requirements, with companies focusing more on ethical sourcing and reducing environmental impacts. This trend is aligned with regulatory developments like the US and UK’s rules on climate-related disclosures and Germany’s Supply Chain Act.

AI as a catalyst for more sustainable supply chains

As we look to the future, the integration of artificial intelligence (AI) into supply chains stands out as a significantly transformative factor. This game-changing technology carries immense potential for enhancing environmental management and operational efficiency. For instance, AI can optimize resource usage, reduce waste and support more informed decision making — all of which contribute to more sustainable business practices. 

Peak’s suite of AI applications can support efforts to directly effect and reduce a company’s environmental impact. By leveraging Dynamic Inventory, for example, you can ensure that you hold only the stock that you need, not the stock that you think you need thanks to optimized safety stock modeling. Peak’s Reorder and Replenishment applications, meanwhile, allow you to minimize the number of orders you may need to make from your suppliers or the number of times you need to replenish your stores or warehouses, reducing unnecessary mileage.

The application of AI and big data is not just about optimization, but also about enabling businesses to achieve their sustainability goals more effectively — and a business that is working efficiently uses its resources sparingly.

Collaboration for greater impact

Another emerging theme is the importance of better collaboration in achieving sustainability goals. The challenges of environmental and social sustainability are too vast for any single entity to tackle alone. This means that there’s a growing trend of partnerships among businesses, governments and nongovernmental organizations (NGOs), who are now pooling resources and sharing knowledge to make a more substantial impact.

A call to action

The journey towards sustainable supply chains is complex and often seems to present insurmountable problems. For supply chain managers and those in the manufacturing sector, the path ahead involves embracing technological advancements like AI, delivering on transparency and collaborating across sectors. As the world grapples with environmental challenges, the role of supply chains in driving sustainable practices becomes increasingly critical. The future of supply chain management is inextricably linked with sustainability, and the time to act is now. AI can ensure that that journey, whilst complicated, can leverage the best technology available.

For those in leadership positions, the message is clear: integrating sustainability into supply chain practices is not just about compliance or public image, but about securing a viable future for businesses and the planet. The intersection of technology, collaboration and strategic planning forms the backbone of this new era in supply chain management — one where sustainability is at the forefront of decision making.

Quote Pricing in the AI era

Peak’s latest AI application, Quote Pricing, takes all of that guesswork and uncertainty away. As a salesperson, Quote Pricing means that you can be sure that not only are you maximizing your chance of winning the quote, but that the prices you provide are fully optimized — both in terms of a specific customer’s needs and your overall business goals.

Access AI-powered dynamic pricing

Static pricing rules are out the window. Quote Pricing leverages your sales and customer data in real-time to generate dynamic pricing that meets your overarching business objectives. This can be customized to a number of options, such as margin or revenue, whilst maximizing the likelihood of a sale converting. It’s also constantly updated to ensure that your sales team has the right price information at their fingertips at all times.

Quoting just got personal

Say goodbye to generic, one-size-fits-all quotes. Quote Pricing personalizes every single proposal based on historic customer sales, individual product attributes and overall business goals. This is calculated on a per client basis, ensuring that each customer and prospect is given the right price at the right time in order to land that all-important sale!

AI-driven pricing insights

Peak’s Quote Pricing AI application is always improving — and the learning doesn’t stop with the sale. It continuously analyzes the results of the quotes it is raising, allowing it to further refine and fine-tune its algorithms and identify new opportunities. It’s like having an AI coach by your side, constantly optimizing and developing your quoting game!

What’s next for AI-powered Quote Pricing?

Our Pricing Intelligence team are already busy working on the next updates to the Quote Pricing application, bringing additional game-changing developments to improve and enhance the full quoting experience. 

Every business is unique, and we believe that a business’ AI should be unique, too. In a future update we’ll be providing users with the ability to continually reconfigure business-specific guardrails as and when your sales and pricing strategy changes, allowing access to even more AI algorithms to ensure that your own AI is driving your CPQ processes. 

This improved functionality, combined with deeper integrations into your business systems such as SAP, ensures that real-time, personalized quote prices are easily and readily available for your sales team to access, whenever and wherever they need them.

If you’re looking to find out more about Peak’s Quote Pricing AI application — one of the many apps on offer from Peak — head to the Quote Pricing page to arrange a quick demo or speak to one of our AI for pricing experts

Optimizing margin vs. stock clearance goals

Maximizing revenue through clearing of stock is a really scary game of chicken and egg. Discounting pricing too much may cannibalize other products in your portfolio, or worse, have no effect on demand at all. Discounting too little, on the other hand, means you may be left with a stock surplus after season end (and then you’re in really big trouble). 

The merchandising and demand planning teams that are tasked with making these crucial margin-based decisions have to take into account a huge number of variables in order to create campaigns they can stand behind. With the latest releases of Peak’s AI applications, this whole process just became infinitely easier. 

With Promotions and Markdown, you can simply build your campaigns based on your chosen products, and allow each application’s sophisticated machine learning algorithms to identify that ideal sweet spot between price change and stock out goals — with a clear forecast on revenue vs. cost for every single one of your products.

No more guessing with price elasticity

We speak to retail experts, merchandisers and demand planners every single day. Plus, a lot of the Peak team have strong knowledge and experience of retail and pricing from their previous roles. From our years of being immersed in the world of retail, we know that the challenge of choosing products for a targeted campaign is hugely complex — it involves huge, data-heavy spreadsheets and a difficulty in understanding, at a product level, if you ever actually made the right choice. 

At Peak, we’re experts in both this problem and the solution, price elasticity. Price elasticity is the impact of product demand based on changing the price. Peak’s tailored, business-specific elasticity models are the foundations of our Promotions and Markdown applications. They ensure you always discount stock in a way that has a positive impact on your company’s top and bottom line, whilst enforcing your business-specific guardrails in its decision-making logic.

What’s coming next?

You can expect to see more explainability in the applications to ensure you always understand the nuanced decision that’s being made. As well as this, there’ll be options for scenario planning based on shifting campaign constraints, empowering you to make a more informed decision on your product-level pricing. And that’s just the start!

We want to hear from you!

Understanding the industries and businesses that we work with is key to the Peak team elevating our AI applications to the next level, and driving more game-changing results for our customers. We’d love you to take this short survey to help us gather data on the pricing market to help us further improve our offering. Thanks in advance!

We’re really excited about these latest developments for Promotions and Markdown, two of the many AI applications on offer from Peak. To learn more, head to our Promotions or Markdown application pages — or sign up for our next B2C pricing demo below

An example of image generated by DALL-E

But despite all of this hype, we’re really only scratching the surface of what generative AI can do — especially in business. To date, generative AI applications have overwhelmingly focused on the divergence of information: they create new content based on a set of instructions; the user enters something into a prompt and they get a response.

When we use generative AI as consumers, our objectives are about having fun, sharing something memorable or saving a bit of time. The applications that allow us to do that have quickly captured the imagination of millions of people around the world.

When it comes to the business application of generative AI, the focus must be about making things more efficient, driving growth or improving the quality of our work. There are some generative AI use cases for business that do just that. For example, sophisticated chatbots deployed in customer service, tools that assist in writing programming code or tools for content generation in marketing. But, the incremental value of generating new content from prompts will ultimately reach a plateau. Applications of generative AI that fundamentally enhance and adapt our business workflows will bring a much greater step change in productivity.

And that’s why we’re so excited to be unveiling Co:Driver, the new generative AI product from Peak.

Co:Driver combines a fine-tuned LLM with a user’s existing AI applications and business data. Co:Driver continuously searches for information, efficiencies, opportunities and anomalies of potential interest to the user.

These are surfaced as bite-sized contextual recommendations, with the user able to choose their preferred next step from a number of AI-generated actions. And through feedback from users, the recommended actions will improve over time.

As well as this, using outputs from a user’s existing AI applications on the Peak platform, users can ask questions about their business in natural language, such as how much stock there is in a certain location or which products are the best performers.

A new paradigm for generative AI in business

Co:Driver is part of a new paradigm emerging when it comes to applying generative AI in business, and we think it will be transformational. The future of generative AI in the enterprise will see it facilitate an innovative and exciting new means to interact with your data and get value from it.

Generative AI will create a new layer between business users and the systems and data that run their operations, bringing together data from across many different formats and making sense of it autonomously. In doing this, the power of large language models will be used for data synthesis more than for content generation. 

With this innovative approach, generative AI can provide business users with real-time, highly-relevant recommendations at their fingertips. Opportunities, challenges and anomalies related to stock holding, pricing or customer experience can be surfaced automatically and help leaders make timely, accurate, AI-driven decisions.

Here’s a practical example…

• Imagine a business that stocks and distributes construction materials such as lumber, mouldings and fasteners 
• The business might have thousands of different products across a network of distribution centers and warehouses; they regularly fill orders from customers and re-order stock 
• The company is using Peak’s Dynamic Inventory application to generate an AI-powered demand forecast and AI-powered recommendations on the correct quantity of stock to hold for each SKU, in each location
• Users typically review and action stock targets, at an individual SKU, category or sub-category level. Sometimes users manually scan the data for outliers, problems or opportunities
• Now imagine that, instead of manually reviewing the recommendations from the AI, the most important actions you need to take are surfaced automatically by Co:Driver. Exactly the information you need, when you need it

Your own AI

Up to now, discussions about different types of AI typically delineate between:

Predictive AI: uses machine learning and statistical algorithms to forecast trends, patterns and predictions from each user’s unique data set, and;

Generative AI: learns the patterns and structure of input data (e.g. text, images) and then generates new data that has similar characteristics.

Often these are presented side by side as if the two shall never meet. In reality, to get the most value for business they have to work together. Customers that work with Peak are building their own AI by training proven business applications on their data and business logic. With the addition of Co:Driver, the value of Peak’s applications is supercharged and they become even more powerful than they would be on their own.

Generative AI can be layered on top of predictive AI providing a new layer to interact with your data and your AI, one that is agnostic of the format of the data. This approach will become transformational in its ability to direct business teams on where to focus their efforts, thereby improving decision-making, boosting efficiency and delivering higher ROI. The combination of fine-tuning an LLM and Peak’s proprietary predictive AI applications will redefine the use of AI in business. 

Co:Driver will be available in private preview in January 2024 — register your interest below.

To leverage the full transformational potential of this emerging technology, retailers need to invest in building their own AI. Not something generic or cookie cutter, but AI that is specific to their unique business, made up of composable blocks that can be re-configured as needed. This will allow AI to ingest data from all systems and push decisions back to all systems.  Putting AI at the heart of the business in this way provides flexibility and creates opportunities to infuse AI across the entire retail value chain.

For any retailer looking to start building their own AI “nervous system”, pricing is a great place to start. Because pricing is by nature a numerical pursuit, it lends itself well to the data-driven and probabilistic approach of AI. Building AI for pricing can also help lay the foundation for additional AI use cases in a retail business. Some important trends around using AI for pricing include:

1. Setting the right price, the first time

Whether we’re talking about homewares, footwear or denim, setting the right initial price must be done using a range of data inputs. Whilst a traditional cost-plus technique applies a crude margin uplift to COGS, modern approaches take into consideration a wide range of datasets. For example, competitor pricing, customer expectations, demand signals, availability across the market and brand metrics can all be used to help determine the optimal initial selling price. Another consideration is the role of distribution partners and the possibility that your initial prices may actually only be a recommendation (i.e. MSRP). All of these factors can be considered in the round by machine learning models in order to determine the optimal price.

2. Breaking out independent effects of price elasticity

The age-old adage that high prices scare away buyers is of course still true but determining the effect of price on sales independent of other variables remains elusive without the help of some sophisticated data science. For example, stock availability and competitor activity can mask the actual price elasticity of your product. If you plot your prices and promotions over time against sales history for the same period, the trends you observe may not tell the whole story. It’s important to set promotional prices with the full knowledge of how price impacts demand in your market and category.

3. Pricing in cohorts and categories

In traditional retail workflows, pricing is usually set at an individual product level but there can be great benefits from optimizing price in cohorts. Data clustering can help determine the ideal cohorts to use, be that category, sub-category, price band, demand or some other combination of factors. You can then move to optimize the pricing and promotion of products so that the impact of changing the price of one product is considered in the context of the full cohort. This ensures individual price changes do not happen in a silo.

4. Managing the proliferation of selling channels and competition

The multitude of ways for consumers to shop means pricing is more competitive than ever. In the future retail pricing will become increasingly contextualised, real-time, and programmatic. Retailers must be equipped for more frequent price changes in order to keep up with competition in digital channels, requiring both a change in mindset and adoption of new technology. Retailers will need to monitor prices and demand across the market, adjust prices, and operationalize those price changes both in store and online. In the fullness of time, a more data-driven and algorithmic approach to buying, merchandise planning and customer segmentation can also help build competitive advantage.

In closing, the opportunities for AI to modernize pricing in retail are truly transformational. Now is the time to invest in an AI platform and AI pricing applications that can build competitive advantage.

But retailers are facing intense headwinds. The cost of products, manufacturing and transportation are all rising; suppliers are often unable to meet delivery timelines; demand from customers is unpredictable; new channels such as e-commerce need to be managed; the imperative to reduce environmental impacts cannot be ignored. And all of this is made that much more difficult with outdated technology.

It’d be foolish to suggest there is a silver bullet or panacea for these woes. But the context we’re operating in does provide some clues for what to do. We’re on the precipice of a revolution, not unlike the dawn of e-commerce in the mid 90s or the launch of the iPhone in 2007: artificial intelligence (AI) is transforming the world around us. Every decision a retailer makes can, and will, be informed by AI — resulting in unprecedented leaps in operational efficiency and improvements in customer experience.

Retailers would be wise to consider how they can use AI to build competitive advantage. Those that don’t invest today risk the same fate as those that ignored the e-commerce trend: they’ll disappear into the retail history books. So, where do they start? Broadly speaking, there are three main themes to adopting AI in retail…

1. Interconnected data as your crystal ball

If only it were possible to know exactly what consumers will want in the future, down to the details of how many, what color, in what stores and what price they’d be willing to pay. The next best thing to having a crystal ball is using your data to manage uncertainty.

Retailers have always been fueled by data, going back to the early days of pen and paper recordkeeping or Lotus 1-2-3 (I’m aware I’m really dating myself here!) The modern AI-enabled retailer must bring together data from different sources to create a single version of the truth that enables data-driven decision making. Supply chain, demand, pricing and customer data can all be combined into data products or a data fabric that allows business applications and end users to access data and insights as needed.

Bringing together data in this way will allow a retailer to create a demand forecast powered by machine learning that becomes the beating heart of the business. With a data-driven and continuously improving consensus on what consumers are most likely to buy, when and how, retail teams can plan their buying, merchandising, allocation, pricing and promotions more effectively.

For example, with a more accurate and more connected demand forecast, the planning team for a major global sportswear retailer can allocate individual SKUs to exactly the right stores on a daily basis, resulting in higher levels of sell-through and increased profits.

2. Probabilistic decision making

This sportswear allocation example illustrates how building AI that leverages a variety of data sources has the potential to disrupt entrenched business concepts, notably “business rules.” Every retailer has these rules and uses them to make decisions. But the truth is that rules alone were never meant to be the basis for good decision making. The constraints of technology have made retail decision making models what they are today.

We’ve ended up with a lot of rules-based systems, not because they work best, but because that’s all we could handle. Legacy technology systems and the rules they brought have not prioritized innovation and creativity, but rather standardization and compliance. Merchandising, buying, planning and pricing have all been slaves to the rules imposed on them by a sub-optimal approach to technology.

But things don’t have to be that way anymore!

The right way of doing things for a retailer no longer means a single right way to the exclusion of other options. Data-driven decision making is probabilistic by nature, with infinite options allowing for more creativity, flexibility and innovation. 

For example, an AI-powered department store could send different marketing messages to each one of its millions of individual customers, or set a different pricing strategy for each one of its thousands of SKUs. The days of planning a range by taking last year’s sales and arbitrarily adding a growth assumption can be left in the past. AI gives you the flexibility to adjust plans quickly and easily. The end result is increased productivity, happier customers and stronger margins.

3. Sustainability

In addition to the bottom line benefits of leveraging AI, there can be enormous environmental benefits. Using AI to optimize buying means raw material use is limited to what you know will sell. Machine learning models can optimize stock movements to and between distribution centers, reducing thousands of miles of unnecessary emissions. And utilization models can help drive down energy, transportation and other resource uses.

Getting started with AI in retail

How does AI fit into a retailer’s existing technology ecosystem? Firstly, we have to consider the state of the tech that they’re currently using today. The systems that run retail businesses may include a myriad of legacy operational and planning systems, plus systems for accounting, HR and much more. On top of this, many retailers will have a patchwork of point solutions for planning and analytics.

To leverage the full transformational potential of AI, retailers need to build a new layer of tech: an AI layer that sits across the value chain and across their existing systems and infrastructure. The ideal technology architecture to do this is a composable one with individual tools and systems working together. This will offer retailers greater flexibility, and create opportunities to infuse AI across the entire retail value chain.

In conclusion, as the tides of uncertainty continue to wash over retailers, the time is now to invest in reimagining retail powered by AI.

For more information on the game-changing potential of AI for retail, download our latest retail whitepaper below.

Data transforms manufacturing and fulfilment

In 2023 manufacturers of all stripes will move at pace to put the days of pencil and paper tracking behind them by adopting cloud-based data-centric approaches for designing and building products. Manufacturers will unify supply chain and demand data, and thanks to AI, will use that to transform and optimise production lines in real time. The end result will be reduced waste, higher quality products and faster production times, all with a lower environmental footprint.

Improving the efficiency of supply chains will be precipitated by a double imperative: environmental as well as economic pressures.

Against a backdrop of reduced consumer demand, manufacturers will start to make choices that they might not have as quickly otherwise, such as using technology to reduce the time to process orders, reduce minimum order quantities, or offer their customers greater flexibility on customisation of items.

And as more of us choose to shop online and a consequently greater share of global retail trade is conducted via e-commerce, innovation in the last mile of the supply chain will be top of mind in 2023. Micro fulfilment will become the dominant investment choice for any business looking to improve distribution to their end consumers. A micro fulfilment centre is a small storage facility to help get inventory closer to the consumer, thereby reducing transportation costs and times. Since they are small, it’s critical to make the right decisions about what stock to store and in what quantity. Using data, there is the potential to make this allocation challenge highly automated, as well as automate the fulfilment centre itself.

Supply chain uncertainty and other headwinds make composable commonplace

The headwinds felt throughout 2022 will spill over into 2023 exacerbating the need for all businesses to be more flexible and resilient. A trend introduced in the last few years – composable – will become the dominant approach both to enterprise software and to building enterprises themselves. A composable approach involves combining individual building blocks together to make a whole. With the near-constant volatility and uncertainty in the world, it’s a sensible approach that provides greater flexibility and resilience.

For example a composable business can be thought of as having many parts, each with their own expertise (e.g. paying invoices, procuring materials, manufacturing widgets, answering customer calls). The emphasis is on re-imagining the business model and then assembly and re-assembly of pieces to get a positive outcome. In a composable business, teams are empowered to compose as they need to in order to meet demand, seize opportunities or overcome obstacles. When it comes to technology, composable means assets, applications or tools each having discrete functionality. 

In 2023, technology buyers such as those with the title CTO, CIO or CDO will increasingly seek to build the infrastructure in their businesses with interoperable component parts: individual discrete tools and applications (including AI applications) that rapidly add value to the business and connect together through cloud-based open APIs, shared data models, and other cooperative frameworks. This approach will supplant the dominance of monolithic legacy systems and usher in a new era of enterprise software that is packaged, sold and implemented in an explicitly cooperative manner.

A CPG that moves in this direction might have many more brands, products and distribution channels catering to myriad consumer needs and desires. Recent comments by Proctor & Gamble (P&G) CIO Vittorio Cretella make it clear this direction is the priority for his business:

The starkness of the choice illustrates the importance of identifying a primary concern and rallying competitive advantage around it.

In either scenario, there is an opportunity for data to help define and fuel that competitive advantage.

The good news for any business looking to get value from their data is that we now live in an era where artificial intelligence (AI) is transforming the world around us and the way we do business. As both the quantity and complexity of decisions increase, so too does the power of technology to augment and enhance those decisions. AI will be the defining technology of our generation because of its ability to enable data-driven decision making at scale.

Given every business is unique, they will each ultimately need their own AI. But to be successful in deploying AI, they will need to take the right approach. Ten years ago the focus was on collecting data with the term “big data” dominating technology conferences. But it’s no longer about having more data, or even better data. And it’s not about increasingly sophisticated algorithms or hiring more data scientists.

But where to start? How does AI fit into an CPG’s existing technology ecosystem? First we have to consider the tech that they are using today. The systems that run CPG businesses may include a myriad of legacy supply chain management and enterprise resource planning systems, such as SAP. And, according to Peak’s AI maturity survey, some CPGs may have invested in modern cloud data infrastructure, for example, migrating their data to Snowflake or AWS. On top of this, most CPGs now have a patchwork of point solutions for planning and analytics.

To leverage the transformational potential of AI, CPGs need to build a new layer of tech: intelligence that sits holistically across the value chain to connect decision making. Rather than a single monolithic system, the future lies in a composable approach to technology with individual tools and systems working together. This offers CPGs greater flexibility, and creates the opportunity to infuse AI across the entire business.

AI applications; delivering results across the value chain

In a composable architecture, individual AI applications can ingest data and then push decisions back into existing systems, uncovering opportunities across the value chain. Below are some examples of the inputs, outputs and business value at different stages of the value chain.

Each AI application deployed by a CPG augments specific decisions. By deploying multiple applications and connecting them together, the benefits of each application are amplified and the whole becomes greater than the sum of the individual parts. There are three stages to developing connected AI: 

1. Connected data:

Bringing together all of a CPG’s data in one place allows previously disparate datasets to work together. By leveraging common underlying data models, multiple AI applications can ingest data from different parts of the business. Linking demand generation and demand fulfillment is one of the best examples of how to do this. There is real power in using customer data to inform the supply chain and vice versa.

2. Connected forecasts:

As AI models are built and tuned to the business, the models create a layer of intelligence that is tailored to that business’ unique needs — with decisions in one application driving the decisions in another. For example, demand forecasts can be generated at various levels of aggregation and used to augment each other. A connected layer of AI can therefore give a CPG unparalleled visibility of demand, supply and customers, along with access to both cost reduction and revenue growth opportunities.

3. Connected journeys:

The final step is to optimize product and customer journeys. For example, location data can help to plan where to place inventory and optimize transportation and warehouse locations; fulfillment times can be used to manage customer expectations and allocate orders; or trends in customer behavior can help a CPG plan promotions.

Integration is the biggest barrier to adopting a modern data stack

Leveraging up-to-date technology within a data stack can deliver benefits that include speed, efficiency and cost savings – providing businesses have the capacity to integrate it.

Conversations at Snowflake Summit highlighted just how difficult many are finding it. Most decision makers we spoke to talked about the challenge of requiring a diverse set of skills and resources internally to get the best from their stack. It’s a balancing act, especially when coupled with the management overhead of integrations and orchestration.

The majority of Snowflake customers adopted its technology because of its simplicity and ‘as-a-service’ model. We heard many people wish they could extend this ease of use to other parts of the stack, including data science, artificial intelligence (AI) and analytics.

Having supported customers deploying AI solutions for over seven years, we couldn’t agree more. It’s one of the reasons we’re so excited about our Snowflake partnership and integration – it gives Snowflake and Peak customers the opportunity to simplify the stack, delivering data warehousing and ready-made AI applications ‘as-a-service.’

Doing even more, directly on Snowflake

Data sets are getting bigger and we’re plugging more applications into them. The bigger the data set, the harder it is to move and the more applications get pulled towards it – that’s ‘Data Gravity’.

It’s a concept that cropped up a lot at the Snowflake Summit thanks to the release of Snowpark for Python and Python user-defined functions. These new features mean that a lot of the computational machine learning workload can be pushed down into the Snowflake platform – drawing applications to the data.

Peak users bridging to Snowflake can run modeling on their own infrastructure, removing the need to move big data sets and providing more sovereignty. In general terms, using Snowflake infrastructure to do more than just ‘traditional’ database transactions provides Peak customers with a larger range of optionality when it comes to developing and deploying commercial AI.

Empower data scientists to overcome the last mile

The vast majority of AI models fail to be productionized, and many of the attendees we spoke to – whether commercial leaders or technical teams – cited this as a major challenge. Summit attendees agreed it’s not just about creating a productionizable model; it’s about empowering data scientists to get models into production themselves.

It’s a problem we designed Peak to crack. Peak unites technical and commercial teams on one platform, facilitating collaboration which promotes data science empowerment, decreases time to value and speeds up the delivery of models that meet a business need.

It’s more than just building models, it’s a cultural change too

A highlight of the event was seeing our CCO, Zoe Hillenmeyer, on stage at the Everest Theater. Consolidating many of the topics we’d heard throughout the summit from a range of industry leaders, Zoe ran a bootcamp for businesses on how to adopt commercial AI.

Here are headlines:

• You can build models rapidly by simplifying your data stack and abstracting infrastructure and integration.
• Empowering data scientists to productionize their models is the key to getting meaningful value from AI projects.
• Productionization isn’t the end of the road. End users need to embrace and utilize the output of the models, and that requires a culture receptive to change and familiar with digital transformation.

Want to read more? You can download Zoe’s presentation slides below.

A great week with great people

It’s easy to forget how intense Snowflake Summit can be but it’s always a lot of fun too. Peak CEO, Richard Potter, shared his personal highlight, “Snowflake Summit was a chance for us to learn as a team. Fueled by the best Mexican food in Las Vegas, we rallied around the booth to talk to as many attendees as possible, cheered on Zoe at her presentation, and we even managed to squeeze in a game of Topgolf! Most excitingly, we’ve been able to introduce our Snowflake x Peak proposition and highlight the value it brings to customers.”

To put that into context, if you were to give every single person on the planet 11 laptops each, the total HDD space would be broadly the same amount.

This huge proliferation of data is also reflected within businesses where, for example, they capture the behaviors of customers on websites, or gather real-time information on the location of delivery vehicles.

This asks questions of how you store all this data. Do you have a number of different databases, each for a different subject area? Do you actually build a data warehouse to support your reporting and analytical organizations?  Do you build a data lake, and empower your data teams to build what they need, however they need it? Do you use on-prem or the cloud? 

These foundational questions on how to organize your data, however, can create their own problems. How can you, as a company, pull together all the insights from these disparate sources of data to improve your business solutions? How can you empower your decision makers with the right intelligence to positively improve the performance of the company? How on earth can we connect all of these disparate data sources?

Enter Peak

We’ve created the Decision Intelligence platform. It’s a new kind of business platform that becomes the place ‘where AI lives’ in a business – slotting in alongside existing systems and applications, rather than replacing them.

This platform builds Decision Intelligence applications that seek to connect these different data sources together, to solve real business problems. But this isn’t just another system, or platform, or workbench that is thrown into the mix; it’s much more than just another data source to tussle with. Peak works in parallel with these data systems, databases, data warehouses and data lakes. We firmly believe that we should leave the data where it is, and bring what is needed into Peak to deliver Decision Intelligence to commercial decision makers.

We’ve developed a wide variety of data connectors that cater to this, from direct connections to various databases, access to Google BigQuery and Facebook, and even something as simple as loading a file.

We’ve even taken that one step further by developing “Bring Your Own Data Layer” technology that directly connects into a Data Warehouse – again, leaving your data where it is and ensuring there’s zero disruption.

There’s no need to change your infrastructure to accommodate Peak, there’s no need to remove or add anything, and there’s absolutely no ripping out required. We work with the data where it is.

(with apologies to xkcd.com/927/)

Your data, your Decision Intelligence

The world continues to generate data at an ever-increasing rate and businesses need to be able to harness this in order to win their sector. Combining all your many data sources together in a way that creates a central system of intelligence – which gets smarter over time as more data is collected and more data sources added – will differentiate between those that hope and those that know.  

Applications developed in this way in our platform can be deployed across an organization in multiple business areas; from sales and marketing, to demand forecasting and planning, through to supply chain management.

Our customers often call Peak the AI brain in their organization, helping them to supercharge their decision making across the whole business. 

Connecting Decision Intelligence with the data that accelerates your business allows you to dominate your industry and win. The best bit? This is done without tearing up the data foundations that have taken you to where you are now.

Peak leaves the data where it is, and our platform takes you into the Decision Intelligence era.

⬡ Read the thoughts on our CEO, Richard Potter, on pioneering the Decision Intelligence category
 Get to know Peak and its capabilities – learn more about our platform here
? Learn more about the journey we take our customers on to introduce Decision Intelligence