What does the future of AI look like in the Enterprise?


Blog by | George Brown, Jet Global

The Future of AI in the Enterprise

The business world is at an inflection point when it comes to the application of Artificial Intelligence (AI). While the technology behind enabling computers to simulate human thought has been developing, at times slowly, over the past half-century, the cost of implementation, readily available access to cloud computing, an practical business use cases are primed to help AI make a dramatic impact on the enterprise over the next few years.

With the potential use cases on the horizon for AI in business, as well as the investment dollars and rate of change currently propelling AI, one thing is clear: you’ll need to get your foundation in place sooner, rather than later, to take advantage of the benefits coming to the business world.

Enter business intelligence (BI) software. By building the foundation now with this readily available, accessible, and affordable software, businesses can prepare themselves for the future while also reaping the benefits of today. After a couple of years with inflated expectations for AI that have yet to materialize, businesses are beginning to ask themselves whether it makes sense to push through a costly implementation that won’t yield tangible results for 2-3 years – when really, they should be focused on implementing BI today, yield results immediately, and layer AI on top of your established  BI data to derive new insights and drive greater benefit once the technology matures/.

So how will BI software help set the stage for AI in your enterprise, and what possible use cases can be gleaned from the intersection of AI and BI?

How Can BI Software Help?

Regardless of where you’re landing in regards to Artifical Intelligence and Business Intelligence, one thing is true: you’ll need to have data to feed to both. Without data to act upon, there’s no ‘intelligence’ in AI or BI. There’s nothing to analyze, or apply a learning algorithm to – when it comes to any intelligence solution, data is the foundation upon which it must be built.

Thankfully, with the widespread of cloud computing and the Internet of Things (IoT), data has never been more readily available in today’s business world. But the vast reams of data generated on a daily basis are presenting a new problem for businesses – what does it mean and which data matters? How should data be tagged, sorted, grouped and analyzed? Which problems do disparate data points speak to? And how can the data be collected across multiple touchpoints, from all retail locations to the supply chain to the factory be easily integrated?

Enter data warehousing. Data warehouses are a means of taking data points from disparate touchpoints (such as point of sale, CRM, inventory, and warehouse management systems), standardizing the data collected, structuring it to extract necessary insights, and running analysis. Enterprise businesses cannot survive without robust data warehousing – data silos can rapidly devour money and resources, and any business still trying to make sense and cobble together ‘business intelligence’ from multiple reports and inconsistent data is rapidly going to lose ground to those businesses with integrated data and reporting.

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however – it’s built on modern data storage structures as the Online Analytical Processing (OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions. Cubes are superior to tables in that they can link and sort data by multiple dimensions, allowing for non-technical users to choose from any number of role-specific and highly contextual data points to uncover new insights and adjust tactics and decisions on the fly. Chances are good that your average non-technical sales agent or purchasing representative will have difficulty joining multiple tables together with a standard report, but with Business Intelligence cubes, all that is required is to drag and drop the metrics and dimensions that matter to them into their own personalized dashboards.

So how is the data extracted? By using Structured Query Language (SQL), the language used to manipulate and extract data stored in cubes. SQL was developed as a standard language to communicate databases, regardless of exactly which type of database was being used, and is ultimately the means by which data in a table is extracted, deleted, updated and managed.

Beyond data warehousing and OLAP cubes, which provide the technical foundation, there are a number of additional components that can help enterprise businesses address their data requirements”

Data modeling: Data modeling is a method of mapping out individual data sources across an enterprise and determining how they need to interact with one another to extract the most valuable business insights. Data modeling can be performed at the conceptual (high-level, related to business objectives), logical (mapping to each business function), and physical (how the actual dimensions, measures, and hierarchies are related within a data cube).

Analytics and reporting: Capturing, structuring, and storing data is good – but being able to analyze and report on it is the ultimate end goal. Business intelligence solutions are capable of providing simple, accessible analytics and reporting functions for end-users, empowering them to find actionable insights they need with little technical expertise (or formal data science training). This also helps business functions avoid unnecessary data logjams and giving them instant access they need to the data they so desperately require.

Data visualization and dashboards: Analytics and reports are a crucial component of business intelligence, but if you’ve ever spent hours poring over a table of values trying to decipher exactly what the data is saying, you’re not alone. With data visualization tools, critical insights are displayed in rich graphical representations that are vastly easier for the human brain to interpret. According to a study by Aberdeen Group, organizations using visualization tools are 28 percent more likely to find timely information than those who rely solely on managed reporting; the same study also found that 48 percent of business intelligence users at companies with visual data discovery are able to find the information they need without the help of IT staff. Dashboards can easily assemble visualizations and reports into customizable displays by end-user or business units, giving individuals instant insight into KPIs that help drive better business performance from the bottom up.

Security, simplicity, speed – these are the three major benefits business intelligence solutions help to drive, and three critical measures of success in enterprise business. While artificial intelligence remains focused on helping computers glean insight entirely on their own, business intelligence is enabling entire organizations to gain access to the data they need to make rapid, informed decisions, and the importance of that in today’s quickly shifting business landscape simply can’t be overstated. In a survey of 2,600 business intelligence end-users, 91% responded that BI gave them faster reporting, analysis or planning, 84% said it enabled them to make better business decisions, and 79% said it improved employee satisfaction.

The Future is (Almost) Here

In the near future, AI algorithms will be able to be seamlessly applied to your existing data stores, unlocking further insight for your enterprise. As highlighted in this 2018 Harvard Business Review article, AI applications in response to business needs fall into one of three categories:

  • Process automation: The most common current application for AI in business is by automating systems and business processes. While previous incarnations of automation focused on exchanging information between systems, AI can level up this ability by actually interacting with the data like a human – either inputting or consuming as necessary. Today, AI ‘robots’ are able to analyze legal contracts and extract relevant provisions, update customer records across a number of disparate systems, and automate customer outreach in response to situational conditions. As these algorithms grow ‘smarter’, businesses will be able to automate even broader swaths of processes.
  • Cognitive insight: Cognitive insight is the ability to apply AI algorithms to vast existing stores of data to extract meaning and identify patterns. While BI software and data stores will undoubtedly provide the ‘diet’ for cognitive insight algorithms, as the algorithms learn, they’ll be able to apply those learnings to broader data sets, react to new data in real-time, identify potential data matches across multiple databases, or manage programmatic ad buying.
  • Cognitive engagement: Cognitive engagement refers to the human-interfacing element of AI – think automated chatbots, knowledge bases, product recommendation engines, and more. Cognitive engagement applications can be used to automate interactions between people and systems, either externally (for customers), or internally (for employers). Most current applications focus on internal engagement as businesses are still apprehensive about the relatively new applications – but, again, as AI development and implementations continue to mature, expect objections to fall by the wayside as businesses will find new ways of using existing data to drive meaningful automated interactions with human beings around the world.

Over the next few years, you’ll see artificial intelligence finally begin to live up to the hype we’ve been hearing about in the business world, and computers will help usher in a new era of productivity and profitability for enterprises on the cutting edge – but only if you have the foundation in place today, and that starts with business intelligence.

Don’t hesitate – ensure you’re setting up your business tomorrow for success today with Jet Global and allonline365. Contact us on  info@allonline365.com or  +27 (21) 205 3650.


Reducing Supermarket Food Waste with Dynamic Pricing

dynamic pricing

The ability to react and cater to consumer demand is nearly always a competitive advantage, but it also comes at a price. Naturally, when a shopper heads to the store to buy their groceries, they expect everything on their list to be available, and their products fresh.

But as supermarkets manage a growing number of products with varying expiration dates, they’re faced with the increasing challenges of selling these items before they expire while making sure they generate as much profit as possible. Failing to accomplish this not only hurts the retailer’s bottom line but also leads to unnecessary food waste. In fact, one-third of the food produced for consumption is lost or wasted globally, and grocers are significant contributors.

In order to save sales and limit losses, grocers often incentivize shoppers to make purchases by discounting products nearing their sell-by date. However, the challenge is that they often struggle to find the perfect pricing and timing balance that will maximize sales and also minimize the discount they need to offer. They walk a thin line of discounting too early or offering discounts that are too high.

With hundreds of thousands of SKUs, internal teams simply can’t manage the complexity of this process. Fortunately, the emergence of AI-enabled solutions makes a responsive price strategy on perishable food far more simpler and more profitable.

Let’s explore how artificial intelligence allows grocery retailers to bypass old roadblocks by enabling dynamic pricing strategies that pad margins and reduce food waste.

Traditional limitations to pricing

Historically, responsive price adjustments were a hassle, or even impossible, especially in physical stores. The process relied on rules-based algorithms that required significant manual oversight.

Picture the pricing gun – employees have to manually walk through the store and change the price on each individual product if there is a sale or special offer on these items. For supermarket and food retailers, the selling period for marked down products is very short, creating a major pain point when attempting to optimize prices in real-time.

These obstacles have led to the development of new tools that automate efforts around identifying optimal prices and enable retailers to make the resulting adjustments quickly.

Optimizing price around sell-by dates

Today, the use of artificial intelligence has led to breakthroughs in markdown pricing. Aggregating demand behavior (historical and current) with inventory information, competitor pricing and sell-by dates allows for pricing strategies that can be optimized in real-time, across all areas of business, and at scale.

For example, bananas that begin to ripen need to be sold while consumers are still willing to pay for them, but not before a new shipment comes in. Providing discounts to shoppers on products that are still fresh gives shoppers an incentive to buy now. This can even be done on a granular, individual store level, as inventory and consumer buying patterns vary based on store geographies. What this means for customers is that products are available at any time at the best possible price, ultimately leading to less wasted food.

Increasing profitability through product affinity

Retailers can also increase profitability by optimizing pricing on products with strong affinity. Analyzing data on products that are sold together, retailers can detect cross-sell opportunities and markdown one product while driving sales of other related products at full price.

As shoppers head to the grocery store for the fourth of July, retailers can find areas to increase sales with perishable and non-perishable items typically paired together. For example, offering a markdown on cherries nearing their sell-by date, if bought with a pie crust, or similarly, full-priced hotdogs and discounted buns.

By linking multiple items, retailers can lower prices of certain products that may have otherwise expired while still increasing the bottom line by ensuring sales of higher-margin items.

Empowering retailers to reduce food waste and better serve shoppers.

When a business deploys dynamic pricing, they become more sustainable – both environmentally and financially. But it also has the benefit of generating loyalty among shoppers who receive the best deals and competitive prices. In addition to time-sensitive food, retailers can also see the advantages of markdown pricing for seasonal items or products with short selling cycles.

With dynamic pricing driven by AI, retailers can gain a holistic view of their entire inventory in real-time, as well as the connections between products, and can optimize their strategies on the fly in order to reduce food waste and better serve shoppers.

allonline365 specializes in retail solutions to help manage your business. We offer solutions that address your current business needs as well as your future ones. Choose a solution that digitizes your business and grows along with you. Contact us on  info@allonline365.com or  +27 (21) 205 3650.


Resource Credit | Progressive Grocer 

Here are some mobile ERP case studies to learn from

mobile erp

As the old saying goes; “for every seller, there’s a buyer”; or in our specific case, an enterprise resources planning platform. More and more people are adopting mobile usage on a daily basis. On average users spend almost five hours per day on mobile devices, the majority of which is spent on mobile apps. Now, let’s consider what and how the technology specifically applies to the ERP segment.

What is a mobile ERP?

First, let’s take a quick refresher on the meaning of what we refer to as today’s mobile ERP. To paraphrase IGI Global, mobile ERP extends traditional ERP tenets and processes by independently collecting and exchanging data via mobile devices and wireless communications mechanisms. Standardized interfaces allow a direct and steady connection to distributed ERP systems, thereby leading to a more flexible and efficient enterprise business processing.

In more simple terms, mobile ERP solutions can be explained as virtual ‘all-the-time-everywhere’ operating platforms that can be leveraged by managers, workers, or customers in real-time. This universal value proposition allows the enterprise to force-multiply any scale of an enterprise; whether its a small ‘Mom and Pop’ shop generating revenues on the order of a million dollars or less, or a multinational industry titan generating billions on an annual basis.

Industry-specific mobile ERP apps

Today’s constellation of mobile apps ranges from the simple to the complex. Nevertheless, all offerings suggest the same business values; enhanced production and the promise of consequently increased revenues. To understand this assertion it is best left to the market itself, so let’s take a look at a couple of mobility-adept industries and their common functions/features applicable to today’s market.

  • Manufacturing – This category ranges from small fabrication operations to large-scale product developers such as auto or equipment brands. Particular functions and features typically involve all record adds/changes.deletes, time/stamp markers, geo-locators, user announcements, and security alarms.
  • Commercial marine operations – this category primarily relates to vessel processes and dock and port facilities. Particular functions and features typically involve all record adds/changes/deletes. time/stamp markers, navigational tracking, communication systems status’, geo-locators, standard/emergency user announcements, and triggered security alarms.
  • Oil and gas development – this category primarily relates to drilling, refining, production and delivery of fossil fuels. Particular functions and features typically involve all record adds/changes/deletes, time/stamp markers, geo-locators, standard/emergency safety announcements, and HAZMAT, and/or triggered security alarms.

As suggested earlier, these mobile ERP features apply to three industrial segments and represent only a small sampling of what mobility means in today’s global industry.

Mobile ERP case studies

Here are some examples to review as you determine how important mobile ERP is to your own business needs. These case samples range from mid to large-scale operations, however, all enterprises were either already involved with a current ERP platform or were applying active mobile capabilities during the various selection/launch/implementation processes.

1.  Transportation supply-chain operator

The company is a leading supply-chain operator in the road construction equipment segment. The company is a mature enterprise, encompassing a 40-year lifespan. Its product line is extensive, primarily supporting global transportation systems developers.

The mobility requirement

The company’s workforce expanded in recent years, requiring a need to streamline its administrative communications capabilities. Its goal was to ensure that all information related to personnel allotments and task assignments were deliverable throughout the company’s business infrastructure, The company also wanted to enhance field staff operations by equipping it with a native mobility capability oriented to enhanced customer support and affiliated reporting.

Baseline scenario

The company’s administrative manager typically assigns sales personnel to individual field sales managers. In turn, these personnel are assigned multiple customers. In the past, at the conclusion of each sales transaction, necessary paper forms were used to gather information, and then execute a manual data entry process using the company’s web portal. As the company grew it began to experience various operational logjams, largely driven by an ever-increasing number of active customers. Subsequently, a related internal investigation found that:

  • Sales personnel spent too much time searching for necessary customer information.
  • Manual data entry processes were redundant and prone to error.
  • Sales status reports experienced delivery lags since legacy processes only allowed for end-of-day batching.
  • New customer record updates were slow and consequently resulted in lost sales.

The company’s solution

The company already had an installed web application system, so after a thorough internal analysis, it proposed an iPad app development for its field staff. The app allowed the company to seamlessly leverage its existing online portal and active data store. Its design allowed for various record options including; customer-lists address books, an ability to register operational events, all the while employing direct geo-location. The design also allowed sales personnel to submit reports in real-time.


After development and implementation were concluded, the company successfully realized all stated goals. Resource Credit: Innomobile

2.  Mid-size retail sales chain

Various metrics relating to today’s retail environment suggest that a minimum of 50% of all retail customers involve themselves with smartphone device use. This non-mobile mid-size retail operator found itself facing increased competition from other mobile-enabled competitors.

The mobility requirement

The company was not mobile-enabled, and consequently was experiencing lost sales opportunities, particularly in the case of targeting young customers. Unfortunately, the company had largely depended on young users to increase brand value by means of word of mouth.

Baseline scenario

The company required a way to enhance its promotion, marketing, and engagement of products to young customers, while at the same time leveraging a seamless resources-based digital infrastructure. The company felt that by tailoring its sales offerings, while enhancing the company’s shopping experience, customers would be more attentive to its product attractions.

The company’s solution

The company engaged a professional mobile developer to ensure that a proper set of operational goals were established. Subsequently, the following solutions were developed and delivered to the company:

  • Mobile shopping cart – this capability allowed users to select and purchase items on-demand.
  • Mobile product search – this capability enabled users to do targeted lookups related to specific items from larger catalog products.
  • Mobile payment – this capability enabled customers to securely pay for purchases.
  • Mobile product ship and track – this capability enabled customers to manage inbound products while allowing the company to maintain active transaction tracking in order to resolve any delivery issues.
  • Wishlist – this capability allowed users to bookmark selected items for future purchase.
  • Store locator – this capability helped customers find the nearest outlet in the event that the customer wanted to leverage the app, but still wanted to do their purchases at one of the company’s brick-and-mortar outlets.
  • Direct support – this capability afforded customers to contact the company’s service center regarding issues or defects, and also afforded the customer 24/7 hotline enhanced with live chat.


Once the company launched its mobile app it reported a significant rise in customer engagement. Customer ease of access led to numerous positive responses from its sales team. Ultimately, customer retention has risen accordingly, in addition to significant increases in new prospect transactions. Resource credit: Innomobile.

3.  Global aviation manufacturer

While the company’s outsourced operations lowered manufacturing costs, it also added complexity and presented a need for rigid controls when managing day-to-day operations. Due to the limitations of a legacy MRP system, and while establishing a newly sophisticated multiple-module ERP platform, much of the company’s work had to be done manually which became increasingly more difficult to manage as the company grew. Consequently, a mobility-adept system was desired as a middle ground.

Baseline scenario

To prevent counterfeit items from entering its supply chain, the company bought a significant number of parts domestically and consigned them to factories in China. The practice required a manual calculation of needed components based on finished assembly usage and inventory levels at the factory. In addition, the company had to calculate the necessary excess to purchase based on historic scrap levels.

If these challenges weren’t difficult enough, communicating with Hong Kong and China operations required them to share and access data around the clock which proved challenging given various time differences.

The solution

The company implemented a multi-module ERP system including; accounting, purchasing, sales orders, and inventory enhanced with mobility.

Once the program was completed the company’s critical data was being shared across functional areas of their business in real-time while maintaining a comprehensive financial status as required. Inventory interfaces with accounting, bill of materials were created that drive demand based on inventory levels across its four factories in China. Additionally, a quality module was included to help the company work through various international compliances, including capabilities such as sub-part traceability and lot coding.

The results

The company’s ROI increased by 50% of sales growth by year four. Resource credit: IQMS

As you can see, industrial mobility and more importantly mobile ERP is becoming a game-changer regardless of the particular business segment. Consequently, it is time to ask yourself, is your enterprise ready to make the jump to mobility lightspeed?


Resource Credit | ERPFocus

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