Stephen Smith's Blog

Musings on Machine Learning…

Posts Tagged ‘business applications

Making Business Applications Intelligent

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Introduction

Today Business Applications tend to be rather boring programs which present the user with rather complicated forms that need to be filled in with a lot of detail. Accuracy is tantamount and there are a lot of security measures to prevent fraud and theft. Companies need to hire large numbers of people to enter data very repetitively into these forms. With modern User Centered Design these forms have become a bit easier to work with and have progressed quite a bit since the original Business Apps on 3270 terminals connected to IBM Mainframes, but I don’t think anyone really considers these applications fun. Necessary and important yes, but still not many people’s favorite programs.

We’ve been talking a lot about the road to strong AI and we’ve looked at a number of AI tools like TensorFlow, but what about more practical applications that are possible right now? My background is working on ERP software, namely Sage 300/Accpac. In this article I’ll be looking at how we’ll be seeing machine learning/AI algorithms start to be incorporated into standard business applications. A lot of what we will talk about here will be integrated into many applications including things like CRM and Business Analytics.

Many of the ideas I talk about in this article are available today, just not all in the same place. Over the coming years I think we’ll see most of these become standard expected features in every Business Application. Just like we expect modern User Centered Design, tomorrow we will expect intelligent algorithms supporting us behind the scenes in everything we do.

Very High Level Diagram of the Main Components of an Intelligent Business Application

Some Quick Ideas

With Machine Learning and AI algorithms there could be many small improvements made to Business Applications, there could be major changes in the way things work, all the way up to automating many of the processes that we currently perform manually. Often small improvements can make a huge difference to the lives of current users and are the easiest to implement, so I don’t want to ignore these possibilities on the way to pursuing larger more ambitious longer term goals. Perhaps these AI applications aren’t as exciting as self-driving cars or real time speech translation, but they will make a huge difference to business productivity and lead to large cost savings to millions of companies. They will provide real business benefit with better accuracy, better productivity and automated business processes that lead to real cost savings and real revenue boosts.

Better Defaulting of Fields

Currently fields tend to be defaulted based on configuration screens configured by administrators. These might change based on an object like a customer or customer group, but tend to be fairly static. An algorithm could watch what a user (or all the users at a company) tend to use and make much more intelligent defaults. These could be based on various contexts of other fields, time/date, current promotions, even news feed items. If defaults are provided more intelligently, then it will save users huge time in data entry.

Better Auto-Suggestions

Currently auto-suggestions on fields tend to be based on a combination of previous values entered and performing a “Google-like” search on what has been typed so far. Like defaulting this could be greatly improved by adding more sophisticated algorithms to improve the suggestions. The real Google search already does this, but most “Google-like” searches integrated into Business Apps do not. Like defaulting, having auto-suggestions give better more intelligent recommendations will greatly improve productivity. Like Google Search uses all your previous searches, trending topics, social media feeds and many other sources, so could your Business Application.

Fraud Detection

Credit card companies already use AI to scan people’s credit card purchasing patterns as well as the patterns of people using stolen credit cards to flag when they think a credit card has been stolen or compromised. Similarly Business Applications can monitor various company procedures and expenses to detect theft (perhaps strangeness in Inventory Adjustments) or unusual payments. Here there could be regulatory restrictions on what data could be used, for instance HR data is probably protected from being incorporated in this sort of analysis. Currently theft and fraud is a huge cost to businesses and AI could help reduce it. Sometimes just knowing that tools like this are being used can act as a major deterrent.

Purchasing

Algorithms could be used to better detect when items are needed to reduce inventory levels. Further the algorithms can continuously search vendor prices looking for deals and consider whether its worth buying now at a cheaper price and incurring the inventory expense or waiting. When you regularly purchase thousands or more items, a dynamic algorithm keeping on track of things can really help.

Customer Data

When you get a new customer you need all sorts of information such as their address, phone number, contacts, etc. Perhaps an algorithm could also search the web and fill in this information automatically (perhaps this is a specific example of better defaulting). Plus the AI could scan various web source (some perhaps pay services for credit ratings and such) to suggest a good credit rating/limit for this new customer. The algorithm could also run in the background and update existing customers as this data changes, since keeping customer data up to date is a major challenge for companies with many customers. Knowing and keeping up to date with your customers is a major challenge for many companies and much of this work can be automated.

Chasing Accounts Receivables

Collecting money is always a major challenge for every company. Much of this work could be automated. Plus algorithms can watch the paying habits of customers to know if say they alway pay on the end of  the quarter, not to worry so much when they go over 30 days. But if a customer suddenly gets credit rating problems or their stock tanks or there is negative news on the company then you better get collecting. Again this is all a lot of work and algorithms can greatly reduce the manual workload and make the whole process more efficient.

Setting Prices

Setting prices is an art and a science. You need to lower prices to move slow moving items out of inventory and try to keep prices high to maximize return. You need to be aware of competitors prices and watch for these items going on sale. Algorithms can greatly help with this. Amazon is a master of this, maintaining millions of prices with AI all over their web site. Algorithms can scan the web for competitive pricing, watch inventory levels and item costs, know where we are in a quarter and how much we need to stimulate sales to meet targets. These algorithms can make all the trade offs of knowing our customer loyalty versus having to be low cost, etc. Similarly this can affect customer and volume discounts. Once you have a lot of items for sale, maintain prices is a lot of work, especially in the world of online shopping where everything is changing so dynamically. With the big guys like Amazon and Walmart using these algorithms so effectively, you need to as well to be competitive.

Summary

This article just gave a few examples of the many places we’ll be seeing AI and Machine Learning algorithms becoming integrated into all our Business Applications. The examples in this article are all possible today and in use individually by large corporations. The cost of all these technologies is coming down and we are seeing these become integrated into lower cost Business Applications for small and medium sized businesses.

As these become adopted by more and more companies, it will become a competitive necessity to adopt them or risk becoming uncompetitive in the fast paced online world. There will still be a human element to monitor and provide policies but humans can perform many of these tasks at the speed and scale that today’s world requires.

For the users of Business Applications, the addition of AI to the User Interactions, should make these applications much more pleasant to operate. Instead of gotchas there will be helpful suggestions and reminders. Instead of needed to memorize and lookup all sorts of codes, these will be usefully provided wherever necessary. I think this transition will be as big as the transition we made from text based applications to GUI applications, but in this case I think the real ROI will be much higher.

 

Written by smist08

July 26, 2017 at 2:03 am

Unstructured Time at Sage

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Introduction

Unstructured time is becoming a common way to stimulate innovation and creativity in organizations. Basically you give employees a number of hours each week to work on any project they like. They do need to make a proposal and at the end give a demo of working software. The idea is to work on projects that developers feel are important and are passionate about, but perhaps the business in general doesn’t think is worthwhile, too risky or has as a very low priority. Companies like Google and Intuit have been very successful at implementing this and getting quite good results.

dilbert-google-20time

Unstructured Time at Sage

The Sage Construction and Real Estate (CRE) development team at Sage has been using unstructured time for a while now. They have had quite a lot of participation and it has led to products like a time and expense iPhone application. Now we are rolling out unstructured time to other Sage R&D centers including ours, here in Richmond, BC.

At this point we are starting out slowly with 4 hours of unstructured time a sprint (every two weeks). Anyone using this needs to submit a project proposal and then do a demo of working code when they judge it’s advanced enough. The proposals can be pretty much anything vaguely related to business applications.

The goal is for people to work on things they are passionate about. To get a chance to play with new bleeding edge technologies before anyone else. To develop that function, program or feature that they’ve always thought would be great, but the business has always ignored. I’m really looking forward to what the team will come up with.

so-many-toys-so-little-unstructured-time-new-yorker-cartoon

We are still doing Hackathons, Ideajams and our regular innovation process. This is just another initiative to further drive innovation at Sage.

Crazy Projects at Google

Our unstructured time needs to be used for business applications, but I wonder what unstructured time is like at Google where they seem to come up with things that have nothing to do with search or advertising. Is it Google’s unstructured time that leads to self-driving cars, Google Glasses, military robots, human brain simulations or any of their many green projects. Hopefully these get turned into good things and aren’t just Google trying to create SkyNet for real. Maybe we’ll let our unstructured time go crazy as well?

Anathem

I’m a big fan of Neal Stephenson, and recently read his novel Anathem. Neal’s novels can be a bit off-putting since they are typically 1000 pages long, but I really enjoy them. One of the themes in Anathem are monasteries occupied by mathematicians that are divided up into groups by how often they report their results to the outside world. The lower order reports every year, next is a group that reports every ten years, then a group that reports every 100 years and finally the highest group that only reports every 1000 years. These groups don’t interact with anyone outside their order except for the week when they report and exchange information/literature with the outside world. This is in contrast to how we operate today where we are driven by “internet time” and have to produce results quickly and ignore anything that can’t be done quickly.

So imagine you could go away for a year to work on a project, or go away for ten years to work on something. Perhaps going away for 100 years or 1000 years might pose some other problems that the monks in the novel had to solve. The point being is to imagine what you could accomplish if you had that long? Would you use different research approaches and methods than we use typically today? Certainly an intriguing prospect contrasting where we currently need to produce something every few months.

My Project

So why am I talking about Anathem and unstructured time together? Well one problem we have is how do you get started on big projects with lots of risk? Suppose you know we need to do something, but doing it is hard and time consuming? Every journey has to start with the first step, but sometimes making that first step can be quite difficult. I’ve had the luxury of being able to do unstructured time for some time, because I’m a software architect and not embedded in an agile sprint team. So I see technologies that we need to adopt but they are large and won’t be on Product Manager’s road maps.

So I’ve done simple POC’s in the past like producing a mobile app using Argos. But more recently I embarked on producing a 64-Bit version of Sage 300. This worked out quite well and wasn’t too hard to get going. But then I got ambitious and decided to add Unicode into the mix. This is proving more difficult, but is progressing. The difficulty with these projects is that they involve changing a large amount of the existing code base and estimating how much work they are is very difficult. As I get a Unicode G/L going, it becomes easier to estimate, but I couldn’t have taken the first step on the project without using unstructured time.

Part of the problem is that we expect our Agile teams to accurately estimate their work and then rate them on how well they do this (that they are accountable for their estimates). This has the side effect that they are then very resistant to work on things that are open ended or hard to estimate. Generally for innovation to take hold, the performance management system needs a bit of tweaking to encourage innovation and higher risk tasks, rather than only encouraging meeting commitments and making good estimates.

Now unlike Anathem, I’m not going to get 100 years to do this or even 10 years. But 1 year doesn’t seem so bad.

Summary

Now that we are adding unstructured time to our arsenal of innovation initiatives, I have high hopes that we will see all sorts of innovative new products, technologies and services emerge out of the end. Of course we are just starting this process, so it will take a little while for things to get built.