Top 3 use cases to infuse AI in your business: A CxO cheat sheet

Artificial Intelligence (AI) is no longer in its infancy; it is an essential business transformation tool to deliver results with impact. AI is changing every aspect of business by bringing data to the center: from AI infused customer and employee experiences to AI infused operations, and, ultimately, protecting the company from external risks and fraud.  

At IBM we are calling this an AI application flywheel. By drawing insights from hundreds of C-level AI engagements, major AI-driven business priorities surface to the top for each C-Suite executive. These include,

  • CEO – who has made a mission to make their organisation an AI driven company.
  • CMO – who wants to revamp marketing with advanced AI led customer engagement.
  • COO – who manages operations and wishes to leverage and apply AI efficiencies.
  • CFO – who wants to safeguard his company from new emerging threats and frauds. 

AI infused customer and employee experience

According to Servion Global Solutions research, approximately 95 percent of customer interactions of the future will be powered by AI. At that scale, it may become impossible to differentiate between humans and virtual agents during live conversation via telephone or online.

Customers and employees alike want to make efficient use of their time. For customers—faster and more personalized service. For employees—the ability to free up time from mundane tasks and focus on building innovation into products and services. Intelligence built into customer service that provides answers quickly and makes the customer feel special is going to be table stakes. Automation built into employee tasks like HR onboarding or team project management will be critical to maintaining a brand’s top position in the market.  Reliability in cognitive and intelligence services—built on a platform that preserves brand integrity while accelerating time-to-market—is key.

Case in point is Prudential, Singapore. They used IBM Watson Discovery and IBM Watson Assistant running on IBM Cloud™ infrastructure to develop an intelligent interface for customers seeking answers to their queries. Watson Expert Assist is the value pattern created by combining Watson Assistant enabled virtual agents with the content analytics capabilities of Watson Discovery to deliver AI infused customer and employee experiences.

Together Watson Assistant and Discovery can transform the way customers begin their buying journey with the brand. Automated engines recommend customers to take the next best step by analysing their behaviour and in turn delivering highly relevant product and service offers. This also helps extend the loyalty of customers by providing them with a digital personal assistant to guide their decision-making and shorten the conversion cycle. 

AI infused operational efficiencies

One of the largest hospitality and real estate firms in Asia has been using IBM Planning Analytics powered by TM1 to streamline planning, budgeting, forecasting and analysis of large finance data sets. Financial forecasts are a critical component of a successful business plan and budgeting process. With increasing demand for instant access to information in a web-driven world, financial managers are expected to offer financial forecasts to predict any financial impact on organization goals and priorities.

AI infused planning analytics provides finance teams capacity to analyse disperse large finance data sets, perform sophisticated calculations to develop accurate financial forecasting models to project expected sales, conduct budget management, and perform initial and ongoing credit analysis.

AI infused risk and fraud 

Today businesses rely on traditional risk scoring systems with simple statistical analysis. Those who use predictive systems do it in an expensive, non-optimized way of limited scalability.

Businesses operate in highly competitive, complex and regulated environment. New rules to comply with make it increasingly important to reduce risks. To stay competitive, businesses are challenged to offer rapid real-time fraud detection as they process high volumes of customer transactions passing through their doors.
Case in point: one of biggest retail chains in Asia is implementing a real time payment fraud system and integrating with POS, financial systems and banking systems. Artificial Intelligence, with built in IBM safer payments solution, identifies complex, nonlinear patterns in large data sets and makes more accurate risk models. These models learn, improving their predictive power over time. In turn, these models integrate with Watson enabled services to route fraud alerts and inform of corrective actions.

Learn how to operationalize AI in your business:

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Matt Newman helps drive better business decisions in a fast-growing industry

This story is part of Analytics Heroes, a series of profiles on leaders transforming business analytics.

Perched on a stool and going over his notes, Matt Newman prepares for an on-camera interview at Data and AI Forum in Miami, Florida. Matt is the Senior Financial Systems Manager at Sunbelt Rentals, the second-largest heavy equipment rental company in North America. Sunbelt considers FP&A (financial planning and analysis) to be critical to their success.

Starting as a Staff Accountant, Matt climbed the ranks over the past decade to his current leadership role and is spearheading great changes with the FP&A organization. In any industry, it’s a well-known fact that anyone who puts their team before themselves is a natural-born leader. And that quality is obviously present in the way Matt approaches FP&A.

At Sunbelt, Matt ensures the company’s financial data and reporting is accurate, stating “We have a mantra in operations: we’re about availability, reliability, and ease for our rental customers. So, we’ve adopted that mantra in FP&A as well, and that’s the heart of my role — to ensure that the data and financial reports are also easily accessible and reliable for our internal customers and the operations management team.” Any inaccuracies have a potentially devastating effect on the business, from finance all the way down to safety of the employees and customers. “Our management team is very busy. They work in an environment that can be dangerous at times, and we don’t want to be a burden to them in FP&A,” says Matt. “We want the planning and reporting process to be as simple as possible so that they can stay focused on one, being safe, and two, taking care of the customer.” In essence, Matt’s successful execution is directly linked to the success of other teams within the organization.

Matt Newman helps drive better business decisions in a fast-growing industryThe call for change

Sunbelt’s executive team saw opportunities in emerging and existing markets since Matt joined them in 2004. In 2016 they devised a five-year plan to manage and sustain growth, called Project 2021. While the executives were putting together an operational plan, the functional leaders of the company—including Matt—were putting together their own plans for supporting that growth. “In the FP&A team, we knew that to support operational growth, we needed to grow headcount, have better data, and of course we knew we needed a financial system that would scale.”

It wasn’t just the FP&A team that required more robust software. Sunbelt’s IT department was also seeking a new business intelligence solution. “I was definitely an advocate for change…but we knew we couldn’t go down this path alone.” After an extensive search for a consultant, and demoing software with multiple vendors, the BI Steering Committee, which Matt sat on, chose QueBIT as an IBM Analytics Business Partner. “QueBIT showed us that the IBM Planning Analytics and IBM Cognos Analytics would fit the bill on both sides of the coin, for the FP&A team and the IT department.”

Once the partnership was finalized, Matt laid out the groundwork to transition to the new platform. “We started by migrating our reporting data. We wanted to take what we currently had, and ‘forklift’ it over into the IBM Planning Analytics software—we wanted it to be so seamless that our customers wouldn’t notice they were using new software.”

Next, came reporting. “We created new cubes and imported a new data set and were able to take our daily and weekly reporting and bring that into the IBM Planning Analytics platform.” With a wide-ranging and highly mobile field staff, Sunbelt needed convenient ways for its users to access information. The answer: iPads. “We didn’t realize or anticipate that we could go mobile so fast,” says Matt. “Having IBM Planning Analytics on the iPad allows our users to access the data and financial reports anywhere, anytime.”

Empowering his finance team

Before switching to Planning Analytics, Sunbelt’s financial team had required manual intervention at month-end to pull in missing data, and they had spent nearly 60 man-hours a month generating and distributing performance statements to more than 1,300 managers. By implementing new solutions, they have drastically reduced the time pulling data, thus saving overall time and cost. “Before this, we relied heavily on spreadsheets. IBM Planning Analytics has empowered me and my team to be the owners of both the data and the processes…It’s no longer a question, is the data right, or is the report right? It’s this is the result, go ahead and take action and improve your business.”

By making the decision to apply an all-encompassing analytics solution, Matt Newman has helped Sunbelt reap the benefits of their investment and is finding continuing success. “It’s changed the way our end users access financial data and reports and enables them to make more educated business decisions,” says Matt. “It allows us to be nimble, so we can adjust on the fly and accommodate those decisions.” Not only has Matt been exceedingly effective in his role —seeing the implementation from conception to execution — Sunbelt has found a sustainable business in their analytics approach.

Being a champion with analytics

Matt learned a lot over the transition. “If I could offer advice to anybody, the first thing I would say is, get your data right. If you don’t, everything else falls apart.” Speaking from 15 years of experience, Matt adds “know your dimensions. If you don’t plan it, then it doesn’t need to be in your planning model. I found there was no need to get too granular, into transactional-level detail. Leave that to the Business Intelligence tools in Cognos Analytics.”

After over a decade of hard work and his role in driving FP&A at Sunbelt, Matt is being celebrated by IBM as an Analytics Hero. And like any strong leader he prefers to share the stage with his team. “I’m honored to be an Analytics Hero, and I think my whole team appreciates the recognition. Since our planning stages, about four years ago, there has been a lot of blood, sweat, tears, and late nights. This is validation that our process is moving in the right direction.”

Learn more about IBM Business Analytics.

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Setting an AI strategy to unlock the value of your data

It’s been said that data is the most valuable resource on the planet. But most companies aren’t getting the maximum value out of their data. If you look at the top three things that are really needed in the marketplace, it’s really been around defining a data strategy, filling the skill shortage and how to operationalize and industrialize your AI. In fact, while AI is helping companies gain competitive advantage in a growing range of industries, 51 percent find optimizing, sustaining and expanding AI capabilities challenging, notes Forrester.

Why is that? The fact is that, in a business context, AI is fairly new. It can also be a bit intimidating. So, I’d like to share some quick thoughts how to build an AI strategy and how to measure its success.

Setting an AI strategy to unlock the value of your data Formulating your AI strategy

Ultimately, of course, the success of any AI project has to be measured in dollars and cents. As you formulate an AI strategy, it’s important to identify and prioritize the use cases that have the greatest potential to generate value in terms of cost savings or net new revenue. Having said that, while it’s appealing to look for a big bang project that promises great benefits, it may not be prudent to go for the biggest bang first. Instead, a good approach is to focus on smaller projects that can deliver the best value in the shortest amount of time. Additionally, these big bang use cases should be broken down into their component parts. The main reason for this is two-fold: first, you need to create value quickly. Second, big projects do not fit into an Agile methodology.

Leveraging Agile methodologies in the data science process is the way to go. With Agile, you can start delivering value quickly, in two-week sprints. If you have some grand overall objective, you can break it down into smaller, lower risk components that have the potential to be duplicated or repurposed in other areas of the business. It’s also important to use good coding practices so that, even if your project is somewhat experimental, the AI can go directly into production, without the code having to be rewritten at the end.

If you love your data scientists, set them free

Companies today are collecting enormous volumes of data. But they are struggling to find the skills needed to operationalize AI. There is a severe shortage of data scientists. Fortunately, however, there are tools that can help you dramatically increase the productivity of your data scientists.

For example, we offer a solution called AutoAI with IBM Watson Studio, which automates many manual steps in the AI lifecycle management process—steps that can consume as much as 80 percent of a data scientist’s time. This solution frees them for the value-added work of optimizing and customizing AI to meet the needs of the business. When you feed data into AutoAI, it identifies the features that are most important. It does feature engineering and model selection, as well as hyperparameter tuning and optimization. AutoAI can be integrated with Watson OpenScale, which can detect and correct both bias and a drift in accuracy in AI models. Both offerings are part of IBM Cloud Pak for Data.

Automating manual steps helps data scientists do more satisfying work and have a bigger impact, and that helps organizations attract and retain the best data science talent.

Another benefit of AutoAI is that it generates Python code which can be re-used with some fine tuning. It enables data scientists to better understand the model and enhance it. Having the Python code available also allows extraction of important parts of the model, such as engineered features. These can be offered in a feature store, such as the one in Watson Knowledge Catalog, a part of IBM Cloud Pak for Data, and then put to valuable further use.

AI for your AI

A great example of how automation helps enhance the productivity of data scientists was offered by Wunderman Thompson at this year’s IBM Data and AI Forum. Their story is one of the most impactful use cases I’ve heard. They are a creative agency with about 200 offices worldwide and they had a serious problem with their data sets, which had about 17,000 features. Every time the company wanted to produce a custom solution for a customer, a data scientist had to determine which of the 17,000 features was right for that particular use case and then engineer them to meet the customer’s needs. It was an intractable problem that they’d been wrestling with for eight years. Then they contacted IBM and the IBM Data Science Elite team, and through using AutoAI, were able to solve the problem and get the AI into production in only two months. The models that were produced outperformed previous models by 200 percent.

Learn more about how they did this by watching my webcast conversation with Wunderman Thompson’s President and Chief Product Officer Michael Murray. And get more tips on scaling AI for growth and innovation.

Get the business team onboard

Finally, as you plan a project, make sure that your business users are involved and will be ready and willing to use the AI once it’s deployed. After all, the best AI in the world has no value if it doesn’t get used. As you gain experience and start delivering results, you can build support internally, and generate the confidence and enthusiasm you need to tackle more and bigger AI projects in the future. 

Share the excitement about AI

The potential of AI is very exciting for those of us who live and breathe data science. But let’s face it, change is hard. Watching competitors pass you by though, is even harder. So, take a look at our client success stories and see how much value AI and data science is bringing to organizations like yours. I think you’ll get excited too. 

Please visit the IBM Cloud Pak for Data web page to learn more about IBM AI capabilities.

Join my webcast conversation with Wunderman Thompson’s President and Chief Product Officer Michael Murray to learn tips on how to scale AI for growth and innovation throughout your organization.

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AutoAI: Synchronize ModelOps and DevOps to drive digital transformation

Welcome to 2020. As we take a look back to reflect on the state of digital transformation in today’s businesses, we can see several key trends emerging. Growth leaders are separating themselves from growth laggards by using AI and machine learning (ML) in modern application development. Below are some statistics provided by 451 Research:

  • Leaders invest in models for digital transformation: More than half the digital transformation leaders adopted ML compared to less than 25 percent of laggards. Furthermore, 62 percent of enterprises are developing their own models.
  • Prevalence of DevOps increases the demand for automation: 94 percent of enterprise companies have now adopted DevOps. Models are becoming integral to the development of enterprise apps—requiring continuous, synchronized and automated development and deployment lifecycles.
  • Data science and DevOps/app teams collaborate more: In 33 percent of enterprises, the data science/data analytics team is the primary DevOps stakeholder.

Based on my recent experience, I am noticing that more application developers are becoming interested in data science and AI, and many have already learned the fundamentals of data science. Business executives are keen on embedding prediction into business, optimizing operations and using automation to augment human capital while enabling their employees to do more with less. However, deploying models into operational systems is a well-known barrier to success. One investment area of interest for tackling operations is to align the cadences of getting a model in production (ModelOps) and an app in production (DevOps).

Intelligent automation can play a pivotal role in aligning model and app cadences. Since we established that AutoAI helps beginners and expert data scientists streamline model development, I’d like to discuss how AutoAI increases yields for model and app investments and orchestrates ModelOps with DevOps.

Automation of AI lifecycle helps your models can produce better outcomes

AI development has a full lifecycle that starts at ideation and ends with the monitoring of models in production. Lifecycle stages include data exploration and preparation, model development and deployment, and optimization and monitoring with a feedback loop. Data scientists, business analysts, data engineers and subject matter experts are key players in this lifecycle. What’s new is that DevOps teams are playing a larger role. In particular, growth leaders are now feeding the models produced from this lifecycle to DevOps to drive greater results at scale.

AutoAI was designed to reduce the more tedious, repetitive, and time-consuming aspects of data science and automate them so that data scientists can concentrate on the parts of the lifecycle where they can make the most innovative contributions. AutoAI also helps those who are just starting out with data science to build models quickly and easily. Beginners can also examine how the models are built and the pipelines are generated. Together, businesses can demonstrate better outcomes with fine-tuned prediction, optimization and automation.

Continuously-optimized models are better suited for cloud-native apps

In the application lifecycle, an app is born from an idea. After that, the development and design teams work with stakeholders to characterize the day and life of an end user and determine how to help him or her solve problems and achieve better results. Once this vision is in place, an app then moves into analysis, design and prototyping as the development team explores how it should work. After that, there is coding and unit testing, user and system testing, publishing and deployment. There will be periodic updates, adjustments for changes in the business and opportunities to address user feedback. AI and ML models can take dynamic interactions into account and present targeted offers that are tailored to each user.

Automation is already making an impact on the application lifecycle through continuous integration, low-code and no-code app development and more. Seasoned application developers can focus on designing innovative solutions without the tedium of hand-coding or struggling to integrate an app with operations, and beginners can design and prototype quickly—without a lot of coding experience. What’s needed is to find a way to integrate AI models into these automated, continuous-integration streams without disrupting them.

Synchronizing ModelOps and DevOps opens up new opportunities

Undoubtedly there is a strong business case for investing to align models and apps. Data scientists use ModelOps. Developers use DevOps. How can the two be synchronized?

ModelOps is where data science meets production IT, and where business value is created. Establishing ModelOps can make the injection of models into apps a more-tuned, repeatable and successful process.  Models have traditionally been deployed in a one-off fashion, and data scientists and data engineers often lack the skills to operationalize models. Application integration, model monitoring and tuning and workflow automation can be afterthoughts. This is why it makes sense to bring model and app development together on a data and AI platform where collective assets and intelligence can be harnessed.

Automation brings data, models and apps together while unleashing data and app talent

Powered by AutoAI, IBM Cloud Pak for Data is ideal for implementing and integrating ModelOps and DevOps. It enables models to be pushed from a data science team to the DevOps team in a regular deployment and update cycle, aligned with continuous integration and deployment to suit business needs. Powered by Watson Studio, Watson Machine Learning and Watson OpenScale, and open by design, Cloud Pak for Data integrates with cloud-native apps and allows you to build and scale AI with trust and transparency.  

AutoAI facilitates collaboration between the data science team and DevOps and app developers, and it reduces the complexity of deploying and optimizing models in production. If you’re in DevOps and app development, you can take the REST API endpoint from Watson Machine Learning and deploy the model while getting increased visibility into usage statistics, model status, and KPIs. Developers can set up the API connection to send more information for scoring and prediction into apps.

More ways to learn about AI and put AutoAI to work

This is just one example of how your enterprise can employ AutoAI to accelerate growth with data science and AI. Discover additional ways in our “10 ways to use AutoAI” eBook. You can also get many tips by viewing on-demand webinars in our 3-part Winning with AI series

Be sure to see our webinar Automated AI lifecycle management and ModelOps for your cloud native applications. It focuses on syncing ModelOps and DevOps and features presenters Matt Aslett of 451 Research and Ruchir Puri, chief scientist of IBM Research.

You can learn more about ModelOps by clicking here.