Unveiling the transformative AI technology behind watsonx Orders

You’re headed to your favorite drive-thru to grab fries and a cheeseburger. It’s a simple order and as you pull in you notice there isn’t much of a line. What could possibly go wrong? Plenty.

The restaurant is near a busy freeway with roaring traffic noise and airplanes fly low overhead as they approach the nearby airport. It’s windy. The stereo is blasting in the car behind you and the customer in the next lane is trying to order at the same time as you. The cacophony would challenge even the most experienced human order taker.

With IBM® watsonx Orders, we have created an AI-powered voice agent to take drive-thru orders without human intervention. The product uses bleeding edge technology to isolate and understand the human voice in noisy conditions while simultaneously supporting a natural, free-flowing conversation between the customer placing the order and the voice agent.

Watsonx Orders understands speech and delivers orders

IBM watsonx Orders begins the process when it detects a vehicle pulling up to the speaker post. It greets customers and asks what they’d like to order. It then listens to process incoming audio and isolates the human voice. From that, it detects the order and the items, then shows the customer what it heard on the digital menu board. If the customer says everything looks right, watsonx Orders sends the order to the point of sale and the kitchen. Finally, the kitchen prepares the food. The full ordering process is shown in the figure below:

There are three parts to understanding a customer order. The first part is isolating the human voice and ignoring conflicting environmental sounds. The second part is then understanding speech, including the complexity of accents, colloquialisms, emotions and misstatements. Finally, the third part is translating speech data into an action that reflects customer intent.

Isolating the human voice

When you call your bank or utilities company, a voice agent chatbot probably answers the call first to ask why you’re calling. That chatbot is expecting relatively quiet audio from a phone with little to no background noise.

In the drive-thru, there will always be background noise. No matter how good the audio hardware is, human voices can be drowned out by loud noises, such as a passing train horn.

As watsonx Orders captures audio in real time, it uses machine-learning techniques to perform digital noise and echo cancellation. It ignores noises from wind, rain, highway traffic and airports. Other noise challenges include unexpected background noise and cross-talk, where people are talking in the background during an order.  Watsonx Orders uses advanced techniques to minimize these disruptions.

Understanding speech

Most voice chatbots began as text chatbots. Traditional voice agents first turn spoken words into written text, then they analyze the written sentence to figure out what the speaker wants.

This is computationally slow and wasteful. Instead of first trying to transcribe sounds into words and sentences, watsonx Orders turns speech into phonemes (the smallest units of sound in speech that convey a distinct meaning). For example, when you say “shake,” watsonx Orders parses that word into “sh,” “ay,” and hard “k.” Converting speech into phonemes, instead of full English text, also increases accuracy over different accents and actively supports a real-time conversation flow by reducing intra-dialog latency.

Translating understanding into action

Next, watsonx Orders identifies intent, such as “I want” or “cancel that.”. It then identifies the items that pertain to the commands like “cheeseburger” or “apple pie.”

There are several machine learning techniques for intent recognition. The latest technique uses foundation and large language models, which theoretically can understand any question and respond with an appropriate answer. This is too slow and computationally expensive for hardware-restrained use cases. While it might be impressive for a drive-thru voice agent to answer, “Why is the sky blue?”, it would slow the drive thru, frustrating the people in line and decreasing revenue.

Watsonx Orders uses a highly specific model that is optimized to understand the hundreds of millions of ways that you can order a cheeseburger, such as “No onions, light on the special sauce, or extra tomatoes.” The model also allows customers to modify the menu mid-order: “Actually, no tomatoes on that burger.”

In production, watsonx Orders can complete more than 90% of orders by itself without any human intervention. It’s worth noting that other vendors in this space use contact centers with human operators to take over when the AI agent gets stuck and they count the interaction as “automated.” By our IBM watsonx Orders standards, “automated” means handling an order end-to-end without any humans involved.

Real-world implementation drives profits

During peak times, watsonx Orders can handle more than 150 cars per hour in a dual-lane restaurant, which is better than most human order takers. More cars per hour means more revenue and profit, so our engineering and modeling approaches are constantly optimizing for this metric.

Watsonx Orders has taken 60 million real-world orders in dozens of restaurants, even with challenging noise, cross-talk and order complexity. We built the platform to easily adapt to new menus, restaurant technology stacks and centralized menu management systems in hopes that we can work with every quick-serve restaurant chain across the globe.

Source: ibm.com

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Breaking down the advantages and disadvantages of artificial intelligence

Artificial intelligence (AI) refers to the convergent fields of computer and data science focused on building machines with human intelligence to perform tasks that would previously have required a human being. For example, learning, reasoning, problem-solving, perception, language understanding and more. Instead of relying on explicit instructions from a programmer, AI systems can learn from data, allowing them to handle complex problems (as well as simple-but-repetitive tasks) and improve over time.

Today’s AI technology has a range of use cases across various industries; businesses use AI to minimize human error, reduce high costs of operations, provide real-time data insights and improve the customer experience, among many other applications. As such, it represents a significant shift in the way we approach computing, creating systems that can improve workflows and enhance elements of everyday life.

But even with the myriad benefits of AI, it does have noteworthy disadvantages when compared to traditional programming methods. AI development and deployment can come with data privacy concerns, job displacements and cybersecurity risks, not to mention the massive technical undertaking of ensuring AI systems behave as intended.

In this article, we’ll discuss how AI technology functions and lay out the advantages and disadvantages of artificial intelligence as they compare to traditional computing methods.

What is artificial intelligence and how does it work?

AI operates on three fundamental components: data, algorithms and computing power. 

• Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. Data is often divided into three categories: training data (helps the model learn), validation data (tunes the model) and test data (assesses the model’s performance). For optimal performance, AI models should receive data from a diverse datasets (e.g., text, images, audio and more), which enables the system to generalize its learning to new, unseen data.
• Algorithms: Algorithms are the sets of rules AI systems use to process data and make decisions. The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming. AI can also work from deep learning algorithms, a subset of ML that uses multi-layered artificial neural networks (ANNs)—hence the “deep” descriptor—to model high-level abstractions within big data infrastructures. And reinforcement learning algorithms enable an agent to learn behavior by performing functions and receiving punishments and rewards based on their correctness, iteratively adjusting the model until it’s fully trained.
• Computing power: AI algorithms often necessitate significant computing resources to process such large quantities of data and run complex algorithms, especially in the case of deep learning. Many organizations rely on specialized hardware, like graphic processing units (GPUs), to streamline these processes.

AI systems also tend to fall in two broad categories:

• Artificial Narrow Intelligence, also called narrow AI or weak AI, performs specific tasks like image or voice recognition. Virtual assistants like Apple’s Siri, Amazon’s Alexa, IBM watsonx and even OpenAI’s ChatGPT are examples of narrow AI systems.
• Artificial General Intelligence (AGI), or Strong AI, can perform any intellectual task a human can perform; it can understand, learn, adapt and work from knowledge across domains. AGI, however, is still just a theoretical concept.

How does traditional programming work?

Unlike AI programming, traditional programming requires the programmer to write explicit instructions for the computer to follow in every possible scenario; the computer then executes the instructions to solve a problem or perform a task. It’s a deterministic approach, akin to a recipe, where the computer executes step-by-step instructions to achieve the desired result.

The traditional approach is well-suited for clearly defined problems with a limited number of possible outcomes, but it’s often impossible to write rules for every single scenario when tasks are complex or demand human-like perception (as in image recognition, natural language processing, etc.). This is where AI programming offers a clear edge over rules-based programming methods.

What are the pros and cons of AI (compared to traditional computing)?

The real-world potential of AI is immense. Applications of AI include diagnosing diseases, personalizing social media feeds, executing sophisticated data analyses for weather modeling and powering the chatbots that handle our customer support requests. AI-powered robots can even assemble cars and minimize radiation from wildfires.

As with any technology, there are advantages and disadvantages of AI, when compared to traditional programing technologies. Aside from foundational differences in how they function, AI and traditional programming also differ significantly in terms of programmer control, data handling, scalability and availability.

• Control and transparency: Traditional programming offers developers full control over the logic and behavior of software, allowing for precise customization and predictable, consistent outcomes. And if a program doesn’t behave as expected, developers can trace back through the codebase to identify and correct the issue. AI systems, particularly complex models like deep neural networks, can be hard to control and interpret. They often work like “black boxes,” where the input and output are known, but the process the model uses to get from one to the other is unclear. This lack of transparency can be problematic in industries that prioritize process and decision-making explainability (like healthcare and finance).
• Learning and data handling: Traditional programming is rigid; it relies on structured data to execute programs and typically struggles to process unstructured data. In order to “teach” a program new information, the programmer must manually add new data or adjust processes. Traditionally coded programs also struggle with independent iteration. In other words, they may not be able to accommodate unforeseen scenarios without explicit programming for those cases. Because AI systems learn from vast amounts of data, they’re better suited for processing unstructured data like images, videos and natural language text. AI systems can also learn continually from new data and experiences (as in machine learning), allowing them to improve their performance over time and making them especially useful in dynamic environments where the best possible solution can evolve over time.
• Stability and scalability: Traditional programming is stable. Once a program is written and debugged, it will perform operations the exact same way, every single time. However, the stability of rules-based programs comes at the expense of scalability. Because traditional programs can only learn through explicit programming interventions, they require programmers to write code at scale in order to scale up operations. This process can prove unmanageable, if not impossible, for many organizations. AI programs offer more scalability than traditional programs but with less stability. The automation and continuous learning features of AI-based programs enable developers to scale processes quickly and with relative ease, representing one of the key advantages of ai. However, the improvisational nature of AI systems means that programs may not always provide consistent, appropriate responses.
• Efficiency and availability: Rules-based computer programs can provide 24/7 availability, but sometimes only if they have human workers to operate them around the clock.

AI technologies can run 24/7 without human intervention so that business operations can run continuously. Another of the benefits of artificial intelligence is that AI systems can automate boring or repetitive jobs (like data entry), freeing up employees’ bandwidth for higher-value work tasks and lowering the company’s payroll costs. It’s worth mentioning, however, that automation can have significant job loss implications for the workforce. For instance, some companies have transitioned to using digital assistants to triage employee reports, instead of delegating such tasks to a human resources department. Organizations will need to find ways to incorporate their existing workforce into new workflows enabled by productivity gains from the incorporation of AI into operations.

Maximize the advantages of artificial intelligence with IBM Watson

Omdia projects that the global AI market will be worth USD 200 billion by 2028.¹ That means businesses should expect dependency on AI technologies to increase, with the complexity of enterprise IT systems increasing in kind. But with the IBM watsonx™ AI and data platform, organizations have a powerful tool in their toolbox for scaling AI.

IBM watsonx enables teams to manage data sources, accelerate responsible AI workflows, and easily deploy and embed AI across the business—all on one place. watsonx offers a range of advanced features, including comprehensive workload management and real-time data monitoring, designed to help you scale and accelerate AI-powered IT infrastructures with trusted data across the enterprise.

Though not without its complications, the use of AI represents an opportunity for businesses to keep pace with an increasingly complex and dynamic world by meeting it with sophisticated technologies that can handle that complexity.

Source: ibm.com

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IBM watsonx Assistant: Driving generative AI innovation with Conversational Search

Generative AI has taken the business world by storm. Organizations around the world are trying to understand the best way to harness these exciting new developments in AI while balancing the inherent risks of using these models in an enterprise context at scale. Whether its concerns over hallucination, traceability, training data, IP rights, skills, or costs, enterprises must grapple with a wide variety of risks in putting these models into production. However, the promise of transforming customer and employee experiences with AI is too great to ignore while the pressure to implement these models has become unrelenting.

Paving the way: Large language models

The current focus of generative AI has centered on Large language models (LLMs). These language-based models are ushering in a new paradigm for discovering knowledge, both in how we access knowledge and interact with it. Traditionally, enterprises have relied on enterprise search engines to harness corporate and customer-facing knowledge to support customers and employees alike. These search engines are reliant on keywords and human feedback. Search played a key role in the initial roll out of chatbots in the enterprise by covering the “long tail” of questions that did not have a pre-defined path or answer. In fact, IBM  watsonx Assistant has been successfully enabling this pattern for close to four years. Now, we are excited to take this pattern even further with large language models and generative AI.

Introducing Conversational Search for watsonx Assistant  

Today, we are excited to announce the beta release of Conversational Search in watsonx Assistant. Powered by our IBM Granite large language model and our enterprise search engine Watson Discovery, Conversational Search is designed to scale conversational answers grounded in business content so your AI Assistants can drive outcome-oriented interactions, and deliver faster, more accurate answers to your customers and employees.

Conversational search is seamlessly integrated into our augmented conversation builder, to enable customers and employees to automate answers and actions. From helping your customers understand credit card rewards and helping them apply, to offering your employees information about time off policies and the ability to seamlessly book their vacation time.

Last month, IBM announced the General Availability of Granite, IBM Research´s latest Foundation model series designed to accelerate the adoption of generative AI into business applications and workflows with trust and transparency. Now, with this beta release, users can leverage a Granite LLM model pre-trained on enterprise-specialized datasets and apply it to watsonx Assistant to power compelling and comprehensive question and answering assistants quickly. Conversational Search expands the range of user queries handled by your AI Assistant, so you can spend less time training and more time delivering knowledge to those who need.

Users of the Plus or Enterprise plans of watsonx Assistant can now request early access to Conversational Search. Contact your IBM Representative to get exclusive access to Conversational Search Beta or schedule a demo with one of our experts.

How does Conversational Search work behind the scenes?

When a user asks an assistant a question, watsonx Assistant first determines how to help the user – whether to trigger a prebuilt conversation, conversational search, or escalate to a human agent. This is done using our new transformer model, achieving higher accuracy with dramatically less training needed.

Once conversational search is triggered, it relies on two fundamental steps to succeed: the retrieval portion, how to find the most relevant information possible, and the generation portion, how to best structure that information to get the richest responses from the LLM. For both portions, IBM watsonx Assistant leverages the Retrieval Augmented Generationframework packaged as a no-code out-of-the-box solution to reduce the need to feed and retrain the LLM model. Users can simply upload the latest business documentation or policies, and the model will retrieve information and return with an updated response.

For the retrieval portion, watsonx Assistant leverages search capabilities to retrieve relevant content from business documents. IBM watsonx Discovery enables semantic searches that understand context and meaning to retrieve information. And, because these models understand language so well, business-users can improve the quantity of topics and quality of answers their AI assistant can cover with no training. Semantic search is available today on IBM Cloud Pak for Data and will be available as a configurable option for you to run as software and SaaS deployments in the upcoming months.

Once the retrieval is done and the search results have been organized in order of relevancy, the information is passed along to an LLM – in this case the IBM model Granite – to synthesize and generate a conversational answer grounded in that content. This answer is provided with traceability so businesses and their users can see  the source of the answer. The result: A trusted contextual response based on your company´s content.

At IBM we understand the importance of using AI responsibly and we enable our clients to do the same with conversational search. Organizations can enable the functionality if only certain topics are recognized, and/or have the option of utilizing conversational search as a general fallback to long-tail questions. Enterprises can adjust their preference for using search based on their corporate policies for using generative AI. We also offer “trigger words” to automatically escalate to a human agent if certain topics are recognized to ensure conversational search is not used.

Conversational Search in action

Let’s look at a real-life scenario and how watsonx Assistant leverages Conversational Search to help a customer of a bank apply for a credit card.

Let’s say a customer opens the bank’s assistant and asks what sort of welcome offer they would be eligible for if they apply for the Platinum Card. Watsonx Assistant leverages its transformer model to examine the user’s message and route to a pre-built conversation flow that can handle this topic. The assistant can seamlessly and naturally extract the relevant information from the user’s messages to gather the necessary details, call the appropriate backend service, and return the welcome offer details back to the user.

Before the user applies, they have a couple questions. They start by asking for some more details on what sort rewards the card offers. Again, Watsonx assistant utilizes its transformer model, but this time decides to route to Conversational Search because there are no suitable pre-built conversations. Conversational Search looks through the bank’s knowledge documents and answers the user’s question.

The user is now ready to apply but wants to make sure applying won’t affect their credit score. When they ask this question to the assistant, the assistant recognizes this as a special topic and escalates to a human agent. Watsonx Assistant can condense the conversation into a concise summary and send it to the human agent, who can quickly understand the user’s question and resolve it for them.

From there, the user is satisfied and applies for their new credit card.

Conversational AI that drives open innovation

IBM has been and will continue to be committed to an open strategy, offering of deployment options to clients in a way that best suits their enterprise needs. IBM watsonx Assistant Conversational Search provides a flexible platform that can deliver accurate answers across different channels and touchpoints by bringing together enterprise search capabilities and IBM base LLM models built on watsonx. Today, we offer this Conversational Search Beta on IBM Cloud as well as a self-managed Cloud Pak for Data deployment option for semantic search with watsonx Discovery. In the coming months, we will offer semantic search as a configurable option for Conversational Search for both software and SaaS deployments – ensuring enterprises can run and deploy where they want.

For greater flexibility in model-building, organizations can also bring their proprietary data to IBM LLM models and customize these using watsonx.ai or leverage third-party models like Meta’s Llama and others from the Hugging Face community for use with conversational search or other use cases.

Source: ibm.com

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How conversational AI can transform IT support

I spent my sophomore and junior years working at NYU’s IT Support Helpdesk and call center. My task was to improve digital experiences for NYU students and professors by solving technical issues they encountered. IT support can be both challenging and rewarding. Support technicians can utilize their technology background to find creative solutions as new issues arise. However, I noticed two prominent problems in the IT support process: repetitiveness and availability.

Problems at the IT Helpdesk

My favorite aspect of the IT contact center was employing creative problem-solving to find new technical solutions. However, through my experience with troubleshooting, I found that users typically shared very common issues with one another. For this reason, their problems were not always “new”. This meant my troubleshooting process was at times very uniform and repetitive. This repetitiveness was evident when users had trouble viewing or accessing certain sites. These customer interactions followed an “if-then” messaging sequence of trying possible solutions until a conclusion was reached.

A key skill of IT support is having the ability to problem-solve creatively. However, tasks like these often felt more algorithmic or methodical. They lacked the human-nature ability to invent a new solution, and rather, implemented a dependable step-by-step set of instructions until a solution was found. This type of customer care was a process that could certainly be automated.

Lack of automation also raised the issue that digital customer care was bound within specified hours. Since student workers also had to account for their time in class, it was difficult to have consistent availability for IT support. The same is true in the corporate world, where many companies only offer customer support between the hours of 9 a.m.–5 p.m. At my helpdesk, this often resulted in professors and students receiving delayed support, and morning-shift personnel having to handle an overwhelming number of tickets from the night before. Helpdesk workers are only human and providing 24/7 support seemed unrealistic. That was, until the introduction of AI chatbots for business emerged on the IT landscape.

How Watson Assistant can help

IBM Watson Assistant is a holistic SaaS solution for creating AI-enabled conversational experiences. It utilizes natural language processing (NLP) to assist customer care and support employees with internal processes. Watson Assistant is a versatile solution for a wide range of services and can be a powerful tool in IT automation.

This omnichannel chatbot solution delivers real-time, consistent, and accurate customer support on a 24/7 basis. No longer is customer support bound to 9 a.m.–5 p.m. hours, and no longer are IT professionals bound to repetitive or mundane tasks. Customers receive the same reliable service they would expect from traditional IT support, while freeing up IT professionals for more creative or valuable aspects of the job.

Watson Assistant seamlessly connects to customer data platforms, enabling data-backed understandings of customer expectations. Integrating these digital channels facilitates delivery of personalized customer experiences, while maintaining consistency in customer satisfaction. 

Using Watson Assistant for stimulating growth and innovation

Watson Assistant is a powerful and scalable tool that revolutionizes digital customer service, while enhancing employee productivity. For example, the green energy giant ENN Group Co. uses Watson Assistant for its 50,000-employee team to streamline the IT service request process. Utilizing Watson Assistant, ENN significantly increases company efficiency, while substantially improving customer experiences. Employing AI-powered chatbots reduces response time and expedites the support process for both customers and employees through effortless messaging. By automating 2,000–3,000 tasks daily, ENN found an increase in employee productivity of 60%. This process is expedited through real-time messaging and customer self-service functionality.

Li Qiang, IT Platforms Executive of ENN Group Co. Ltd claims, “Our AI automation platform provides employees with personalized AI skills, helps employees perform daily tasks, frees them from repetitive daily tasks and unleashes their creativity and imagination.” This automation allows employees to focus on higher-value activities, stimulating growth and innovation.

Watson Assistant is valuable across various industries and for small, midsize, and large businesses alike. Its integration into different companies has proven highly successful, transforming customer experiences and streamlining IT support processes. The implementation of Watson Assistant can revolutionize the IT industry, while providing application potential for numerous other commercial sectors as well.

Source: ibm.com

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IBM Watson Orchestrate: Unlocking new levels of productivity for every employee

The world is changing faster than ever, and the way we work needs to keep up with the possibilities that new technologies bring to our day-to-day work. Companies that want to stay competitive need to help their employees quickly build new skill sets and adapt to changing market conditions.

Read More: C9560-680: IBM Control Desk V7.6 Fundamentals

Workforce demographic trends compel us to reimagine how we work. A large share of the worker population—Millennials and Generation Z—grew up using technology in their day-to-day lives, and they expect the tech they use at work to function the same as it does in their personal lives.

Those businesses that do not adapt may be overly exposed to worker shortages and productivity. An analysis from the U.S. Bureau of Labor Statistics shows that the U.S. labor force participation rate is projected to decline from 61.7% in 2021 to 60.1 % in 2031, fueled by the aging population trend reflected across the world.

Sport Clips Haircuts reimagines talent acquisition

Sport Clips Haircuts—a leading hair salon with almost 1,900 stores in the U.S.—recognizes the modern-day challenges of staffing and employee retention. CEO Edward Logan has put technology at the forefront of the company and considers AI and automation to be a great way to support franchise owners, which is a top priority for the company.

Customer demand is high and every chair without a stylist at Sport Clips is a lost opportunity to make a customer happy. However, recruiting for the stylist role and finding the right talent can be challenging. Sport Clips wanted to help reduce the recruiting burden on franchise owners by expanding their qualified talent pool and streamlining outreach to passive candidates, which led them to reach out to IBM and ThisWay Global—a candidate sourcing and matching platform with an expansive network of over 8,500 communities.

Franchise owners were onboarded to the solutions in under an hour and now have the ability to manage their end-to-end recruiting process through a single user interface. Through chat-style interactions, users are able to initiate automations (known as skills), including the following:

◉ Create a new job requisition from commonly posted positions.

◉ Socialize the job listing on job boards like Indeed and LinkedIn (paid subscriptions may be required).

◉ Identify qualified passive candidates using ThisWay Global and email up to 300 candidates within minutes to invite them to apply.

“Driving transformation for our franchisee owners is a top priority. Can we make a process easier, faster and with fewer errors to help them be successful? With IBM Watson Orchestrate, we streamlined passive candidate outreach. What used to take three hours can now be done in just a few minutes,” said Edward Logan, CEO & President of Sport Clips Haircuts.

Introducing IBM Watson Orchestrate Enterprise Edition

IBM Watson Orchestrate is a cloud-based solution that helps companies empower their people to change the nature of their day-to-day work. The new Enterprise Edition includes an expanded set of skills and a new ability to import existing automations, including those from IBM Robotic Process Automation. The release introduces digression—the ability for Watson to multi-task so it can handle multiple requests at the same time.

Get work done quickly with pre-built skills

Skills are foundational to the Watson Orchestrate platform—think of them as units of automation. It can be as simple as adding a row to Excel or as complex as onboarding a new employee with the many tasks involved— collecting I-9 information, ordering a new computer and even setting up a welcome meeting with the team. The new Watson Orchestrate Enterprise Edition offers over 30 new pre-built/pre-trained skills for SAP SuccessFactors, Oracle HCM and Workday.

Reuse existing automations

IBM Watson Orchestrate can help you get the most out of your prior automation investments. Easily import existing automations that use the OpenAPI specification. These automations can be from IBM or other third-party vendors. After importing, developers can train skills and define natural language utterance phrases quickly.

Support more use cases with IBM Robotic Process Automation

IBM Watson Orchestrate Enterprise Edition comes with entitlements to IBM Robotic Process Automation (RPA) to facilitate automating unique use cases across your organization. Watson can sequence your custom skills with pre-built flows or create new workflows dynamically.

How many things can you do at once?

Multi-tasking—it’s essential in today’s work environment. To truly transform work, we need tools that can keep up. Watson can now work multiple requests in parallel. For example, a recruiter can ask Watson to schedule interviews and then continue work with Watson while the task is being worked in the background.

Beyond the chatbot

IBM Watson Orchestrate isn’t a chatbot, which typically executes dialog trees that terminate when the user response cannot be found in the tree. Watson has an advanced natural language processor to understand intent, break down requests and guide you through a dynamically generated sequence of steps to complete a task. If Watson needs more information or to clarify ambiguity, it asks. When unable to find a skill, Watson guides users to find a new skill or import an existing skill to complete that task.

Source: ibm.com

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It’s 2023… are you still planning and reporting from spreadsheets?

I have worked with IBM Planning Analytics with Watson, the platform formerly known as TM1, for over 20 years. I have never been more excited to share in our customers’ enthusiasm for this solution and how it is revolutionising many manual, disconnected, and convoluted processes to support an organisation’s planning and decision-making activities.

Over the last few months, we have been collecting stories from our customers and projects delivered in 2022 and hearing the feedback on how IBM Planning Analytics has helped support organisations across not only the office of finance – but all departments in their organisation.

The need for a better planning and management system

More and more we are seeing demand for solutions that bring together a truly holistic view of both operational and financial business planning data and models across the entire width and breadth of the enterprise. Having such a platform that also allows planning team members to leverage predictive forecasting and integrate decision management and optimisation models puts organisations at a significant advantage over those that continue to rely on manual worksheet or spreadsheet-based planning processes.

A better planning solution in action

One such example comes from a recent project with Oceania Dairy. Oceania Dairy operates a substantial plant in the small New Zealand, South Island town of Glenavy, on the banks of the Waitaki River. The plant can convert 65,000 litres of milk per hour into 10 tons of powder, or 47,000 tons of powder per year, from standard whole milk powders through to specialty powders including infant formula. The site runs infant formula blending and canning lines, UHT production lines, and produces Anhydrous Milk Fat. In total, the site handles more than 250 million litres of milk per year and generates export revenue of close to NZD$500 million.

Oceania Supply Chain Manager, Leo Zhang shares in our recently published case study: “Connectivity has two perspectives: people to people, facilitating information flows between our 400 employees on site. Prior to CorPlan’s work to implement the IBM Planning Analytics product, there was low information efficiency, with people working on legacy systems or individual spreadsheets. The second perspective is integration. While data is supposed to be logically connected, decision makers were collating Excel sheets, resulting in poor decision efficiency.”

“CorPlan”, adds Zhang, “has fulfilled this aspect by delivering a common platform which creates a single version of the truth, and a central system where data updates uniformly”. In terms of Collaboration, he says teams working throughout the supply chain managing physical stock flows are being connected from the start to the finish of product delivery. “It’s hard for people to work towards a common goal in the absence of a bigger picture. Collaboration brings that bigger picture to every individual while CorPlan provides that single, common version of the truth,” Zhang comments.

The merits of a holistic planning platform

While the approach of selecting a platform to address a single piece of the planning puzzle – such as Merchandise Planning, or S&OP (Sales and Operational Planning), Workforce Planning or even FP&A (Financial Planning and Analysis) – may be a organisations desired strategy, selecting a platform that can grow and support all planning elements across the organisation has significant merits. Customers such as Oceania Dairy are realising true ROI metrics by having:

 

◉ All an organisation’s stakeholders operating from a single set of agreed planning assumptions, permissions, variables, and results

◉ A platform that supports the ability to run any number of live forecast models to support the data analysis and what-if scenarios that are needed to support stakeholder decision-making

◉ An integrated consolidation of the various data sources capturing the actual transactional data sets, such as ERP, Payroll, CRM, Data Marts/ Warehouses, external data stores and more

◉ Enterprise-level security

◉ In the cloud, as a service delivery

My team and I get a big kick out of delivering that first real-time demonstration to a soon-to-be customer, showing them what the IBM Planning Analytics platform can do. It is not just the extensive features and workflow functionality that generates excitement. It is that moment when they experience a sudden and striking realisation – an epiphany – that this product is going to revolutionise the painful, challenging, and time-consuming process of pulling together a plan.

What is even better is then delivering a project successfully, on time and within budget, that delivers the desired results and exceeds expectations.

If you want to experience what “good planning” looks like, feel free to reach out. The CorPlan team would love to help you start your performance management journey. We can help with product trials, proof of concept or simply supply more information about the solution to support your internal business case. It’s time to address the challenges that a disconnected, manual planning and reporting process brings.

Source: ibm.com

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Call Center Modernization with AI

Picture this: A traveler sets off on a camping trip. She decides to extend her RV rental halfway through her trip, so she calls customer service for assistance, but finds herself waiting minutes, then what feels like hours. When she finally does get a hold of somebody, her call is redirected. More waiting follows. Suddenly her new plan doesn’t seem worth the aggravation. Now, imagine the same scenario from the agent’s perspective, dealing with a dissatisfied customer, scrambling for information that takes time to collect. Instances like these are far too common—the debacle ends up being costly for the company, and frustrating for both customer and agent.

Conversational AI solutions for customer service have come a long way, helping organizations meet customer expectations while reducing containment rates, complexity, and costs. It starts with bringing AI into the mix and ends with more cost-efficient operations and more satisfied customers.

So how can conversational AI help fulfill customer expectations in today’s ever-demanding landscape?

When you deploy conversational AI in your call center, you get:

1. Increased customer and agent satisfaction. Think of the example above—long wait times and unanswered questions can only lead to frustrated customers and agents and slower businesses. With leading natural language understanding (NLU) and automation leading to faster resolution, everybody wins.

2. Improved call resolution rates. AI and machine learning enable more self-service answers and actions and help route customers who need live agent support to the right place – continuously analyzing customer interactions to improve response. Agents benefit from this assistance too; empowering them to perform at their best when call traffic is high. Ultimately, improved resolution rates mean better customer experiences and improved brand reputation.

3. Reduced operational costs. With the capabilities of AI-powered virtual agents, you can contain up to 70% of calls without any human interaction and save an estimated USD 5.50 per contained call. This is money saved for your business, and time saved for your customers.

Not all AI platforms are built the same

On the lowest rung of the AI ladder, you have rules-based bots with limited response function. For example, you want to know if your telecom provider offers an unlimited data plan, so you call customer service and are given a set of basic questions following strict if-then scenarios—“…say yes if you want to review service plans; say yes if you want unlimited data.”

Climb up one rung, and there’s level two AI with machine learning and intent detection. You accidentally type “speal to an agenr”— but the virtual assistant understands your intention and responds properly: “I will connect you with an agent who can assist you.”

Then there’s IBM Watson® Assistant—the always-learning, highly resourceful virtual agent. Watson Assistant sits at the top—level three. Level three offers powerful AI that has unparalleled data and research capabilities.

The Watson Assistant deployed at Vodafone, the second-largest telecommunications company in Germany, exhibits level-three capacities—in addition to answering questions across a variety of platforms, such as WhatsApp, Facebook and RCS, Watson Assistant answers requests pulled from databases and can converse in multiple languages. It mines data, customizes interactions and is continuously learning. “*Insert Name*, transferring you to one of our agents who can answer your question about coverage abroad.” 

With Watson AI, you can expect more for your call center: 24/7 support, speedy response times and higher resolution rates. Seamlessly integrate your virtual agent with your existing back-end systems and processes, with every customer channel and touchpoint, without migrating your tech stack—IBM can meet you wherever you are in your customer service journey. Watson AI offers:

◉ Best-in-class NLU

◉ Intent detection

◉ Large language models

◉ Unsupervised learning

◉ Advanced analytics

◉ AI-powered agent assist

◉ Easy integration with existing systems

◉ Consulting services

All these features work in concert to redefine customer care at the speed of your business.

Why add complexity when you can simplify with AI? 

According to a Gartner® report, in 2031, conversational AI chatbots and virtual assistants will handle 30% of interactions that would have otherwise been handled by a human agent, up from 2% in 2022. To remain among the leaders, modern contact centers will need to keep up with AI innovations. Of course, like Watson, leading businesses are constantly learning, analyzing, and striving to become better.

Watson Assistant plugs into your company’s infrastructure, is reliable, easy to use, and always there to provide answers and self-service actions. Take Arvee, for example, an IBM Watson AI-powered virtual assistant for Camping World, the number one retailer of RVs. When customer demand surged early in the global pandemic, Camping World deployed Arvee in their call center and agent efficiency increased 33%.  Customer engagement also increased by 40%.

Similarly, IBM is working together with CcaaS providers like Nice to make it even simpler to build, deploy and scale AI-powered virtual voice agents.

Watson Assistant helps streamline processes and create agent efficiency—and when calls go to human agents, they can deliver higher quality personal service. Remember that aggravated customer from earlier? With the power and capabilities of Watson Assistant, she can enjoy her time camping—goodbye hold music, hello sounds of nature.

Source: ibm.com

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ESPN, IBM Consulting and the power of data-driven decision making in fantasy football

Fantasy football has been around since the 1960s, when a part-owner of a professional football team gathered with friends to “draft” athletes into fantasy leagues and accrue points based on the actual performance of those players in their real-life games. It was an early effort to gamify the experience, have fun with friends, and increase interest in the football season.

It worked. Today, fantasy sports aren’t just fun and games; they’re an $8.8 billion-dollar business. An estimated 45 million Americans play fantasy football, dedicating nearly seven hours a week to researching players and managing their rosters throughout the season. And ESPN is the undisputed champion of the fantasy football platforms, with a record 11 million players managing more than 17 million teams.

Read More: C2010-825: IBM Rational Team Concert V6

“We want ESPN to be the destination for all fans playing Fantasy Football, whether it’s their first time or they’ve been managing a league for 20 years.,” said Chris Jason, Executive Director, Product Management at ESPN. “To meet that bar, we have to continuously improve the game and find ways to enhance the experience with new innovations.”

To keep that constant innovation moving, ESPN has been working closely with IBM Consulting over the last six years, designing, developing and delivering new features that enhance the user experience. In particular, ESPN is eager to develop ways to serve up insights that help fantasy players make great roster decisions.

“Football produces a massive amount of data,” says Stephen Hammer, Sports CTO, distinguished engineer, ES&iX, IBM Consulting. “There are 1,900 players in the league. Every time they take the field, they produce a whole series of data. And that’s just the structured data. Millions of blogs, articles, and podcasts about football are produced every season. And those contain important insights as well.”

To get a handle on all this data, ESPN worked closely with IBM Consulting to visualize the kinds of features and insights end users were looking for. They used IBM Design Thinking to understand the different personas of fantasy football players and map out the various user journeys they undertake. And they worked in the IBM Garage model, a proven methodology for co-creation that accelerates the innovation process.

The teams co-created two solutions, Player Insights with Watson and Trade Analyzer with Watson.

Player Insights combine analysis of structured data like scores and statistics, with AI-powered analysis of media commentary using Watson Discovery. This results in comprehensive and user-friendly insights designed to help fantasy managers understand a player’s boom or bust chances, or to assess how injuries will affect their lineup.

Trade Analyzer with Watson uses those same analyses to evaluate potential trades between fantasy managers. When one fantasy manager proposes a trade, Trade Analyzer examines the strengths and weaknesses of their team and shows which positions the manager needs to fill to make their team stronger.

In just the first week of the 2022 season, more than 6 million trades were proposed on ESPN’s platform. And last year alone, IBM served up more than 34 billion AI-powered insights through the ESPN fantasy app.

“This is the same technology we’re using to help clients transform data into insight in every industry,” says Hammer. “Whether it’s fantasy football or financial services, it’s all about data-driven decision making.”

Source: ibm.com

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Is your conversational AI setting the right tone?

Conversational AI is too artificial

Nothing is more frustrating than calling a customer support line to be greeted by a monotone, robotic, automated voice. The voice on the other end of the phone is taking painfully long to read you the menu options. You’re two seconds away from either hanging up, screaming “representative” into the phone, or pounding on the zero button until you reach a human agent. That’s the problem with many IVR solutions today. Conversational AI is too artificial. Customers feel they’re not being heard or listened to, so they just want to speak with a human agent.

IBM Watson Expressive Voices 

Luckily, there is a way to fix that problem and make the customer experience more pleasant. With IBM Watson’s newest technology of expressive voices, you will no longer feel like you’re talking to a typical robot; you’ll feel like you’re talking to a live human agent without any of the wait time. These highly natural voices have conversational capabilities like expressive styles, emotions, word emphasis and interjections. Not only do these voices relieve the customer frustration of feeling like they’re talking to a bot, but they also contribute to the goal of call deflection from human agents. It’s a win-win for customers and businesses.

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Best suited for the customer care domain, the voices will have a conversational style enabled by default; however, the voices also support a neutral style which may be optimal for other use cases (newscasting, e-learning, audio books, etc.). Have a listen to the expressive voice samples below:

◉ Expressive Emma voice

◉ Expressive Emma voice with empathetic style

◉ Expressive Emma voice with uncertain style

◉ Expressive Emma voice with cheerful style

Emotions, Emphasis, Interjections

As humans, we convey emotion in the words we speak, whether we realize it or not. We tend to sound empathetic when apologizing to one another. We sound uncertain when we don’t know the answer to something, and perhaps cheerful when we finally discover the answer. The ability to convey emotion is what makes us human. IBM Watson’s expressive voices can express emotion in order to better convey the meaning behind the words, ultimately reducing customer frustration when dealing with today’s phone experiences. Your voice bot will sound empathetic when telling the customer their package is delayed or cheerful when they’ve successfully helped the customer book an airline ticket.

Emphasis is another important aspect of human speech. Did you say Austin or August? Did you say you lost the card ending in 4876? IBM expressive voices support word emphasis so that your bot can better convey the desired meaning of the text. Users can indicate the location of the stress with four levels – none, moderate, strong, and reduced.

Interjecting with words like hmm, um, oh, aha, or huh is another feature of human speech that IBM expressive voices now support to enable an interaction that feels more natural and human-like. The new expressive voices will automatically detect these interjections in text and treat them as such without any SSML (Speech Synthesis Markup Language) indication. There’s an also an option to disable the interjections when it’s not appropriate (e.g., ‘oh’ can be used to spell out the number 0 or as an interjection).

How to Get Started with Expressive Voices

Expressive voices and features will be available in US-English first in September 2022, followed by other languages in early 2023. The US-English expressive voices are Michael, Allison, Lisa, and Emma. For customers using the V3 version of Michael, Allison, or Lisa, switching to the expressive voices shouldn’t cause disruption as it will still sound like the same speaker, but with a more natural and conversational style. It’s easy to start using the new voices – simply indicate the voice name in the API reference, just like any other voice.

In summary, IBM’s new technology of expressive voices is the next level of conversational AI. It checks the box when it comes to an engaging and natural experience that mirrors that of a human agent. The new voices relieve the customer frustration of feeling unheard and drive call deflection from human agents.

Source: ibm.com

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How IBM Consulting and the US Open evolve the fan experience and accelerate innovation

IBM® has been the official technology partner of the US Open Tennis Championships for more than three decades, and the relationship goes much deeper than courtside logo placement. It’s an ongoing partnership delivering world-class digital experiences to fans, built on IBM’s open, flexible technology platform. “We need to constantly innovate to meet the modern demands of tennis fans, anticipating their needs, but also surprising them with new and unexpected experiences,” says Kirsten Corio, Chief Commercial Officer at the United States Tennis Association (USTA).

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Year after year, IBM iX, the experience design arm of IBM Consulting™, works with the USTA to integrate technology from dozens of partners, automate key business processes and use the power of artificial intelligence (AI) to transform vast quantities of tennis data to deliver key insights.

Bringing fans closer to the game they love

This year’s tournament features colorful personalities and compelling stories. But as host of a leading spectator event viewed by nearly 10 million people every year, the USTA is charged with delivering ever more engaging experiences. IBM Consulting asked: How can we use the digital experience of the US Open to serve the USTA’s mission and grow the game of tennis? How can we better serve fans with live scores, stats and player information while they watch a live match? How can we deliver the answers they need? How can we provide relevant and timely insights they can’t find anywhere else?

These questions led to several innovations: The IBM Power Index with Watson ranks player momentum and combines performance and punditry, queried through IBM Watson® Discovery, to create a “Likelihood to Win” prediction and highlight compelling matchups. Match Insights with Watson delivers head-to-head pregame analysis of every match, using natural language generation to translate historical statistics into easily read sentences. And US Open Fantasy Tennis, enriched with Match Insights, lets fans create and follow their own fantasy team.

A collaboration that drives innovation

Creating digital experiences that drive enthusiasm requires a human lens. To achieve that, the US Open digital strategy team partners closely with IBM iX, one of the largest business design consultancies in the world. IBM iX uses collaborative design thinking brought to life by the IBM Garage methodology — an end-to-end model for accelerating digital transformation — to address challenges within a variety of management frameworks including lean startups, human-centered design, agile and DevOps.

Stage 1: Co-create

At the co-create stage, squads agree on the nature of the challenges, prioritize them and conceptualize solutions. For example, for the 2022 US Open, a top priority was providing more explainability to the “Likelihood to Win” prediction.

Stage 2: Co-execute

At the co-execute stage, development teams build minimum viable products or solutions, and test them. Using this process, IBM and USTA developed the “Win Factors” feature, which shows the top three variables affecting the prediction such as head-to-head record, winning record on this surface or Power Index rating.

Stage 3: Cooperate

The cooperate stage is not simply about operational maintenance. It’s about ongoing performance management, improvement and product development. The USTA and IBM Consulting cooperate virtually year-round to develop and refine the digital experience, starting with a debrief after the tournament asking questions such as Where did we succeed? What could be improved? How can we be more efficient and effective?

Beyond solving the problems at hand, co-creating with IBM Garage can be a transformative experience for organizations, helping them prioritize their development queue, iterate solutions and evaluate them in a cycle of ongoing improvement. Using this method, IBM and the GRAMMYs delivered artist insights for live coverage based on IBM Watson analysis of millions of articles. IBM and the Masters® built a digital platform to scale the capabilities of the Masters Digital team.

Over 30 years in, IBM and the US Open continue to overcome new challenges and engage fans with new experiences. For a tournament, fan expectations and technology that are always evolving, this partnership keeps the USTA ahead of the ball.

Source: ibm.com

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Building a platform of innovation to transform golf data into predictive insights

The Masters Tournament is steeped in tradition. From Amen Corner to Butler Cabin, it seems everywhere you look at Augusta National Golf Club, history is staring back at you. But the Masters has another, more forward-looking, tradition: innovation.

Since 1934, the Masters has pushed the boundaries of innovation in golf, from low-tech inventions like the under-over scoring system, to award-winning technology like serving up every shot, from every player, on every hole through the Masters app. And to help the Masters continue to define the future of digital experience in sports, IBM Consulting developed a next generation “platform of innovation.”

The Masters has always had a clear and consistent vision for the digital experience on Masters.com and the Masters app. Among other things, they want to bring digital “patrons” closer to the Tournament with meaningful insights harvested from data. To do this, IBM and the Masters Digital team worked closely using the IBM Garage™ methodology, a way of co-creating solutions to a variety of business problems. The teams constructed a powerful platform that uses hybrid cloud and AI technologies to transform vast quantities of data into insights.

The result is a digital experience that generates player insights that bring patrons closer to the players they love. Using IBM Cloud Pak® for Data® and Red Hat® OpenShift® to manage the flow of structured and unstructured data, the platform applies the natural language processing capabilities of IBM Watson to analyze millions of statistics and articles, identifying and delivering insight right to the player pages in the app.

In addition, the teams built AI models that could analyze six years of historical Masters data on hundreds of players and more than 120,000 shots. These models generate predictions of every player’s score in every round. Users of the app can then use those projections to help select their Masters Fantasy foursome.

These new features do more than enhance the fan experience. They also automate critical elements of the editorial workflow for the Masters Digital team, which allows them to scale their capabilities and focus their attention on the most urgent, creative work.

These new features join other recent fan favorites, like My Group, Track, and the AI-generated Round in Under Three Minutes, all of which make the Masters digital experience one of the best in all of sports. And they extend the tradition of partnership between IBM and the Masters.

Source: ibm.com

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What’s next in AI-assisted governance, risk and compliance

“You need a technology plan that’s aligned with your risk and compliance objectives,” says Heather Gentile, Head of RegTech Offerings, Data and AI at IBM. In an episode of the executive video series “Compliance Over Coffee,” Gentile and Brian Clark, co-founder of regulatory knowledge platform Ascent, discuss the partnership between Ascent and IBM OpenPages with Watson to handle governance, risk and compliance (GRC) for clients. The two discuss the responsibilities that come with digitalization, proactive measures for compliance, the importance of trust, and what’s next in compliance trends.

Gentile points out the two sides of the trend toward total digitalization. “With ‘going digital,’” she says, “we see organizations collecting more information about their clients than ever. On the good side, you have a lot of data available for AI analysis. The challenge is, you need a data governance framework that allows for the secure collection and organization of that data so that it can be securely leveraged.”

Using the predictive capabilities of AI, organizations can get more proactive with their compliance strategies. IBM OpenPages integrates with Ascent to bring obligation data into OpenPages and help organizations look ahead, rather than simply react. “You can’t always predict where the Administration is going to go with their new legislation,” Gentile says. But there’s an opportunity to start earlier, make plans and involve stakeholders in a more collaborative approach to compliance. “The lines between first line, second line, third line are really starting to blur now,” she says. “If you can anticipate the risks accurately, you’re less inclined to have an audit issue to clean up later.”

“By combining Ascent’s knowledge with IBM OpenPages, we’ve created an integration that helps make the process of compliance more seamless, repeatable, and scalable than ever before,” says Brian Clark, President and Founder at Ascent. “Ascent’s RegulationAI solves an actual business problem, and our partnership with IBM focuses on maximizing this impact.

“From ‘Compliance Over Coffee’ to the IBM RegTech Summit, we are working together to help the market distinguish between smoke and mirrors and true value-add technology. Ultimately, we’re on a mission to help firms ‘de-risk’ their business in a cost-effective and accurate way.”

Gentile emphasizes the advantage of the IBM approach to AI, which emphasizes trust and transparency, breaking open the “black box” of AI. Many organizations are eager to adopt AI models to support business strategies, she says. Those organizations need to have control of, and insight into, how the models operate. “You can set everything up with the best of intentions from a governance perspective,” she says, “but a big piece of people being able to accept AI is through effective controls.”

To scale GRC solutions, financial services firms are looking to the cloud and hybrid cloud. That, says Gentile, is where they can leverage containerization on the Cloud Pak for Data platform. Now that IBM OpenPages is part of Cloud Pak for Data, OpenPages has a direct integration with Watson Knowledge Catalog to address data governance.

Gentile points out GDPR regulation from the EU, and the intense work that organizations went through to comply. This work isn’t over, thanks to similar data privacy laws in states such as California, as well as potential federal regulation under the new administration. But Ascent and IBM OpenPages can make it easier. With anticipatory requirements and control suggestions from Watson Natural Language Classifier inside of the OpenPages UI, based on a repository of regulatory data, clients can save time on data mapping and administration, so they can focus better on analysis.

Gentile places regulatory compliance within IBM OpenPages paradigm of infusing AI throughout an entire organization. Clients can use OpenPages to optimize the compliance process end-to-end. “We’re seeing more and more that IT is not just a stakeholder in the risk and compliance buying decisions, but more of a decision maker and a collaborator.” That makes it important to align an organization’s GRC objectives with its technology plan. There’s a lot of work ahead, and organizations that automate, integrate and optimize end-to-end will come out ahead.

Source: ibm.com

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IBM researchers use epidemiology to find the best lockdown duration

We finally have vaccines, but prevention strategies and mitigation of spread of the virus will stay for the foreseeable future, in the form of lockdowns. While effective for helping to deal with disease spread, the duration of lockdowns during the current pandemic has been typically chosen through empirical observation of symptoms.

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But is it the best way?

Our team at IBM Research, in collaboration with the team of Dr. Ira Schwartz at the US Naval Research Laboratory, aims to provide an arguably more accurate approach to the optimal duration of lockdowns, based an epidemiology theory.

In a recent paper Optimal periodic closure for minimizing risk in emerging disease outbreaks published in PLoS One, we describe a new technique to calculate the optimal duration of a periodic lockdown during an outbreak of an infectious disease where there is no cure or vaccine. Our findings are different from the lockdown durations widely applied during COVID-19.

Using an epidemiological model and a new mathematical formulation, we’ve assessed the optimal duration of a lockdown to help minimize the spread of the virus — and found that it can vary between 10 and 20 days rather than the inflexible and imprecise current protocol of two weeks.

The rationale of the 14 day duration

During the current pandemic, nations often have imposed lockdowns based on the time it takes for symptoms to appear. This is estimated to be, at most, two weeks. The lockdown would then be periodically reassessed.

However, our findings are different.

We show that an optimal, data-driven way to help control an epidemic is by closing businesses, schools, and other public meeting places for a period roughly equal to two to four times the mean incubation period, or between 10 and 20 days, based on measurable local health factors. After that time, these places can be reopened for about the same period, until the outbreak is controlled and the disease is eradicated.

Importantly, this period depends on the so-called disease reproductive number, or R0, a measure of the potential of the disease to spread in a population. When R0 is larger than 1, the disease spreads and triggers an outbreak. When R0 is smaller than 1, the disease dies out after having been put under control.

We’ve found that the higher the value of R0, the longer the lockdown needs to be to curb the spread, and vice-versa. We’ve also found that when the reproductive number exceeds a certain threshold, the spread cannot be controlled by periodic lockdowns. This observation, which has never been suggested until now, may have important consequences not only for the current COVID-19 pandemic, but also for the next one, whenever it may happen.

“Control theory” for lockdowns

Not much work has been devoted to the lockdown duration until now. Some recent papers have suggested strategies for lockdowns, but they were mostly computational in nature. Our work, on the other hand, introduces a mathematical framework based on the theory of epidemiology for the assessment of the effect of lockdowns. As such, its application is general and can be used not only for COVID-19, but for any disease for which a periodic shutdown may be necessary to contain and slow community spread.

We used a mathematical approach called control theory, widely used in engineering (for example, for the design of aircrafts and ships), biology and artificial intelligence. We assume that the incidence of the disease — the number of infectious cases per day — is something that can be ‘controlled’ using periodic lockdowns as ‘controllers.’ We then determine the conditions a lockdown needs to meet for the total incidence to be minimized over the course of the outbreak.

Paired with a predictive model, like the one used in IBM Watson Works’ Return to Work Advisor that mixes rigorous epidemiological theory with AI, we believe that our research results can potentially make a difference between a large outbreak and a small one.

It’s clear that to control an outbreak of an infectious disease when there are no vaccinations or treatments, breaking contact is a must. We hope that our work will help to further reduce the contact rate and pave the way to determining an optimal cycle of lockdowns when the next pandemic hits.

Source: ibm.com

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