The Union

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Orchestration is a complex process that involves multiple steps to successfully deploy and update software features for customers. It requires communication with customers, training them appropriately, automating deployment and production processes, and continuously monitoring the progress of the project. 

By focusing on exceptions and updating automation processes periodically, one can ensure that users can use a feature and get value from it. If help is needed with orchestration, experts are available who can provide guidance. 

I recently had a conversation with Chris Kraus about orchestration, the customer success aspect, and how to deal with exceptions. Chris gave us great insight on how to manage the complexity of orchestration so that the customer can use a feature and get value from it. 

He suggested that we focus on exceptions and updating automation processes periodically, to ensure success. He also reminded us that orchestration is an ongoing process and it should be monitored continuously to get the desired results. 

Key Points:

• Orchestration is the process of managing and coordinating all the steps needed to get an update or feature into a customer's hands 
• It involves communicating with customers and managing training, as well as automating deployment and production.
• The ultimate goal of orchestration is to ensure that users can use a feature and get value from it. 
• When orchestrating, it is important to focus on exceptions and update automation processes over time as needed.
• It is also key to periodically check up on the progress of the process and take any necessary steps to expedite things that may have gone wrong.
• For help with orchestration, reach out to experts who can provide guidance. 

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Enterprise uses for artificial intelligence continue to expand. Companies are now using AI to automate processes in customer service, human resources, IT, and supply chain to make more informed decisions to improve profits and customer service.

But, is AI necessary?

Companies like Krista, IBM, and your competitors think so.

I recently had a conversation with Manish Sampat about his experience working with IBM Watson. Manish is the Global Sales Leader for IBM Watson and he shared with me how AI can be used to enhance processes, and customer service, what ethical considerations need to be taken into account when implementing AI, and how we can upskill our people to build better AI-led processes.

Key Points:

• IBM is committed to helping workers transition into the new normal with skills training and global initiatives for AI development.
• Humans will remain a critical part of the decision-making process; AI is here to assist us in our tasks, not to replace us. 
• Executives should act quickly, have a strategy in place, and make sure data is available to adopt AI and remain competitive.
• CIO’s office, data science team, chief customer officer, chief marketing officer, and chief digital officer should all work together when overseeing AI adoption. 
• A variety of departments within a company should collaborate effectively to ensure the ethical use of AI. ​​

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Evaluating artificial intelligence (AI) products can sometimes be confusing. New AI products appear every day and are challenging to determine which products are truly powered by AI and which are veneers on top of the latest generative AI. The AI hype caused the US Federal Trade Commission (FTC) to issue a warning to software vendors to "Keep your AI claims in Check," so they don't overstate AI capabilities. Therefore, you need to understand the relationships among all of the different AI terms so you can make smart decisions about bringing AI into your business. 

FTC Warns About Misleading AI Claims 

The FTC is warning emphasizing the importance of transparency and accuracy when making claims about artificial intelligence (AI) products and services. The FTC is concerned that misleading AI claims may harm consumers and undermine trust in the technology. To avoid potential issues, companies should take care to substantiate AI claims, disclose the technology's limitations, and protect user privacy.

1. Companies must have a reasonable basis for their AI-related claims, including claims about performance, data privacy, and data security. They should be able to substantiate their claims with reliable and relevant evidence. 
2. The FTC emphasizes the importance of transparency in AI decision-making processes. Companies should clearly explain to consumers how decisions affecting them are made, what data is used, and what factors are considered. 
3. Companies should consider external validation to bolster the credibility of their AI claims. This might include certifications, third-party testing, or expert evaluations. 
4. Software companies should avoid making overly broad or unsupported claims about AI in products and services. 

How to Know When Products Actually Use AI

Determining how a product or service uses AI begins with understanding and differentiation among AI terms. If you don't understand the capabilities, the inputs, the outputs, the training data, and what they mean, then how are you going to make informed decisions?

We will help you evaluate AI products and arm you with questions you can ask AI vendors to make sure what they are delivering is really AI and not hard-coded logic.

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Third-party supply chain risk is a significant concern for many organizations in today's global economy. This risk arises when a company relies on third-party suppliers for goods or services, and it can manifest in several ways, including financial, legal, reputational, and operational risks. Companies measure third-party supply chain risk using various methods, including supplier surveys, site visits, and data analytics. However, managing third-party supply chain risk is challenging due to the sheer number of suppliers, their complex supply chains, and the constantly evolving risk landscape.

To improve the process of managing third-party supply chain risk, companies can take several steps. First, they can establish clear policies and procedures for selecting and monitoring suppliers, including conducting thorough due diligence and regularly assessing their performance. Second, companies can use technology to automate data collection and analysis, such as using data analytics tools and software to manage supplier relationships. Third, companies can work with their suppliers to improve their risk management practices, such as providing training and resources to identify and mitigate risks. Finally, companies can develop contingency plans to address potential disruptions in their supply chain, such as identifying alternative suppliers or building up inventory.

Overall, effectively managing third-party supply chain risk is critical for businesses to protect their reputation, reduce financial risk, and ensure a more resilient supply chain. By taking proactive steps to manage this risk, companies can mitigate potential disruptions and ensure the continuity of their operations.

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As businesses become increasingly reliant on third-party vendors for IT services, it is important to understand and manage the potential risks associated with this type of relationship. A misstep in managing these relationships can have significant consequences, including loss of data or access, financial losses due to downtime or disruptions in service delivery, and even reputational damage. Therefore, businesses need to understand the importance of properly managing third-party IT risks to protect their business and customers.

What are some of the challenges in managing third-party IT risk?

Organizations today face a variety of risks associated with third-party IT, from data breaches to ransomware attacks to IT outages. Managing these risks can be a challenge, as organizations must take into account the security of any external providers they work with and ensure that proper protocols are being followed. In addition, they must carefully weigh the costs and benefits of allowing external parties access to their information and technology systems. To effectively manage third-party IT risk, organizations must be aware of the risks associated with it, identify any potential threats, and implement appropriate measures to mitigate them. Moreover, they must ensure that the proper protocols for managing access are in place and that there is adequate oversight.

Measuring third-party risks is challenging

• Lack of visibility
• The complexity of vendor relationships
• Rapidly evolving threat landscape
• Shared responsibility
• Compliance

Operationalizing governance, risk, and compliance (GRC) software can bring its own set of challenges. Some of these include: 

• Cost
• Training requirements
• Data security
• Complexity
• Vendor lock-in
• Regulatory compliance
• Platform integration

Managing third-party risk is a process

To effectively manage third-party IT risk, organizations need to develop a comprehensive risk management program that includes policies, procedures, and communication. GRC software packages can help with this process, but they come with their own set of challenges such as cost, training requirements, data security, complexity, vendor lock-in, regulatory compliance, and platform integration. Organizations need to consider these factors when implementing a GRC system to ensure successful implementation and ongoing risk management.

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The Wall Street Journal recently published "Pressure Mounts on CIOs to Build More AI Apps," stating ChatGPT's popularity is causing business leaders to ask for more AI apps. The demand is so high chief information officers say they cannot keep up since building, training and rolling out AI models is so expensive and takes too much time.

In this episode, we talk about the challenges when deploying AI and how you can use a process oriented approach to create more models and more value without needing a data scientist for every project.

• Challenges of implementing AI projects
• How to deliver more AI projects faster
• Generate faster time to value by focusing on smaller, easier-to-build models
• Generate clean data as part of your business process workflow
• Optimizing your AI projects for Impact
• Using a platform approach to prioritize AI projects

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As technology evolves and we enable AI to make more decisions for us we need to be confident it’s leading us in the right direction. Automated systems are increasingly being for all types of use cases in HR, sales, IT, and cybersecurity to speed up business and provide better services. However, while AI-assisted answers can be incredibly helpful, it is important to ensure these systems are properly trained and have the right context to give us accurate responses and advice. We need to build trust in AI decisions. Trusting AI means understanding that there are some inherent risks involved with relying on it for critical decisions.

Trusting AI requires context

The most important factor when considering whether or not to trust AI is context: What is the AI being asked to do? What information does it have access to? Is it capable of making accurate decisions based on this data? For AI to be trusted, it must be trained properly and given access to the right data.

Without sufficient context, AI could make errors that would have otherwise been avoided. For example, if an organization’s HR department was using AI-assisted vacation time calculations, but the program didn’t understand how seniority or job title impacted these decisions, it could lead to unfair results. Similarly, if a company was relying on an automated threat assessment system but did not provide it with enough data about past threats and similar situations, then the AI would likely miss potential dangers and leave the organization exposed to risk.

For AI-assisted systems to be trusted and used reliably there needs to be sufficient training and understanding of the context. Organizations should make sure that the AI has access to the right data and is well-trained in the tasks it is being asked to perform. It is also important to remember that AI can only be as accurate and reliable as the data it has been given, so organizations must be diligent in ensuring they are providing consistent and up-to-date information.

Understanding AI limitations

Trusting AI means understanding its inherent limitations, such as its inability to think outside of what it’s been trained on or account for changes in context. As technology continues to evolve, having an understanding of these risks will be essential when making decisions about how much we rely on automated systems. With proper training and sufficient context, however, AI can be an invaluable tool for organizations of all sizes.

For example, AI can be used to automate mundane tasks such as customer service emails or accounts payable/receivable inquiries. By providing the AI with enough information about the scope of these tasks, it can quickly and accurately answer questions that would otherwise take a human employee much longer to respond to. Similarly, AI-assisted systems can process large amounts of data in seconds and allow organizations to better understand their customers’ needs and preferences.

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AI-driven business processes are becoming increasingly popular, as they can increase business velocity. Using AI to respond to email and omnichannel messages allows businesses to respond quickly and efficiently while providing a higher level of customer service. However, AI by itself is not always the answer since there are two paths you can take with this technology - task-based automation and conversation-based automation.

Task-based automation involves creating simple rules for computers to follow when answering emails, like sending out standardized responses or performing basic administrative tasks. This solution provides an efficient way to provide basic customer support but doesn't provide much benefit beyond that. Conversation-based automation, on the other hand, uses natural language processing (NLP) and machine learning (ML) algorithms to understand the context and intent of customer emails to provide an appropriate response without the need for a person.

To train AI-based resolution systems to understand your company-specific language and context, you need to provide the appropriate training data. This can include providing sample emails from your customers so that machine learning algorithms can learn how to classify them according to different categories. Additionally, you can also use feedback loops so that your employees can manually reply or verify the machine-generated reply before it's sent to the customer.

Using AI to reply to emails provides you with several benefits, like immediate response times, greater customer satisfaction, and cost savings. For companies looking to implement AI-based solutions for their customer support needs, understanding how to properly train systems is key to achieving maximum benefits. As AI technology continues to evolve, we will see even more improvements over time. In any case, using AI to reply to email is an important tool for businesses that are looking to stay competitive in today's marketplace. 

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ChatGPT is a revolutionary new language processing technology that has revolutionized how artificial intelligence can interact with people. Developed by OpenAI, ChatGPT uses natural language processing techniques to allow AI agents to engage in conversation with users in a more human-like way.

But, generative AI like this eases one step in a business process.

Your business processes span multiple people, teams, technologies, and AI. How will you implement conversational tech like ChatGPT into a process?

John Michelsen, Chris Kraus, and Scott King discuss how ChatGPT helped the world realize how far this technology has come and what you need to prepare for when bringing it into your enterprise.

Topics covered in this episode:

• Where is AI like ChatGPT headed?
• ChatGPT created a new level of technology awareness
• What ChatGPT can’t do
• What is the enterprise use case?
• Are other AI models going to proliferate inside a business and cause more issues like too many apps?
• AI-generated code doesn’t deliver business outcomes
• People are still part of the process
• The AI needs to understand the context to deliver a customer’s outcome
• Where should people start with chat-like AI models?

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