Key Learnings:

• The ROI Trap: Why using a trillion-parameter model for menial data extraction is killing your budget.
• The API Burden: How to avoid the massive development cycles triggered by model deprecation.
• Dynamic Orchestration: Using Krista to select models based on speed, accuracy, cost, and risk in real-time.

Continuous Learning: Why GenAI alone can't learn your business, and why you need a platform that does.

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 🔍 What you'll learn:

• Why AI initiatives often fail and how to avoid fragmentation
• The three core functions your organization must master: memory, reasoning, and action
• A step-by-step approach to build cognitive capability, starting small and scaling smart
• How to platform AI agents so they share memory and
  context
• Real-world business implications, from operations to approvals
• Why “technology should be invoking us, not the other way around” 

🎯 Who this is for:

• VPs of IT, CIOs, digital transformation leaders, AI architects, growth executives — anyone tasked with making AI meaningful rather than simply busy.

👉 If you’re tired of isolated AI pilots and ready to think with AI instead of chasing hype, this episode is for you.

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We break down the difference between exposing data and improving outcomes, and why generic AI won't scale. You’ll hear how AI that understands your workflows can anticipate needs, suggest actions, and drive results—without writing endless prompts or building from scratch.

If you’re still treating AI like a tool that needs babysitting, it’s time to rethink the strategy.

Topics covered:

• Why blocking AI from learning limits your ROI
• The risks of generic intelligence in enterprise settings
• How to train AI like you train your people
• Real-world examples of AI reinforcing strategy by learning context

Learn how to make AI work for your business—starting with your data.

Listen now and subscribe for more episodes on making AI work in the enterprise.

#AI #EnterpriseAI #Automation #BusinessIntelligence #AgenticPlatforms #AITraining #LLM #ProcessAutomation #DigitalTransformation

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 Key Takeaways:  

• Recordings alone don’t cut it—context is king  
• Enterprise data (emails, chats, CRM) powers smarter decisions  
• Orchestration automates action from unified knowledge

Want to elevate your meetings? Check out Krista’s conversation agents, AI-led knowledge management, and orchestration at https://krista.ai/solutions/conversation-agent/.

Subscribe for more tips on turning data into results!  

#EnterpriseKnowledge #MeetingAutomation #Orchestration #AI #BusinessStrategy  

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🔹 Topics Covered: 

 ✅ The biggest security risks with AI agents 

 ✅ How to ensure compliance with regulations like GDPR & SOC 2 

 ✅ The real reason some AI platforms are "free" (and why you should be cautious) 

 ✅ How AI can reduce human error and insider threats 

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John Michelsen shares insights into the rapid advancements of AI, emphasizing that while you can’t predict every step, embracing change and adopting AI iteratively are essential for staying competitive. Learn why small, practical steps can help you build the muscle for rapid technology adoption, and how leaders and organizations can align to create a culture of continuous learning and growth. 

From AI’s potential to elevate human roles to the pitfalls of waiting too long, this discussion highlights why it’s not about knowing the future—it’s about being prepared to meet it. 

Key Takeaways: 

• Why you don’t need to know your future to start leveraging AI. 
• How leadership alignment accelerates AI adoption and innovation. 
• The importance of starting small, scaling fast, and building agility in your organization. 
• Real-world examples of businesses turning AI into a competitive advantage. 

The future isn’t about replacing humans—it’s about unlocking their potential. Watch now to learn how to lead with confidence and thrive in an AI-driven world. 

#AI #FutureOfWork #Innovation #Leadership 

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Highlights include:

 ✅ The differences between Google’s business-focused approach and Microsoft/OpenAI’s developer-centric guides.
 ✅ Privacy concerns and the complexities of integrating tools like Google Workspace or Azure.
 ✅ Why prompt engineering is both an art and a science—and not always the best use of time.
 ✅ How automation can eliminate repetitive tasks, improve customer support, and free your team to focus on what matters most.

Join the conversation and learn how to navigate the world of generative AI without wasting time on unnecessary complexities.

📎 Links to prompting guides mentioned in the video:

• Microsoft Prompting Guide
• OpenAI Prompting Guide
• Google Gemini Prompting Guide

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Join Scott King, Chris Kraus, and John Michelsen as they explore the challenges of traditional machine learning, the limitations of generative AI, and how Krista’s automated machine learning framework builds machine learning for you. Learn how Krista enables businesses to deploy AI quickly and cost-effectively without needing expensive data science teams.

Discover how to identify high-ROI processes, streamline operations, and let machines handle repetitive tasks, so your team can focus on high-value work.

Ready to get started? Visit Krista.ai to learn more about building AI solutions tailored to your business.

#AI #Automation #MachineLearning #GenerativeAI #KristaAI #DigitalTransformation #BusinessInnovation

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They detail the benefits of NLP techniques such as lexical matching, entity extraction, and context-aware data sorting, which help technology leaders move beyond rigid rules and regular expressions. Discover how NLP allows organizations to identify essential data points, streamline workflows, and reduce human error, all while improving the accuracy of information extraction.

Learn how Krista integrates these NLP advancements to simplify document processing, freeing your team from complex rules and enabling faster, more reliable decision-making. Ready to take your document processing to the next level? Watch now to see how NLP is revolutionizing data extraction with the power of context.

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In this episode, John Michelsen shares real-world insights, including a case study from a European healthcare organization that improved its document processing accuracy from 65% to 82.5% by implementing AI. Learn why businesses must act now to adopt these technologies, streamline workflows, and stay ahead of the competition. This episode offers actionable steps for using AI to automate repetitive tasks, enhance data extraction, and unlock new growth opportunities.

Like, subscribe, and share to stay informed about how AI is transforming the future of business operations.

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They discuss:

• Three key document understanding use cases that can transform how your organization manages data
• Real-world examples of AI speeding up invoice processing and handling complex documents
• The role of NLP (Natural Language Processing) in automating workflows and improving efficiency

Whether you’re looking to streamline operations, reduce manual work, or better leverage the data within your organization, this episode provides actionable insights to get started with AI-driven document understanding.

🔔 Subscribe to The Union Podcast for more episodes on AI and business technology!

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AI Integration in HR: A Hot Topic 

One of the central themes of the SHRM 24 conference was the role of artificial intelligence (AI) in transforming HR functions. Michael highlighted the enthusiasm and curiosity among attendees regarding AI's potential to revolutionize various HR processes. Popular use cases discussed at the conference included AI-driven talent acquisition, employee engagement, and predictive analytics to foresee employee turnover. The overarching message was clear: AI can automate repetitive tasks, freeing HR professionals to focus on strategic initiatives that enhance the employee experience. 

The Evolving Role of HR Professionals 

Traditionally, HR roles have not been heavily tech-centric. However, the integration of AI into HR functions is changing this dynamic. Michael noted that HR professionals now need to become experts in AI technologies and be involved in every AI-related conversation, especially those impacting people. Effective AI integration in HR involves using AI to enhance roles, streamline processes, and make data-driven decisions. Standardized, validated approaches to assessing and matching skills with job opportunities are also essential. 

SHRM and Krista: A Strategic Partnership 

A highlight of the conference was the announcement of a strategic partnership between SHRM and Krista to deploy AI solutions for SHRM members. Michael provided insights into this partnership, explaining how Krista was chosen as the AI vendor to build SHRM's member-facing AI engine. This AI tool aims to leverage SHRM's extensive knowledge base, accumulated over 75 years, to provide members with advanced capabilities such as document analysis, understanding, comparison, and drafting. The partnership is set to empower SHRM members with AI-driven superpowers, enhancing their efficiency and effectiveness in various HR tasks. 

Key Takeaways for HR Professionals 

The SHRM 24 conference provided insightful knowledge for HR professionals. Michael Haske emphasized several key takeaways: 

• Embrace AI: HR professionals should not shy away from AI but instead embrace it as a tool to enhance their roles and improve organizational efficiency. 
• Focus on Upskilling: With rapid technological advancements, continuous learning and development are crucial. HR professionals should prioritize upskilling and reskilling to stay relevant. 
• Be at the Forefront: HR should be involved in all AI-related decisions within the organization, ensuring that AI implementations are human-centric and aligned with organizational goals. 

The SHRM 24 conference highlighted the transformative potential of AI in HR and the importance of addressing the skills gap. With strategic partnerships like that of SHRM and Krista, the future of HR looks promising, with AI playing a central role in driving efficiency and innovation. As Michael aptly put it, "It's time for HR to have a seat at the table on an enterprise-wide basis when it comes to AI decision-making." 

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Challenge: Navigating Complex Policies and Regulations

How AI Can Help: AI-powered tools like Krista act as knowledgeable assistants, instantly providing accurate answers to employee questions about your company policies, benefits, and regulations. This not only saves time for HR staff but also ensures consistency and accuracy in responses.

Employee Self-Service: Empowering Employees with Information

How AI Can Help: AI-powered self-service portals allow employees to quickly access the information they need providing accurate answers and guiding them through complex processes like leave requests or benefit enrollment. This empowers employees and frees up HR staff to focus on strategic initiatives.

Candidate Experience: Attracting and Hiring Top Talent

How AI Can Help: AI-powered tools can automate job postings, applicant screening, and interview scheduling, making it easier for candidates to apply and for HR or local management to identify the best fit. Additionally, AI can help tailor the candidate experience based on individual preferences and communication channels like SMS or omnichannel.

Recruiting and Tracking: Streamlining the Hiring Process

How AI Can Help: Krista orchestrates processes across different systems, automating tasks like resume screening, interview scheduling, and progress tracking. Having software run the process instead of people ensures a smooth and efficient hiring process rather than manually keeping track of all of the steps.

Employee Onboarding and Offboarding: Ensuring Smooth Transitions

How AI Can Help: Krista automates onboarding and offboarding workflows, ensuring that tasks like systems access, equipment setup, and paperwork are automated and accounted for. This improves the onboarding experience and reduces the risk of data breaches when employees leave.

Process Orchestration: Streamlining HR Operations

How AI Can Help: Krista orchestrates complex HR processes, combining multiple tasks into a streamlined workflow. For example, an employee can request vacation, and Krista automatically handles approvals, calendar updates, and notifications. This improves efficiency, reduces errors, and frees up HR staff for more strategic work.

Conclusion

AI and automation can alleviate many common bottlenecks in HR processes. Leveraging tools like Krista can help HR professionals streamline operations, enhance the employee experience, and focus on strategic initiatives that drive business growth.

Embracing AI is not just about efficiency; it's about empowering your workforce and adapting to the evolving landscape of work. As AI continues to advance, HR departments that leverage these technologies will be well-positioned to lead their organizations into the future.

Ready to explore how AI can transform your HR processes? Contact us to discover the art of the possible for your organization.

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What is LLM Jailbreaking? 

LLM vandalism refers to manipulating large language models (LLMs) to behave in unintended or harmful ways. These attacks can range from stealing the underlying model itself to injecting malicious prompts that trick the LLM into revealing sensitive information or generating harmful outputs. 

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Takeaways

·      Building your own GenAI system and app is not as simple as building traditional web or mobile apps.

·      GenAI technology is rapidly evolving, with new models and updates being released frequently.

·      The frameworks and APIs used to interact with the models can change rapidly, requiring constant maintenance and upgrades.

·      Ingesting and understanding unstructured data, like images and PDFs, is more complex than it seems.

·      Using a platform like Krista can provide the necessary tools and expertise to handle the complexities of building GenAI apps and automations.

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• AI skepticism remains a hurdle: While interest in AI is high, doubts about accuracy, safety, and trust persist. This emphasizes the need for accurate, transparent, explainable AI models with validation and governance. 
• Focus on time savings for overworked teams: A major draw of AI is automating repetitive tasks and finding pain points. This frees up Third Party Risk Management (TPRM) teams to reduce friction with the business and tackle the increasing burden of assessments, including Nth party risk. 
• Contract risk: a critical area for AI application: AI's ability to analyze and extract data from complex contracts fills a significant gap, helping manage risks often overlooked by traditional risk management programs. 
• Earning trust in AI is key: Risk management professionals crave solutions that are accurate and reliable. AI adoption depends on providing transparency, demonstrating explainability, and building confidence through meticulous validation. 
• Strategic empowerment: AI isn't about replacing risk managers but enabling them to make proactive, informed decisions about risk. This transforms the profession and opens the door to embracing calculated risks for the organization's success. 
• The journey starts with the basics: Organizations often need help finding where to begin. Understanding how AI automates assessments and pinpointing specific pain points is the first step toward targeted solutions. 

The Shared Assessments Summit, a leading risk management conference, brought together experts to discuss the latest trends and best practices. Sam Abadir, a risk management and governance, risk & compliance (GRC) solutions specialist, and Jason Eubanks, a risk consulting manager, were among those in attendance. In this article, we explore key takeaways from the conference, focusing on how artificial intelligence (AI) and automation can transform your approach to risk management. We explain how AI-powered tools, like Krista, can automate repetitive tasks, improve knowledge accessibility, and shift the focus of risk management professionals to strategic activities. We will also explore AI's potential to unlock new ways of approaching risk. By using AI and automation, risk management professionals can streamline processes, improve efficiency, and contribute more effectively to the success of their organizations. 

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Takeaways

Definition and Scope of AI Copilots

• AI copilots are identified as tools based on generative AI technology, designed to assist in various tasks by generating or completing content based on given inputs. They are differentiated from other AI applications like predictors or categorizers.

Applications and Benefits

• AI copilots can assist in coding by generating initial code drafts, helping to speed up the development process, though the generated code may require optimization for efficiency.
• In customer service, AI copilots can help draft email responses or summarize customer interactions inside of a single application.
• In legal applications, AI copilots can summarize meetings or draft documents, though it raises concerns about the skill development of junior lawyers.

Challenges and Considerations

• The proliferation of AI copilots across different platforms and tasks (e.g., coding, customer service, email management) could lead to challenges in managing, governing, and integrating these tools effectively within organizations.
• There’s a risk of over-reliance on AI, potentially reducing human oversight and quality control, especially in critical tasks.
• There are concerns about AI’s potential for misuse, such as generating inappropriate or harmful content, though it was noted that current applications are not designed to act autonomously in such a manner.

Perspectives on the Future of Work with AI Copilots

• The inevitable increase in the use of AI copilots across various job functions emphasizes the need for careful management to avoid overwhelming users.
• The potential for AI copilots to significantly reduce routine tasks and allow professionals to focus on more complex and creative aspects of their work was seen as a positive development.

Adaptation and Learning

• A learning curve is associated with effectively utilizing AI copilots, including understanding how to prompt and interact with these tools for optimal results.
• Choosing the right AI tool for specific tasks is important to prevent inefficiency and confusion.

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Key Takeaways 

• Retrieval augmented generation works by integrating large language models (LLM) with real-time data retrieval to provide accurate, contextually relevant responses, which reduces computational and financial costs associated with inaccurate responses 
• RAG fills knowledge gaps by using vector databases for better information retrieval and regularly updating knowledge libraries to maintain response accuracy, addressing the limitations of static data in AI models. 
• The practical application of domain-specific augmented generation use in industries like retail and e-commerce, telecommunications, and manufacturing demonstrates improved service delivery. 

Unlocking LLM Potential with Retrieval Augmented Generation 

RAG is a method that significantly enhances the capabilities of LLMs. RAG functions as a prompt engineering technique, enriching the output of LLMs by integrating an information retrieval component into your systems of record and data sources like CRM, HR, and external knowledge bases. Doing so provides AI systems with timely, accurate, and domain-specific data - a marked improvement over conventional large language models that often operate with static or outdated training data. This improves the LLM’s ability to generate accurate responses and limit hallucinations. 

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What does the internet say about AI?

What do the AI pundits think will happen?

We were curious, too.

In our quest to understand what was being predicted for AI in 2024, we reviewed a set of diverse sources to analyze and merge a myriad of predictions to provide a consolidated overview. This article cuts through the noise, delivering a straightforward perspective on AI trends. We've factored in common predictions and outliers, providing you with a balanced view of who predicts what when it comes to AI.

The Sources Behind AI Predictions

In our review of AI predictions, each source offered distinct insights reflecting their unique perspectives. I've linked each of the sources from Adobe, Forrester, Gartner, IBM, IDC, LA Times, NVIDIA, PWC, TechCrunch, and TechTarget that we reviewed and categorized. 

• IBM emphasized predictions at an enterprise level, focusing on how AI would reshape business operations and strategies.
• Gartner and Forrester focused on the impact of AI on individual task levels, highlighting how AI could enhance personal efficiency and workplace dynamics.
• IDC provided a more IT-centric view, exploring how AI would aid IT professionals in their roles, with an emphasis on shifting outcomes and the emergence of conversations as the standard user interface.
• LA Times, PWC, and TechTarget brought attention to the coming of age of open-source AI, stressing the importance of ethical AI and the need for transparency in AI operations.
• NVIDIA presented a broader spectrum of insights, reflecting the diversity of opinions from the 17 experts they consulted, covering a wide range of AI applications and implications across various sectors and disciplines.

The AI Landscape - A Consensus View

Across the board, experts agree that generative AI is set to skyrocket this year, bolstering productivity and spurring innovation. Businesses are bound to see a significant shift towards multimodal AI, which invites a more natural interaction with technology using voice, images, and text. As these technologies advance, tight AI regulation is expected to emerge, guiding their integration into the market. The consensus is clear — AI is not just a fleeting trend but an innovation that is fueling economic growth and investments.

Outliers - Unique Predictions and Their Significance

Not all forecasts follow a common thread. Gartner casts a spotlight on AI's role as an emerging economic indicator of national power by 2027. Meanwhile, TechCrunch raises concerns about AI's potential misuse in the 2024 elections. NVIDIA equates the race for AI supremacy to a new space race. These outlier predictions, while not widely echoed, provide insights for businesses to consider, presenting both opportunities and warnings.

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Different departments, leveraging their unique expertise, now seek information from third parties to manage diverse risk types. Third-party risk management expert, Tom Garrubba, practical advice to assist companies in tailoring third-party risk management activities to their size, risk profile, and risk management necessities. Regardless of where the organization situates third-party risk management, the ultimate responsibility rests with the third-party risk manager and the business owner. They must identify the necessities and required documentation for each vendor, enabling a thorough assessment and due diligence or ongoing monitoring. 

The assessment process presents challenges for both the vendor and the risk manager, often requiring over 40 hours to complete and validate. Midsize companies dealing with dozens to hundreds of third parties quickly face the reality of these complications. Additionally, vendors often feel overwhelmed with assessment requests from their many customers and may instead issue a "customer assurance packet" containing broad information sets for you to sift through to identify potential risks. 

Third-party risk management is essential, even for industries not legally required to do so. Those lacking a robust strategy and supporting technology risk overloading their vendors with assessments and distracting internal teams. Furthermore, if you operate in a regulated industry, expect your strategy and technology to face scrutiny eventually.

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Our guest speaker Julie Mohr is a principal analyst at Forrester covering IT service management and enterprise service management. Julie shares how knowledge management practices are evolving and how GenAI is accelerating change. Julie spotlights the shift from old-school waterfall techniques to agile knowledge management and describes how GenAI is set to overhaul how companies capture, update, and apply their knowledge. 

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Generative AI is hot and has ignited our imaginations. However, it's important to highlight that there are other AI capabilities, like predictors and categorizers, that can produce significantly more value, particularly in enterprise settings. But, these capabilities aren't new; they have been around for quite some time and have proven their worth in many business applications. Predictors, for instance, are excellent for forecasting numbers or categories based on historical data, while categorizers excel in sorting data into predefined groups. Both play a vital role in enhancing efficiency and decision-making in businesses, demonstrating that while generative AI is indeed captivating, it is not the most valuable AI player.

Key Takeaways:

1. Generative AI vs Other AI Models: While generative AI has garnered a lot of attention and hype, there are other AI models, such as predictors and categorizers, that can offer substantial value in enterprise settings. 
2. Practical Applications of Predictors and Categorizers:
   • Predictors: Used for predicting numbers or categories based on historical data. 
   • Categorizers: Used for categorizing data into predefined categories. 
3. Bridging the Gap for Business Users: There is a need to make AI more accessible to business users, not just data scientists. 
4. Data Quality and Availability: Successful implementation of AI models requires good quality data.
5. Building Trust in AI Models: For AI models to be successfully adopted, users need to trust their predictions and recommendations. 
6. Starting with AI in Business: Businesses looking to implement AI should start by identifying processes that can benefit from predictors and categorizers. 

Questions for Reflection:

1. Identifying Opportunities for AI: In what areas of your business could predictors and categorizers be applied to improve efficiency or decision-making?
2. Building Trust in AI: How can you involve business users in the AI implementation process to build trust and ensure the accuracy of the AI models?
3. Data Quality and Preparation: What steps can you take to ensure that you have access to clean and relevant data for training your AI models?

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Generative AI (GenAI) is promising unparalleled advancements and efficiencies for many types of use cases.  Boards and CEOs continue to experiment with the technology and imagine how it can improve workforces and increase throughput. The Wall Street Journal highlights how CEOs are pressuring CIOs and technology leaders to urgently install generative AI for fear of being left behind and CIOs are feeling the heat. However, with the dynamics and complexity of adopting generative AI in enterprise settings, it becomes clear that managing expectations is just as important as it is about technological integration.

Generative AI Sets False Expectations

The simplicity and efficiency of generative AI in personal use often paint an unintentionally misleading picture in an enterprise setting. When CEOs and other non-technical leaders personally interact with tools like ChatGPT, they're introduced to the potential of the technology in an uncomplicated, straightforward context. This magical experience often sets false expectations, leading them to question why such technology isn't already integrated into the broader systems of their companies. However, the reality is that scaling these tools for enterprise needs is a vastly more intricate process. It's akin to the difference between cooking a meal for oneself versus catering for a large event with complex dietary restrictions; the underlying task is the same, but the scope and complexity are dramatically different. This lack of understanding between personal uses and the intricacies of enterprise deployment highlights the need for clearer communication about the capabilities and limitations of AI tools in a business context.
The Intricacies of Enterprise Implementation

Deploying generative AI in an enterprise setting is more than meets the eye. While individuals might find generative AI to be a convenient solution for isolated tasks, integrating it within a business's broader systems demands addressing a series of complex challenges. As John points out while a user might see generative AI as solving 100% of a personal problem, it only covers about 5% of the challenges in a business context. The vast majority of the work comes from:

• Content ingestion: Importing data correctly is a massive challenge, especially when dealing with varied content like text, tables, images, and metadata. Properly importing, categorizing, and managing this data is a colossal task that requires precision to ensure you prompt an AI model with the right context and information.
• Real-time access: Unlike personal use scenarios, where static data is sufficient, enterprises operate in dynamic environments and require real-time data, which means integrating AI models with existing systems in a nimble and adaptable method.
• Data security:  Enterprises deal with vast amounts of sensitive data, and any AI model must operate securely within existing frameworks, ensuring that access is limited to only the appropriate roles and parties.
• Scalability and cost: Experimenting with public interfaces is free or inexpensive but deploying these models at scale can be extremely costly so enterprises need to be able to manage these costs and justify the investments.

The journey towards integrating generative AI in your enterprise is simple if you plan effectively and leverage the right tools. It involves more than simple adoption—it demands understanding, strategic planning, careful deployment, and continuous assessment. With the right approach, clear use cases, strong data governance, skillful training, and vigilant monitoring, generative AI can be effectively integrated to drive considerable value to your business, fostering innovation, and giving your organization a competitive edge.

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How to Evaluate and Test Different LLMs

Now that we have identified some key factors to consider, let's look at how you can evaluate and test different LLMs in a matter of minutes.

1. Start by identifying your specific use case for an LLM. This will help you narrow down the list of available options. A great first use case is an employee assistant since it is straightforward and more than likely you have the data to support the test. We chose this use case in Comparing Large Language Models for Your Enterprise: A Comprehensive Guide.
2. Gather the documents related to your use case. These documents will serve as the basis for generating questions to test the LLMs. If you want to run a similar test to ours you can use your employee handbook or another resource that you are familiar with.
3. Import your documents into Krista and write down the questions that you would like to ask of your data. If you have FAQs available based on your document, then you can use those questions, different ones, or a combination.
4. Choose 2-3 LLMs that you want to compare and run each question through them, recording their responses. Krista provides you with a conversational interface to ask questions about your document sets. 
5. Evaluate the accuracy, performance, and cost of each model's responses. 
6. Consider any other relevant factors, such as data security, when making your final decision.
7. Repeat the process with different sets of questions and documents to thoroughly test each LLM. If you want to connect a system like a ticketing system, CRM, or email inbox, contact us for help.

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LLMs, like BARD, OpenAI GPT-3.5 and 4, Llama, or Flan, require a diligent and persistent approach to testing and refining prompts. Their variable performance for different use cases reveals the critical need for consistent testing and evaluation to achieve accurate results. Therefore, prompt engineering isn't a static task. It's an evolving practice that isn't an exact engineering principle. It's part science and part art, therefore it requires constant attention and refinement. Whether you're a seasoned professional in the field or just starting out, this article and corresponding podcast offer examples, and valuable insights into the challenges and solutions in prompt engineering.

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In our experience, LLMs are primarily used to distribute static content, but static content queries only cover about 20% of the answers users seek. The majority of queries require real-time information. For instance, asking for a current cash forecast at 10 a.m. on a given day will have a different answer hours, if not minutes, later. Or, "Which sales opportunities have a chance of slipping into the next quarter?" Answers to these questions reside in your finance and accounting or customer relationship management software systems. You can't train an LLM on this fluid data but you can prompt an LLM with the data from these systems by integrating an LLM with your backend systems. This in essence will limit LLM hallucinations since you are prompting it with your real-time data to generate an answer that contextually makes sense to the person asking the question given they have permission to read the data.

Moreover, when individuals pose questions like these, they often aim to initiate a whole workflow. For instance, the sales opportunity question mentioned earlier about which deals may slip, cannot be resolved by referencing static content. Such requests will involve triggering other systems or human workflows that fall outside of an LLM's purview. A sales manager or chief revenue officer will seek to initiate some type of action if deals are slipping so they can maintain the sales forecast. They may want to offer a discount to accelerate a deal or offer an incentive if excess inventory is available. 

It's essential to realize that while LLMs are an important part of the solution, they are not the entire solution. To handle queries related to static content, real-time information, and integrated systems or workflows, you need to marry LLM capability with other systems. With this integrated approach, you can limit LLM hallucinations, ensuring the AI system provides more accurate and beneficial responses.

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How High Performers Achieve Results with Generative AI

McKinsey's report summarizes 1684 survey responses and highlights how companies are using generative AI, some of the opportunities they are taking advantage of and several concerns with the technology and how it integrates into applications and workflows. The report authors split the responses and highlight how differently high performers are leading the way in the use of AI. High performers are organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption. They're able to quickly identify opportunities and capitalize on them with a data-driven mindset, leveraging AI solutions to rapidly develop new products, services and processes--not just save money.

High Performers Are Using Generative AI Differently

How are high performers achieving a 20% lift in earnings with generative AI? This is a big lift and demonstrates that AI is truly transforming businesses. Interestingly, the survey showed that high-performing organizations state they focus on building new products using AI rather than cost savings. Building new products opens adjacent markets or enables one to grab more share and spend from current customers. I'd like to see this data further segmented by industry or use cases since they are getting such a lift. I assume that the high performers are likely to be companies with dedicated data science teams who understand how AI can contribute to product development. Companies just starting with AI might be more focused on potential cost savings, given their lesser familiarity with AI's potential.

Concerns with Generative AI are Consistent

As with any technological advancement, there are apprehensions surrounding generative AI adoption. Is it secure? Are the outputs accurate? Where does my data go? These are valid concerns and the report states generative AI inaccuracy, cybersecurity, and IP infringement as the top concerns. However, these risks can be mitigated by using AI integration platforms like Krista to help govern data, and prompt generative AI with data and answers from your internal systems thereby reducing inaccuracies or hallucinations, and cybersecurity risks.

How Are Companies Using Generative AI?

Of the individuals surveyed for the report, most have used generative AI at least once and one-third use it regularly. Industries using generative AI the most include tech, media, and telecom sectors followed by financial services and other business services. This suggests that the more data-intensive an industry is, the higher the usage of AI, and therefore a greater opportunity to make sense of the business and build new products.

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In this episode of The Union Chris and I discuss predictive analytics and how to use it to help you make better decisions. We explored how advanced analytics can help business process owners and IT professionals boost their decision-making prowess. The most important topic in this episode is using machine learning to build machine learning. It's possible to capture and interpret business process data to streamline and improve them over time. This capability helps automate decisions that, until now, many may not have recognized as automatable.

Automating HR Processes with Machine Learning

In a given process, predictive analytics can supplement or could replace a manual decision with an automated one, increasing efficiency and speed. For instance, when an employee requests vacation, a machine learning model can evaluate multiple variables rather than a manager looking up the same information - such as the employee's leave balance, work capacity needs, and crew shift patterns - to make an informed decision about if or when the employee can take a leave of absence.

Enhancing Cash Flow Predictions

We also discussed an intriguing case in finance where predictive analytics helped improve the decision-making process. Accounting can tally the invoices sent out, but it's finance that often struggles to predict when these invoices will be paid. Machine learning can analyze patterns in past behavior to predict when a customer is likely to pay an invoice. This insight can assist finance in determining cash flow so they can pay invoices or invest. This kind of prediction is impossible to achieve manually, especially for large organizations with thousands of vendors. With machine learning assisting in evaluating data and helping predict outcomes you can boost accuracy and get a better grip on cash flow forecasting.

Strategic Decision-Making with Predictive Analytics

Then we tackled the topic of strategic decision-making. Companies need to move beyond using predictive analytics for making isolated decisions. Instead, they should harness it to drive strategic actions. For instance, once finance knows which invoices are likely to be paid soon, they can offer an early payment discount to accelerate cash flow. It's all about leveraging the predictive power of analytics to achieve business objectives and streamline operations.

Envisioning the Future of Predictive Analytics

Looking ahead, predictive analytics will continue to enhance business decisions as companies move processes to software. As more organizations recognize its potential, we'll see a shift from simply making faster decisions to automating actions based on these decisions. The future is about integrating predictive analytics within business processes to drive automated, data-driven outcomes to improve the ways businesses operate.

The Indispensable Value of Predictive Analytics

Predictive analytics and automation have the potential to automate decision-making, predict future trends, and drive strategic actions, all of which will revolutionize the way businesses operate. Embracing predictive analytics is no longer a choice; it's imperative for those seeking to boost efficiency and enhance both employee and customer digital experiences.

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The power of automated workflows

A major component that tends to get lost in the AI conversation is the power of business workflows. These are the cogs and gears that keep an enterprise running smoothly, but they're often overlooked. Consider a scenario where a company needs to manufacture a specialty item for a valuable client that isn't on the standard price list. How can you effectively orchestrate such a process that is outside of the norm? Different decisions will need to be made. Other concurrent processes will need to be modified or stalled. How do you keep everyone informed in such a situation to make sure you provide superior customer service at the lowest costs?

Your people cause process delays

Now, let's extrapolate this to the reality of remote work and multiple stakeholders in a process. Many times the complexities of business operations across time zones or business units can lead to inefficiencies, especially if the responsibility of coordination falls on a single individual. Moving well-orchestrated workflows to software could greatly simplify these processes, allowing you to keep your global operations moving at machine speed.

In the absence of well-orchestrated workflows, we run into several issues. For instance, if someone is on vacation, who steps in to approve and follow up on their tasks? Without a well-defined process, tasks could easily fall through the cracks, leading to delays and errors.

Automating communication offers immediate ROI

But there's a solution within your reach - intelligent automation. Intelligent automation offers immediate ROI by identifying areas within business processes that can be streamlined. By automating tasks, you set the stage for AI integration, thereby further enhancing your operations. There's a wealth of evidence supporting the ROI of automation, from case studies to quantitative data. This prepares you for your iterative AI journey and paves the way for a smarter, more productive workforce.

You need good processes and data for AI

To sum it all up, while AI is undoubtedly a game-changer, we need to keep our focus on the big picture. AI can help us make better decisions based on good data, but the underlying processes that service our customers and employees are just as crucial. We can make immediate impacts by taking the time spent on emailing and follow-ups and transferring them to software. By focusing on orchestrating our workflows, we can improve our efficiency, better serve our customers and employees, and make room for transformative technologies like AI.

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Generative AI assists in front and back office tasks

The line between back office and front office operations is blurring as generative AI takes on a more significant role. AI's ability to assist in automating tasks previously managed by humans in areas such as customer service is remarkable. For example, consider the scenario where AI meticulously examines incoming documents in a loan application process, distinguishes them, and then forwards them to a loan officer for approval. AI aids professionals by automating routine tasks, allowing them to focus on higher-priority matters.

Generative AI ROI

One cannot overlook the financial implications of AI. In certain regions where labor costs are low, the financial burden of resolving a problem with AI may outweigh the cost of human labor. However, relying on humans in this context merely postpones the inevitable adoption of AI.

High-value work, which is the cornerstone of any successful business operation, deserves our attention. Every employee is often bombarded with requests from peers and external stakeholders. Amid these demands, it's important to prioritize. Generative AI can help by taking on tedious tasks, freeing up individuals to focus on high-value projects that require creative and critical thinking.

Generative AI innovation accelerated

When reflecting on McKinsey's previous reports, the advancement of natural language understanding technology surpasses all expectations. Previously, experts predicted that AI technology capable of human-like tasks would emerge in 2027. However, as of 2023, we are witnessing this transition happening right now. This leap signifies a four-year acceleration, which has tremendous implications for businesses worldwide.

What are the practical uses of Generative AI?

The question remains: How should businesses perceive and respond to this accelerated development? While some might dismiss it as mere hype, the practical use cases of AI, as outlined in various research papers, are too compelling to ignore.

In one instance, a Krista partner who sought ten potential use cases for AI from customers ended up identifying a hundred viable scenarios, each with a distinct dollar value. With the rapid advancement of technology, waiting and watching is no longer an option. If you don't adopt AI now, your competitors will, leaving you trailing in their wake.

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AI Should Involve People in the Same Context

Any AI conversation should maintain a 'shared context'. A shared context refers to the ability of an AI system to maintain a consistent understanding of a situation across multiple interactions and even multiple users. For example, in a customer service scenario, a shared context would allow a customer service agent to pick up where a previous interaction left off, saving the customer from having to repeat information. 

Maintaining a shared context across multiple interactions is crucial since customers may require help from multiple systems or people spanning several sessions. Therefore, AI systems should be able to manage long-running conversations and provide previous knowledge. Many conversations are not resolved on the first attempt so any AI system should be able to recognize that it is the same conversation and maintain the same context. 

AI Should Know Who Is Allowed to Know What!

When implementing generative AI for customer or employee assistants, it is critical to understand who has access to what data and for what reasons. With many different stakeholders involved, from employees to managers to end users, you cannot afford to expose data to those who should not have access to it.

For instance, a call center agent may need to consult with a manager to approve a customer interaction or an offer. This decision may involve accessing customer data that the agent does not have the privilege to view. An automated AI workflow can handle this situation by learning how such decisions have been made in the past and using that knowledge to decide on its own, without the need for human intervention. However, it's essential to ensure that AI does not provide answers to those who should not have them, maintaining data privacy and security. You wouldn't want employees to ask the HR or payroll systems questions about employee health, benefits, or salary information. 

AI Conversations Should Transfer from Chat in a Browser to Mobile Platforms to SMS

Today's customers expect a seamless experience across all platforms, whether it's desktop, mobile, or SMS. By enabling 'long-running conversations' across these platforms, businesses can enhance omnichannel customer experiences while also governing the data that supports them.

Effectively governing AI data involves maintaining a shared context across many people, understanding who is allowed to access what data, and managing data across multiple user interfaces. By focusing on these areas, you can reap the benefits of AI while also ensuring your data is managed effectively and securely.

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We must view AI not just as an intelligent service that generates content, but as an operative tool that propels actions and leads to substantial business outcomes. AI should be more than an impressive chatbot; it must guide your customers and employees based on the next best actions for the outcome they are seeking. This is a fundamental concept in the realm of AI: operationalization and optimization. And, this AI shouldn't merely exist in the realm of conversations but rather connect to real-time systems, actively engage with multiple people, and most importantly, facilitate actions that drive positive business outcomes.

This journey of operationalizing AI starts with a simple, yet important, concept. Eventually, you will want customers and employees to have one-on-one, one-to-many, or many-to-many conversations with your real-time systems. Doing so provides them the capability to complete an outcome by themselves. In essence, what we are striving for is an AI automation system that does more than just chatter—it must "do" things. Chris and I delve into this intriguing aspect of AI in this second of three episodes, breaking down AI complexities and understanding its capacity to "do" things, which ultimately leads to measurable outcomes.

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This is the first of three podcasts where we explore quickly connecting AI solutions. This first episode discusses three enterprise requirements for connecting generative AI into your business to improve employee and customer experiences.

1. You must connect generative AI to enterprise systems in real-time 
2. You must be able to instantly integrate and interchange different AI tools and services
3. You must be able to efficiently synthesize vast data volume

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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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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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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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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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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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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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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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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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• Innovation requires change and businesses need abilities to respond quickly
• Most application and automation changes are brittle and require an SDLC every time a change is made
• Allowing businesses to configure rules outside of integration technology increases their responsiveness and velocity

Low-code platforms cause testable events

Low-code platforms create testable events whenever changes are made. This is a problem. These platforms have focused on developing code and app construction instead of maintaining apps and lowering the total cost of ownership. Therefore, changing code or modifying apps built with low-code platforms involves a lot of IT resources like developers, testers, dev environments, and integration environments.

Most platforms are extensible and allow integrations, but the process is brittle since the business logic and integration methods are coupled together. If you have ever seen a step in the process that included "paste your code here", then you understand what we mean.

To make this process smoother, what's needed is something akin to Excel or WordPress that allows building blocks without coding the wiring or integrations and business logic together. This would avoid having a testable event triggered every time you make a change as with certain other platforms. Understanding how these platforms differ from others is an important consideration when it comes to choosing the right way to automate processes across different systems and teams. 

Your automation contributes to your technical debt

Your automation efforts can contribute to technical debt if you are using traditional automation platforms which are essentially a thin veneer over VBScript, JavaScript, or code generators. These changes require coding and testing, however, it is possible to achieve the same velocity with a different architecture pattern. 

For instance, using natural language processing between the configuration of building blocks and the programming logic can alleviate many problems associated with change. This layer allows you to invoke backend systems in much the same way as an Excel function is called - without actually needing to call any programming logic. 

Having this kind of high-velocity change in automation projects allows businesses to keep up with their needs and avoid having to pull down previously created automation that they cannot maintain due to a lack of the ability to test. By distinguishing between the integration technology and the business layer technology, it is possible to avoid triggering a full SDLC to test changes. 

This approach can help reduce technical debt and increase business velocity.

Change without the long-tail

Businesses need to find ways to make changes and automate tasks quickly and cost-effectively without triggering a huge testing cycle. By using modern technology such as natural language processing between the configuration of building blocks and the programming logic it is possible to invoke backend systems in much the same way as an Excel function is called - without needing to call any programming logic. This helps reduce technical debt and increase business velocity by separating automation change management from functional testing.

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Your IT group is increasing complexity, not reducing it. But, it’s not their fault.

Companies contain a myriad of departments and divisions, all with their own specialized teams of workers. Processes are designed to be as efficient as possible, often at the expense of flexibility. And information is collected and analyzed to a level of detail that would have been unimaginable just a few years ago. Amid all this complexity, it’s easy to lose sight of the fact that businesses exist to produce something – whether it’s a product, a service, or simply a profit. In the quest for efficiency and productivity, companies sometimes forget that the goal is to produce results, not just to follow a set of rules. When businesses ask IT to build new apps, it increases complexity and often hard codes rules into the process. Then, tomorrow, you may find yourself bogged down by your own bureaucracy and struggling to achieve your true potential.

App explosion contributes to complexity

Building more apps to support new business needs creates more complexity, not less. Apps, SaaS, low-code apps, and automation have created a more complex landscape than ever before. And while some argue that this complexity is a necessary evil, it introduces more risk and can lead to problems down the road. One of the biggest problems with so many apps is that it makes things more difficult to change. If you want to add a new feature or make a change to a business process running across an app, you have to wade through a lot of code and figure out how everything fits together. Many times, the processes are hard-coded to the app making you choose between innovation and stagnation. If you add an app to avoid the inherent complexity, you add more complexity for tomorrow. If you modify an app or automation to avoid waiting on IT to build another app, it still takes a long time to edit and test your new processes. You have left yourself with two poor choices and both of them from having too many apps for too few developers.

More apps create more work

Enterprises are already struggling to keep up with the sheer volume of apps they have. Therefore, it’s hard to imagine why anyone would want to add more to the mix. CIOs are already stretched thin, and their IT teams are spread even thinner. Can you imagine a CIO stating,

“My company has 65 apps on the average knowledge worker’s desktop. I’d like to get to 75 apps by the end of the year.”

No.

So ask yourself: Are you adding or removing complexity?

Listen to why John Michelsen states building more apps is the wrong way.

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Utilizing chatbots with natural language processing and entity identification can help ensure your customer’s needs are met quickly and accurately. Chris suggests that businesses should think about using more intelligent chatbots as a way to analyze customer interactions and provide better services, rather than just deflecting inquiries to keep from talking to expensive agents.

Digital leaders should focus on using conversational AI to increase customer satisfaction. This includes utilizing natural language processing to understand customer intent, context, and business rules to ensure customers receive the assistance they need promptly. Ultimately, these strategies should help businesses create more meaningful customer interactions

By following these simple steps, businesses can start to build trust and loyalty with their customers by providing the best services possible. It’s an investment that will pay off in the long run! Whether it’s conversational AI or other strategies, businesses should continue to strive for customer satisfaction.

We hope this episode provided some useful insights into the benefits of using conversational AI in chatbots. Thanks for listening! See you next time!

Links and Resources

• What is conversational AI?
• 5 ways Forrester predicts AI will be “indispensable” in 2023
• Gartner Predicts Conversational AI Will Reduce Contact Center Agent Labor Costs by $80 Billion in 2026

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To do this, companies must create a process that makes it easy for users to understand and interact with their products. This means creating an orchestrated process that users are used to or “more of the same” where the product guides them one step at a time in a conversation. Additionally, companies need to ensure that their products provide feedback when something goes wrong or when the user needs assistance. This helps to ensure that users don’t become frustrated and that they can successfully adopt the product with minimal training.

The benefit of taking this approach is that it allows companies to deploy products quicker, with less training required. This means that users are able to get up and running faster, leading to increased adoption and greater value. Furthermore, this approach reduces the need for manual integration allowing companies to quickly add new features and create a better user experience at lower costs.

Ultimately, with the right approach to digital transformation, companies can create better products, faster and with less effort. This leads to improved customer satisfaction, increased adoption rates, and greater returns on investment. By taking the time to think about the process differently, companies can ensure that their digital investments are successful and make the most of their efforts.

Links and Resources

• What is conversational AI?

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