Building your own GenAI system and app requires a deep understanding of the rapidly evolving technology and the complexities involved. It is not as simple as building traditional web or mobile apps. GenAI is constantly changing, with new models and updates being released frequently. This means that the frameworks, behaviors, and APIs used to interact with the models can change rapidly, requiring constant maintenance and upgrades. Additionally, the process of ingesting and understanding data, especially unstructured data like images and PDFs, is more complex than it seems. Assuming that maintaining the infrastructure and quality of GenAI apps is similar to your existing projects can lead to expensive costs and time-consuming maintenance cycles. Using a platform like Krista can provide the necessary tools and expertise to handle these complexities and allow businesses to focus on solving their specific business problems instead of maintaining a custom-built solution.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.More at krista.ai

Key Takeaways 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. More at krista.ai

AI copilots are generative AI engines that assist users in point tasks such as writing emails, summarizing customer cases, and generating code. AI copilots can be used in a variety of business functions, including marketing, customer service, and software development. However, AI copilots assist one person with one task at a time. They improve personal productivity but are not effective at transforming business processes or using more powerful AI solutions like predictors and categorizers.TakeawaysDefinition and Scope of AI CopilotsAI 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 BenefitsAI 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 ConsiderationsThe 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 CopilotsThe 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 LearningA 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.More at krista.ai

In Third-Party Risk Management (TPRM), adopting Artificial Intelligence (AI) presents both an opportunity and a dilemma. One, if you should use AI, and second, for what tasks. I talked with TPRM experts Sam Abadir and Tom Garrubba about responses from a recent poll among approximately 1,000 risk management professionals. We reviewed the questions and responses and offered insights and opinions based on the results.More at krista.ai

Retrieval augmented generation (RAG) is revolutionizing AI by infusing language models with timely and relevant external data. This technique is pivotal in delivering not just intelligent but informed AI responses. In this podcast, Chris and I explain what RAG is, how it functions, its impact on AI’s performance, and the challenges it helps overcome. 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. More at krista.ai

2024 AI PredictionsWhat 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 PredictionsIn 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 ViewAcross 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 SignificanceNot 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.More at krista.ai

Most third-party risk lifecycles adhere to a similar pattern: planning, due diligence, contract negotiations, ongoing monitoring, and termination. However, the management and responsibility of these processes differ significantly across organizations. Traditionally, the information security department carried this burden, but recent events like Covid, regional wars, political changes, and socially-focused laws have broadened organizations' risk perception beyond just IT. They now include geographical, reputational, concentration, and compliance risks. 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.More at krista.ai

Embracing technological change via automation and artificial intelligence (AI) is no longer optional; it's a necessity. Delaying AI use in your company can hinder progress and put you farther behind your competitors. However, embracing AI adoption is not without its apprehensions. Your concerns about unknown outcomes and hallucinations are valid but are easily overcome with the right security, accuracy, performance, and cost strategies to limit your risk and exposure. Integrating AI is about continual progress over perfection, focusing on the transformative power of automated processes, rather than the pursuit of unattainable perfection. We will show you how to overcome AI fear, build confidence, choose the right process for AI and guide you toward the first steps for adopting AI.More at krista.ai

Generative AI (GenAI) is influencing nearly all processes in our businesses and none so much as knowledge management. Employees want a better experience and they have found by already experimenting with GenAI; ask a question, and get an answer. But, the answers and the knowledge delivered to them via the public interfaces aren't always correct. 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. More at krista.ai

Generative AI vs Predictors and CategorizersGenerative 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: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. 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. Bridging the Gap for Business Users: There is a need to make AI more accessible to business users, not just data scientists. Data Quality and Availability: Successful implementation of AI models requires good quality data.Building Trust in AI Models: For AI models to be successfully adopted, users need to trust their predictions and recommendations. 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:Identifying Opportunities for AI: In what areas of your business could predictors and categorizers be applied to improve efficiency or decision-making?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?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?More at krista.ai

Explore how AI orchestration is revolutionizing Third-Party Risk Management (TPRM). Learn how combining AI technologies, like document understanding, NLP, and generative AI, with process orchestration improves risk management practices. Discover how AI can read vendor documentation, complete assessments, identify risk outliers, and provide comprehensive data processing in minutes rather than weeks. Sam Abadir explains how this automation not only enhances risk awareness but also significantly reduces costs and improves efficiency. Using an AI-led orchestration platform to automatically populate assessments and create issues helps your risk managers perform more assessments in less time therefore increasing your risk awareness.More at krista.ai

The Pressing Demand for Generative AI in EnterpriseGenerative 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 ExpectationsThe 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 ImplementationDeploying 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.More at krista.ai

In an era where Artificial Intelligence (AI) is transforming business processes, companies face a significant dilemma: whether to hire expensive AI specialists or pivot towards more cost-effective and scalable options. With industry giants like Netflix offering compensation packages as high as $900,000 for AI Product Managers, the perceived value of AI expertise is high. However, smaller companies wanting to apply AI might be scared off by these numbers, raising questions about the viability and affordability of such roles. This article explores the financial implications of implementing AI, the challenges of AI adoption, and the benefits of choosing the right data platform for AI implementation. We delve into some AI use cases managed through automation versus those handled by specialized AI engineers, and describe the role of platforms like Krista in making AI accessible to companies regardless of their size.More at krista.ai

Different LLMs have varying strengths and weaknesses. Understanding the strengths and weaknesses of different language models is crucial, as it allows you to select the most efficient tool for your specific needs. When you evaluate and test various LLMs, you are essentially comparing their ability to answer different types of questions, their performance, and their cost-effectiveness. This comparison isn't merely an academic exercise but a critical step in identifying which model can best serve your needs in real-world applications. This process is straightforward and achievable with the right tools and guidance, so everyone can benefit from the advancements in artificial intelligence.How to Evaluate and Test Different LLMsNow 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.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.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.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.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. Evaluate the accuracy, performance, and cost of each model's responses. Consider any other relevant factors, such as data security, when making your final decision.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.More at krista.ai

The prompt engineer. Do you need one? Can you train your people to generate prompts? Can this be automated? Well, it depends.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.More at krista.ai

To effectively limit LLM hallucinations, you need to treat LLMs more like journalists instead of storytellers. Journalists weave stories using factual, real-time information. Similarly, you need to feed your LLMs with real-time information to ensure their generated content aligns more closely with reality. Storytellers on the other hand don't require real-time information, since they are creating tales in a fictional world. 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.More at krista.ai

In this episode of The Union podcast, Chris and I review McKinsey &Co.'s "The state of AI in 2023: Generative AI’s breakout year," exploring its impact on various industries and job functions and how it's shaping the future of work.How High Performers Achieve Results with Generative AIMcKinsey'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 DifferentlyHow 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 ConsistentAs 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.More at krista.ai

Companies evaluating AI want the most accurate machine learning models (ML) to predict or summarize business outcomes. However, many times achieving high levels of accuracy and confidence inflates expenses and erodes value versus adding to it. In this episode, Luther Birdzell and Scott King discuss what is and what is not AI, evaluating ML model accuracy against costs, and how to maximize your ROI when building or maintaining your models.More at krista.ai

Embracing Predictive Analytics in Decision-MakingIn 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 LearningIn 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 PredictionsWe 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 AnalyticsThen 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 AnalyticsLooking 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 AnalyticsPredictive 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.More at krista.ai

There's a lot of AI buzz and businesses everywhere are feeling the pressure to integrate this exciting technology. However, amidst all the hype, there's a need to clarify a few things. First off, AI isn't a panacea for all business woes. It needs a foundation of sound data and processes to be effective and it's intended to assist rather than replace human decision-making.The power of automated workflowsA 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 delaysNow, 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 ROIBut 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 AITo 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.More at krista.ai