How do you make AI and business intelligence systems user-friendly?

Any “AI” enabled use case could be reduced to one of those 2 categories: Automation (execute workflows automatically) or augmentation (allow the user to do more than what their time and capacity allow). 1. Make it clear: The user needs to feel on top of the experience. Make it clear when the result is an AI suggestion vs data-factual 2. Rethinking the workflow does not necessarily automate existing workflow steps 3. Not everything needs to be automated. Know what annoys the user to automate it or augment it.

I like to problem solve using frameworks as it ensures structured and well rounded perspective. For example, using the PESTEL framework considers Political, Economic, Sociocultural, Technological, Environmental, and Legal factors that impact AI and BI systems. This lens ensures that beyond usability, these systems align with regulations, adapt to societal changes, remain financially viable, and are environmentally conscious. By diving into these aspects, we guarantee that AI and BI systems are thoughtfully designed to be user-friendly, ethically sound, and sustainable, aligning seamlessly with the preferences and values of the users they serve.

They’re all great recommendations. There’s also a bit of design, interaction, and processing of information in this. Instead of hard accuracy values that business users can’t relate to (97.4%), change your scale (??????) to guide your users to the decision you want them to take.

Imagine Business Intelligence (BI) as a tool that helps people look at data from the past. It organizes this data and creates reports and charts to show what's been happening. It's really good at dealing with structured data, which is like organized information that fits into boxes. Artificial Intelligence is like a clever helper that tries to understand things like a human does. It can learn from all sorts of data, even if it's messy and not organized. AI can also guess what might come next and do tasks automatically. So, when BI and AI team up, they become a powerful combo. They help businesses make smart choices by looking at what's happened before, what might happen in the future, and all sorts of data in between.

Making AI and business intelligence (BI) systems user-friendly involves several key strategies. -Intuitive Interface: Design a user interface (UI) that is intuitive and easy to navigate, with clear menus, buttons, and visualizations. -Personalization: Allow users to customize their dashboard and reports based on their preferences and needs. -Natural Language Processing (NLP): Implement NLP capabilities to enable users to interact with the system using natural language queries and commands, making it more accessible to non-technical users.

When designing an interface for human-AI interaction, it is very important to make clear that AI is a machine, and that it’s responses might incorporate error in the form of bias and variance. There is an uncanny valley to be avoided, and best interface will stay away from confusing the user.

As any other revolution, the AI one will also bring many new solutions and use cases into our lives. The one thing that I find different now, is the pace. It's probably going to be fast and dynamics, more than ever before. I believe that designing a smart interface, that includes graphics and icons, is a must. However, adding short videos, is an added value. If the entire world is moving to more and more video-based solutions and higher video consumption (vs. text and pictures)- why not using it for the next generation of AI solutions and interfaces as well?

A streamlined interface in any healthcare tool is not just about aesthetics; it directly impacts the efficiency and accuracy of care delivery. Particularly in wound care, where timely interventions can be crucial, a cluttered or complex interface can delay treatment and hinder optimal patient outcomes. By simplifying the interface, caregivers can quickly access relevant data, make informed decisions, and focus on what truly matters: the patient. Thus, a minimalist, user-friendly design is not a luxury but a necessity in wound care technology, ensuring that caregivers can seamlessly integrate these tools into their workflow without any unnecessary hurdles.

Simplify the dialogue between human and machine to the max but without going simplistic. Having an intuitive and decluttered user interface to engage with AI solutions does not mean to have minimal guidance features, menus and buttons but a balance with accessability of information through voice or text in the most human centric manner.

Visual aids, like charts and graphs, play a crucial role in facilitating data comprehension. They transform complex information into digestible visual representations, aiding users in grasping insights quickly. Additionally, the integration of Natural Language Processing (NLP) holds immense potential. It empowers users to interact with systems using natural language, making the experience more conversational and user-friendly. This capability can foster an intuitive and engaging way for users to query, retrieve, and manipulate data within AI and BI systems.

In a wound care facility, clinicians make vital decisions daily that can profoundly affect patient outcomes. When introducing an AI-driven solution, it's essential for these healthcare professionals to understand not just the 'what' but the 'why' behind its suggestions or outputs. When clinicians comprehend the reasoning of the tool, they can confidently integrate its insights with their expertise. This synthesis of human expertise and AI-backed logic can lead to more precise, timely, and effective interventions, elevating the standard of care in wound treatment facilities.

"Explainability" should be evaluated at both the overall model level (e.g. partial dependence plots) and at the prediction level (e.g. SHAP values). Different audiences have different concerns about model behaviours, so having a variety of metrics in your toolkit will enable you to address whatever questions come your way. Also, keep in mind that your non-technical audience is unlikely to know how to interpret diagnostics such as the ones described above. You need to translate and simplify the message for them!

Transparency provides insight into the inner workings of algorithms, demystifying the process for users. Interpretability methods render complex models intelligible, shedding light on how input variables influence outcomes. Explainability adds context, highlighting key factors driving particular conclusions. Employing storytelling and narratives amplifies user engagement. Data and insights are communicated in a compelling manner, weaving a narrative that resonates with users' experiences and objectives. Practical examples and scenarios further drive home the value proposition, illustrating how the system addresses real-world challenges and empowers users to attain their goals.

In AI and BI ecosystems, a prevalent challenge is user skepticism due to opaque outputs and recommendations. Addressing this, 'Explainable AI' modules have proven invaluable. They don't just provide outcomes but elucidate the decision-making process behind them. Incorporating illustrative case studies and narratives further bolsters this understanding, transforming perplexed users into informed advocates. It's evident that in the realm of AI and BI, transparent rationale is as crucial as the accuracy of the recommendations. Users don't just want results; they seek comprehension of the underlying 'why' to trust the given 'what'.

It is not only about explaining the logic, but also about explaining how the system has been built, particularly in regards to data. Training data for AI or source data for BI systems can sometimes be opaque, yet transparency in the data is a necessary prerequisite before explaining the logic of the system. This is often complicated by the fact that there is rarely (never) a single source of data as a result of the way in which organisations have evolved and transformed over time.

A doctor's expertise, cultivated over years of education and hands-on experience, shouldn't be overshadowed by AI; rather, AI should be a complementary tool. By ensuring that AI platforms offer flexibility, doctors can tailor the tool's assistance according to their unique needs and preferences. For instance, if a system suggests a particular treatment based on data analysis, doctors should have the autonomy to either follow that recommendation or use their judgment to take an alternate route. Offering this blend of advanced technology and human autonomy ensures that AI serves as an enabler, not a replacement, preserving the indispensable human touch in healthcare.

Configurability and comfort. That is the game! AI and BI systems should be intelligent enough to offer a comprehensive view by default. Configurability then becomes the icing on the cake, allowing users to fine-tune their experience by toggling controls as they see fit. It's not just about providing options; it's about delivering an optimized experience right out of the box, with the flexibility to adapt.

User autonomy is crucial in the dynamic world of AI and BI. Customization features, such as filters and settings, consistently lead to higher engagement, since users feel they are steering their experience rather than being passive recipients. In order to foster a two-way interaction, feedback loops are essential; they allow real-time adjustments based on user inputs, which enhances the accuracy and relevance of the system. A growing focus on data rights has also highlighted the importance of opt-in/opt-out mechanisms. Systems that prioritize user consent not only adhere to regulatory standards but also establish trust. The axiom holds true: Empowering users with control isn’t just ethical, it's good business.

Additionally, it might be important to provide user with the possibility to rate responses and reports, to enable a feedback loop. Users are usually interested in the improvements of the system performance and are generally open to contribute with feedback if this is easy and friendly. Interface, and timing, are important towards this objective. ??

Empowering users with control and choice is a cornerstone of a user-friendly system. It recognizes that while AI and BI systems are powerful, they should serve as tools that complement human intuition and decision-making, not replace them. By providing customization options, users can tailor the experience to their unique needs and preferences, making interactions more relevant and efficient. It also fosters a sense of ownership and trust, as users know they have the final say. After all, AI and BI are most effective when they amplify human capabilities rather than dictate a one-size-fits-all approach.

In AI and BI rollouts, guided learning proves pivotal. When a BI tool debuted at a large firm, its potential was hampered by low adoption, attributed to a challenging learning curve. Addressing this involved introducing an onboarding process, interactive tutorials, and immediate tooltips. Gamification, like badges and challenges, further drove user engagement. Additionally, social features led users to build communities, sharing analytics and insights, amplifying the tool’s value manifold. The takeaway? Beyond features, user education and fostering a sense of community are key to unlocking potential.

A BI tool I encountered had a feature called "Data Discovery Quests." These were essentially guided challenges that users could embark on, leading them through various features of the platform. As users progressed, they earned badges and unlocked advanced functionalities. This gamified approach not only made the learning process fun but also ensured users were exposed to the full breadth of the tool's capabilities. By the end of their quests, many users were not just familiar with the tool but also deeply engaged, turning them from casual users to power users. The integration of learning and discovery can drastically boost user competence and confidence.

"Test, iterate, repeat" is a golden rule for AI and BI implementations. An AI tool rolled out in a retail sector initially showed suboptimal engagement. Usability testing revealed some features were not intuitive. A/B testing, a method to compare two versions of the tool, pinpointed precisely which features needed refinement. After tweaks, engagement metrics soared. User feedback loops provided real-time insights into this iterative process. By embracing agile methodologies, the system could be rapidly updated in response to this feedback, ensuring timely improvements. What's the lesson? User-centric testing and iteration are essential to unlock the full potential of AI and BI tools.

AI and BI systems are never static and should be constantly evolving as new data arrives. AI is trained on historical data sets, but what may have been a good model a month ago may not necessarily be so today. People, organisations and technology are evolving constantly, so AI and BI systems need to be able to adapt to take this into account. This can be achieved either automatically (through mechanisms such as threshold-based model retraining) or manually (through careful performance monitoring).

Check with customer service support what are the issues users have the most problem with or check the FAQ section what get more traffic. In addition, ask new employees to review the product, mostly within areas that are not in their department. Once you have the info and data, use AI and BI to improve the product/service to be user friendly

There are several ways to improve user friendliness in AI, such as including NLP ( Natural Language Processing) as much as possible, with natural language-based interactions, and also for example, including drag-and-drop interfaces. There are tools also for saliency ranking as regards HCV ( Human Computer Vision). The integration with games, such as its Gamification Software and Cloud needs also to be taken into consideration, and this could be very useful also for interfaces and user easiness and friendliness in AI educational tools.

To enhance user-friendliness in AI and business intelligence systems: Employ an intuitive UI design that presents complex data clearly and in a visually appealing manner. Enable personalization to let users tailor the system to their preferences, promoting a more personalized experience. Integrate natural language processing for easy interaction, allowing users to query the system using natural language. Offer guided assistance through tutorials, tooltips, and contextual help to aid users in utilizing the system effectively. Maintain ongoing training resources and support materials to ensure users stay informed about system updates and features.