You're trying to improve your business' AI capabilities. How can you find the right platform?

Here are 5 key steps to defining your goals: 1. Define your objectives and goals for AI implementation. 2. Identify the main problems or opportunities you want to address with AI. 3. Determine the expected outcomes and benefits for your business. 4. Establish metrics for measuring the success of your AI projects. 5. Use these defined goals to narrow down your options and select suitable AI platforms.

Establish clear and specific goals for implementing AI within your business. Whether it's optimizing operations, improving customer engagement, or enhancing data analytics, articulate measurable objectives. Defining goals helps in aligning AI capabilities with your business strategy, making it easier to evaluate potential platforms based on their ability to meet these specific objectives.

As ever, start by clearly defining your specific needs and goals. Research different AI platforms, focusing on those aligning with your business objectives - keep your current and future goals as your north star. Consider ease of integration with your existing systems, time to learn, market resource cost, and the level, and responsiveness, of technical support offered. It's also wise to look into the platform's scalability, ensuring it can grow with your business. And look at the providers strategic roadmap to ensure you are both going in the same direction. Additionally, seek recommendations from industry peers and consult AI experts. Finally, explore trial versions to test suitability before committing to a financial contract.

Many businesses follow AI trends in pursuing outcomes that have benefited other companies. While the fast follow method could be useful, I would argue it's crucial to precisely define your AI objectives, use cases, and assess your existing talent and resources. Identify the most relevant use case for your business, then conduct a time bound trial to measure the ROI and AI project. Finally, establish clear goals and understand your organization's unique qualifications and capabilities for AI integration. Stay true to your organizations AI goals and objectives.

Start by clearly outlining what you aim to achieve with AI in your business. Whether it's enhancing customer experience, automating processes, or gaining insights from data, having clear objectives will guide you in choosing a platform that best suits your needs.

I would first start by identifying the key areas in the business where AI can be applied and list down the specific challenges or opportunities in each area. Then describe what a successful implementation looks like for each one of them and connect the expected outcomes to tangible business benefits like cost savings, revenue growth, customer satisfaction improvement, etc. One thing that I have found helpful is defining clear, measurable indicators of success for each application area. and this could be either KPIs, ROI, customer metrics, or operational efficiencies.

Using AI in your business today is inevitable! Here is how I would start today if I am starting over, with the journey of incorporating AI tools within the business: 1- Define your WHY? As in why do you need AI tools/platforms exactly? Determine 1 area only, where AI can help you the most & the fastest, and impact can be measured clearly. Only then, start evaluating the platforms .. 2- While choosing a platform, make sure it aligns well with your team's skills, your customers, your market geography & the overall context of your business. Not every AI tool is for every business.. 3- Don't just follow the hype with each new AI tool! Get curious, test it first & only include it in your AI stack if it really adds value to your business!

This should be Rule #1 in every business but unfortunately, it isn't and has to be often repeated. The reason its so important and especially in AI is because the journey is not linear. Call it a "scenic route", a very...scenic route. One of the best ways is to engage an advisor or consultant in the space you are in or trying to penetrate. AI is simply too vast and growing daily to just see what happens. Have a plan with all of your data points you need to achieve your goal. Sometimes you can accomplish that internally but many times not. A consultant in this space can help you define your goals and achieve them efficiently and effectively if you cannot yourself. But without well thought out and defined goals, you waste invaluable time.

"Don't just improve AI capabilities, enhance your business capabilities using AI." How do you find the right platform to do this? Identify your business needs first. Is it automating processes, enhancing customer experience, or extracting insights from big data? Consider your team's technical skill level. For those with advanced skills, TensorFlow or PyTorch offer deep learning capabilities. For a more user-friendly experience, platforms like Microsoft Azure AI might be suitable. Think about integration and scalability. Platforms like IBM Watson and Google Cloud AI may be helpful in these areas. Also, how will this investment impact your budget? Example, AWS's flexible pay-as-you-go model can be a cost-effective solution.

Evaluate the resources you have available, including your team's technical expertise, budget, and existing infrastructure. This assessment will help you determine which AI platform aligns with your resource capabilities and how much investment in training or additional infrastructure might be needed.

Evaluate the resources available within your organization, including budget, expertise, and infrastructure. Determine the level of technical expertise among your team members and identify any gaps that may require external support. Consider the scalability of the AI platform in relation to your business growth. Understanding your resource landscape ensures that the chosen platform aligns with your capabilities and supports effective implementation.

When enhancing your business' AI capabilities, identifying the optimal platform is crucial. From a legal perspective, ensuring data compliance and privacy is paramount. Selecting a platform with robust security measures and compliance features is non-negotiable. On the AI front, consider the skills within your team. A platform with user-friendly interfaces and comprehensive documentation can facilitate smoother integration. Legal-wise, having contracts that clearly define data ownership and usage rights is advisable. Budget considerations are vital. Evaluate whether a platform offers a scalable pricing model aligned with your business growth. Hope this helps.

The beauty of AI is that it levels the playing field, even for organizations with resources constraints or lack of sufficient budgets to build AI platforms. Here is why: 🤖 AI multiplies the brilliance of decision makers, developers, data scientists, content creators, etc. 🤖 AI serves as catalyst for creative inspiration 🤖 AI resources required over time diminish as teams become “AI-ready” 🤖 AI can reduce the costs of third party vendors 🤖 AI can make you resource rich Overall, there is a misconception that AI implementation is costly, resource-intensive, and risky. While this may be true in certain instances, adopting a growth mindset towards AI integration in your organization can yield dividends.

Assessing existing business resources and evaluating them to see what areas AI can improve is an essential step. It helps businesses evaluate how ready they are for AI improvement, taking into consideration existing infrastructure, size or quality of data they have, existing standards, and skill level of their employees. It also makes it easy to identify areas that would really need AI improvement.

This touches on some of my point in prompt 1, you need to know where you are at the beginning of your AI project and the reality of how long it will take to get there based on resources. Now, you can obviously add them as you need and thats often the most efficient. But keep in mind the timeline of these types of things and that AI is moving faster than almost any other industry in history. So be prepared to pivot on your resource needs as needed. At the same time, pivoting from one platform to another is not easy and can be incredibly difficult. Try to choose the right one from the start because if you do, it can grow as you do and you will not ever have to entertain switching platforms because you are missing valuable tools or otherwise.

In my experience to find the right AI platform for your business, start by assessing your available resources, including budget, technical expertise, and data infrastructure, to determine what type of platform (cloud-based, on-premises, or a combination) aligns best with your needs and capabilities.

Here are the 5 key points for determining your resources: 1. Resources include data, skills, budget, and time. 2. Choose a platform based on available resources. 3. Platforms offer varying levels of automation, customization, support, and scalability. 4. Limited data and skills may lead to a preference for pre-built models and easy-to-use tools. 5. More resources allow for building, training, and deploying custom models with flexibility and control.

Evaluate Resources: Assess the data, skills, budget, and time you can allocate to AI initiatives. Understand your resource constraints and preferences, as they will influence the type of platform that best fits your business needs. Consider Automation and Customization: Depending on your resources, evaluate whether you need a platform with more automation for users with limited data or skills, or a platform offering greater customization and control for those with more resources.

Top 10 considerations when comparing platforms: 1. Consider the types and quality of models offered or supported. 2. Check if the platform covers the domains and tasks of interest. 3. Evaluate if state-of-the-art algorithms and frameworks are used. 4. Assess the ease and speed of model development and deployment. 5. Determine the time it takes to create, train, test, and deploy a model. 6. Consider the amount of coding or configuration required. 7. Evaluate the performance and reliability of the platform's models. 8. Take into account data security and privacy. 9. Ensure compliance with relevant regulations. 10. Consider how the platform monitors and updates models over time.

--Model Types and Quality: We scrutinize the range of models offered, ensuring they align with our project's domain. It's vital that these platforms support state-of-the-art algorithms and frameworks that can tackle the specific tasks we're interested in. --Development and Deployment Process: Time efficiency is key. We evaluate how long it takes to go from model creation to deployment, including the amount of coding or configuration needed. --Performance and Scalability: Assessing the accuracy, robustness, and scalability of the models is crucial. We need a platform that not only delivers high-performance results but can also scale with the growing demands of our project.

Thoroughly assess the features and functionalities offered by various AI platforms. Consider factors such as machine learning capabilities, data integration, scalability, and compatibility with your existing systems. Evaluate user interfaces, ease of integration, and the platform's ability to address your specific business needs. A detailed comparison ensures that the chosen AI platform aligns with your requirements and provides the necessary tools to achieve your defined goals.

Once a use case has been identified by a team or organization, then they can specifically identify which AI platforms to compare. For instance, if a team intends to employ generative AI for content creation targeted at executive audiences, they can explore tools that leverage retrieval-augmented-generation (RAG). In this context, platforms such as ChatGPT4 or Jasper.AI can be assessed for their ability to generate content by drawing from an organizations publicly available documents as source material ensuring accuracy and compliance. After comparing the output from both platforms, organizations can make an informed decision about which generative AI platform aligns best with their requirements for serving their executive audiences.

After setting clearly defined goals and assessing existing business resources, it is important for businesses to compare which AI features or functionalities currently exist that best meet their identified areas of improvement. Model performance, functionality, speed, scalability, frameworks, ethics, privacy, and compliance are all areas to make good comparisons.

When comparing features and functionalities of AI platforms, focus on their capability to meet your specific business needs. Look at the range of AI technologies offered, like machine learning, natural language processing, and automation. Evaluate the ease of integration with your existing systems and data compatibility. Assess the scalability of the platform to ensure it can grow with your business. Consider user-friendliness and the level of technical expertise required. Check for customisation options and support services provided. Analyse performance metrics and security features to ensure robustness and data protection. Comparing these aspects will help you choose the most suitable AI platform.

Model Coverage: Examine the types and quality of models offered by each platform. Ensure that the platform covers the domains and tasks relevant to your business needs, and assess if it uses state-of-the-art algorithms and frameworks. Ease and Speed of Development: Consider the ease and speed of model development and deployment. Evaluate how quickly you can create, train, test, and deploy a model, and assess the level of coding or configuration required. Performance and Reliability: Evaluate the performance and reliability of the platform's models. Consider factors such as accuracy, robustness, and scalability. Also, assess data security, privacy, and compliance features, along with how the platform monitors and updates models over time.

Evaluate your existing resources, including budget, expertise, and infrastructure. Determine if you have the necessary resources to implement and maintain an AI platform effectively.

On the platform front, the new things that are coming out in the AI space are becoming more straightforward to understand and providing more realistic outputs. So, it's important to keep track of new developments so that futuristic plans can be tweaked accordingly. This is achievable by contnuous assessment of models, platforms, and functionalities, irrespective of whatever stage we are in AI implementation,

Research and analyse customer reviews and feedback for each AI platform under consideration. Pay attention to reviews from businesses with similar needs and challenges. Assess user experiences, customer support, and the platform's overall reliability. Real-world feedback provides valuable insights into the practical performance and user satisfaction, helping you make an informed decision about the suitability of each platform for your business.

Research, research, research. But do so quickly and efficiently. The research is a portion left by many in AI because they don't want to do the painstaking work it usually entails. But there are plenty of tools out there that can greatly assist in this process. Reading reviews is not enough, not even close. Use them as part of the process but they cannot be the sole part of the process. See if you can get interviews with platform customers to get their thoughts and experience with the AI platform you are considering. Two, maybe 3, is all you need. Then, verify some things quickly with your research, decide, get to work.

Leverage community feedback regarding evolving AI platforms when possible. I test different platforms like ChatGPT4, Bard, Jasper.ai, Claude.ai, etc. Lately, my focus has been on their ability to create long-form content. Currently, ChatGPT4 excels, particularly with its multimodal retrieval augmented generation (RAG) features, though other platforms aren't too far behind. I also monitor their adoption rate among business leaders, developers, and legal teams, and how these models evolve over time. This approach helps drive LLM model integration decisions while adhering to company policies for responsible AI use, protecting IP, and avoiding hallucinations, data toxicity, and prompt injections. Trying models is your #1 best friend!

Customer Experiences: Look for customer reviews, testimonials, and case studies that provide insights into the experiences of businesses similar to yours. Assess both strengths and weaknesses of the platform from the perspective of real users. Seek References and Demos: Ask the platform providers for references or demos. This allows you to see the platform in action, understand its capabilities, and assess how well it addresses your specific business problems or needs.

Seek Reviews and Testimonials: Look for detailed reviews and testimonials from other users. They often highlight the platform's strengths, weaknesses, and real-world performance. Analyze Case Studies: Review case studies to understand how the platform has been applied in similar scenarios or industries. This provides insights into its practical utility and effectiveness. Check Ratings: Ratings can give a quick overview of user satisfaction and the platform's overall reputation. Request References or Demos: Ask the provider for references or demos. Direct Inquiries: Reach out to current or past users, if possible, for firsthand accounts of their experiences.

Considering customer feedback is essential when selecting an AI platform. Look for detailed reviews and testimonials that reveal both the platform's strengths and potential shortcomings. Case studies offer valuable context on its real-world applications. Don't hesitate to request references or live demonstrations to gain a firsthand understanding of the platform's capabilities and how they match your specific business requirements. This can significantly enhance your decision-making process.

Customer references and industry expertise play a crucial role in ensuring the chosen platform is a reliable and strategic investment for your business. When researching these case studies, it is valuable to observe reviews from companies facing comparable issues and requirements. Further, spending time to collaborate with platforms by being transparent and clearly communicating your intent for your business's AI capabilities will ensure you find the optimal platform for your business's needs.

Evaluating customer reviews and feedback is like tapping into a goldmine of practical insights when choosing an AI platform. Reviews and case studies often reveal real-world strengths and weaknesses that might not be apparent in product specifications. They provide a glimpse into user experiences, helping to gauge the platform's effectiveness in practical scenarios. Asking for references or demos is also prudent, offering a hands-on feel of the platform's capabilities tailored to your needs. In my experience, this approach not only aids in making an informed decision but also helps anticipate and plan for potential challenges in implementing AI solutions.

Ditch the star ratings and think beyond the box! Don't just skim reviews - seek industry-specific insights from companies similar to yours and tackle problems like yours. Look for detailed feedback on the platform's strengths, weaknesses, and real-world performance across diverse use cases. Don't be afraid to ask questions directly! Supplement your research with independent analyst reports, online communities buzzing with user experiences, and even social media chatter to get a well-rounded understanding of the platform's culture and customer satisfaction. Remember, choosing the right AI model goes beyond features - it's about finding one that truly aligns with your needs and sets your project on the path to success.

This part of the process is usually the most fun, and also the most frustrating if things don't go according to plan. While the prompt suggests a free trial, those have become few and far between and be sure to read the T's & C's before committing to the ones still out there. For me, pilots are the best because you can choose your audience as well as have proper expectations set for what you're hoping to achieve from your pilots. I want them to tell me everything they don't like, what features they want, what will make their job easier and better in regards to the tool, and more. That feedback is priceless and I emphasize as much when onboarding them. Of course I want to hear what they like also but the pain points are far more valuable.

Before committing to a long-term contract, conduct pilot tests or trial periods with short-term subscriptions for the selected AI platforms. Implement the platform in a controlled environment to assess its performance, user-friendliness, and alignment with your goals. Gather feedback from your team and iterate on the implementation based on insights gained during the testing phase. This iterative approach ensures that the chosen AI platform seamlessly integrates into your business processes and delivers the expected results before full-scale adoption.

This is top of my list. The number of tools has proliferated, as has the number of LLMs. The only way to establish which is best for your business is to try a few and assess performance. However, testing has to be systematic. We are seeing a number of businesses that should know better make basic IT deployment errors that cause brand embarrassment. It's not necessary. Dev Ops discipline is required.

As someone who's witnessed countless project triumphs and stumbles, let me tell you: testing and iterating is the ultimate weapon. You need to experience the platform firsthand to feel its strengths and weaknesses under your data and goals. Here's what you need to do: Free trials and sandbox environments: Experiment with features, train models on your data, and see how the platform handles your specific challenges. Pilot projects: Pick a small, manageable project that aligns with your overall goals and dive in with the chosen platform. You'll understand its integration, performance, and impact on your workflows. Embrace the iterative loop: Testing isn't a one-time thing. As you learn and gather data, refine your approach and strategy.

Você já comprou um carro sem fazer test drive? Não deveria... 😂 Faça trials e valide se a ferramenta atende suas necessidades antes de fazer um compromisso financeiro! 💰

Organizations need to take an agile approach. Try out different tools for a similar case study and see which tool performs best for the given task at hand. Through our exploration, we've found different tools perform remarkably better in some cases and terribly in others. We are at the gold rush/wild west stage of democratized AI which means there will continue to be exponential leaps forward in shorter amounts of time.

NONE of the AI platforms are simple plug and play. The more you learn about how AI works, how to write prompts, how to check all content the AI writes, you're not ready to make a decision on a platform. Before you even start testing platforms, it is important to study the art of prompting and to become informed about the latest features in AI. Once you are confident that you know how to write effective prompts, you're ready to try a couple of platforms. ChatGPT and Perplexity are my two go to's to start any project. Don't take the first response your prompt gets as the only answer. Tell it "tell me more," "go deeper," or "more" to get the platform to delve further into your prompt.

Free Trials and Demos: Utilize free trials, demo accounts, or sandbox environments to explore the platform's features without long-term commitments. Pilot Projects: Conduct experiments or pilot projects with your own data and objectives to assess the platform's performance and result delivery. Validate Assumptions: Use testing to confirm your initial assumptions about the platform's capabilities. Identify Gaps: During testing, look for any gaps or issues in the platform's functionality. Optimize Strategy: Use insights from testing to refine and optimize your AI strategy.

The loop is an important method within design thinking. Build, observe, improve. Too often we assume, that we have 100% empathy with the end user, but this is far from the truth. Testing + iterating beats perfection.

A critical thing you need to consider is whether the tool you are going to use requires prompt engineering skills. Prompt engineering is both a skill and art of communicating with AI, and I would argue one of the most important skills to learn these days. I would go as far as saying that not having at least the basic skills of prompt engineering in the next few years will become the same as not being able to use a phone today. Having prompt engineering skills, allows you to have a much broader set of tools you can use to be competitive in this new world, and these tools are much more flexible and powerful.

Further, it is important to consider the long-term viability of the vendor/SaaS company offering the tools. As you delve into the process of selecting an AI platform, it's crucial to thoroughly assess the vendor's background, reputation, and long-term viability. Investigate the vendor's track record in delivering consistent updates, addressing customer concerns, and adapting to technological advancements. A reliable vendor with a clear roadmap for the platform's evolution instils confidence in its commitment to supporting your business's AI journey over the long term. Evaluate the vendor's stability, support infrastructure, and responsiveness to ensure sustainable continuity as your business scales.

When trying to improve their business AI capabilities, organizations can leverage the expertise of well established companies that can serve as a trusted advisor to help them expand upon their business capabilities. As an example, organizations can leverage Google Cloud Platform's Vertex AI or Gemini AI solutions with the support of AI experts to identify a use case, build an MVP in weeks, and achieve business outcomes. The journey of improving your business' AI capabilities doesn't have to be done alone.

Improving business' AI capabilities starts with identifying the right AI platform that aligns with your specific needs and goals. Assess type of AI applications you intend to develop, such as natural language processing, image recognition, or predictive analytics. Research, compare platforms like IBM Watson, Google Cloud AI, Microsoft Azure AI, and Amazon Web Services for their features, scalability, and support. Consider ease of integration with your existing systems and level of technical expertise required. It's also crucial to evaluate the cost-effectiveness and the quality of customer support provided. Attending webinars, reading case studies, and seeking recommendations from industry peers can also aid in making an informed decision.

Start by diving deep into AI-centered communities. They are the heart and soul of AI development and an excellent resource for real-time insights. Follow thought leaders in the field who are unassuming, yet are constantly experimenting with AI. Be selective of those with a substantial audience however, as there is a substiantial amount of so-called "experts". How can someone be an expert in a space with 1000 new companies and 4000 new research papers every month. The path to finding the perfect AI platform is not a solitary journey. Find those guides that will meet you where you are presently in your AI journey, and explore together.

a. Scalability: Ensure that the chosen AI platform can scale with your business as it grows. Scalability is crucial for long-term success. b. Integration: Check if the platform easily integrates with your existing systems and software. Seamless integration can save time and resources. c. Customization: Determine whether the platform allows for customization to meet your unique business needs. A one-size-fits-all solution may not be sufficient. e. Support and Training: Assess the level of support and training offered by the platform provider. Adequate training and support are essential for

Consider customer segments. Design AI capabilities around each type of customer?The wrong AI platform can cause security concerns, reduced scalability and more. You wouldn't choose a platform built for the retail industry that's actually built for the manufacturing industry because capabilities and needs are stark. Manufacturing has needs like Predictive Maintenance, Inventory management etc. Whereas the retail has needs along Personalized shopping experiences, AI-assisted store associates etc. Examples 1. Manufacturing. Siemens Mindsphere 2. Retail. HCL Commerce Neither one of those platforms or companies cater to the other industry. So if you were serving both customer segments you would want something versatile enough across sector. #ai

In my opinion, elevating a business's AI capabilities necessitates a thoughtful approach to selecting the right platform tailored to its unique needs. First and foremost, a clear definition of business objectives is crucial – whether the goal is to enhance operational efficiency, deliver superior customer experiences, or enable data-driven decision-making. Once these objectives are established, a thorough assessment of technical requirements becomes imperative, considering factors like algorithm complexity, data processing capabilities, scalability, and seamless integration with existing systems.

- Assess if your data is sufficiently clean, labeled, and integrated to support AI. - Audit if your team has the right analytical and technical skills to deploy AI. - For many applications (e.g. ML models), seek AI platforms that provide transparency into how outputs are determined. - Factor both subscription and professional services / staffing costs over multi-year horizons. - Assess if you require cloud for scalability or if sensitive data necessitates on-premise servers. - Ease of integrating AI with surrounding data and applications stacks important. - If use cases may scale, ensure platform can grow flexibly.

My daily activities involve identifying areas for improvement, both in processes lacking automation and those already automated. The development of Artificial General Intelligence or superintelligence is always guided by specific business use cases. It's crucial to continually evaluate and re-evaluate the implemented models to effectively address real business challenges and ensure the success of our AI models. Therefore it's essential to conduct a thorough assessment of your business needs and align them with the capabilities of various AI platforms. Look for a platform that not only meets current requirements but also offers scalability and adaptability for future growth and changes in business dynamics.