Previously available exclusively to tech giants, artificial intelligence is now making its way into more organizations’ business processes. Now democratized, artificial intelligence technologies can be used by all businesses to modify customer experiences, meet the changing needs of the market, soothe the pains of employees relying on guesswork – bring real value.
It’s not a matter of rumors that AI adoption can be burdened with the risk of falling short of expectations. However, we have a solid urge to appeal for: AI is innocent. Don’t blame technology for human mistakes.
Artificial Intelligence is an R&D project, and we should handle it in the R&D way. The key factors influencing the project thriving do not regard the thousands of dollars spent. But, for instance, how much data you have collected or chosen solutions for a given case. So: what should we do to make your product succeed? Well…
Let’s make AI a strategic move.
AI projects are much like experiments – and in order to get on board with AI adoption, you need to be ready to test ideas, and let some of them fail
Don’t underestimate the human factor of AI – it’s created to augment your team, so you need to get them ready for being more data-driven and AI-friendly
Identifying the proper use case is one of the common bottlenecks to successful AI adoption – don’t let it happen to you! Find the model that will solve your most burning problems
The AI Sprint is a short-term, fixed process where we help you discover the potential for artificial intelligence in your organization, identify the most promising use cases, build the base for your data strategy, test and assess the solutions to show you what they’re worth.
First things first: we start with a talk. It is crucial to communicate well in AI projects, at every step of the way. It’s crucial from the very beginning to make sure everyone has a full understanding of the business, the pains, the hopes, and the assumptions.
Knowing your operations and your struggles, we can list artificial intelligence solutions that have the best chances to bring you quick wins and open up the path to company-wide AI adoption. We find the right algorithm for you looking at your objectives, requirements, and data.
The data science team performs the AI research – exploratory data analysis (EDA) – to discover patterns, spot anomalies, and test hypotheses. With the results, we have a full picture of what the data represents and how it can be used in the most efficient way.
There’s the moment of truth. We test various machine learning algorithms to find the best fit. We assess the probability of successful implementation of the selected solution, and we get you ready for the next steps with tailored recommendations for your AI journey.
Convince your stakeholders that artificial intelligence is worth the shot with the findings and results of the AI Sprint.
Organize relevant elements of AI adoption and standardize the way your company will work with AI projects.
See how to move forward strategically to make the most of AI adoption.
Get evidence that artificial intelligence can (or cannot) deliver tangible value to your organization.
The PoC has shown you whether the idea is viable but also gave you a taste of what it’s like to work with external data scientists. The base of your data strategy and recommendations lay the groundwork for a successful, strategic implementation.
Now it’s time to bring the idea to life.
Your AI Sprint has tested a variety of models that can solve your problem, and at the end, you see which AI-based solution performs best. No matter if it’s a recommender system to suggest your customers the next purchase, predictive models to assess the risk of churn, dynamic pricing to increase your margin profit, speech recognition to improve customer service, or some other solution utilizing machine learning, we have a process that will help you bring it to life.
Gain valuable insights about future events and use artificial intelligence to predict e.g. customer churn or employee turnover to be more proactive.
Give your customers personalized, AI-based recommendations of products or content to boost their engagement, improve customer service, and increase sales.
Adjust your prices when needed – automatically. Dynamic pricing allows for a higher margin and better conversion rates.
Assess the value of items using AI, evaluate the probability of conversion (lead scoring) or verify credit risk (credit scoring).
Handle customer queries more efficiently with the help of NLP-powered chatbots. Gain more understanding of users with sentiment analysis.
Artificial intelligence is not limited to the models described here – and it’s not about what model seems to be the right fit, but what proves to be the right fit. Whatever your right AI model is, we’ll find it!
What does your project need? A scoping workshop is a great starting point for the AI development process – it help you list all the requirements and jobs to be done. Thanks to that, we can build a team tailored to your needs: including data scientists, software development engineers, designers, or a Scrum Master, estimate the time to deliver, and manage the work efficiently.
With the team selected and educated on your project requirements, it’s time to start the work! During a kickoff meeting, you’ll get to know the team a little better and discuss all the essential points of your collaboration in detail.
Data science projects are managed slightly differently since the models have to learn, but our teams are always loyal to the Scrum methodology. Don’t forget about your role in the development! It’s important for you to participate in the process – go to “Your AI development team” to see why.
The AI model has been trained and tested, your team is learning how to use it right, so it’s time to go live. Depending on the path you’ve taken (stand-alone AI-powered solution or model integration into an existing system), the steps towards launch can be different. And once it’s live, all that’s left is to use AI to the fullest!
AI development involves the work of the data science team, software development engineers, and scrum masters, but it also includes you at the heart of the project – it is all done in collaboration with you and your team to meet all expectations. Artificial intelligence is not just the algorithms – it’s how it works within your organization, including getting onboard with data-driven culture.
Your domain expert (e.g. head of marketing) or an engineer. Or both. The people who will later work with the model in their daily tasks should participate in the process, too.
Watching over the process, questioning assumptions, suggesting ever better solutions to problems. A person who will consult you if needed.
Data science projects aren’t limited to data work. Software engineers will build your dashboards, interfaces, or a whole new AI-ready program to allow for easy use of the model.
A person who makes sure the team’s work goes as smoothly as possible and facilitates communication between the AI development team and your team.
An absolute must in any data science project. The person who’ll take your data and bring it back to changed like it’s had a makeover.
Need a pretty dashboard? Want to build a full app? The designer will help you turn your ideas into reality that is pretty, usable, and intuitive.
The decision-making person responsible for the success of the project – and involved in the process to accept increments, ask and/or answer questions, specify needs.
The PO proxy is a person who will support you in defining requirements, communicating your needs clearly, and maximizing value derived from the product.
Developing your AI solution can be difficult from the very beginning, and that’s exactly why you might need help kicking off. You might need advisory on the use case, technical know-how to understand what’s achievable, or development power to get the project running.
At Neoteric, we address these needs, building teams adjusted to your requirements, listening to your needs, and making sure communication is always honest and clear. This way, a strong partnership is built: our team works alongside yours to develop an AI-powered solution living up to every expectation.
Artificial intelligence is a significant technological trend that shapes the web development landscape. Over the last few years, AI-powered modules have become one of the most desired features of software platforms and applications. As such, they require an adequate system architecture to work smoothly – which has a strong impact on how these systems are developed.
However, many web and mobile applications are not AI-powered and are not intended to be, at least in the near future. They can still benefit from AI-assisted development, e.g. Quality Assessment & code review.
There are certain factors that impact the final cost of such a project. These include: using off-the-shelf components, team composition, quality of data, project scope, costs of the infrastructure, integration, and maintenance, etc.). That’s why it is so difficult to assess this cost without knowing the details.
In general, the costs of AI projects vary from $10-20K for a simple Proof of Concept, which will help you validate the idea of using AI to solve a specific business problem, to $50-500K for the full implementation. The reliable estimation needs to start with having a closer look at your needs, requirements, and possibilities to assess the solution’s complexity and the work it requires.
There is no fixed team size for AI development projects. Depending on your requirements, the team composition in an AI project may vary. Also, note that the AI development team may not need to be limited to data scientists and a scrum master. If you need your AI models to integrate with a system you already use, you will need a software engineer(s). If you need to build new dashboards, interfaces, or whole apps to allow for easy use of the model, you will also need designers.
If you want to build an AI-powered web application, you need to start with designing the architecture that will make it possible to integrate various AI models.
To make it “future proof”, we suggest you not focus solely on the models that you’re starting with. Don’t focus only on the temporary needs. Instead, try to identify all the places that have the potential to be AI-powered in the future – regardless of the data you have right now or your current business processes, needs, and priorities. Thanks to that, you will be able to plan the whole architecture of that new system in a way that will make it possible to integrate other artificial intelligence models in the future.
Artificial intelligence changes web development in many ways. On the one hand, it is transforming how web apps are developed. AI-powered tools for developers can automate some tasks (therefore reducing the development time), improve the code quality, or even suggest optimizations in design based on users’ behavior. On the other hand, it requires web apps and systems to be written in a certain way that enables integration with artificial intelligence models. For sure, it is an important trend that will have a significant impact on web development.
Machine learning uses various types of algorithms: neural networks, linear regression, logistic regression, clustering, decision trees, random forests, etc. The choice of a specific algorithm should be adjusted to your project. Often, it is necessary to test a few algorithms to find the one that is most suitable for the specific use case.
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