Advanced AI Solutions for YOUR technological leadership

We offer consulting services in the area of ML/AI project management, conceptual design, analysis and preparation of data-sets as well as the enablement of Controlled Application Spaces.




Use AI/ML as a driving force to become a leader in your industry!

The general idea is, that AI can be applied to improve various business processes: enhance or enable products development, support management in making better decisions, automate processes, provide insights to the market situation and propose strategic moves to position the organization better. AI can be used to reduce effort and costs, improve speed to market, streamline logistic, drive design and innovation, reduce complexity and change relationships with players in the eco-system.

A company might want to better understand the behaviours and decision making process of its customer and it could use machine learning to analyze highly granular data on the various transactional data about its customer. It might also be interested in analysis and predictions about customer loyalty, potential churn and counter the findings with appropriate measures. The company may also decide to use customer provided feedback as well as industry trends and innovation insights to improve product design and portfolio. We are only starting to understand the many things we can do with AI.

We are here to help you achieve all this.

Your Lakeside Analytics Team



The race for the first Super-Intelligence is on. But first things first. An exponential growth in AI applications and Data Science projects with thousands of dedicated teams has created a demand for a disciplined approach to creating autonomous systems that come with a built-in threat,  which is no smaller than the possible extinction of mankind.


Companies should prepare for the highly likely scenario, in which competitiveness will be driven by the company´s AI´s capabilties. Soon we will see machines enter the arena of judgement and decision making, thereby producing new data to learn from. Whatever we do, be it nice or naugthy, will become "training data" for any learning machine as well.


AI algorithms will soon decide about win or loose, no matter what the question. The data-sets they have been trained on will determine the quality of such decisions. Getting the best data-sets possible is key to creating autonomous systems that some day may decide about your companies faith. Controlled Application Spaces give you just that.

The Race is on! Try not to be last one with a cutting edge AI.


A good AI/ML strategy entails clear objectives, data-driven decision-making, rigorous evaluation, seamless deployment, ethical considerations, adaptability, and a culture of innovation. The relationship between elements in such a strategy creates a ripple effect throughout the company, leading to transformational growth, improved efficiency, enhanced customer experiences, and innovation leadership, ensuring sustainable success in the AI-driven era.

The AI/ML strategy becomes the catalyst for revenue growth, cost reduction, improved efficiency, enhanced customer experiences, and innovation leadership. By nurturing a culture of data-driven decision-making and continuous improvement, the company becomes a pioneer in leveraging AI technologies to achieve its strategic goals. The benefits of the strategy significantly outweigh the costs as it empowers the company to thrive in a dynamic, data-centric world. The AI/ML journey becomes a driving force behind the company's achievements, shaping its future and positioning it as a visionary leader in the industry.


Competitive Advantage through ML & AI - Don´t miss your Chance!

See examples on how various industries create value using highly advanced algorithms, well prepared data-sets and controlled application spaces.


Getting into AI requires a project, data and some champions around the organization. From a project management perspective, many traditional aspects need to be considered as well (regardless if you go waterfall, agile or something new): you want to clearly understand the business objective, you need to identify the gap (starting where you are right now, going to somewhere new) and its consequences, you need to get your data and data-sets right (streamlined, integrated, cleaned, prepared), you want to have proper controls and exit strategies in place, you want to create a change movement that aligns your organization to whats coming and last-but-not-least you want to make sure that you have skilled, available and motivated experts on your team. This may not sound like a milestone plan, but here´s your first lesson learned: The AI will tell you when its done!


If you look around you may spot a relatively high number of cases, where the results or successes in AI developments are leave a lot of room for improvement, the put it mildly. Clearly there are some projects that have a lot of success, but there are two or three times as many, that aren´t getting anywhere. What´s the difference you may ask. Interestingly, the difference is neither the tools and technologies used (they all us pretty much the same) and it is also not the skills, experience and capabilties of the team-members (they are all experts). So what is it? Bottomline: what makes an AI project work is the data, not the code, the model or the desired functionality. There is already wide agreement that projects need to move from a model-centric to a data-centric approach. But this move also needs to take place in the heads of those who manage the project, especially the stakeholders and customers (internal or external). If you agree that the benefits of an AI is the insights that it gains from its data-sets, then you are already one step ahead.


What sounds rather discrete will eventually turn out to be extremely pervasive and all-encompassing. Except for when the project is closed-up and focussing on something that has no ties into improving the way the organization works, the need for high and tight integration is eminent. Large Business Intelligence projects have over and over shown how important a well planned data strategy (along with consideration of its heterogeneous systems, bespoke interfaces and many satelite projects) is. Without all hands on deck (technological, resource-wise and political) the project is doomed to fail. A BI project may still come to life in a reduced and isolated kind of application, but that´s limiting its potential. The difference is the horizon of data that is needed. While a BI project reports on the status quo, the Machine Learning and Artificial Intelligence objective is different: its about using as much data as possible, in a cleaned-up and smoothly prepared way to predict, judge and eventually derive decisions from it. Not only is the value of such projects greater, when the data is complete, comprehensive and conclusive, but also trust in its findings increases when each and every aspect is included. Any algorithm that is targeted for autonomous decision making, needs to create confidence and a sense of security, even though, eventually, it may mature beyond what is humanly comprehensible.


The question that it all comes down to is "what would an AI do?" If we accept that, like in the case of the algorithm that beat human players at "GO", at one point the algorithm reaches a maturity that will be incomprehensible by us humans, then ultimately we need to think about what may happen, once the AI if ahead of us. Will we be able to control anything at all? Of course, many applications restrict the AI to just a few functions. But some might not. Lakeside Analytics offers a 2 hour Seminar leading you through a number of already existing AI applications, with a focus on the perspectives that each area offers as well as a critical look at what it takes to stay in control. Contact us at info@lakeside-analytics.com.

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