The world of artificial intelligence is hyperactive and constantly evolving. Is there a day gone without you hearing about #AI? Certainly not if you follow our feeds :-) (biZNov blog, linkedin biZNov, twitter @biZNov_fr).
Constant monitoring and relentless questioning, this is the mindset that must be displayed to develop and implement a winning AI strategy adapted to your business. It is because AI has demonstrated its positive impact on many business, in many domains, that it is worth the cost of investing the energy needed to constantly monitor this subject.
Today, there are more and more "on-shelf" solutions in SaaS mode, with "ready-to-use" features. This makes the adoption much easier.
There are of course many software development services companies that offer to build your custom AI.
And then you have the option to build your homegrown solution with your team. How do you get started? What skills/tools will you need in the short, medium, and long term?
What justifies you should be moving towards one solution or another? First, consider your business strategy, your company valuation, your customers, and your means of differentiating yourself from your competition. Also unique, are your ambitions.
Is there an off-the-shelf solution that fits the unique needs of your business? An impressive number of solutions are already listed: already 546 by the end of 2020 for Europe only (see the pictures at the bottom of this article from European AI Startup Landscape ). How do you identify the right solution among this forest of existing solutions?
What is the level of adaptation is required? How does this solution enable your employees to be involved in the overall system performance? Does it include an active human-in-the-loop improvement capability? What solution is tailored for the volume of data of your company? What infrastructure to use (on premise, cloud, hybrid), for the learning phase, for inference? Once deployed, and your company is threatened to become a unicorn (and we wish so ;-) ), will it scale accordingly or will you have to start over?
And then, assuming we have found the ideal solution on paper, how much does it cost to develop, adapt, deploy and also maintain? Because your company's environment data is not frozen, how often should you update the prediction/classification/aggregation models that are the basis for the new services you are going to create?
If you need your custom solution, who is the right partner to process your data from yesterday and tomorrow? Does this partner use algorithms that are state-of-the-art and/or best suited to your constraints? Are this partner's teams skilled to listen to your needs, with the right AI/ML expertise? Are relevant open source tools used?
Are the solutions or providers used capable of guaranteeing the security and confidentiality of your business' data and your customers' data?
And tomorrow, will the algorithm used to train your models always be the best answer to your needs or will you have to migrate to achieve even better performance? Will the initial return on investment always make sense or justify stopping, or evolving your services?
All of this is a lot of questions to ask, but it's because we ask ourselves the right questions, we find the right answers and we give ourselves the best chance of success (50% of AI projects still fail in 2020).
It is very complicated to adopt #artificialintelligence, to deploy it and secure all the expected benefits. It is a nascent industry with scopes in all areas, this explains the abundance of information on the subject.
biZNov is here to help you sort thru all the noise, avoid all the pitfalls and make your AI project a success.
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