As much as we hear about data science and artificial intelligence, not even 10% of companies have implemented any form of it in their organization yet. It’s not like there aren’t options available, as there are countless solution providers that offer machine and deep learning, NLP, and automation services for interested companies. If that’s not the issue, then what is? Here are a few reasons why more companies aren’t implementing AI as much as they could be.
You don’t know what they want yet
Where do you even get started with implementing AI? Between automation, sentiment analysis, analytics, and more common processes, many businesses aren’t even sure what to do. Take time to think about what’s realistic i.e. “low hanging fruit” and take it from there.
You don’t have control over your data
You have some type of data. Somewhere. Somehow. You think. Right? Each organization is different and has different forms of data – which would infer what you do with it. Maybe you have lots of text data in the form of survey responses, or maybe you have purchase history from thousands of customers. Take the time to really decide what you want to do with your data – once you take inventory of it.
You suck at hiring
You’re not a data science pro yourself, so maybe you’re not the person who should be doing the sentiment analysis. You’ve put a few job listings out there, but they’re vague, self-contradictory, and describe a job that doesn’t even exist. Do your homework, look into different job titles, and decide what you want your next hire(s) to focus on. You don’t hire a writer and expect them to do graphic design, so you wouldn’t hire a machine learning engineer to perform text analytics.
You aren’t thinking about your end goals
You shouldn’t be implementing AI just because all the cool kids are doing it. You can’t just “throw some money at a machine learning service” like throwing out a bad marketing campaign. It’s not so simple. Decide what you want as the end result. Do you want a streamlined customer experience? Do you want to automate some internal processes? Decide what you want and then figure it out from there.
Your leadership isn’t up-to-date
Decision-makers should be fully aware of the decisions they’re making, including deciding what to do in regards to AI. This doesn’t mean that every decision-maker needs to get a degree in AI, but they should know the key terms, the industry, the goals, possibilities, and so on. Talk the talk so you’re making the right decisions moving forward. Don’t blame the interns if it all goes wrong.
There are too many platforms
Choosing an AI product or service isn’t like picking a new game console where there are only a handful of options. There are countless amazing AI products and services out there, all with their own pros and cons for each purpose and organization. Do you want a simple one for sentiment analysis? Do you want to completely restructure your CRM to be more automated? Do your homework, learn about AI solution providers, and do your reading before making a purchase or signing a contract.
Learn about Implementing AI and AI Solution Providers at the ODSC Europe 2021
If any of these problems sound familiar, then maybe we can help. At ODSC Europe 2021, we have our Virtual AI Expo where you can see some awesome machine and deep learning, data science, and artificial intelligence service providers. Talk with them, check out demos, get free trials, and learn everything you need to know to make informed decisions that’ll transform your business instantly.
Some partners include
– KNIME: Builds software to create and productionize data science using one easy and intuitive environment, enabling every stakeholder in the data science process to focus on their other job responsibilities.
– DataRobot: DataRobot offers an advanced enterprise AI platform that democratizes data science and automates the end-to-end process for building, deploying, and maintaining artificial intelligence and machine learning at scale.
– SAS: Offers a variety of analytics solutions that help organizations derive greater value from their data, identify what is working, and what needs to be improved.
– Microsoft Azure: Achieve your goals with the freedom and flexibility to build, manage, and deploy your applications anywhere. Use your preferred languages, frameworks, and infrastructure to solve challenges large and small.
– ClearML: One open-source suite of tools that automates preparing, executing, and analyzing machine learning experiments. Bring enterprise-grade data science tools to any ML project.
Other partner machine learning solutions include: Neo4j, Appen, RavenPack, HPCC Systems, Manta, dotscience, and more to come.