Enterprise ai
Here is my choice of the machine learning and artificial intelligence books to read if you start with the subject of artificial intelligence. These are not academic textbooks.

Enterprise AI — what you should know

Businesses across the globe are investing a lot of money in artificial intelligence (AI). According to IDC, the worldwide spending on artificial intelligence is expected to reach $37.5 billion by this year. The spend on AI is only expected to reach $97.9 billion in 2023. What we are essentially seeing is a CAGR of 28.4% over the 2018-2023 period.

This massive rise in AI spend is partly due to a growing realization of AI’s potential in driving business growth and a growing fear of missing out on these advantages that AI has to offer. Businesses have become to acknowledge the competitive advantage they have over their peers, thanks to AI. Most early adopters of AI claim that the technology is empowering them, making work better.

By 2023, AI and deep-learning techniques will be the most common approaches for new applications of data science. Gartner

This is true because cognitive technologies have a business-altering potential. Despite how promising AI can be, only a few organizations seem to be successful in building and implementing this technology. According to Gartner, 46% of CIOs have plans to implement AI but only 4% have brought this to reality.

The success of AI depends largely on its execution. If done right, it can help enterprises remove friction in their business processes and jump ahead of their competition. Large organizations need to be proficient in a wide range of practices such as strategy, choosing the appropriate problems, building a strong data foundation, and cultivating the mindset to be open to experimentation. Only then can enterprises experience success with respect to AI.

5 Things You Should Know About Enterprise AI

  • Future-proofing your data strategy
  • Prepare for the long haul
  • Put humans at the center of your strategy. Always.
  • Build a diverse team
  • Put data at the forefront

#1 Future-proofing your data strategy

For AI to be enterprise ready is to future-proof your data strategy a well-connected data strategy. A connected data strategy requires that data can be securely ingested, the intelligence is actionable, and there is context to the interpretations drawn from data. These questions will help you check whether your data strategy is future proof.

  • Can the platform enable you to convey information using data easily?
  • Can you detect patterns quickly in the data?
  • Can the platform provide real-time intelligence?
  • Can you connect data from various sources for a 360-degree view?
  • Is the platform built on open source technology?
  • Are all compliance regulations taken care of?

#2 Prepare for the long haul

Successfully building an AI system and implementing takes time. Enterprises usually have access to a lot of data, but the challenge is that this data is usually siloed. Smart enterprises that are aware of this short-coming, take a multi-year approach to acquiring data and building a strategy. This means, collecting and compiling data from various sources and over a long duration of time.

When preparing for the long-haul, another aspect that needs to be considered is the data storage. It is sensible to opt for cloud-based offerings. Cloud-based storage allows enterprises to explore diverse use cases as the AI strategy progresses over a long period of time.

#3 Put humans at the center of your strategy. Always.

AI is not meant to replace humans. It is only meant to enable humans to focus on higher impact tasks and create more job opportunities. A successful implementation of AI should do just that a partnership with humans and not a replacement. A report by ZipRecruitershowed that 81% of employers prefer to hire a human over implementing a fully autonomous system. The report also stated that AI could replace only 15% of the work human employees perform. A right balance is needed between using AI to automate tasks and to augment the capabilities of the workforce.

#4 Build a diverse and multi-disciplinary team

Enterprise AI requires a more holistic approach because the overall project combines efforts in collecting data from siloed sources, mining data, strategizing the efforts, identifying patterns, writing algorithms, testing, implementation, and automation. People who are experts at each stage of the process are required to create and deploy a fully functioning AI system.

Here are the 18 skills needed for the successful deployment of an AI system right from ideation to delivery:

  • Architecture
  • Infrastructure Engineering
  • DataOps
  • Machine Learning Science
  • Machine Learning Engineering
  • DevOps
  • Backend Engineering
  • Frontend Engineering
  • Security Engineering
  • Quality Assurance
  • Release Management
  • UI Design
  • UX Design
  • Project Management
  • Product Management
  • Technical Writing and Documentation
  • Compliance and Legal Operations
  • Business Leadership

#5 Put data at the forefront

Artificial intelligence is shaking up businesses and this momentum will only build and get stronger as we step into 2020. Hence, it becomes essential that enterprises empower employees within the organization and encourage them to think about how data and technology can be leveraged to build a smart business. Despite all this, data will remain at the forefront as artificial intelligence evolves. It is an irreplaceable driving factor for the success of AI. Enterprise need to begin taking a smart approach to data right from now in order to reap the benefits in the near AI-powered future.

Closing thoughts

Enterprises will continue investing in AI and keep a close eye on the returns this technology gives. There is a significant amount of effort out into improving AI capabilities, giving businesses the competitive edge. It is important to address some of the common and vexing questions that most enterprises have.

Should we make incremental investments in AI or aim to position ourselves as experts in AI initiatives?

What would be a better option to pick and develop from the existing pool or talent or seek external help?

Should we face the complexities that come with AI head on or be more cautious with our approach?

The answers to these questions depend on where the enterprise is in its AI journey and how it wants to tackle these impending challenges. A good way to approach this by examining early adopters of AI and how they have fared in their AI initiatives so far. This way informed decisions can be taken to leverage the benefits of AI.

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