Artificial Intelligence & Machine Learning (AI/ML)

Artificial Intelligence & Machine Learning (AI/ML)
How do you move your AI workloads to the cloud?

Top Answer: AI workloads are best suited for cloud and the journey should actually begin from Cloud native platforms like tensorflow, AWS sagemaker, Azure ML etc. These platforms are built to handle massive data workloads and can expedite the training cycles for ML.

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If you had a magic wand - what's the #1 daily business challenge you'd eliminate?

Top Answer: Without a doubt - Technical Debt! It's a ball and chain that creates an ever increasing drag on any organization, stifles innovation, and prevents transformation.

If you are a current SAP customer, when do you plan to migrate to SAP S/4HANA?

Top Answer: No plan to migrate soon.

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What are real ways enterprises will consume AI in %YEAR%?

Top Answer: Many AI-based applications are finally making the jump across the hype cycle from ‘promising’ to ‘delivering’ real value. Chatbots are one that have made a dent in 2017 and will continue to do so in 2018. Messaging has proved to be a simple, and extremely effective way to communicate and interface with information. Message based apps leverage a commonly used medium to provide a conversational interface to complex workflows. This has already dramatically simplified how enterprise users interact with applications and collaborate at work, especially as more and more millennials enter the workforce with drastically different expectations for software than their prior generation.

But adding AI to chat takes us beyond just simple question and answer commands. Instead of typing “File HR Ticket” to a SupportBot, an employee can simply describe their actual challenge, e.g. “what is our maternity leave policy?” Bots can handle complex NLP, decompose the question, search for potential answers across multiple applications/ backends, and instantly provide the user with the right answer. This greatly simplifies workflow and massively boosts productivity and customer/ employee satisfaction.

Zooming out a bit, the bigger picture going into 2019 is very clear. AI will be built in as a layer across every application going forward — just as “mobile” used to be a standalone category, it is now seen as a necessary component of any software product.

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Innovation:  AI Investment and AdoptionInnovation: AI Investment and Adoption

AI/ML investments are well underway at leading companies and show no signs of slowing. But are these massive investments generating the business results they promise? Your IT peers tell all.

Thinking about deep machine learning, how ready is your data (cleaned, prepped and labeled) for compute-heavy data models?

Top Answer: Probably depends on age and size of business. Ours has divisions over 100 years old or acquired less than 2 years ago. We struggle with data quality even within a division. Note that our smallest is probably 25,000 employees and largest 40,000. Total company 130k people, 15 major ERPs and probably 20 minor ERPs. We still have debates on Customer address and shipping fields sometimes during consolidations. My estimate 35% of the data we would like to use is ready

What are your biggest concerns as AI capabilities become a standard industry offering? 

Top Answer: If AI is done right, it's a huge opportunity for technology; if we get it wrong it’s a huge slippery slope. There's already been evidence, in some cases, of the consequences involved if we mess up broad aspects of AI: a few years ago, Microsoft put out a chatbot that didn't have the right monitoring and people were seeding it with harassment and hostile language. That basically trained the chatbot to say inappropriate things back to people because people had flooded it with all this negative stuff. There have been instances of AI that have already had severe consequences, like the criminal risk assessment algorithms that are used by some courts to determine if people will reoffend. There was a research study done by a group of PhD students that found that this system was substantially discriminatory against African-Americans because of the way in which the data was put into it. It used machine learning to determine somebody's likelihood to recommit crimes based upon the anchor point of a historical bias, which the machine then learned to continue going forward. I've worried a ton about that at Cylance and even during my time at Intel.

Will AI Ops take IT jobs away?

Top Answer: I think IT professionals in general are very fearful their jobs are going away. The message that I would deliver is AI-Ops can help make you look smarter, augment you, and make you significantly more efficient in meeting your OKRs. Don't be fearful of it. The Overlords are not coming for your job.

Do you use AI to manage IT complexity?

Top Answer: You may look at IT4IT framework for more structured approach for managing ITs own complexity