eWeekChat

A2: Companies’ comfort level with AI is still very early. I was recently talking to an enterprise that is known to have a massive AI team and have done a lot to incorporate AI in their business. Only to find out most of the AI models are still in the lab.

A2: #AI was once considered a back-office fundamental. But with the accelerated pace of digital transformation, #IT service & operations teams are using #AIOps to become more agile & proactive, to better anticipate challenges.

@victorthu Not surprising at all! But perhaps a waste of money??

A2. Companies are starting to get more comfortable with AI but it still has a ways to go. A recent Forrester study revealed that in 2019 only 54% of companies were using some form of AI, in 2020 that number grew to 69%. So again movement in a positive direction.

I think there's a spectrum here. I thinks orgs are definitely comfortable with the *idea* of AI. So there's maturity in that sense. I don't think we have hit maturity when it comes to identifying all the possible ML use cases within their operations.

A2: The #AI market is still very nascent, especially when operationalizing #IT.

@BCorrIoT #AIOps definitely on the upswing.

A2: Having said that, conversations about AI are getting easier, more realistic and more holistic as well

Yes, @JamesMaguire, there's definitely a sense of concern especially the lack of ROI with such a huge investment. So if a large company is struggling, the smaller ones will feel the pain even more acutely.

A2: They’re getting practiced in ML mostly. DL and true AI are frontier tech far from commercialization across the market.

@victorthu AI still feels like a tool for larger enterprise. But the SMBs are eager to catch up.

A2: By holistic, I mean customers are more open to thinking about AI as much more than primarily model development.

Yes @Adewunmi , very true. Compared to a few years ago where many AI vendors are selling vaporware vs. today more enterprises are better educated on the topic.

definitely. A recent study showed that 41% of enterprise IT use over 10 tools for performance management and monitoring. Hence the need to introduce #AIOps.

A2: Companies have become more comfortable using AI within the past 3-4 years, mainly attributed to the popularity of RPA. But, by itself RPA is not AI

definitely. A recent study showed that 41% of enterprise IT use over 10 tools for performance management and monitoring. Hence the need to introduce #AIOps.

@BCorrIoT That is a blizzard of tools. I think larger companies in particular want "best of breed" on everything. Which does drive AIOps.

@BCorrIoT Companies have also become comfortable using chatbots, but those who are digital natives spot them right away and the younger population gen-z and millennials tend to hate them! #ai #eweek

A2: maturity comes from collaboration and an ecosystem but not being locked to underperforming tools.

@RyRaiker The problem with chatbots is that they are artificial but not intelligent ;)

@RyRaiker not to mention increased collaboration between business and IT professionals in digital business initiatives demands new practices, policies and technologies.

A4: An audit can help pre-determine what a successful #AIOps implementation looks like for an organization. This includes aligning your AIOps initiative to address current needs such as more effective event noise reduction & faster probable cause analysis.

A4: Start with the fundamental. What business problem are you trying to solve and why AI. Some business challenges may not require sophisticated AI models.

A4: This is a little counterintuitive but I would say don’t focus on just AI deployment. Take a holistic approach and assess your entire machine learning lifecycle. Think MLOps alongside questions of how can I deploy more quickly and effectively.

@victorthu I agree. Just because you have a AI hammer, doesn't mean every problem is an AI nail.

A4: Data drives change! Organizations need to look at how processes are actually occurring. This is done by looking at the timeline of events in a process. It can also find blind spots and common bottlenecks prime for AI fixes

A4: Invest in DL and true AI now as a differentiator, as ML know-how and execution will become more standard and buyable.

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@Adewunmi It's true that AI strategy has to consider the whole. It's not a stand alone project.

@BCorrIoT I always love this saying. People think they solved it all with one tool - they say it takes a village and it does, it truly does

@BCorrIoT Hard agree! And I think an excessive focus on PoCs can sometimes make it easier to miss non-viable ML projects/ use cases

@RyRaiker " Data drives change!" Basically the motto of our era.

And the other thing is, don't fall in love with super high accuracy with your models. Sometimes the actual business results between 80% accuracy and 90% accuracy is not material. It's better to just deploy them!

beyond all that .. maybe “keep it simple” - You know … “hey team let us not try to boil the ocean” even small AI wins can make a major impact on the enterprise

Yes, and that means data ingestion, it means recognising and planning for differences between the conditions in your development environment versus production. It also means taking a more cross-functional team approach to developing AI solutions

Exactly @RyRaiker!! You can definitely get AI wins starting with taking smaller bites.

A7: Specialists that solve a level of the tech — ML, DL, AI — with clear applications and business cases.

A7: The big cloud guys have built a series of complex tools that require expertise to assemble. Like supermarkets offering just anything as long as you have chefs who knows how to cook.

Instead, the more exciting players are the startups that are providing gourmet meals!

Q7: it might not be who you suspect. But the best way to evaluate leaders is through analyst reports. AI is an umbrella tech that covers a variety of enterprise tech. and it plays a huge role in intelligent process automation.

@victorthu You mean low code no code?

A7: Well, for obvious reasons I would say Cloudera :). One reason for that is that data is so critical to delivering high-quality AI. Making data ingestion as easy as possible regardless of where that data sits - on prem or in the cloud, is the first step

@RyRaiker on the note of analysts : In RPA for example, Gartner has recognized UiPath, BluePrism and Microsoft as leaders.

A7: @BMCSoftware is a proud leader in this market & deeply committed to #AIOps. Our AIOps solutions apply #ML & predictive capabilities across IT Ops & #DevOps environments for real-time, enterprise-wide observability, insights, & automated remediation. (1/2)

Not exactly @JamesMaguire. There are tools offered by startups today that allow AI models to go into production quickly without having to assemble your own infrastructure.

@RyRaiker For intelligent document processing, Everest Group has named ABBYY and Automation Anywhere as leaders, and in process mining NelsonHall recognizes ABBYY.

A7: I also think while the plethora of highly specialised, point solution MLOps tools are exciting, maturity demands consolidation and a platform approach is one of the best ways to do that.

Amazon, Google, and Microsoft all continue to lead in enterprise AI with a large portfolio, often through acquisition or through investing in R&D.

A7: It's a large and diverse set of tech and depends on what you want to achieve. Some are good for out of the box regression models for getting started and other are better at more complex uses cases like finding similarities in large sets of data.

@RyRaiker Many would argue that the cloud players will "win" the AI war. But -- there are so many sectors, they can't prevail in all of them.

A7: significant considerations for #AI are cross-domain observability & actionability, parsing out event “noise”, and intelligent alerting & #automation. (2/2)

cloud is a must, but even startups can access and deploy cloud first

Agree, the cloud players provide the infrastructure but not the end-to-end for complex business problems.

@RyRaiker I think this is true. I also think though that analyst results serve more as a north star. Orgs still have to develop the skills to assess their own needs against the features of the product. That means they have to have the in-house talent, or buy in help, to do so