eWeekChat

Expanding Your AI Deployment
JOIN US: Discuss strategies for building out your enterprise AI usage.
   a month ago
#eWeekchatData Analytics JOIN US: Discuss the challenges, potential and best practices for data analytics.
James Maguire
Q2. What’s companies’ comfort level with AI? Is there anything approaching maturity?
Victor Thu
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.
Bill Corrigan
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.
James Maguire
@victorthu Not surprising at all! But perhaps a waste of money??
Rik Chomko
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.
Ade
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.
Bill Corrigan
A2: The #AI market is still very nascent, especially when operationalizing #IT.
James Maguire
@BCorrIoT #AIOps definitely on the upswing.
Ade
A2: Having said that, conversations about AI are getting easier, more realistic and more holistic as well
Victor Thu
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.
Chris Ehrlich
A2: They’re getting practiced in ML mostly. DL and true AI are frontier tech far from commercialization across the market.
James Maguire
@victorthu AI still feels like a tool for larger enterprise. But the SMBs are eager to catch up.
Ade
A2: By holistic, I mean customers are more open to thinking about AI as much more than primarily model development.
Victor Thu
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.
Bill Corrigan
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.
Ryan Raiker
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
Bill Corrigan
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.
James Maguire
@BCorrIoT That is a blizzard of tools. I think larger companies in particular want "best of breed" on everything. Which does drive AIOps.
Ryan Raiker
@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
Ryan Raiker
A2: maturity comes from collaboration and an ecosystem but not being locked to underperforming tools.
Bill Corrigan
@RyRaiker The problem with chatbots is that they are artificial but not intelligent ;)
Ryan Raiker
@RyRaiker not to mention increased collaboration between business and IT professionals in digital business initiatives demands new practices, policies and technologies.
James Maguire
Q4. What advice would you give companies to help expand their AI deployment?

(edited)

Bill Corrigan
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.
Victor Thu
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.
Ade
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.
Bill Corrigan
@victorthu I agree. Just because you have a AI hammer, doesn't mean every problem is an AI nail.
Ryan Raiker
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
Chris Ehrlich
A4: Invest in DL and true AI now as a differentiator, as ML know-how and execution will become more standard and buyable.

(edited)

James Maguire
@Adewunmi It's true that AI strategy has to consider the whole. It's not a stand alone project.
Ryan Raiker
@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
Ade
@BCorrIoT Hard agree! And I think an excessive focus on PoCs can sometimes make it easier to miss non-viable ML projects/ use cases
James Maguire
@RyRaiker " Data drives change!" Basically the motto of our era.
Victor Thu
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!
Ryan Raiker
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
Ade
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
Victor Thu
Exactly @RyRaiker!! You can definitely get AI wins starting with taking smaller bites.
James Maguire
Q7. What vendors are the leaders in the enterprise AI market, as you see it? Why?
Chris Ehrlich
A7: Specialists that solve a level of the tech — ML, DL, AI — with clear applications and business cases.
Victor Thu
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!
Ryan Raiker
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.
James Maguire
@victorthu You mean low code no code?
Ade
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
Ryan Raiker
@RyRaiker on the note of analysts : In RPA for example, Gartner has recognized UiPath, BluePrism and Microsoft as leaders.
Bill Corrigan
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)
Victor Thu
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.
Ryan Raiker
@RyRaiker For intelligent document processing, Everest Group has named ABBYY and Automation Anywhere as leaders, and in process mining NelsonHall recognizes ABBYY.
Ade
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.
Ryan Raiker
Amazon, Google, and Microsoft all continue to lead in enterprise AI with a large portfolio, often through acquisition or through investing in R&D.
Rik Chomko
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.
James Maguire
@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.
Bill Corrigan
A7: significant considerations for #AI are cross-domain observability & actionability, parsing out event “noise”, and intelligent alerting & #automation. (2/2)
Ryan Raiker
cloud is a must, but even startups can access and deploy cloud first
Bill Corrigan
Agree, the cloud players provide the infrastructure but not the end-to-end for complex business problems.
Ade
@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