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A8: The economy today is a good forcing function for enterprises to stop treating AI like a toy.

In 3 to 5 years, enterprises who pivoted from building their own tools will advance much rapidly as they focus on delivering real results rather than just doing lab research.

A8: The market and competition will solve ML. Innovators will begin to properly position themselves in the DL and true AI segments.

A8: There is a lot of potential for #AI – especially its predictive capabilities. Additionally, we expect to see AI ethics standards proliferate among the tools & consortia as governance around AI starts to codify.

A8: It will certainly grow but it will need to adapt to be more explainable and proactively alert organizations for bias in their models/data.

A8: My prediction/hope is modest. I think we'll see more standardised practice around MLOps and DataOps. And I think a corollary of that is an easing of the deployment gaps so many orgs are currently grappling with.

@BCorrIoT "governance around AI" -- that's a big topic.....

A8: I guess we will see iRobot come to life… no. No. No! I suspect you will see remote work grow, while production increases. As inflation continues, more companies will be looking for cost savings which AI can deliver. Those who remain stagnant will be beat!

@RyRaiker Good point on inflation driving AI adoption. Clearly yes.

This is a really good point. I think that governance is both internal and external (i.e. from regulators and civil society-based auditors)

Yes @JamesMaguire we are seeing this in conversation with both private and public sector customers.

@BCorrIoT This is a really good point. I think that governance is both internal and external (i.e. from regulators and civil society-based auditors)

A8 (continued) I think these AI tools will be driven by the rise and commoditization of process mining which will mean process and data understanding for every business operation and tech stack.

@BCorrIoT Still on the governance front. I really hope regulation keeps up. I think it's a useful forcing function for building better, more robust AI.

Exactly @Adewunmi , in fact this is critical for AI to gain wider adoption.

@victorthu but why hasn’t it been seen yet?

@Adewunmi Regulation varies from region to region. For example the EU is getting ahead of this problem right now.

@Adewunmi I'm pessimistic about AI governance. Too much money, too many ways to work around regulators. And what's their authority?

@RikChomko Yeah, I agree. I think the path to this is a holistic one though. I don't think we'll see tools emerge that do this well i.e. no silver bullet. I think better ML practice is at the root of this and the cost and pain of maintenance may help drive this too.

@Adewunmi the EU has done a really amazing job of looking at these angles https://www.abbyy.com/blog/legal-regulation-of-art...

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

A6: One challenge in deploying #AI & #ML for analytics is that the analysis is only as good as the #data & the data science. Applying algorithms on unprepared data is likely to find patterns, but patterns without purpose are not the goal & won't provide value.

A6: #AI with data analytics is a must – it’s how you’ll report on KPIs, know when, where and why human intervention is needed to continuously improve AI models. Peter Drucker said you can’t improve what you don’t measure (or something like that lol) match made!

A6: I do! I think both data scientists and business analysts are seen as 'end users' of data ops and we are seeing more data engineers in customer orgs asking about how to avoid duplicating effort when it comes to serving them

@RyRaiker Yes, the famous Drucker quote is at the center of AI and data

A6: AI is critical to getting insight out of big data-level volumes. AI will help well-staffed companies deliver the unmet promise of data analytics.

@RyRaiker marry them and never divorce them, it will only get better! #artificialintelligence

A6: We see #AI & data analytics playing a huge role at the edge as data is parsed and acted upon in settings like smart buildings and factories.

@RyRaiker A marriage made in heaven :)

@BCorrIoT But can the edge devices crunch the data at the needed level? (I think so...)

A6: Sojust for reasons of efficiency, there'll be at least a union of processes/tools etc. Also, I think the output of these two disciplines are often complementary and ideally should be used together. I think as decision makers get more savvy, they'll do this more

@Adewunmi Well here comes #processmining to solve some of those challenges

A6: I also think this union speaks to the growing demand for more democratisation of AI

Agree, we are seeing more and more sophistication of both processor, battery and storage in modern-edge devices across industries (eg. computer vision in smart cameras).

@Adewunmi SMBs and citizen developers want more of the democratization of AI.