eWeekChat

A6. Enterprise AI is driving the development of cloud and cybersecurity technologies these days

A6 • AI can be used to identify potential threats, detect malicious activity, and respond to security breaches

A6. • AI can automate many processes involved in cloud and cybersecurity, such as patching, compliance, and vulnerability scanning

A6: Any sector & technology can stand to benefit from #AI. AI tools can provide visibility & generate proactive insights or automatic remediation across the entire application structure, from cloud to data center to security monitors.

A6 AI can be used to automate the configuration and deployment of cloud services and to monitor and analyze user behavior

A6. AI-driven analytics can be used to gain insights into user behavior to better protect systems and data

A6: Cybersecurity solutions have been adopting AI effectively for some time now. We can expect AI to seep into every enterprise stack rapidly - cloud, CRM, marketing, middle office etc.

A6. In networking, cloud access gives u scalability & crowdsourcing as storing data on hardware is hard. You’ll also want models that provide network insights that incorporate security and app level behavior & anomalies.

A6. Another option that #cloud provides is the ability to learn from other. Peer insights is really useful for optimization purposes...

@rfpioinc Spot on. In our space its logical to go from network to adding security insights...

Q4 well let's chat about bias, in reality, the everyday data the organizations collect is poor and holds no significance of its own.

A4. When addressing AI challenges, a disciplined approach is essential. Early in the project it is essential from a perspective of getting the right data sets together, doing the data wrangling, and being able to build the models with the associated exploratory Data

A4: Focused vendors with data advantage will dominate their corresponding verticals.

A4 Analytics...

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@BMCSoftware Good point - Data Scarcity!

A4. o Having a process where you are doing this in a disciplined and defined manner is uncommon but it is a differentiator for us.

A4. It takes time, but a before & after visual helps. From an org’s perspective, seeing how problems are solved more quickly or that a help desk is taking fewer calls is key. The ability to measure the effectiveness of #AI (or anything for that matter) can’t be beat

A4. o Once you start scoping up, then the data engineers become even more important especially when you have demanding real time needs, audacious throughput, and the dynamic scale up and down of large parallel processing environments as most of our clients do.

@Trentf_CA Definitely agree--being able to measure efficacy and results is vital.

@bonnieholub often an afterthought.....IMHO analytics need to go hand in hand with the ML

@PhilipAWCooper agree

A8. We’ll be having the same conversation but the goal posts will have shifted again!

A8. I think it will be a disciplined domain with key sub-skills that are being applied now by data scientists. I believe it will also include knowledge, engineering environments, and other intellectual power tools to make data scientists even more productive

A8. More scale; more use cases; more creative applications - the bar will be raised. Laggards will be out-competed.

A8: We’ll see #AIOps morph into #ServiceOps. AIOps takes monitoring data & uses it to find the root cause, whereas ServiceOps takes observational data & imbues it with ticket data to create better efficiencies between people & technology.

A8. Low- and no-code tools will be the norm allowing non-technical folks to create fantastic solutions.

A8. Hope that data from more parts of an org are brought together to help decipher where #AI helps. i.e., in addition to network & endpoint data, leverage user feedback, weather reports to re-route WAN traffic if needed... The world is data, so use it

A8 hell might even will become a part of foreign policy.

A8. LLMs will have run out of training data ;-)

Thanks for the great discussion! Signing off from BMC – Erhan Giral

Thanks for organizing!

Thanks for having us! Wonderful discussion.

@JamesMaguireThanks for organizing this chat. It was a great discussion

Thank you for hosting such an informative discussion!

Thank you for organizing. It is a pleasure to be here.

Really fun; hope it was useful and engaging

A9. If you haven’t bitten the bullet, bite it. You will lose out as the bar rises and laggards won’t be competing on even terms.

A9 i. We are at an inflection point for exponential growth. This is the point where growth changes from looking like it is linear and increasing in a predictable curve to exponential where there are possibilities abound

A9: Trade data volume for insights with #AI. #AIOps is key to manage escalating IT complexity with a proactive enterprise approach & can help #IT teams reduce alarm noise, find root causes faster & determine the actions to take.

A9. Feedback is key. Models and insights are made better when humans tell data scientists and UI experts (if a GUI’s involved) what they like. This is exciting new technology that’s not set in stone so get engaged & make #AI what you need it to be for you

A9. Don’t be dogmatic about build vs buy – if your business applications have solid embedded AI capabilities use them; build what’s unique to your business.

A9. ii.This inflection point was predicted by Hans Moravec in his book Robot: Mere Machine to Transcendent Mind. Hans had predicted that we would be at this curve now so it might not be a huge surprise to people who have read his book but it is a very exciting time

A9 We’ll see a greater emphasis on responsible AI and ethics. That is going result in some great conversations

A9. If you want to drive productivity this is your #1 tool. Human + machine wins....it's what humanity has done for millennia, embrace it.

A3. Different concerns for various challenges. When it comes to operating a network it’s not cost as you get #AI in your mgmt solution. It’s getting Admins comfortable with trusting the insights & using #AI to augment what they do.

A3 Computing Power is huge and I would also say trust..How a specific set of inputs can devise a solution for different kinds of problems

A3 Costs are usually justifiable, I think Bias is the biggest issue

A3: (1/2) Cost will always be a factor, but with huge surge in operational data volumes, the real struggle is for #IT teams to identify #AI solutions that align their silos/sources of truth.

A3: (2/2) Traditionally cost have been a huge issue, mostly aggravated by data quality issues. That’s why we had to normalize and integrate all observability data sources to a unified model before we an apply #AI.

A2 Generic Answer - AI model adoption, business goals, and user acceptance. Real answer, reduce costs.

A2: Generative #AI (#ChatGPT & LLMs) have clearly been a focal point for the last few months. Applying this to enterprise scenarios like extracting useful information from logs & tickets to answer user queries/suggest the next best action.

A2. Fortunately, it’s not all about #ChatGPT & #AI bots. 8-) I’d say that automation & ability to create efficiencies is driving adoption. In networking space the idea of delivering an improved service level experience without a major overhaul or spend

@BMCSoftware I'm seeing ChatGPT confuse people so we're having to educate our sales ppl to explain differences in what we deliver