Artificial Intelligence

Artificial Intelligence
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.

Is more data always better?  Are companies collecting an unnecessary amount of consumer data?

Top Answer : The core of a CSO role is effective risk management and risk tolerance: business empowerment and risk tolerance alongside it. And I think if the perception around data is simply “more is better, more is more, the end,” it becomes difficult to have an informed risk tolerance around the acquisition of data. If there's no liability and there's a touring test kind of analog that has to happen in the courts for AI and ultimately the company behind it to be rendered liable, I look at this and I think, "What is legal's responsibility here for the implementation of AI?" And then we as cyber practitioners, if there is no liability behind certain algorithmic implications what then, for the larger status? And how do you form risk tolerance atop that.

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How should I manage AI expectations with my CEO?

Top Answer :

When most people ask about AI (Artificial intelligence), they are really asking about ML(Machine learning)[1]. In any case, following are some suggestions on setting expectations with your CEO

  1. This is a capability that is worth developing internally, as in, this is unlikely to be a fad that goes away. So ask for a separate team/budget for this. It does not have to be excessive. Most companies will do well to start with a team of around 5 data scientists/analysts.
  2. Educate your CEO and the e-staff on the reality vs. hype in this space. If needed get external consultant and spend several hours with your CEO on this. Go into the math a bit so it is demystified and does not remain something esoteric.
  3. Lead with business outcomes that you want to enable. ML/AI is not a panacea, it is not going to make you a business that you fundamentally are not. The onus should be on business leaders to think generatively about how ML/AI can help them.
  4. You will have to try large number of projects to develop this muscle. So ask your CEO to be prepared for several failed initiatives. So, set expectation that rapid iteration and quantity of projects is what you will optimize for in the first 6 months.
  5. Set expectation that ML is dependent on availability of data. If you don't have much data, you are unlikely to build a model that generates magic from nothing.
  6. Depending on the technical level of your company, set expectation that there needs to be a cultural change to make data based decisions part of your core DNA. The CEO (and e-staff) should lead by example.

Few things for you to do, watch out

  1. Look hard at data you have but have not been able to use successfully. For instance, many companies have machine sensors that generate large amounts of data that go into a black hole. Revisit these large data stores and ask what new things can be done with them.
  2. ML is doomed to fail if the underlying data is bad. Pay attention to your data infrastructure and get that to a good place constantly. For clarity, I am not suggesting a big bang, "build a data lake" project. But ensure that all data sources needed for business outcomes in #3 are in a good place and then build from that
  3. Often when people say they want ML, they mean they want better analytics as in they want more reliable quicker answers to questions they have. Test if this is the case and you can do really well by hiring strong analysts as opposed to data scientists
  4. Pair key business leaders with analysts who can answer any data related question for them and can educate them on how to interpret data. It is surprising that there are many leaders who do not understand terms like p-value and correlation coefficient. Getting leaders moderately savvy, goes a long way in changing the organization culture.

[1] On definitions Artificial intelligence is the term used when machines do things that are usually considered the realm of humans. Machine Learning is when a machine gets better at performing a task with experience. ML is one of the ways to build AI. Deep learning is a subset of machine learning that uses neural networks.

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Will AI eliminate human inefficiencies?

Top Answer : I for one haven't seen the data that shows AI eliminates a major selection of manual human inefficiencies. I've seen it on individual processes and business use cases, but I wonder if for us as technology and cybersecurity leaders there is a missed cost on the skilling side, the human side, the training and adaptation side for even using this technology. And what role do principals play in that? Do they aid creating structure on the top? Where does that leave us?

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AI startupsAI startups

This report was created to for IT Executives interested in surveying which startups their peers find interesting and have worked with.

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Disruptive TechnologyDisruptive Technology

This report dives into how 450 IT Executives are thinking about disruptive technologies like AI, ML and IoT.

Can AI improve sales operations?

Top Answer : I'm sure the answer is yes, and it's probably yes more than I can even articulate because it's mind boggling what AI will probably be doing in not just 10 years, but 2 years from now. There's some obvious use cases. We probably spend the bulk of our time doing some type of reporting and it's such a time suck. AI is going to, I hope at least, give us so much time back because, A, just the consolidation of data, but then B, telling us what it means instead of having to stare at it for hours at a time.  It’ll automate a lot of our worlds, in terms of some of the fire drills and the tactical stuff that we do day in and day out. This will enable us to spend more time thinking longer term and more strategically. I'm blown away at what it can do today, and that’s  just the tip of the iceberg. I'm sure AI is going to come into everything: compensation, forecasting and analytics, trends, and capacity and territory management, etc. I'm excited, but I don't have a lot of real world, hands-on experience with it at this point.

What are the top three sales ops tools you use?

Top Answer : I feel like a lot of tools over promise and under deliver. And it always comes back to Salesforce. I actually went through experience where, in my previous company, we migrated from Salesforce to Dynamics, and there were some strategic reasons for it. But then what ultimately happened is, two years later, we got acquired and the venture capital firm looked at it and said, "Well, enough of fun, we're migrating back to Salesforce." So two years later, we migrated back to Salesforce. I feel it has this level of simplicity combined with some aesthetic rightness to it. And whoever is trying to build something on top of it, there's all these smart things which start working around it, but it just doesn't have that same flavor. So I can't tell you three, I'll just combine it all to Salesforce.

In the past, black box third party vendors have left security in the dark. How can security personnel ensure that vendors are honest and the AI/ML they are selling is legitimate and ethical?

Top Answer : Be aware of it and encourage a healthy dialogue around what's put on the table. I think  the tangible outcome is if we're communicating frankly to the vendors we're interfacing with that we expect more and would like transparency. I think there's an evolutionary angle and I truly believe there's a tangible good to doing that.

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