eweekchat

A9: Responsible AI will continue to grow in importance with companies becoming more transparent with their AI practices. Organizations will focus more on the ethical data collection, data usage and model outputs.

A9. #DataOps, an agile approach to data management, will be adopted widely by organizations as an enabler of value realization for DnA programs, which have historically been successful approx. 1 in 5 times across enterprises

@unsolvedmistry There is enormous pressure for #ethics in #AI

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A9. Data Thinning. The world can't keep up with data creation due to Edge, IoT, Digital Transformation - analytics software (with the help of GPUs) will gain significant traction next year.

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A9. 2022 is the year that privacy will become much more real for many more people as healthcare data takes center stage. This will drive the convergence between privacy and security - throughout all systems, not just point solutions as innovation and litigation collide.

A9 Climate change & sustainability have taken center stage at the global level. While the tech industry has, in some cases, taken steps to be greener, there will be greater pressure to implement more aggressive steps toward clean tech production & processes.

A9. IT consumption will begin a major shift from a CapEx to an OpEx model.

@Llanor_A How about tech companies helping to SOLVE these problems - that would be even better!

A9. The scale of this years’ exploitations coupled with the consequences of ransomware will renew diligence in patch management

@larry_lunetta hasn't that already shifted?

@brucekornfeld Yes!

@brucekornfeld working alongside legislators, there are answers to be had - let's solve things and not simply complain about them. That's why we are here!

A9. The scale of this years’ exploitations coupled with the consequences of ransomware will renew diligence in patch management #eweekchat

@ktcoppins A9 totally agree on healthcare at center stage... but may mean very different things in US, Europe, developing world!

@Raj_Samani patch management and data classification. Know where your valuable stuff is, protect that.

@szisk which adds a degree of difficulty for multinationals for sure!

@Llanor_A A9 Interesting on climate change, since cloud & blockchain are **huge** consumers of electric power.

@ktcoppins To some extent. It will be much more than simply how to pay for IT: flexible consumption, managed services, etc.

@szisk There will be a reckoning.

@larry_lunetta agreed - still much to do underneath the "Capex/opex" discussion. Thanks for the clarification.

A8 Continue and grow faster. The world hasn't been digitized yet. Paper is still being used in business. Tech continues to make it easier - LC/NC, GPUs for faster processing....the list goes on. We are just at the beginning!

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A8: More companies will continue to learn and refine their AI strategy as this time of digital transformation grows larger. We have seen budgets increase year after year and expect them to continue to rise.

A8. The concept and the execution will continue and accelerate. I hope the term goes away...and I'm in marketing.

A8. Grow. Effects and advancements of pervasive intelligence and enterprise automation will be felt more quickly and globally because capabilities are not only increasing but becoming more significant and measurable

@larry_lunetta Funny

A8 Digital transformation will continue to grow. Many enterprises are still exploring how to effectively & economically implement it as well as carve paths to retiring legacy systems. That will take time.

A8 For many companies digital transformation is an incremental process that will take years.

A8. Not sure how to answer this one, but I will say that there is WAY more tech debt in the world than we think. Lots of legacy (20 year old+) stuff running factories and companies (probably more than one NetWare server out there still serving packets...) So yes - will continue

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@larry_lunetta A8 Digital transformation is already way beyond markting boundaries, though. Spoke w/ a big CPG today doing virtual product testing.

@ktcoppins No doubt the enormous tech debt -- lurking, and growing...

A8. COVID changed all the rules and forced transformation on every organization so it is now a fact of life.

@larry_lunetta Yes, COVID accelerated digital transformations and AI strategies for many organizations.

@Llanor_A Slow transformation is a painful (but true) message and a kick in the pants. Big Gap between aspirations and reality!

@jamesmaguire there is a reason why the mainframe will outlive many of our careers

@larry_lunetta Every company is a tech company

@ktcoppins Agree, the challenge now is to manage multi-generational IT while transforming.

@szisk Yes. There is also the employee buy-in factor. People have to be able to effectively use these systems.

@larry_lunetta I like that term - will use that instead of "Digital Transformation" and move on to "migrating away from multi-generational accidental IT architectures" #marketing

A7. Real-time data acquisition and analytics will become increasingly important given decreasing value provided by historical #data that have been somewhat useless in supply chain planning

A7. Data will continue to grow at a nutty pace, organizations will continue to stockpile it, replicate it and lose it without any deep understanding of the relative value of it. Data Privacy and Data Security cannot be an afterthought.

A7. Data analytics moves to the edge. Today, most analysis is done in the cloud which requires massive amounts of data to be sent from the edge to the cloud. Data growth is primarily at the edge.

A7 Data will continue to be the shining star as model evaluation and tuning becomes a focus for many organizations. There is an essential need to continually train AI models. Right now, about 90% of models are updated at least monthly according to our report.

Q7. As mentioned, data will grow at the edge with IoT-powered use cases driving real time decisions and actions.

A7 Like low-code AI, AutoML will grow in the enterprise. ML is already a complex set of processes that can be labour intensive. Automated ML intros a set of prepackaged methodologies to make producing ML models easier + faster.

A7 Enterprise stakeholders w/o programming skills (data analysts, IT engineers, etc.) will be empowered to create accurate ML models to help automate various aspects of the business.

@unsolvedmistry Yes, data is still the "fuel" for things like AI and larger the relevant data set, the better the AI outcomes.

@larry_lunetta It's a new way of thinking for most IT departments. The cloud isn't the center anymore.

in volume, considerably higher the demand will be to determine more context and actionable insights.

Q6. We are seeing the democratization of tech with the popularity of low code + no code giving access to app creation + evolutions that were once complex.

Q6. The advent of low-code AI allows users to create friendlier tech solutions that are intuitive, easier to interact with, and integrate with large systems.

A6. The movement of AI/ML to the edge. No need to send data around the world to get value - do it locally. Take action fast.

A6. Many vendors will continue to overhype it, consumers will underestimate the power and presence of it and business will continue to find innovative ways to harness it.

A6. AI continues to get more grounded in productive use cases vs. theory. Also, IT teams will become more educated and savvy about how to best use AI and how to separate "AI Washers" from real solutions.

A6. #ML and #AI are essential for businesses transforming into #autonomousdigitalenterprises. With ML-driven IT Operations, businesses can obtain insights from ever-increasing data & drive cost and efficiency gains (1/2)

@Llanor_A A6 on low-code AI... does that raise issues of transparency, accuracy, governance if AI goes self-service?

A6. Businesses will increasingly use AI and ML-based alert noise reduction, root cause analysis, and #predictive recommendations to ensure service performance and optimization (2/2)

A6 AI lifecycle management will be a focus for organizations to strategically partner with companies that offer solutions. The process of build, train, validate, deploy, monitor is a continuous cycle and organizations who want to be industry leaders will focus on it

Also, there is an increasing recognition of the social and cultural implications of AI that will influence how the technology is used and how the results are interpreted.

@szisk A6 There is always some negatives when we go in this direction. I don't believe it will be completely self serve. There will be and has to be some governance.

@unsolvedmistry A6 agree on lifecycle management, but will organizations look to specialists (e.g. DataRobot) or hope for help from Einstein, Cortana and the like?

@larry_lunetta couldn't agree more, without fully operationalized #AI and #ML use cases the hype will fade fast

@larry_lunetta We are definitely seeing an increase in practical applications of AI and will continue to as teams look to improve efficiencies.