Is AI just a load of fakenews, or the real deal?Test your knowledge of myth vs. realitywith industry luminary, Sandeep Dadlani,Chief Digital Officer of Mars, and analystlegend Phil Fersht, CEO and Chief Analyst ofHFS Research
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Introduction
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Is AI just a loadof fake news,or the real deal?
There have been decades of hype in AI – a promise oftranscendent capability against a backdrop oforganizational inertia. But where there has beenmyth, there has also been reality.
Mars, the approximately $40 billion global consumerproducts and services company, will soon becelebrating its 200+ successfully scaled AI projectsand capabilities with its first-ever Mars AI Festival.
The size of this number clearly indicates that this isno pandemic-induced AI adoption or digitaltransformation, but merely an acceleration of strongdigital foundations. In fact, there is no such thing as adigital strategy at Mars, per se.
Sandeep Dadlani, services industry luminary andMars Chief Digital Officer confirms:
“We have a growth strategy and a vision that isrooted in a purpose as to where the company needsto be five years from now. Digital is only a way toaccelerate towards that. We talk about 100x: Howcan I grow 100 times faster? It is all about speed,pace, and purpose...
Our purpose is to build a better tomorrow based onhow we do business today. For example, our Petcaresegment wants to build a better world for pets andMars Wrigley believes better moments make moresmiles.”
Speed, pace, and purpose indeed >>
The Mars AI Festival isa week-long, all-virtual, immersiveexperience andlearning series withthe likes of SatyaNadella, CEO ofMicrosoft, as itsopening keynote;Stuart Russell, MissyCummings, and MattLundren as some ofthe attending AIexperts; andapproximately 100,000Martians expected toattend. This is noneighborhoodbarbeque.The festival has theopportunity to be notonly Mars’ largestcompany conferenceever but its mosttransformational.
The purpose of this piece is two-fold.
1To test your abilities to filter the reality aboutartificial intelligence (AI) from the myths, orin other words–the truth from the AI
hype. Consider it AI–101 in preparation for Mars'inaugural AI Festival ahead
2To offer a roadmap for your AI journey. Wewill present the 5 key techniques and someassociated use cases from the horses'
mouths: Mars' Chief Digital Officer, Sandeep Dadlani,and HFS' CEO and Chief Analyst, Phil Fersht
Good luck and godspeed!
The State of AI
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Phil Fersht, CEO andChief Analyst, HFSResearch: Let’s starttalking about the state ofAI, where we really aretoday, and some of theprogress we’ve made.And then we can get intothe reality and the myths.
Sandeep Dadlani, ChiefDigital Officer, Mars:Hey, Phil. Thanks forhaving me. You know,earlier this year, the
Guardian published thisop-ed, and it began bysaying, “A robot wrotethis entire article. Are youscared yet, human?”
You had DeepMinddeveloping AlphaGo, thatdefeated the best Goplayer in the world. Youhad IBM’s Deep Bluedefeating Garry Kasparovin chess earlier; and thiswas OpenAI’GPT-3–onemore milestone that
instilled hype and fearabout AI.
Multiple sources pointout that what was notpublished by theGuardian was thatOpenAI’s GPT-3 hadactually spit out eightessays, and the editors atthe Guardian had to slicethrough differentversions of the eightessays to come out withthe one op-ed.
The State of AI
Sandeep Dadlani, Chief Digital Officer, Mars, in conversation
with Phil Fersht, CEO and Chief Analyst, HFS Research
Question 1: Single-purpose AI canbe scaled with automation. Truth orhype?
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Truth
Hype
Effectively, it was editedand curated by humanbeings, and I think that’sthe real state of AI.
When somebody tries toattempt to ask itcontextual, philosophicalquestions, the responseslacked context. So, I stillfeel that we are a long
way away from the hypearound large scale,omnipotent, intelligentplatforms.
We are beginning to trainthem with a lot moredata than we ever couldimagine, and that’sexciting, but they are stillsingle-purpose, narrow AI
tools. They still do whatyou train them to do. Theidea of general-purposeAI is very, very, very faraway yet.
What do you think? Doesthat resonate with you?
"So yeah, I think there’s a lot of hype surrounding AI.But when we look at the true possibilities,
they are absolutely phenomenal.This will get more and more exciting in the years to come." ~ Phil Fersht
Question 2: General AI is becomingavailable in large-scale platforms.Truth or hype?
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Truth
Hype
Phil: Yeah. I do think that.It’s about having muchbigger clarity of purposefor what you’re trying toachieve in an enterpriseand being able to createalgorithms which allowsome self-remediation,some self-determination,for your systems to workfairly independently ofhaving constant human
oversight. I mean, that’sthe basic premise.
I think a lot of today’s AI ispredefined. It’s accessinglogic in containedmechanisms and systems.It’s being able to unleashthat into the uncontainedand leverage data outsideof the organization–in away that is obviously
secure–that will reallyextend intelligence, andthought, and the ability topredict.
We talk about theOneOffice Organization alot, and the whole pointbehind OneOffice is howto become ananticipatory organization.How do you really have
that ability to makedecisions more quicklythan everybody else?Building out that ability isoften done by simplifyingthan by addingcomplexity, especially intoday’s environment.
So yeah, I think there’s alot of hype surroundingAI. But when we look at
the true possibilities, theyare absolutelyphenomenal. This is onlyreally unravelling in thesetimes and is going to getmore and more excitingin the years to come.
The true concept of AI isonly just emerging.
RESEARCH HIGHLIGHT
Top-line growth is a big motivator for AIAI’s potential to impact revenue growth isimmense. It can improve customer experiencesand customer lifecycle management programs,augment sales and marketing efforts, and providethe opportunity to use data strategically to createentirely new revenue streams or business models.Customer and revenue linked-AI programs featurein both strategic and operational goals for manyenterprises today.
In my opinion, theexisting concept of AItoday is, like, “Buy thisproduct, buy thisplatform, buy this suite,and you get AI.” Youabsolutely don’t. Youhave to design yourbusiness, and sometimesyour personal life, theway that you want the
outcomes to be achieved,and then you build fromthere. Right?
You have to have very,very solid automationwithin the way youdesign your processesand operations, and thenyou can start to reallybuild out the logic using
technology and smartalgorithms to makeyourself and yourbusiness smarter thanyou are.
Sandeep: I agree. If avendor walks in and tellsme that they have abroad enterprise AIplatform, I actually won’t
take the meeting,because I know there’snone that exist. In fact,every AI project is acustom project in someways, by definition. Thereare tooling platforms, butthey are just startingpoints to actually doprojects to solveproblems with AI.
I love the idea of beingclear about your purpose,being process led, andhaving good data. Andbecause of automation,being able to then reallyscale single-purpose AI,which is a very realisticmodel of how you lay outthe AI platforms.
RESEARCH HIGHLIGHT
HFS data suggest that as an industry, we arestarting to see more engagements make it toproduction builds after several years of learning onwhere and how to best apply AI.
Sure, there are tools;there are accelerators. Infact, some of the latesttools which my team andI enjoy are tools thatallow you to optimizeamong various machinelearning models orchoices, but by far, theyare defined by thepurpose, or the problemyou are clear aboutsolving.
Let me throwsome realenterpriseexamples, to bringcolor to whatyou’re saying...
"I love the idea ofbeing clear aboutyour purpose,being process led,and having gooddata."
~ Sandeep Dadlani
Forecasting Use CaseCLARITY OF THE PROBLEM AND LIMITINGFACTORS
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Sandeep: In one of the business units inMars, we were trying to improve supplychain forecasting. This is a very commonAI use case. But when we went in andlooked at the broken processes, werealised that the demand planner, theinventory planner, the production planner,etc., they’re just not looking at the sameinventory data together.
Forecasting Use CaseCLARITY OF THE PROBLEM AND LIMITING FACTORS
Question 3: Application ofAI starts with clarity ofbroken processes. Truth orhype?
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Truth
Hype
Question 4: One of thelimiting factors forapplying AI in an enterpriseis the lack of talent [ortalent shortage]. Truth orhype?
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Truth
Hype
Question 5: One of thelimiting factors forapplying AI in an enterpriseis the lack clean label datafor training. Truth or hype?
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Truth
Hype
This is not an AI problem. This is a dataproblem and an automation problem,first.
Being super clear about the problem firstallowed us to provide inventory visibilityand the same data to all of these roles.That itself improved the forecastingaccuracy by more than two thirds of theproblem that we had. Then we did put AI,we put a very specific optimization AI tool,but that was literally 10 or 20 percent ofthe solution of improving forecastaccuracy.
In Mars, we take it one step further; weare also clear about the Associate, or theemployee, that we are amplifying. Whoare we taking 100x? Who are we drivingfaster?
We usually put a 1080-pixel, high-definition picture of that person, and wesay, “She is the one that we are reallytrying to amplify” and then we are clearabout the problem statement. We either
RESEARCH HIGHLIGHT
No matter where a company is on the maturity spectrum, top challenges to emergingtechnology adoption are not about technology. Tangible business cases are far morecritical now than ever before.
use design thinking or other techniquesand then figure out what combination oftools is needed to scale it, to automate it,and to solve it.
Now, in that experience, a few things havecome to light. The biggest limiting factornow is not the lack of AI models or the lack
of expertise; there is enough expertisegoing around, outside and inside thecompany, or any enterprise. The biggestlimiting factor is the ability to define aproblem clearly. The biggest limitingfactor is also the availability of clean label
data for training.
This is usually ignored; almost every AIscientist will tell you that 90 percent oftheir work gets throttled by thatavailability of clean label training data. Wehave spent the last three years withtremendous investments in trying to help,
to whatever extent possible, having a lot oftraining data available for the problemswe are trying to solve. We now have200-plus bots in the company, as well,helping solve specific problems incustomer engagement, in e-commerce,
"The biggest limiting factor is the ability to define a problemclearly. The biggest limiting factor is also the availability ofclean label data for training."
RESEARCH HIGHLIGHT
Data management is a critical part ofenterprise AI products. Historically,many enterprises did not have any datastrategy across their organizations.Enterprises collected some data andstored it in silos but utilized it poorly.The first step to getting a benefit froman AI product is fundamental datahygiene—making sure that datacollection and preparation includesparse and noisy data handling, dataintegration, and data approximation.For some emerging use cases, there isno past data available. Having minimaldata means slow progress towardaccurate AI models while more data iscollected.
Diagnostics Use CaseGOING BEYOND PROCESS, OPERATIONS,AND STRATEGY
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Sandeep: We have a diagnostics divisionthat actually processes x-rays forvets–about 30 million x-rays a year. Thereare not enough vet radiologists in theworld, and the radiologists that do the x-ray processing are strapped for time.
The way we framed the problem was,“How do we help make the radiologistsmore effective?”
When we observed the radiologists, werealized that, in the first 30 seconds, everyradiologist just oriented the x-ray in termsof top-down, or bottoms-up, and so on.
Now, if you think of the problem to solve,saving 30 seconds for one x-ray is not a bigdeal, but saving 30 seconds of 30 million x-rays–that’s a lot of radiologists’ time thatcan be used for far better purposes.
Diagnostics Use CaseGOING BEYOND PROCESS, OPERATIONS, AND STRATEGY
In the first sprint, literally using open-source techniques and not too muchoutside help, we solved that. Then the nextsprint went on to say, “How do we help theradiologists zoom in on where theproblem areas are on the x-ray?”
Long story short, in about ten sprints, weare getting to a point where the AI can getdangerously close to diagnosing and
recommending the actual problem. In thiscase, it could be cancer; it could be otherdiseases. That part of the business reallyhas a clear purpose: A Better World forPets.
The purpose is clear; the radiologist is clearthat she is the one we are amplifying; andthe models are easy. We have figured outhow to process 30 million x-rays' data inour data lakes, and I find that to be asuccessful experiment and a scaledsuccess for both AI and Edge Computing.
Between each successful experiment,there are many failed experiments, don’tget me wrong, but they all follow theprinciples that you laid out earlier. Doesthat make sense?
Phil: It makes a lot of sense, and I thinkthe one thing that’s really becoming
clear, from our conversation, is before youeven need a strategy, you need a purpose.
Question 6: Before youneed an AI strategy youneed a clear purpose. Truthor hype?
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Truth
HypeYou need acrystal-clearpurpose ofwhat visionyou're trying toachieve.This has been why so much of AI has beenhype over the last 30, 40 years–becauseuntil you’re truly prepared to redesign theway that you run your operations andprocesses to achieve your true purpose ofthe enterprise, you’re never going to getthe true benefit from the software.
Critical Skills andAugmentedIntelligence
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Phil: When we look backover the last decade,back to the onset ofcloud, we’re now trying tofinish up getting into thecloud properly.Everything we’ve done inthe last decade hastechnically been a bigpile of bunk.
Well now, I don’t think itcompletely is, becausewe’ve developedinvaluable experienceand knowledge duringthat time. What we didn’tdo is execute against it,we’re only executingagainst it now.
Critical Skillsand AugmentedIntelligence
One of the silver liningsof this pandemic is theforced need to go andabsolutely redesign howwe run processes andoperations in ourbusiness, and we’regetting away from thisstigma of IT versus non-ITpeople.
When we get into skills,we get into what we’re
doing as people, andwe’re finally starting tosee the beginnings ofthis interplay between ITand business withinorganizations. We seenow the need to becomedigitally fluent.
That means, on thebusiness side of thefence, that you have to beable to translate the
understanding of digitaltools to create new waysto serve customer needsand drive value; you needto examine yourcompany’s strategy andoperations right in thecontext of technology,and on the IT side, you’vegot to understand thebusiness context behindwhat you’re doing to besuccessful.
When we get into skills, we get into what we’redoing as people, and we’re finally starting to seethe beginnings of this interplay between IT andbusiness within organizations.We see now the need to become digitally fluent.
Question 7: Digital fluency is theability to understand andcommunicate information derivedfrom data. Truth or hype?
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Truth
Hype
We’re finally starting tosee this coming together,where the smartestbusiness managers in theworld aren’t computerprogrammers, they don’twrite lines of code, butthey really understandthe business they’re inand the decisions theyneed to make. They needto have the ability to turnintelligence andknowledge of the
business into thistechnological capability,where they can go outand get data, right acrossthe world, to supporttheir ability to makesmart decisions quickly.
That’s what this is allabout. It’s beingintelligent and lettingthe technologyaugment humanintelligence.
RESEARCH HIGHLIGHT
While both top-line and bottom-line focused initiatives are underway, lessthan 1% of enterprises are considering eliminating headcount as theirprimary objectives of using AI technology. The adoption of AI, for mostorganizations, is about doing more with less—augmenting and enhancingthe productivity of their current workforce to help them make betterdecisions and reduce mundane, “robotic” tasks from their workstreams.
When we look at theskills that we have todevelop for the worldwe’re moving into, this iswhere AI becomesextremely relevant. HFShas defined six core skillscategories for the futureor work. (Exhibit 1). We
have to look at the newskillsets, which includecapabilities like yourappetite for change, youradaptability, your agility,your resilience. I’vementioned digitalfluency.
Your initiative is a skillsetnow, it’s an ability to be aleader. How do you leadduring times of change?How do you offeropinions? How do youoffer direction? Whatabout your ability for self-development, which I
think is absolutelycritical–having anaccurate assessment ofyourself and seeking outnew knowledge and skillswhen you need it?
With emerging AItechnology, you don’tneed to have a computerscience degree; you don’thave to have algorithmiccapabilities at the PhDlevel to be a genius atthis. You need to have theability to navigate workand collaborate withlimited supervision.
We look at other skillsthat are coming into play,like problem solving, thatare more important thanever. That’s when we startthinking more aboutdesign thinking and
scenario planning.
Another area you and Ispend a lot of time with,Sandeep, is socialinfluence. How do wenegotiate better andimprovise agreementthrough exploration?How do we developbetter socialconnections? How do weget better at persuadingpeople and articulating?
Certainly, the whole idearound values is critical,such as ethics, humility,and respecting andrecognisingexperience–somethingwhich I don’t think hasbeen very strong inenterprises. Inclusion isso important, as areresponsibility, trust, and
Exhibit 1: Skills Categories for the future-forward organization.
These are all theattributes that are goingto make AI real, versusthe world we came from,where it’s selling dreamsin software platforms,where we charge a lot ofmoney, and you go andfigure out how to fulfilthose dreams. Now it’smoving to a very differentreality, where we trulyaugment humanintelligence to makemuch smarter decisionsin our business lives andin our personal lives.
Sandeep: I love thebreadth of topics you justcovered. Let me illustratesome of them and bringcolor to them.
You talked aboutpersonal effectiveness. Ijust received an emailfrom Microsoft, on myanalytics platform, whichtold me that in a typicalworking week, I have acollaboration level of 70%.I use 13% email, 10% chatsand calls, and 47%meetings. I can now
discover more about myworking style by clicking“further”; it says that 97%of my online meetingsare joined on time, Phil,and 100% of my meetingswere during workinghours. Now, it does saythat I have only 30% of atypical week available tofocus, and I need to focusa lot more.
This is an AI algorithm.Whether it’s accurate ornot, it’s directionallytrying to learn from my
habits and everybodyelse’s habits in my peergroup, and it’s trying togive me a decentindicator. Whether I useit to change my prioritiesor my working style is upto me. But, you know,think about how well it’s
augmenting my personaleffectiveness, which I feelis pretty interesting, andMicrosoft has been greatto work with us on thesekinds of things.
The other thing youtalked about was digital
Question 8: Talent can developproblem-solving skills by learningabout analytics and AI. Truth orhype?
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True
Hype
fluency. I’ll give you astory from early in myMars stint.
We had a Udacitymachine learning course,which was originallyintended for only 30technical people, but theemail accidentally wentfrom my email inbox toall of Mars, which at thattime was made up of100,000 Associates. Andwe were like, “Well, it’sgone to all of Mars, but,you know, who wants tolearn machine learning?”
And to our shock andsurprise, there werethousands whoresponded–people indifferent functions, insupply chain, in sales, inmarketing, and in vethealth. Everybodywanted to learn machinelearning.
I think there is a generalsense, for every Associatein every company now,that this is coming–thatwhat they use today asMicrosoft Excel, perhapstomorrow will be PowerBI, or Tableau, and the
day after tomorrow it willbe AI. So, you’d better geton with it as our jobs getredefined. And that wasvery inspirational to me,because I did not expectall these Associates tocome and say, “I want tolearn machine learning.”
In fact, in our AI festival,both Microsoft andUdacity are helping usconduct a “Basics of AI”training course for all ourAssociates attending. Canyou imagine? Thousandsof Associates trained onAI.
We first took all of our130,000+ Associatesthrough a journey ofuser-centricity anddesign thinking, soexplaining how to framea problem. Now we aretaking them through ajourney of analytics, AI,and so on, so that theyknow how to solveproblems. So, this wholecycle reinforces ourdigital engine.
Every companytoday wants tolearn how to gofaster as anorganisation.Some of it has todo purely withleadership, as yousaid, because ifyou lead better,you run faster, buta lot of it also hasto do withupskilling. Youcan’t change allyour talent. Youhave to upskill allyour talent; youhave to believe inall your talent tothen drive forwardand movemeaningfullyahead.
RESEARCH HIGHLIGHT
Upskilling for outcomesAccording to recent research conducted by HFS, the urgency to develop new skills inhouse through training has increased significantly,versus external approaches like crowdsourcing, recruiting new hires, and recruiting temporary staff. Approximately 65% of leaders nowexpect their staff to develop skills through in-house training and to learn on-the-job and engage with learning and developmentresources (Exhibit 2).
Exhibit 2: The urgency to develop new skills in-house and learn on the job
RESEARCH HIGHLIGHT (continued)
In the same study, executives were asked to rank the most sought-after people skills post-Covid. Creativity, problem solving,communication skills, agility/adaptability, and data science/analytics came in as the top 5 (Exhibit 3).
Exhibit 3: Most important skillsets to unleash organizational potential post Covid-19
The AI VendorLandscape
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Sandeep: Let’s talk about the vendorlandscape a little bit.
We are working with a number ofmedium-sized analytics vendors, like MuSigma and Fractal, on doing large-scalemachine learning work, so I’d say thatmachine learning is now very common.The larger SIs will do it, provided youcherry-pick the right people in the SIs tocome forward…
The AI Vendor Landscape Question 9: The AI vendorlandscape consists ofenterprise platform players,mid-sized vendors doinglarge-scale machinelearning work, and start-ups for specific solutions.Truth or hype?
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Truth
Hype
We work with a number of start-ups, andwe enjoy that, because a lot of the start-ups come in and build models for veryspecific solutions. They’re not underpressure to make it the be-all end-allsolution for all AI projects. And that I enjoy,because, just based on our discussion, wewere very clear that it is unique, narrow,purposeful AI that helps us solve problemswhen we know the problem clearly in aparticular domain. And so we really enjoythat part.
We enjoy working with Microsoft, inparticular, because Microsoft helps usdemocratise AI, given their integrationwith GitHub, their integration withLinkedIn, and their integration with all theopensource components. Given that60-70% of our footprint is on Azure, we cannow pull Azure ML tools–anyone in thecompany can–to then learn and startdoing their own AI experimentscomfortably. And we like that relationship.
In fact, as part of our AI festival weare launching the AI for All Martiansplatform based on Azure ML Studio.
Similar relationships are obviously onthe horizon, with other enterpriseplatform players, but that’s thevendor landscape as I see it.Whether it’s Amazon, Microsoft, orGoogle, there are enterpriseplatforms; then there are these mid-sized vendors that really takemachine learning everywhere; andthen there are these startups thatare doing very advanced AI for veryspecific needs.
Does that vendor landscaperesonate with you?
Phil: It does a lot, actually, and a lot of itcomes down to the DNA of the vendors.They all have the technical chops inspades; it’s more about how you apply it.Ultimately, enterprises want to havetrusting relationships, like you’re doing.You’re teaming up with lots of great, nichecompanies with genius pools of talent inthere. Right? You’re not going to hirethose people yourself. They’ll leave in fiveminutes.
You have to partner with these companiesto make you great, and that’s where Ithink a lot of this is moving from a vendorlandscape scenario.
You need to have a great software partnerwho builds products in the way that youcan leverage them, and then havespecialist partners to bring in and fill thosegaps. It could be big partners, littlepartners – who cares anymore? It’s the
whole ecosystem of building out thoseskillsets around you to get the best out ofyour existing talent.
We talked a bit about skills and digitalfluency, and digital fluency is absolutelycritical. You can’t achieve AI success by justbringing together the best PhDs and themost intelligent minds; you have to bringtogether the business minds, the drive,the purpose, and the partners.
RESEARCH HIGHLIGHT
Context matters. We cannotoveremphasize the importance ofdomain expertise. One size will notfit all problems. Domainunderstanding is fundamental forthe success of AI products. Mostenterprise AI products are built forspecific use cases and industries. So,understanding business processes(domain lexicons, ontologies) isimportant for the effective use of AI.
HFS sees an ecosystem of serviceproviders, product vendors andstartups, and enterprise softwareplatforms emerging to cater to thegrowing need for automation, AI,and analytics (the Triple A Trifecta).
Major cloud providers such as Microsoft and Google are certainly an influential partof the technology advancement in AI, as Sandeep highlights, along with manyinnovative services and product startups that are creating combinations of domainand technology expertise to carve a niche for themselves in this competitivemarketplace.re…
Bringing it alltogetherTHE ROADMAP TO A SUCCESSFUL AIJOURNEY
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Sandeep: If we use thetechniques that we justdiscussed, the human-centricity, the purpose-led work, the cleardefinition of the problem,the clear availability ofdata to train the AI, and
all these platforms andvendors, then theopportunity we have, inany organization’sjourney, is to completelyredefine businessmodels, even fortraditional companies.
And that’s very exciting.That learning journey iswhat we at Mars arelooking forward to. Thatpart of AI is definitely nothype.
Bringing it alltogetherTHE ROADMAP FOR A SUCCESSFUL AIJOURNEY
>>
The opportunity wehave... is to
completely redefinebusiness models,
even for traditionalcompanies.
HUMAN-CENTRICITY
Having clarity about whoyou are amplifying whoare you solving for
DEFINITIONOF THE PROBLEM
Having clarity whenframing the problem –what you are solving for
PURPOSE-LEDWORK
Having clarity around thevision you are trying toachieve – why you areultimately creating asolution
AVAILABILITYOF DATA
“Quantity of databecomes decisive indetermining the overallpower and accuracy of analgorithm” – Kai Fu Lee,AI Superpowers. Data isthe how behind AIcapability
PLATFORMSAND VENDORS
This is your technologyand partner ecosystem
WHO WHAT WHY HOW WHERE
Addendum:Questions andAnswers
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Truth or Hype: Question 1
Single-purpose AI canbe scaled withautomationTruth or hype?
Correct Answer: Truth
You can scale single-purpose AI withautomation – this is a very realistic model ofhow to lay out the AI platforms
Back to question
Truth or Hype: Question 2
General AI is becomingavailable in large-scaleplatformsTruth or hype?
Correct Answer: Hype
We are a long way away from the hype aroundlarge scale, omnipotent, intelligent platforms.We are beginning to train them with a lotmore data than we ever could imagine, andthat’s exciting, but they are still single-purpose,narrow AI tools.
Back to question
Truth or Hype: Question 3
Application of AI startswith clarity of brokenprocessesTruth or hype?
Correct Answer: Hype
Application of AI starts with clarity of the problemyou are trying to solve from the point of view of theend-user.
Back to question
Truth or Hype: Question 4
One of the limitingfactors for applying AIin an enterprise is thelack of talent [or talentshortage]Truth or hype?
Correct Answer: Hype
There is enough expertise going around, outsideand inside the company, or any enterprise. Thebiggest limiting factor is the ability to define aproblem clearly.
Back to question
Truth or Hype: Question 5
One of the limitingfactors for applying AIin an enterprise is thelack clean label data fortrainingTruth or hype?
Correct Answer: Truth
Almost every AI scientist will tell you that 90percent of their work gets throttled by thatavailability of clean label training data
Back to question
Truth or Hype: Question 6
Before you need an AIstrategy you need aclear purposeTruth or hype?
Correct Answer: Truth
You need a crystal-clear purpose of what visionyou’re trying to achieve. This has been why somuch of AI has been hype over the last 30, 40years–because until you’re truly prepared toredesign the way that you run your operations andprocesses to achieve your true purpose of theenterprise, you’re never going to get the truebenefit from the software.
Back to question
Truth or Hype: Question 7
Digital fluency is theability to understandand communicateinformation derivedfrom dataTruth or hype?
Correct Answer: Hype
Digital fluency is the ability to examine yourcompany’s business model, strategy, andoperations in the context of technology.
Back to question
Truth or Hype: Question 8
Talent can developproblem-solving skillsby learning aboutanalytics and AITruth or hype?
Correct Answer: Truth
User-centricity and design thinking can help talentlearn how to frame a problem; analytics, AI, (and soon) can support talent in learning how to solve forproblems.
Back to question
Truth or Hype: Question 9
The AI vendorlandscape consists ofenterprise platformplayers, mid-sizedvendors doing large-scale machine learningwork, and start-ups forspecific solutionsTruth or hype?
Correct Answer: Truth
There are enterprise players with enterpriseplatforms; mid-sized vendors that provide large-scale machine learning solutions, and start-upsbuilding advanced AI models for very specificneeds.
Back to question
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Thank you for reading
Is AI just a load offake news, or thereal deal?
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