Real-world use cases with real results
An eye for AI
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This report surveyed 119 UK IT leaders involved in AI strategy or implementation at their organisation from a wide range of industries, including finance, manufacturing, education, government, and more.
Artificial intelligence (AI) and machine learning (ML) continue to be product feature checkboxes within the technology industry today. Organisations are left to discern which are purely marketing buzzwords and which offer genuine business value, asking questions such as:
What conditions are needed to ensure return on investment (ROI) from AI can be achieved in the near term?
How far have organisations gone in establishing a culture led by advanced data analytics?
How can they take the next step?
This report, featuring dedicated end-user decision-maker research, cuts through the hype surrounding AI and explores what organisations are achieving with the technology. We look at AI use cases, how to overcome barriers, and how to take AI from proof-of-concept to revenue-generating success stories. Finally, this report will delve into the importance of employing the right hardware infrastructure in ensuring AI benefits are fully realised.
Introduction
4
are not confident
%
The most successful use cases identified are natural language processing, followed by deep learning, data analytics, and predictive analytics.
ICON
%
Key Findings
ICON
80 per cent of IT leaders agree their organisation is in the early stages of AI adoption.
%
96 per cent of organisations are at least interested in employing AI.
ICON
%
The main factors shaping AI strategy are cost reduction, decision-making speed and confidence, and customer insights.
ICON
%
A third of respondents say their AI initiatives have seen a significant ROI.
%
ICON
The improved management of risk regulatory compliance, alongside customer insights and meeting customer needs are leading benefits of AI.
%
While it is widely recognised that AI holds great promise for delivering service benefits, and with many organisations already seeing gains from implementing intelligent data analytics and customer services, some are simply paying lip service. Moreover, most are typically in the early stages of their AI adoption maturity.
AI state of play
80% of our staff are working from home or shared external spaces
COVID-19 has caused a 10-15 times increase in remote working
We have a lack of support when we need it most
The increase in remote working has greatly increased demand for better endpoint security
Just 7 per cent of surveyed organisations have fully implemented AI, however, 96 per cent are at least interested in establishing an AI strategy for their organisation. The majority of respondents report their company is in the trialling stage, indicating adoption is relatively early or has stalled. This is unsurprising given 80 per cent agree their AI adoption is stuck in the initial stages.
Importance of endpoint device decision makers
AI adoption maturity
The use cases for AI are extensive, but organisations today face a common challenge – how to get from concept to success with the least cost and risk.
Going beyond proof of concept relies on a careful rollout, with key needs across resourcing, budgeting, operations. This is often a graduated and iterative process. Evidently, IT leaders recognise the benefits of AI implementation, and report which factors are pushing their strategy.
Shaping strategy
A distributed workforce makes a Zero Trust security strategy even more important
The importance of providing secure remote access to data, applications, and networks to ongoing organisational success
When asked, how does Zero Trust fit into your endpoint management processes, responses were varied.
Remote working presents a serious risk to our network security
Intelligent reporting also enables efficient marketing for organisations. AI produces actionable customer insights, allowing organisations to understand their users’ behaviours. In this way, they can build and deliver top-level experiences that not only meet their needs (a gain highlighted by 56 per cent), but also drive revenue through strong relationships and attract new lines of business.
Understandably, the gains reported by respondents are all influenced by cost considerations. Whether that is reducing costs through intelligent decision-making or through offering top-level customer interactions supported by intelligent insights. When asked what factors are shaping their AI strategy at their organisation, costs and the impact of efficiency and automation are top of mind for all IT decision-makers surveyed.
Future strategy
Our organisation will utilise AI for as many tasks as possible. All new projects will look at AI from the outset
We want to be more focused on automating processes to save both customer and employee time
Once we have established a framework, we will significantly increase our AI use
As AI technology matures, we will use it more and make more strategic use of it
Biggest endpoint device buying challenges
Organisations utilising AI underline the role it has played in enabling new lines of business and revenue streams. 78 per cent of respondents at least somewhat agree AI has directly impacted their competitive edge, with a third strongly agreeing. Clearly, investment in AI solutions can have a significant impact on efficient processes as well as business revenue in the long-term.
For example, the use of AI solutions in healthcare has profound impacts on patient care. Using AI embedded into X-Ray technology, diagnoses are faster, user productivity is aided, and patient experience is enhanced. The intelligent medical imaging processes can detect conditions while optimising workflows and automatically prioritising cases. Overall, using AI the speed and accuracy of imaging is significantly improved. One task, autorotation, is already seeing significant benefits for the healthcare industry. Technologists must manually rotate images to view scans or screenings in the correct orientation, however, with automation it is estimated 70,000
Organisations must harness AI to extract value from data, but challenges abound. Data pre-processing, from discovery to breaking down silos, to quality control and managing it from edge to cloud, come first. Taking the right approach to modelling, from analytics to machine or deep learning, with the right technology and expertise comes next.
Intel provides a holistic and open path forward, addressing the full data, modelling & deployment pipeline, with the freedom to compute on whichever architecture is best. Take to the open road in AI with us, starting with getting the most out of the only x86 CPU with built-in AI acceleration.
Sponsor Insight
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Conclusion
With dispersed workforces becoming the norm, the security perimeter has been shattered, and organisations must ensure they are effectively managing and monitoring endpoints. Employees and the devices they use are the primary attack routes into an organisation – particularly when connected to weaker home networks.
Additionally, given the proliferation and sophistication of attacks today, it has never been more important to secure devices. When operating a true Zero Trust model, anything trying to access an organisation’s network or servers should be assumed untrustworthy until shown otherwise.
With typical endpoint refresh cycles spanning three to five years, IT decision-makers must consider their Zero Trust aspirations as part of those buying processes today, as well as how their endpoints are secured and managed.
IT leaders are striving for a data-driven decision-making culture – one that is increasingly underpinned by AI.
While most organisations are still in the preliminary stages of AI maturity, interest is huge. IT leaders recognise the gains to be made when using AI technology. From insights to security, across the board AI can support myriad objectives across organisations in all sectors.
Evidently, high-level data analytics is key to unlocking data value, but this is underscored by strategy and infrastructure.
A successful AI program depends on a mature data strategy and infrastructure on the one side, and the right technology and hardware partnerships on the other. IT leaders evolve from proof-of-concept to real-world success by establishing the most fertile soil for business case benefits. They must establish which services will benefit from AI adoption and the technology that must underpin it.
Find out more
Developed for the IT professionals of today and tomorrow, Intel vPro is built for business. The manageability technologies allow IT to keep a highly dispersed workforce patched and protected, whilst the stability features allow for consistent rollouts and reliable lifecycle management. So, no matter where your users find their office, managing a fleet is made easier. With each component and technology designed for professional grade, IT can be confident with tools to enhance everyone’s productivity and help secure their business’ data.
Introduction
Key findings
AI state of play
Shaping strategy
CX and intelligence
Future strategy
Intelligent reporting also enables efficient marketing for organisations. AI produces actionable customer insights, allowing organisations to understand their users’ behaviours. In this way, they can build and deliver top-level experiences that not only meet their needs (a gain highlighted by 56 per cent), but also drive revenue through strong relationships and attract new lines of business.
Understandably, the gains reported by respondents are all influenced by cost considerations. Whether that is reducing costs through intelligent decision-making or through offering top-level customer interactions supported by intelligent insights. When asked what factors are shaping their AI strategy at their organisation, costs and the impact of efficiency and automation are top of mind for all IT decision-makers surveyed.
55%
14%
49%
44%
Supply chain issues
Hybrid/remote working
Budget constraints
User expectations
Security concerns
Strongly disagree
Somewhat disagree
Neither agree nor disagree
Somewhat agree
Strongly agree
3%
9%
25%
24%
39%
Strongly
disagree
Somewhat disagree
Neither agree nor disagree
Somewhat agree
Strongly agree
0%
12%
32%
63%
22%
Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better. To learn more about Intel’s innovations, go to newsroom.intel.com and intel.com.
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4%
21%
30%
16%
7%
1 = not at all satisfied, 10 = extremely satisfied
Endpoint ROI satisfaction levels
1 2 3 4 5 6 7 8 9 10
1%
2%
1%
1%
8%
12%
32%
27%
12%
6%
RETURN TO HUB
Introduction
Key findings
Challenging landscape
People problems
The importance of capable hardware
Conclusion
Sponsor info
Using devices past their life cycle could impact reliability, productivity and security
Keeping these stakeholders on board, while also enabling them to work productively and securely may require a refresh rethink.
FUTURE STRATEGY
Securing endpoints is difficult due to a dispersed workforce
1 = not at all important, 10 = extremely important
1 2 3 4 5 6 7 8 9 10
1% 1% 1% 1% 2% 2% 7% 21% 26% 39%
How does Zero Trust fit into your endpoint management processes?
It’s a key requirement
It’s a key part of our strategy
It is becoming integral
It doesn’t fit into our processes at the moment
It is not something we have explored
It requires a change in philosophy to an ‘always verify, never trust’ model
Without it, we would struggle to manage all our resources effectively
Under consideration
Something we need to adopt in the future
Currently planning our approach to this in our cybersecurity strategy
It’s hard to say, we’re not sure how yet
We are getting there. It will hopefully become a core element
Currently not included but considered necessary going forward
IT leaders’ strategies for the upcoming years
Our plan is to considerably increase our AI investments year on year
We will accelerate our AI adoption and identified which business processes can benefit from it
Strong authentication of all users regardless of location but with minimal friction
Full automation. No input from the user. The infrastructure manages the whole relationship
Invisible
Constant authentication and analysis with real time alerting and automated response to breaches
What does an effective Zero Trust model look like at your organisation?
19%
16%
3%
49%
18%
13%
Strongly disagree
Somewhat disagree
Neither agree nor disagree
Somewhat agree
Strongly agree
0%
32%
12%
5%
51%
Real benefits
The continued prominence of hybrid working has meant end users are more reliant on their devices than ever before. Away from the office environment, endpoints must be easy to use, performant and secure. Computers that are not fit-for-purpose will result in unsatisfied employees, lower productivity levels and will add to the workload of IT teams.
Remote working presents a serious risk to our network security
IT decision-makers must consider their Zero Trust aspirations as part of those buying processes today, as well as how their endpoints are secured and managed.
We surveyed 130 IT decision-makers involved in managing, testing, evaluating, specifying, recommending, or procuring endpoint estate products at their organisations. The majority were IT managers, IT directors and
C-suite professionals.
21%
When developing their endpoint refresh strategy, organisations should evaluate their existing endpoint needs, how these could change in the future, how new devices will fit with current ones and how best to optimise new endpoints.
Out-of-sequence refreshes may be required to ensure those across your organisation are equipped with the necessary technology to do their jobs correctly.
A number of reasons may have impacted this. Firstly, a wide-spread talent shortage in the technology space may mean AI initiatives or strategy have been put on the back burner in favour of more traditional digital programmes. Overstretched, time-poor IT teams are typically prioritising tasks that ensure effective day-to-day management at their organisation. Consequently, innovation can be stymied.
Budget constraints are also a pervasive issue, not least as we enter a period of economic challenge, and may prevent organisations from fully committing to new initiatives if they are feeling financial pressures.
However, many real world AI use-cases are demonstrating cost savings that will ultimately help the bottom line – making
ongoing investment the wiser course for those organ-isations looking to make efficiency gains. The cost and time savings generated from automating processes, not to mention the insights created by intelligent reporting, are significant benefits.
For those already using AI at their organisation, the most prominent use cases are data analytics and natural language processing, followed by cybersecurity and predictive analytics.
At the other end of the scale, very few organisations are using AI in marketing, manufacturing, or in job candidate selection – though it’s important to note these areas are largely industry-specific.
1st
2nd
3rd
4th
IT teams
End-users
Financial decision-makers
Procurement personnel
End users are more vocal and more knowledgeable in what they want and need
Management of risk and regulatory compliance is the leading benefit being seen by organisations using AI. Building on intelligent reporting, a valued advantage of AI adoption, IT leaders can rely on tooling to bolster security defences, monitor events, and detect and remediate issues.
In a time of frequent, sophisticated cyberattacks, this is especially important for today’s digital-first economy. AI identifies anomalies and deviations from established patterns, immediately detecting threats, breaches, and harmful actors. Organisations using AI can predict potential vulnerabilities and prevent problems to a much higher degree of accuracy than humans can. This is evidenced by 44 per cent seeing significant gains in taking proactive, predictive decisions.
For example, retailers looking to enhance their customer processes are increasingly turning to AI. ‘Know Your Customer’, an integral part of securing user interactions, is significantly improved using machine learning techniques, streamlining data sharing and compliance checks at scale. This is especially important for organisations dealing with large amounts of sensitive data wanting to remain competitive, efficient, and productive. Banks and building societies often face problems with inconsistent documentation from accounts and applications, leading to dissatisfied customers and time-consuming processes. However, with intelligent tooling, Know Your Customer information can be digitised, streamlined and reviewed without relying on manual processes, saving on costs and improving efficiency.
Management of risk/regulatory compliance
67
Customer insights
56
Meeting customer needs
56
Taking proactive, predivive decisions
44
Top benefit areas for those integrating AI (selected 3 maximum)
Reporting
33
Performance tracking
22
OS updates
29%
Challenge today
More challenging over next two years
64%
53%
49%
35%
34%
16%
The needs and opinions of employees should be factored into all endpoint buying decisions, as they are interacting with the devices in question on a daily basis and younger employees are increasingly factoring equipment provisions into career decisions
6%
20%
43%
23%
8%
Likelihood of changing endpoint vendor/hardware partner in the next two years?
Somewhat unlikely
Neither likely nor unlikely
Somewhat likely
Very likely
Very unlikely
The increased speed of data analysis and subsequent reporting time means AI insights are rapid while also accurate and reliable. Enabling responses faster than human oversight gives organisations the ability to do more with less, expand their operations, and redistribute resources from human-intensive processes. In this way, organisations can be confident in decision-making, a factor shaping a third of organisation’s AI strategies.
AI’s impact on the working environment is significant too. It can take over the mundane, administrative tasks freeing up employees to deal with issues requiring specialist expertise and distinctly human creativity. IT managers are likely to expend the majority of their time and effort on coordinating and controlling the day-to-day management of their IT workloads. With AI technology, the time-consuming and routine processes are taken care of, and core business objectives can be prioritised. Similarly, the speed and confidence of decision-making facilitated by AI is key for a third of respondents.
Respondents to Computing’s latest research in this area identify competitive advantage as an important strategy driver. Through reporting, performance tracking, proactive and predictive decision-making, the ability to spot future business opportunities is an important benefit of AI. Capable of learning and training
Older machines are being retired slightly earlier than the normal refresh cycle
34%
User expectations
Budget constraints
Hybrid/remote working
Supply chain issues
Security concerns
OS updates
16%
35%
49%
53%
64%
Just over half say hardware specification is vital for effective AI and analytic workflows.
ICON
%
2%
5%
14%
35%
45%
14%
More challenging over next two years
64%
53%
49%
35%
34%
16%
Challenge today
“My organisation is still at the early stages of AI adoption”
Somewhat agree
Neither agree nor disagree
Somewhat
disagree
Strongly
disagree
Strongly agree
OS updates
Data analytics
78%
Natural language processing
67%
Cybersecurity
44%
Predictive analytics
44%
Customer services
33%
AI adoption rates per area
Data analytics, including both descriptive and diagnostic uses, allow organisations to accurately summarise and visualise historical data. Using dashboards and reporting tools, detailed queries can be made to identify trends and causation for issues. AI establishes relationships between variables, recognising and actioning patterns, while isolating any anomalous data. It is perhaps the most obvious use case – with AIs far better placed to quickly process and interpret large quantities of data, and spot patterns or anomalies, than the human eye.
Recognised and actioned patterns in data sets can be further used to train AI models to then make intelligent predictions. Similarly, natural language processing yields data for AI to enable text or speech reading, analysing, and recognition.
When asked to rate the success of current AI applications, those that have the greatest implementation, score best. This indicates that organisations utilising AI are confident in its capabilities and that there is room for development for the less common use cases.
1 = Not at all successful, 10 = extremely successful
Use case success (average score)
Natural language processing
7.9
7.7
7.6
7.6
7.0
7.0
6.4
6.7
6.3
5.7
Reduced cost through efficiency/automation
53%
Speed/confidence of decision-making
33%
Customer insights
30%
Competitive advantage
28%
New business models
17%
Factors shaping AI strategy
based off historically acquired data, AI can generate numerous insights that are objective and rooted in organisation-specific, tailored data.
User behaviour, decisions, and more are all intelligently tracked and collated to produce straightforward, actionable insights. Based on this, recommendations can be made and organisations can see what is performant and what needs improving. This plays a role in attracting new customers too, as they are drawn to services that can be tailored to suit their preferences based on their interactions. Performance enhancements also improve user experience, both for customers and employees, supporting quick, reliable, and user-friendly interactions.
For example, organisations can utilise deep learning and historical data to analyse customer interactions and make intelligent, customised recommendations. AI collates previous and real time user behaviour with other contextual variables that may impact purchasing decisions to improve customer experience and sales. Fast food restaurants can compile guest ordering choices and combine preferences with weather, time, location, and more, to generate personalised recommendations. In this way, they can feature ice cream on a hot day, soup on a cold one and recommend meal packages based on what is already in their basket.
Future strategy
When asked how their strategy will change in the next few years, IT leaders were keen to convey AI is top of mind. This is unsurprising given the benefits that are expected and being realised by organisations.
We see a greater opportunity to expand with more tasks being automated where possible
We will continue working with the trials and determine which use cases are worthy of further investment
Real benefits
button clicks a year are saved, amounting to roughly three working days of time that was previously wasted before autorotate capabilities.
Organisations that have fully implemented AI overwhelmingly agree that it has already had a significant positive impact, with 88 per cent at least somewhat agreeing in its impact and a quarter strongly agreeing.
For those that are currently implementing AI, the benefits are already being seen. 63 per cent ‘somewhat agree’ AI has already positively impacted their organisation in a significant way.
Those in the trialling/incubating stage also report substantial advantages, with a third at least somewhat agreeing AI has already had a significant impact on their organisation.
Strongly
disagree
Somewhat disagree
Neither agree or disagree
Somewhat agree
Strongly agree
5%
7%
25%
36%
28%
Organisations that have fully implemented AI
Organisations that are currently implementing AI:
Strongly
disagree
Somewhat disagree
Neither agree or disagree
Somewhat agree
Strongly agree
5%
7%
25%
36%
28%
Organisations that are trialling or incubating AI:
Significant
Somewhat
Mixed
Too soon to tell
None
33%
0%
22%
33%
12%
ROI from AI initiatives at those organisations that have ‘fully implemented’
Building on firm
hardware foundations
The better the hardware, the smoother and more precise your AI tooling will perform. We’ve seen the benefits that performant AI can achieve and the successes that organisations are already seeing come to fruition. Assessing current hardware and upgrading where necessary must fit into AI strategy.
The staggering positive influence of AI is further supported by the ROI being seen.
A third have seen a significant ROI on AI initiatives, with 22 per cent reporting a slight return. Another third say ROI has been mixed, highlighting that some initiatives have been stronger than others. 12 per cent admit it is too early to tell which is unsurprising given the intricate nature of AI cost returns and the relatively immature market. It can be hard to see what has been made directly more efficient. Over time, more efficient operations, incident response, and insights will become more apparent.
All of the organisations that have fully implemented AI have seen a return on investment – none have seen no return.
Strongly
disagree
Somewhat disagree
Neither agree nor disagree
Somewhat agree
Strongly agree
0%
33%
22%
22%
“Hardware specification is vital to effective AI and analytics workflows”
A successful, cost-effective AI strategy is built on high performance hardware. Choosing the right technology and infrastructure is crucial in analysing large amounts of data quickly and accurately.
For AI, two of the most important factors that enable meaningful performance are memory management and parallelisable compute capabilities. Reliable environments mean important insights can be rapidly accessed, queried, and learnt from, while it is still actionable. At a time when data centre energy use is under more scrutiny than ever and costs are mounting, it’s vital that processors and supporting hardware platforms are efficient across all workloads. Without strong infrastructure in place, you’re falling at the first hurdle.
Organisations value hardware specification in this space, with roughly 60 per cent at least somewhat agreeing that it is vital to effective AI and analytics workflows. To go from proof of concept to real-world scalability while minimising costs and maximising returns, it is important to leverage the appropriate technology and infrastructure.
AI assistants
33%
Marketing
22%
Manufacturing processes
22%
Job candidate selection
11%
4.9
4.9
Deep learning
Data analytics
Predictive analytics
Robotic process automation (RPA)
Marketing
Cyber security
Customer services
AI assistants
Job candidate selection
HR
Manufacturing processes
Sales
22
Sale of data assets (monetisation)
11
Spotting future business opportunities
11
Technology upgrades
16%
Revenue and profits
14%
Approach to risk
13%
Improved working environment
13%
New products
11%
Attracting new customers
8%
Company reputation
5%
Attracting talent
5%
We felt we needed one
3%
Organisations that are currently implementing AI
Organisations that are trialling or incubating AI
“AI implementation has already had a significant positive impact on my organisation”
22%
Hardware foundations
0%
Unsure
Fully implemented
Currently implementing
Trialling/incubating
Planning
None, but interested
None and no interest
Strongly disagree
Somewhat disagree
Neither agree nor disagree
Somewhat agree
Strongly agree
None and no interest
4%
33%
45%
11%
11%
0%
“AI has enabled new lines of business/revenue streams at my organisation”
5%
25%
0%
0%
42%
25%
11%
Conclusion
Sponsor info
67%
56%
56%
44%
33%
22%
22%
11%
11%
The most successful use cases identified are natural language processing, followed by deep learning, data analytics, and predictive analytics.
ICON
%
4%
21%
21%
30%
16%
7%
AI adoption maturity
Just 7 per cent of surveyed organisations have fully implemented AI, however, 96 per cent are at least interested in establishing an AI strategy for their organisation. The majority of respondents report their company is in the trialling stage, indicating adoption is relatively early or has stalled. This is unsurprising given 80 per cent agree their AI adoption is stuck in the initial stages.
A number of reasons may have impacted this. Firstly, a wide-spread talent shortage in the technology space may mean AI initiatives or strategy have been put on the back burner in favour of more traditional digital programmes. Overstretched, time-poor IT teams are typically prioritising tasks that ensure effective day-to-day management at their organisation. Consequently, innovation can be stymied.
Budget constraints are also a pervasive issue, not least as we enter a period of economic challenge, and may prevent organisations from fully committing to new initiatives if they are feeling financial pressures.
However, many real world AI use-cases are demonstrating cost savings that will ultimately help the bottom line – making ongoing investment the wiser course for those organ-isations looking to make efficiency gains. The cost and time savings generated from automating processes, not to mention the insights created by intelligent reporting, are significant benefits.
For those already using AI at their organisation, the most prominent use cases are data analytics and natural language processing, followed by cybersecurity and predictive analytics.
At the other end of the scale, very few organisations are using AI in marketing, manufacturing, or in job candidate selection – though it’s important to note these areas are largely industry-specific.
Data analytics
78%
Natural language processing
67%
Cybersecurity
44%
Predictive analytics
44%
Customer services
33%
AI assistants
33%
Marketing
22%
Manufacturing processes
22%
Job candidate selection
11%
AI adoption rates per area
Data analytics, including both descriptive and diagnostic uses, allow organisations to accurately summarise and visualise historical data. Using dashboards and reporting tools, detailed queries can be made to identify trends and causation for issues. AI establishes relationships between variables, recognising and actioning patterns, while isolating any anomalous data. It is perhaps the most obvious use case – with AIs far better placed to quickly process and interpret large quantities of data, and spot patterns or anomalies, than the human eye.
Recognised and actioned patterns in data sets can be further used to train AI models to then make intelligent predictions. Similarly, natural language processing yields data for AI to enable text or speech reading, analysing, and recognition.
When asked to rate the success of current AI applications, those that have the greatest implementation, score best. This indicates that organisations utilising AI are confident in its capabilities and that there is room for development for the less common use cases.
The use cases for AI are extensive, but organisations today face a common challenge – how to get from concept to success with the least cost and risk.
Going beyond proof of concept relies on a careful rollout, with key needs across resourcing, budgeting, operations. This is often a graduated and iterative process. Evidently, IT leaders recognise the benefits of AI implementation, and report which factors are pushing their strategy.
Management of risk/regulatory compliance
67
56
Customer insights
1 = Not at all successful, 10 = extremely successful
Use case success (average score)
Natural language processing
Deep learning
Data analytics
Predictive analytics
Robotic process automation (RPA)
Marketing
Cyber security
Customer services
AI assistants
HR
Manufacturing processes
7.9
7.7
7.6
7.6
7.0
7.0
6.7
6.4
6.3
5.7
4.9
4.9
56
Meeting customer needs
33
Reporting
Management of risk and regulatory compliance is the leading benefit being seen by organisations using AI. Building on intelligent reporting, a valued advantage of AI adoption, IT leaders can rely on tooling to bolster security defences, monitor events, and detect and remediate issues.
In a time of frequent, sophisticated cyberattacks, this is especially important for today’s digital-first economy. AI identifies anomalies and deviations from established patterns, immediately detecting threats, breaches, and harmful actors. Organisations using AI can predict potential vulnerabilities and prevent problems to a much higher degree of accuracy than humans can. This is evidenced by 44 per cent seeing significant gains in taking proactive, predictive decisions.
For example, retailers looking to enhance their customer processes are increasingly turning to AI. ‘Know Your Customer’, an integral part of securing user interactions, is significantly improved using machine learning techniques, streamlining data sharing and compliance checks at scale. This is especially important for organisations dealing with large amounts of sensitive data wanting to remain competitive, efficient, and productive. Banks and building societies often face problems with inconsistent documentation from accounts and applications, leading to dissatisfied customers and time-consuming processes. However, with intelligent tooling, Know Your Customer information can be digitised, streamlined and reviewed without relying on manual processes, saving on costs and improving efficiency.
78%
Natural language processing
67%
Cybersecurity
Predictive analytics
44%
Customer services
33%
AI assistants
33%
Marketing
22%
Manufacturing processes
22%
Job candidate selection
11%
AI adoption rates per area
Data analytics
78%
Natural language processing
67%
Cybersecurity
44%
44%
33%
AI assistants
33%
22%
Manufacturing processes
22%
Job candidate selection
11%
AI adoption rates per area
34%
44%
Improved working environment
13%
Natural language processing
New products
13%
Customer insights
Competitive advantage
New business models
Attracting new customers
8%
Company reputation
5%
Attracting talent
5%
AI assistants
The increased speed of data analysis and subsequent reporting time means AI insights are rapid while also accurate and reliable. Enabling responses faster than human oversight gives organisations the ability to do more with less, expand their operations, and redistribute resources from human-intensive processes. In this way, organisations can be confident in decision-making, a factor shaping a third of organisation’s AI strategies.
AI’s impact on the working environment is significant too. It can take over the mundane, administrative tasks freeing up employees to deal with issues requiring specialist expertise and distinctly human creativity. IT managers are likely to expend the majority of their time and effort on coordinating and controlling the day-to-day management of their IT workloads. With AI technology, the time-consuming and routine processes are taken care of, and core business objectives can be prioritised. Similarly, the speed and confidence of decision-making facilitated by AI is key for a third of respondents.
Respondents to Computing’s latest research in this area identify competitive advantage as an important strategy driver. Through reporting, performance tracking, proactive and predictive decision-making, the ability to spot future business opportunities is an important benefit of AI. Capable of learning and training based off historically acquired data, AI can generate numerous insights that are objective and rooted in organisation-specific, tailored data.
User behaviour, decisions, and more are all intelligently tracked and collated to produce straightforward, actionable insights. Based on this, recommendations can be made and organisations can see what is performant and what needs improving. This plays a role in attracting new customers too, as they are drawn to services that can be tailored to suit their preferences based on their interactions. Performance enhancements also improve user experience, both for customers and employees, supporting quick, reliable, and user-friendly interactions.
For example, organisations can utilise deep learning and historical data to analyse customer interactions and make intelligent, customised recommendations. AI collates previous and real time user behaviour with other contextual variables that may impact purchasing decisions to improve customer experience and sales. Fast food restaurants can compile guest ordering choices and combine preferences with weather, time, location, and more, to generate personalised recommendations. In this way, they can feature ice cream on a hot day, soup on a cold one and recommend meal packages based on what is already in their basket.
We will accelerate our AI adoption and identified which business processes can benefit from it
Our plan is to considerably increase our AI investments year on year
We see a greater opportunity to expand with more tasks being automated where possible
We will continue working with the trials and determine which use cases are worthy of further investment
Reduced cost through efficiency/automation
53%
Speed/confidence of decision-making
33%
Customer insights
30%
Competitive advantage
28%
New business models
17%
Technology upgrades
16%
Significant
33%
Somewhat
22%
Mixed
33%
Too soon to tell
12%
None
0%
ROI from AI initiatives at
those organisations that have
‘fully implemented’
Organisations must harness AI to extract value from data, but challenges abound. Data pre-processing, from discovery to breaking down silos, to quality control and managing it from edge to cloud, come first. Taking the right approach to modelling, from analytics to machine or deep learning, with the right technology and expertise comes next.
Intel provides a holistic and open path forward, addressing the full data, modelling & deployment pipeline, with the freedom to compute on whichever architecture is best. Take to the open road in AI with us, starting with getting the most out of the only x86 CPU with built-in AI acceleration.
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IT leaders are striving for a data-driven decision-making culture – one that is increasingly underpinned by AI.
While most organisations are still in the preliminary stages of AI maturity, interest is huge. IT leaders recognise the gains to be made when using AI technology. From insights to security, across the board AI can support myriad objectives across organisations in all sectors.
Evidently, high-level data analytics is key to unlocking data value, but this is underscored by strategy and infrastructure.
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AI state of play
New business models
17%
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