In the wake of the COVID-19 crisis, many businesses are accelerating their digital transformation plans to gain a competitive edge. Yet, scaling up and achieving the full potential of any emerging technology can be difficult. Drawing on lessons learned deploying technology solutions to some of the world’s leading businesses, Rob Smith, CTO of award-winning cloud services provider Creative ITC, explains how the growing trend of as-a-Service IT models is enabling organisations to harness the potential of AI to achieve greater ROI.
Uptake of artificial intelligence (AI) and machine learning (ML) is continuing to rise at a rapid rate as businesses progress their digital transformation plans. Offering firms new ways to accelerate and improve decision-making, efficiency and customer service, two thirds of companies plan to invest more in AI over the next three years.
Some of the largest investment in AI is set to come from the banking and finance firms. Spurred on by pandemic challenges, half of UK banks plan to invest more in AI, and global annual spending on AI by banks and finance firms is predicted to reach $64.03 billion by 2030.
AI has proven its value particularly in in middle office areas such as risk management, payment fraud and debt analysis. Institutions can automate their credit evaluation processes, resulting in faster loan decisions. AI and ML deployments are also helping firms to drive down fraudulent financial transactions, flagging suspicious patterns to expedite necessary interventions. These areas will be where the industry will allocate much of its investment over the next three years to developing automated threat intelligence and fraud analysis applications.
As AI gathers momentum, organisations in other industries are developing solutions to solve more complex challenges. Chatbots are becoming more prominent across the retail sector and robots and autonomous vehicles are being deployed to reduce risk to workers in manufacturing and warehouses.
In architecture, engineering and construction, the newfound ability – to quickly interpret data and make informed decisions with minimal human input – is a game changer. As well as boosting productivity through workflow automation and making busy construction sites safer, there’s also a role for AI to inform designs, aid materials selection and improve how plans are translated into completed structures. With increased use of drones by construction teams and growing adoption of advanced cameras by site workers AI can easily knit together captured images and videos into usable 3D models and compare with planned designs to rapidly identify deviations. Advanced analytics enable millions of permutations of how project schedules and costs will be affected to ensure the right action is taken and minimise risks and delays.
Moving beyond individual structures, AI can also be deployed successfully at large scale developments to enhance urban planning and highlight sustainability opportunities, analysing and optimising views, daylight, wind flows, traffic movements, parking and more.
Not a silver bullet
The transformative potential of AI is widely recognised, and it’s not surprising that the recent
AI in a Post-COVID-19 World report revealed that 72% of leaders feel positive about the role it will play in the future. However, unlocking the full potential of new technologies is always challenging in practice. Although many firms are achieving successful results from AI, scaling up enterprise-wide remains unattainable for many. Trying to scale up AI solutions often reveals underlying problems, meaning AI projects frequently over-run, overspend and fall short in terms of results.
The most common barriers to scaling up AI are:
- Legacy infrastructure issues
Half of business and technology leaders say their legacy processes and technologies do not support AI. Huge processing requirements swamp data centre and network capacity, causing latency issues or even outages. Trying to share actionable insights with stakeholders in multiple locations can reveal further weaknesses in legacy IT infrastructures, which haven’t been designed to share such datasets securely at speed and scale. Frequently, this leads to poor user experiences and collaboration challenges.
- Unforeseen costs
New technologies are continually straining IT budgets. Three in five IT leaders say lack of understanding at board level and commitment towards investing in AI is a major obstacle hindering expansion plans. Of course, total cost of ownership (TCO) doesn’t stop with acquiring the AI solution itself; it also includes implementing and maintaining the right IT infrastructure and integration systems to support long-term AI deployment.
- Skills shortages
Lack of AI skills in-house is a major concern for half (48%) of businesses. To optimise AI workloads and allow an organisation to realise its potential business benefits, specialist IT skills are required. Most firms aren’t able to employ and retain large, multi-skilled IT teams, or devote adequate resources to ensure long-term AI success.
Overcoming obstacles
Cloud adoption is one route enabling IT teams to access the latest technologies and computing power required for AI. Research shows that the organisations achieving the biggest gains from AI are taking more advantage of cloud infrastructure than their peers. The best performing businesses deploy two thirds (64%) of their AI workloads in public or hybrid cloud, compared with 44% at other companies.
Businesses are increasingly turning to Infrastructure-as-a-Service (IaaS) solutions to gain on-demand access to cloud-based systems and specialist skills for successful AI deployment. This gives them newfound agility, while offloading the burden of hardware costs and upgrade burdens to a managed service provider (MSP). A specialist MSP will boost in-house resources, taking away the headache of designing, implementing, managing and optimising IT infrastructure and AI systems. The MSP route quickly pays back with savings on data centre space, infrastructure, licensing, support, training and headcount, providing a fully-managed service in a predictable, monthly OpEx model.
Futureproofing your AI investment
When transitioning to the cloud and an IaaS model to boost your organisation’s AI capabilities, remember not all clouds or cloud services providers are the same. Seek out a provider with a strong track record in in your business sector who will offer a tailored, fully managed solution to meet industry and regulatory requirements, allowing you to retain data and workloads on-premise, while accessing the latest technologies across public, private and hybrid cloud environments.
Scrutinise the fine print to ensure you’ll benefit from access to the latest technologies and regular updates, rather than having to invest in expensive upgrades during the contract. Take a close look at their technical credentials, too, to be confident they can offer the right IaaS solution with ongoing management, optimisation and UK-based 24/7 support. In particular, check their expertise involving advanced graphics processing units (GPUs) capable of handling vast and complex workloads simultaneously, which are essential to high-performance, hyperscaled computing for rapid AI and real-time business analysis.
As AI deployment gathers pace, demand for extra infrastructure capacity is increasing and cloud adoption rates continue to rise. Some of the world’s leading businesses are spearheading a shift towards as-a-Service IT models in order to achieve greater return on their investment in these new technologies. IaaS is empowering ever more effective handling of complex and shifting AI workloads, with stress-free management. The result is flexibility, speed and scalability on a realistic budget and time frame, enabling firms to unlock greater operational and strategic benefit and stay ahead of the competition.
