Artificial intelligence has been part of business optimization discussions for so long that it is often hard to believe that the technology is often more of a potential digital modernization project than one which is proving its value across the connected society.
There is little doubt that AI has a significant role to play in not only required digital transformation but also improved customer experience. If you take the time to listen to any AI evangelist, the consensus is that companies need to start aggressively pursuing strategies that will demonstrate both ROI (return on investment) and the integration of automation in business workflows. The caveat is that AI adoption rates remain low, even though most decision makers are more than aware of its potential.
According to a recent report from Gartner, there is already a limited window for companies to derive competitive advantage from AI.
A survey of more than 300 CFOs and finance leaders in May of this year found that 90 percent of respondents expected to use the lessons learned from the pandemic to invest in AI solutions. In addition, the analyst noted that there was an increased awareness that functional digitization goals would only be reached if more experimental projects were put on hold in favor of those that deliver results in the not-so-distant future.
For example, the report said, organizations that use AI to identify which customers are prone to paying their bills late are not doing anything mind-blowing with the technology. Granted, being able to reduce the chance of an outstanding debt by proactively reaching out to customers has an impact on cashflow and demonstrates clear ROI, but a true transformation would be to see what customers have a history of delinquent payment at the sales stage and prioritize those customers accordingly.
“There’s nothing wrong with using AI to modernize the finance function. It’s very important work,” said Clement Christensen, director in the Gartner Finance practice. “However, the most impressive rewards of AI will fall to the CFOs who think bigger about how the technology can fundamentally change the way their company does business.”
With that in mind, we should take a few minutes to look at why AI is both a strategic priority for decision makers and a technology that still has an almost glacial adoption rate.
Problem Solving Comes First
If we think about required modernization versus deploying AI as part of a competitive advantage, then the companies that start with a problem that needs solving first – as opposed to one that needs “modernizing” – are likely to be the ones that understand the digital journey that they need to take.
One problem for most companies is that they know the benefits but have either slowed or curtailed their AI plans, a report cited by TechRepublic said. Juniper Networks’ latest study – “AI is set to accelerate … is your organization ready” – addressed this curious juxtaposition, with 95 percent of executives stating that their companies would benefit from AI, but only 6 percent of those people said that AI -powered solutions were part of their daily workflows. In addition, most respondents – around 73 percent – said that they were struggling to even get their projects started.
The underlying concern, the report said, was that the advantages of AI were often overshadowed by the challenges involved in implementing it. Respondents cited three ever-present adoption inhibitors: AI-ready technology stacks, workforce readiness and AI governance. To make things even more problematic, there was a defined choke point in the ingesting, processing and managing of data to feed the AI itself.
This problem is one that repeats itself on myriad occasions. As many as 50 percent of all AI projects fail to go from prototype to production, while the scalability of the projects is often hindered by not really having a contextual idea of what the AI should actually do. More often than not, it is the harvested data that is putting a spanner in the works.
Manish Mistry, our CTO, explained the conundrum in his most recent article for Forbes. He noted that the expected AI revolution is less advanced than it should be, with adoption rates often glossing over the less-than successful implementation of the tech itself. And while AI remains a strategic priority, he said, it is “hard not to think that we are stumbling along in the dark.”
“Effective implementations rely on good data, and for most organizations, this presents a challenge. Basically, their data is currently not up to the job,” Manish said. “First, the volume of data available is vast and continues to grow. Secondly, only 20% or so is useful — the so-called “business critical” data. Thirdly, accessing that data, cleaning it up and categorizing it is an arduous, time-intensive task that can quickly lead data teams down a rabbit hole.”
In other words, it’s hard for businesses to know which piles of data are going to be useful until they start organizing them. Companies are often at various stages of a potential AI journey, which is makes it vital that they work with digital partners that understand both the ever-evolving AI landscape and the core areas of AI-focused services – machine learning and pattern recognition, natural language processing and computer vision and image processing.
Leveraging the Competitive Edge
At Apexon, our digital engineering teams have a deep understanding of all three of these services. And, unsurprisingly, it is data that is the platform for innovative and game-changing solutions.
A key factor in the creation of unique and engaging customer experiences, for instance, are the insights that are generated by mobile app usage, digital clicks or transaction data posting. To date, our work with companies such as Lifescan (Blood glucose data), Westhill (Home instance data) and Urgent.ly (auto insurance data) has helped them achieve positive outcomes for not only their customers but also business optimization strategies.
These individual projects have involved facilitating the highest standards of data protection, mitigation of risks and fraud, spam detection (using AI-Machine Learning algorithms) personalization services.
In addition, we have developed a configurable AI algorithm for a leading national bank, created a smart scheduling solution for a well-known healthcare company and worked with a life sciences company that specialized in cancer diagnostics and patient treatment. We also built a healthcare-centric accelerator – the Healthcare Data Analytics Platform (HDAP) – to help companies harness their data assets quickly while complying with regulatory requirements.
All these solutions were designed to give the companies a competitive edge, simply by using data and AI services to alleviate demonstrated choke points.
AI Attracts Investment
If you want further proof that AI is not only one of the hottest new kids on the block but also one that can provide companies with the competitive advantage that they seek, then you only have to take a quick glance at the amount of money that is being invested into AI-focused start-ups.
According to the latest quarterly report from financial analyst CB Insights, Q2 2021 alone recorded more than $20 billion in investment in Ai firms, with more than 41 percent of all investment in US-based companies. Much of this funding was in so-called “mega-rounds” of $100 million or more, while 24 companies attained the unicorn status for the first time – a unicorn is a company that has a market valuation of more than $1 billion, as we know.
Cybersecurity and processor companies were the major beneficiaries of this funding largesse, the report said, while finance, insurance, retail and packaged goods all received significant capital injections. Healthcare was the single biggest sector for investment, attracting around 17 percent of all deals in Q2 – a more detailed dive into the numbers can be found in this recent VentureBeat article.
These numbers should not come as a surprise. AI has been part of Gartner’s annual hype cycle for years, for example, with the analyst continually highlighting the tech as one that can be both a facilitator of forward-looking business optimization projects and a digital engineering asset that is scalable, transformative and, ultimately, trustworthy. The latter is of particular significance, as AI has always been painted as the scary thing from the movies.
Over the last decade, however, what some may consider as science fiction is increasingly a scientific reality, with the connected society happy to leverage the tools that exist in the digital ecosystem. Mark Weiser, the so-called father of ubiquitous computing, said as much back in 1991 when he commented that the most effective tools are often the ones that work quietly in the background – non-fictional AI such as Amazon’s is helpful, intuitive, conversational and (almost) invisible.
Proactive, Not Reactive
AI is built to follow a set of basic rules – what, who, where, when why and how – and those rules provide a framework that understands the bigger picture. In addition, organizations that rely on data to augment both business processes and customer experience will become increasingly reliant on the insights that thinking machines can provide. The digital ecosystem that we all live in has a finite capacity to evolve and cognitive technologies are the ones that are most likely to deliver business value.
To put it bluntly, companies that don’t see AI as an information gatherer and a problem solver are missing the point. AI can observe how we move, the things we say and the decisions we make. All these insights play into how business leaders can not only optimize their brand awareness but also augment the customer experience with digital technologies that have been designed to deliver value.
The rise of the machines may still be rooted in the dystopian visions of the future that Hollywood loves to produce, but the simple truth is that AI is not looking to take over the world. Once you understand that it can be the invisible helper that takes your company to the next level, then it becomes the difference between simply modernizing a business and true digital transformation.
To learn more about how to improve your organization’s use of AI in data analysis and customer experience, then check out this link.
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