Why Emerging Tech is Failing to Deliver Expected Returns

The Digital IQ survey, carried out by PWC, has revealed that technological progress in business has stagnated since 2007; even dropping in recent years. The score, “a measure of an organization’s capability to get strategic value from technology investments” led to the conclusion by PWC that even though companies today are smarter about emerging tech, the challenges of integrating new technology into the business have increased.

They also introduced what they believe are the “Essential Eight”; based on motivations to adopt technology in the future.

  • Adoption is driven by strategy – which varies greatly by industry sector and business model [as it should!]
  • Cost cutting and disruption power – are the main drivers for technology selection
  • Skill levels are not keeping up with technology investment – with training and development budgets failing to drive sufficient engagement in new technologies to meet investment ROIs.

So why have investment levels in emerging tech [as a percentage of overall technology spending] stagnated?

With emerging tech representing 17% of total digital technology budget in 2007, is it reasonable that it sits at a similar 18% in 2017? Although business leaders are aware of the highly disruptive, transformational impact emerging AI-related technology will have in the next 5-10 years, there is either a lack of awareness of the degree of this disruption, or other competing, more urgent investments that are keeping investment at this level.

The reason given by PWC is that companies are still investing heavily in the previous group of emerging technology [cloud, mobile, and analytics], with these technologies now classified outside of the category.

The one significant statement in the article that reinforces my own findings is that “Most organizations have still not dedicated specific groups and senior leaders to focus on creating real business value from their companies’ investments in emerging technologies”. Bingo!

As a pioneer in business intelligence capabilities, including analytics, I found that adoption and ROI were typically both lower than forecast. I believe both were largely impacted by these new technologies being managed by IT. In my book BI Strategy Guide, I outline the options and benefits of various models of driving more value from BI. Organisational inertia is always a problem when new ways of working are introduced into companies, but never was there a time when leaders need to wake up and realise that the current operating and cultural models within large organisations is seriously holding them back. IT, as an organisation, needs a complete redesign.

The waves of new technologies crossing the landscape at present are not the kind of underlying IT platform that IT was traditionally charged to manage. Whilst cloud and mobile may fairly be classified as ‘enterprise platform’ technologies, they are not as abstracted from daily work as the traditional bank of servers once were. Whilst it is easy to abstract cloud away as just another way of serving business applications, mobile has significantly changed the way we work. So too will the cognitive technologies rolling into the market as artificial intelligence continues to evolve. Where mobile has added new dimensions in where we can work, AI will change the way we work, the type of work we do, and how we make decisions. These are largely cultural changes, but they require transformational changes in approaches to training and development. IT needs to be able to focus on what they are very, very good at – and release them from the political complications of emerging technologies.

A second factor holding back adoption of new technologies are in their ‘ease of use’. The interfaces of most AI applications are still a long way from being humanised. We cannot realistically expect every employee to understand statistical mathematics. However, this is typical of applications designed by technical engineers, for normal human use. I am also highly confident that this will change over the next few years – as deep learning capabilities remove a large proportion of the need for data scientists to continually update analytical models. With a bit more thought into #theHumanFactor, user interfaces to AI applications will be more ‘wizard-like’, removing the need to understand which type of analytics best suits the question needing to be answered.

The current statis indicates a relatively passive approach to innovation and the significant difference AI related technologies will have. This provides a tremendous opportunity for more forward thinking, agile companies to take the lead and redefine industry norms. So what’s your strategy – regenerate or crash?

Read PWCs full article – 2017 Global Digital IQ Survey: Emerging tech insights


Leave a comment

Your email address will not be published. Required fields are marked *