As technology continues to boom, we will start to see convergence and an ecosystem starting to emerge where technologies will need to work as one to be effective. Businesses will find it more challenging to continue updating processes with old legacy systems and architecture. It is only a matter of time where they will reach a critical mass, where whole infrastructures will need to be replaced to really offer the best services to their end customers.
Innovation and infrastructure
New AI and automation innovations lead to additional challenges such as big data requirements to effectively show value of these new technologies. Future tech trends rely on a comprehensive backbone being built. Businesses need to take stock and begin looking at rolling out priority technologies that can be deployed and developed continuously. Organisations must have a longer-term vision of implementation rather than the need for immediacy and short-term gains. Ultimately these technologies aim to create more intelligence in the business to better serve their customers. As a result, new groups of business stakeholders will be created to implement change. These will include technologists, business strategists, product specialists and others to cohesively work through these challenges.
The data challenge
AI and automation continue to be at the forefront of business’s minds. The biggest challenges are that automation is still in its infancy, in the form of bots, which have limited capabilities without being layered with AI and machine learning.
For these to work cohesively businesses need huge pools of data. AI can only begin to understand trends and nuances by having this data to begin with, which is a real challenge. Only some of the largest organisations with huge data sets have been able to reap the rewards. Organisations like Microsoft and institutions like NASA have been successful.
Compliance and governance
One of the critical challenges is technology governance. Businesses are acutely aware that these issues must be addressed but orchestrating such change can lead to huge costs, which can spiral out of control. For example, cloud governance should be high on the agenda. Cloud offers new architecture and platforms for business agility and innovation, but who has ownership once cloud infrastructures are implemented? What is added and what isn’t? Recent research from Pega on the new world of work found that 51 percent of organizations would spend on cloud-based solutions or AI.
But AI and automation can make a huge difference to compliance, data quality and security. The rules of the compliance game are always changing, and technology should enable companies not just to comply with ever-evolving regulatory requirements, but to leverage their data and analytics across the business to show breadth and depth of insight and knowledge of the inner workings of their business, inside and out.
In the past, companies struggled to get access and oversight over the right data across their business to comply with the vast quantities of MI needed for regulatory reporting. Now they are expected to not only collate the correct data but to be able to analyse it efficiently and effectively for regulatory reporting purposes and strategic business planning. There are no longer the time-honoured excuses of not having enough information, or data gaps from reliance on third parties, for example.
AI governance is business critical, not just for regulatory compliance and cyber security, but also in diversity and equity. There are fears that AI programming will lead to natural bias based on the type of programmer and the current datasets available and used. For example, most computer scientists are predominantly male and Caucasian, which can lead to conscious/unconscious bias, and datasets can be unrepresentative leading to discriminatory feedback loops. Gender bias in AI programming has been a hot topic for some years and has come to the fore in 2020 again within wider conversations on diversity. By only having narrow representation within AI programmers, it will lead to their own bias being programmed into systems, which will have huge implications on how AI interprets data. As a result, new roles will emerge to try and prevent these biases and build a more equitable future, alongside new regulations being driven by companies and specialist technology firms.
Humans versus AI?
As AI and automation comes into play, workforces fear employee levels will diminish, as roles become redundant. There is also inherent suspicion of AI among consumers and certain business sectors. But this fear is over-estimated, and, according to leading academics and business leaders, unfounded. While technology can take away specific jobs, it also creates them. In responding to change and uncertainty, technology can be a force for good and source of considerable opportunity, leading to, in the longer-term, more jobs for humans with specialist skillsets.
Automation is an example of helping people to do their jobs better, speeding up business processes and taking care of the time-intensive, repetitive tasks that could be completed far quicker using technology. There remain just as many tasks within the workforce and the wider economy that cannot be automated, where you need a human being. As Google Chief economist Hal Varian says, “Automation doesn’t generally eliminate jobs. Automation generally eliminates dull, tedious, and repetitive tasks. If you remove all the tasks, you remove the job. But that’s rare”.
Businesses need to review and place initiatives in play to upskill workforces to create augmented workforces. Reflecting this, a survey on the future of work by cloud software specialists for customer engagement, Pega, found that 67% of businesses plan to invest in robotic process automation, 68% in machine learning, and 80% investing in perhaps more mainstream business process management software.
Putting customers first
There is also growing recognition of the difference AI can make in providing better service and creating more meaningful interactions with customers. Another recent report examining empathy in AI from Pega saw 68% of survey respondents say they trust a human more than AI to approve bank loans. 69% felt they were more likely to tell the truth to a human than AI, yet 48% of those surveyed see the potential for improved customer service and interactions with the use of AI technologies.
2020 has taught us about uncertainty and risk as a catalyst for digital disruption, technological innovation and more human interactions with colleagues and clients, despite face-to-face interaction no longer being an option. 2021 will see continued development across businesses to address the changing world of work and the evolving needs of customers and stakeholders in fast-moving, transitional markets. The firms that look forward, think fast and embrace agility of both technology and strategy, anticipating further challenges and opportunities through better take up of technology will reap the benefits.
About the author
Gurpreet is a passionate business development specialist with over 10 years’ experience assisting global organisations with their marketing needs, consulting on campaign strategy, media planning, market research, thought leadership and events. His approach focuses on aligning the clients marketing objectives to their business needs to achieve the desired outcomes. His experience includes leading the technology practice at Longitude (Financial Times Thought Leadership division) and creating a marketing services proposition at Future plc.Back to Blogs