Published date: April 28, 2022
Expert article by Gunjan Agarwal, Software Engineering Manager
Picture a Formula 1 racing car. It’s crafted for high speeds and has the use of the most advanced engineering and technology. But what happens when the brakes do not work?
This is a high wire balancing act that most organisations perform these days. In a world that preaches the mantra of ‘disrupt or be disrupted‘, it becomes really easy to adopt every emerging technology. But without a structural basis of stability, even the most advanced of companies could just go into a tailspin.
Today, technology, often overshadowed by new inventions, remains a crucial element. According to a McKinsey study, it was revealed that 92% of the respondent companies indicated that the business model has to be revised after the integration of new technologies. Whether it’s AI transforming the service industry or blockchain enhancing security in finance, businesses must adapt to these technological shifts. However, an exclusive focus on innovation carries inherent risks that must be managed.
Finding a way out of the duality of “fueling innovation…” while “protecting the core business” has always required a sophisticated combination of risk, strategy and operations. In this article, we will examine how companies may be able to address this problem, provide concrete examples and strategies to manage the risks effectively.
Exploring the Risk Dimensions of the Technology Innovation Continuum
The implementation of new technologies leads to the emergence of several risk aspects which can potentially undermine business stability. These risks are typically not self-contained; more often than not, they overlap and multiply each other to form a conglomerated risk factorial. By deconstructing and thoroughly analysing these interconnected risks, organizations can develop well-formulated mitigation strategies.
1. Implementation Risks
As the companies are trying to move towards the implementation of lower-level edge technologies, the associated problem of integration, compatibility and up-scaling forms part of the strategy. New technologies such AI, Cloud, and IoT tend to integrate with systems that were not designed for them and makes future technological integration even harder. Integration issues in compatibility and surprises stemming from glitches could be profoundly detrimental, resulting in time spent on maintenance and downgrades in work output.
For instance, a banking institution that sought to implement AI based credit evaluation was confronted with the problem of constant system downtimes and inaccuracies during the scoring due to legacy systems. However, a phased and gradual approach to system integration proved beneficial, as it allowed system stabilisation and the resolution of glitches before full integration.
2. Cyber Security Risk
Due to being relatively new, many technologies lack fully developed security parameters and therefore become a security risk. The paradigm shift of cloud is great but it raises potential threats to systems when shifting of data is done from on premise systems to off premise environments. Additionally, risk factors which include data breaches, cyber attacks, and violation of regulatory compliance become apparent due to the lack of security frameworks that are designed for the newly introduced technologies.
Example: In 2022, the Ronin Network, an Ethereum sidechain developed for the popular NFT game Axie Infinity, experienced a significant security breach. Hackers, later identified as the North Korean group Lazarus, exploited vulnerabilities in the network’s validator nodes, gaining control over five of the nine nodes. This majority control allowed them to authorize unauthorized transactions, resulting in the theft of approximately $615 million worth of cryptocurrency.
This incident underscores the critical importance of implementing robust security frameworks in emerging technologies. The Ronin Network’s reliance on a limited number of validator nodes without adequate security measures made it susceptible to such an attack. To mitigate these risks, it is essential for enterprises to adopt comprehensive security protocols, including full encryption and stringent access controls, to protect sensitive information and maintain regulatory compliance.
3. Operational Risks
Workflow interruptions as well as poorly done training can hamper productivity and create problems. Workers need time to get used to the changes in systems, and without careful training, organisations will have low goodwill and increase in mistakes.
Example: In 2022, a study published in BMC Health Services Research highlighted significant operational challenges faced by Swedish healthcare leaders during the implementation of artificial intelligence (AI) systems. The research identified three primary categories of challenges:
- Conditions External to the Healthcare System: Factors such as legal regulations, data privacy concerns, and the availability of external support influenced the adoption of AI technologies.
- Capacity for Strategic Change Management: The necessity for robust leadership and strategic planning was evident, as many organizations lacked the internal capacity to manage the transformative changes associated with AI integration.
- Transformation of Healthcare Professions and Practices: The introduction of AI required significant shifts in professional roles and daily practices, necessitating comprehensive training and adaptation periods for staff.
4. Financial Risks
Implementing new technologies often leads to financial challenges that can strain company budgets. Inadequate financial forecasting may result in unforeseen expenses, such as cost overruns and delays. A study revealed that 25% of contact center projects experienced cost overruns and delays, with overruns averaging 90% of the original budget. Additionally, hidden costs related to system maintenance, integration complexities, and employee training can further impact budgets.
Example: The cost of migrating their data to the cloud was greater than what they predicted, leading over 55% of the businesses adopting cloud infrastructure to suffer an unplanned increase in costs. In order to avoid such a scenario, firms need to adopt a more specific budgeting approach and periodically review costs during every stage of the technology life cycle.
5. Breach of Standards
Another common issue of concern pertains to standards that are delayed or poorly developed in relation to technology, making it difficult for companies, particularly those in the finance and health sectors. A systematic approach needs to be taken towards adoption of new technology otherwise this can invite penalties and erode the trust of consumers.
Example: The stricter regulatory frameworks in the global financial markets have arisen amid drastic changes in how the financial industry works, especially with increased interest in decentralised finance (DeFi) and digital assets. Moving resources to this market requires high-speed innovation for compliance with a large number of requirements. As evidenced by previous actions of financial authorities, for example, fine or suspension of the licence for not meeting standards.
6. Organisational Risks
Restriction of technology is often met with multiple organizational problems that can greatly stifle successful adoption and execution. One of the most critical issues is the reluctance of employees to adapt to changes. Nearly half of business leaders consider this to be the top challenge in regard to digital transformation initiatives. This reluctance can originate from a fear of losing a job, lack of comprehension, or uneasiness towards new approaches. Moreover, there is a skills gap within the workforce; 47% of employees do not consider themselves digitally competent which adds a high level of stress towards newer workplace tools. This gap requires clearly defined training and development strategies in order to provide employees with the necessary skills. Additionally, the demands placed on IT budgets may substantially shift during the implementation phase as they have to manage integration, provide support, and troubleshoot a host of other issues. If appropriate planning is not thought out in advance, this intense burden becomes a recipe for strained resources, burnout, and reduced functionality which is detrimental to the overall success of the new technique implementation.
Example: In 2022, a study focusing on family-owned businesses revealed significant organizational challenges during the adoption of artificial intelligence (AI). Employees expressed apprehension about potential job displacement due to AI integration, leading to resistance against new technologies. Additionally, companies situated in remote areas faced difficulties attracting the necessary talent for AI implementation, further hindering progress. These obstacles underscore the necessity for comprehensive change management strategies, proactive support for IT staff during adoption phases, targeted upskilling initiatives, and phased assistance to ensure effective technology integration.
Strategies for Innovation Risk Management
The risk and threat posed by the adoption of the technology is a risk that needs strategy opportunities on how best to develop it and apply it to the organisation with these gaps bridged, below are some of the strategies that are proven to have worked well in issues of innovation:
1. Conduct IT Assessment
It is important to evaluate the specific technology, its stage of development, and whether it is suitable for the company in the long run. Diligence is not just an examination of the market perspective and competitors, but it also envisions the operational, financial and overall assessment of a technology including the systems in place.
For example, when a large retail company decides to implement a machine learning inventory system, the first step is to evaluate its history in the selected market, as well as the degree of its reliability and the possibility of integrating it. They noted that there was a success of cost reduction in the like retailers and thus saw justification of the investment to pilot and subsequently implement
the system.
2. Rollout and Testing on Sections of the Organization
Organisations can undertake a new technology in a section of the organisation to confirm its feasibility. This process allows an organisation to minimise unnecessary change and assist to identify the challenges earlier on. Testing of the operational aspects first through small scale IT in organisations guarantees thorough examination of its possibility among more members of the organisation later on.
For example, Netflix began its gradual transfer towards AWS while ensuring the territory’s stability at every stage. Rather than moving everything at once, Netflix started by transferring supplementary services, and later on, important aspects, such as the core functions, were included. In doing so, it assisted in minimising risks and allowed for moving into the cloud without hitting any bumps, setting standards as far as large scale migrations to the cloud are concerned.
3. Strong Testing and Monitoring Systems
Unit tests as well as system integration tests and security risk assessments are all designated as testing protocols which assist in ensuring that weaknesses are eliminated and strong performance is assured. Once a system is up, routine checks assist in providing a clearer picture, thus assisting in the easy identification of the possible avenues for failure.
Example, the Cybersecurity Journal notes that continuous control and evaluation cut security issues by 65%. According to the Journal, this reduces the risk of potential issues such as fraud. In fact, popular banks employ real time due diligence controls on the block chain to avoid fraud in the first place by scrutinising activities.
4. Employee Training and Change Management
For a company to remain steady, its workers must be trained to use its new systems. Research shows that efficiency is easier to come by when companies invest into the proper training of its workers. Furthermore, the programs make sure that individuals are warm to the idea of the change, making life easier while the transition occurs.
Example: It was not simply the technological offering that drove the 30% increase in operational efficiency of the firm that was using a real-time tracking system IoT device, but extensive training of all team members on how to use the new system as well. This effort, which was complemented by change management frameworks, contributed to the 30% rise in operational efficiency metrics.
5. Setting Up a Governance structure
When technology is governed properly, good decision-making, accountability and risk management can be exercised across the entire technological lifecycle. In such a case there are boundaries to what is deemed as reasonable within the scope of integration, resource deployment and new technology usage as it is perceived to help achieve the strategic goal of the organisation.
Example: In the drug discovery process AI helps the pharmaceutical industry albeit under a governance framework meant to meet the healthcare and ethical requirements. By establishing a governance framework, firms are able to review and fine-tune the AI dose calibration and algorithmic output to be fair and timely with compliance to legal expectations that keep changing.
Real-World Examples in Managing Innovation Risks
Netflix’s Relocation Experience to the Clouds: The gradual transition to the cloud by Netflix enabled her to accommodate usage patterns without losing containment. As a result, when services were turned on when and where they were required, Netflix avoided major outages, which underscored the need for a phased strategy for lowering operational risk.
AI in Healthcare: Use of AI for diagnostic work is changing the healthcare sector, however, focusing on risk structured approach is still essential. According to research commissioned in 2022 and published in the journal of Healthcare Informatics Research, AI-enabled diagnostic instruments had more accurate diagnostics up to 20%, but also pointed to the necessity of adequate validation to avoid administration and ethical issues.
Blockchain in Finance: Blockchain’s capacity for facilitating safe transactions has interesting prospects in the future. A report by Deloitte, on the other hand, indicates that 39% of fintechs in the United States named regulatory risks as the main barrier for advocacy of blockchain. It’s critical to address these challenges through effective risk containment strategies in order to reap the benefits of blockchain technology.
Autonomous Vehicles: The concept of self-driving cars is expected to greatly change the transport sector but some profess security concerns. Research shows that autonomous vehicles may be responsible for reduction in accidents, but the complex issues of ethics and laws need to be thoroughly addressed by the algorithms before mass uptake is realised.
Conclusion
As companies seek more ways to grow, managing the risks associated with technology implementation and stability requires discipline. Planned processes such as thorough due diligence, roll out strategies, timetabling, and governance processes are examples of other measures that seek to avert a scenario where innovation undermines growth. In a fast-changing digital landscape, where competition requires constant change, such companies would be in a sound position for growth and robustness in the long term.
