By Vamsi Kora, Chief Data and Analytics Officer, Apexon
AI, among the fastest-moving sectors in high-tech, presents enterprises with tough decisions on which initiatives to prioritize. According to a recent PwC report, AI could contribute a whopping $15.7 trillion to the global economy by 2030. AI technologies have has already brought significant, transformative benefits to businesses in every sector, yet significant productivity gains are still to come. The World Economic Forum, for instance, predicts that labour productivity in developed countries can improve by as much as 40% through 2035 thanks to the impact of AI, while IDC expects that by 2026, 60% of Indian enterprises will leverage AI, resulting in a 20% productivity uplift for workers.
In short, businesses’ intelligent, strategic choices made now will impact their ability to modernize and outpace their competition. In addition, harnessing AI technologies will require businesses to combine skilful technical implementation while being sensitive to culture change while establishing an ethical framework for the new AI era.
As AI innovation accelerates, focusing on these four areas is critical to maximizing value and gaining a competitive advantage.
1. Trusting the Tech
AI bias, lack of accountability, unanticipated behaviour and the sheer complexity of AI systems are just some of the concerns around increased AI adoption. Therefore, creating a culture of trust will be critical to AI adoption for both consumers and employees. The analyst firm, Gartner, calls this concept AI TRiSM – AI trust, risk, and security management – and predicts that by 2026, organizations operationalizing AI TRiSM will attain a 50% improvement in adoption, business goals, and user acceptance.
Trust can’t be magicked into existence: businesses must lay the foundations with a governance framework of policies and tools that demonstrate AI model governance, reliability, trustworthiness, efficacy, fairness, robustness, and data protection. Organizations prioritizing transparency (model explainability and transparent data privacy practices, for example) and investing in upskilling the workforce to implement AI to its fullest advantage succeed in assuring trust in their AI models.
2. A Self-service Approach to Software Development
The pace of digital development and delivery has been speeding up since development teams switched to Agile methodologies. However, in the last few years, as digital initiatives have rocketed thanks to the mega-digitalization triggered by the pandemic, enterprises have struggled to meet the demand for digital products and services.
Platform engineering is an emerging approach to software development and delivery that utilizes self-service workflows, which in turn enable rapid delivery for digital initiatives. By providing reusable tools and services powered by AI, platform engineering accelerates digital transformation, improves the developer experience, and enables organizations to realize business value faster.
With platform engineering, AI and automation technologies are embedded into engineering tools and services. These reusable components help enterprises speed up the development of new products, lower maintenance efforts needed for platforms and increase developer productivity. Gartner estimates that by 2026, 80% of enterprise software engineering organizations will establish platform engineering teams internally to support developers and improve DevOps collaboration.
3. Increasing Agility with Adaptive AI
The new breed of deep learning, neural networks use real-time feedback to adjust for new data, real-world changes, and modified goals by revising their code post-deployment. This “adaptive” AI enhances user experience, better business outcomes, and faster deliveries. Furthermore, these systems improve business agility significantly. While the benefits of adaptive systems are clear, for organizations with relatively low AI maturity, adaptive AI may require a significant upgrade on their current tools and practices.
4. AI-powered sustainable IT
At a time when all business leaders are under pressure to act on climate change, all IT leaders – and AI pros in particular – will have an important role to play in driving meaningful improvements. Governments, investors, customers and employees are all increasingly concerned with how organizations can advance their environmental, social, and governance (ESG) goals. AI technologies are already helping enterprises become greener by automating and streamlining processes and modeling more efficient ways of conducting business. This trend will continue: AI systems are increasingly helping organizations reduce their carbon footprint, for example, with the use of embedded IoT devices that reduce power consumption, or greenhouse gas management systems that harness an organization’s disparate ESG performance data into a single unified view with recommended actions to help them meet their goals.
Implementing AI to Boost Productivity and Value
AI adoption has been steadily rising for many years thanks to increasingly sophisticated algorithms, data volumes, and improved computing power. Despite the inevitable hype surrounding AI, implementing these tools has already helped organizations realize tangible, wide-ranging improvements with more significant gains expected in the near future.
However, productivity gains, like other benefits from AI, will need to keep pace with user adoption and acceptance. The most significant AI concepts will challenge organizations structurally, culturally, ethically, and technically. Prioritizing these will help enterprises achieve their digital transformation and business agility objectives while also ensuring transparency, trust, and social responsibility.