In our last Tech Navigator, we looked at how organizations can be part of building the human-centric future. Now we need to move that on to the next stage: empowering humans and creating exponential impact by becoming AI-first entities.
No organization can afford to ignore AI, and many are already deploying it in some way. AI is not just another technology, but one that will upend the way organizations make money and remain competitive.
An AI-first company is made out of four building blocks. These are: the AI experience, AI engineering excellence, AI that's responsible by design, the AI operating model
AI-first firms rethink everything about how they're experienced, from using AI-led assistants to reimagining customer journeys with AI at the centerWatch the video
We explore next-generation software development and platform engineering. We also discuss the advent of AI operations, Data and MLOps for high velocity productsWatch the video
We explore the challenges posed by the explosion of AI, and how organizations will need to have robust processes in place to ensure that the risks are properly managed and mitigatedWatch the video
What does the future AI-first operating model look like? We discuss how to bring about the changes that are neededWatch the video
Incorporating AI assistants and 'in-the-flow' tools has the potential to unlock value for individuals and for the whole organization.
AI assistants and adaptable dashboards mean that humans have an interface that works for them.
Foundation models will help humans with their work, whether that's document analysis, drafting content or working with code.
More than 80% of employees say that AI makes them more productive
AI enables better, faster and more automated processes, which in turn leads to better outcomes. Smart companies are using AI as an opportunity to look at everything from processes to customer journeys.
Customers that are offered a fine-tuned customer journey spend 140% more than those with the poorest experiences.
Coders are in short supply, and many are over-worked, reducing productivity and efficiency in software development.
Tools such as OpenAI's Codex, GitHub's CoPilot, and OpenAI's ChatGPT can be used to build code and find bugs. Platform engineering can help speed up development and reduce burn-out by providing developers with a self-service platform.
Infosys believes that platform engineering can save up to 40% of developers' effort building products, and enable up to 25% faster time-to-value
AI systems should evolve and improve over time, but many don't have this capability. To get there, AI models will have to do three things: 1. Acquire up-to-date knowledge, 2. Advanced reasoning, and 3. Actuation – or acting in the real world.
Only 15% of firms achieve evolutionary AI design capabilities
AI is now part of our lives, from building products for businesses and consumers to scanning CVs for recruitment and managing remote workers, and from drug discovery to diagnostics.
Yet security and governance are only just catching up with the explosion of AI functions.
Organizations are increasingly conscious that they need to have thoughtful governance in place when using AI. However, as with security, it is vital that good governance and an ethical framework are baked in and part of the process, and not retrofitted after problems have surfaced.
Training datasets should include a representative cross-section of society: it is impossible to make fair automated decisions if training datasets exclude minority populations
Ethical AI demands the highest standards of governance and depends on transparency, reproducibility and compliance across the business.
Risk of bias in AI is a top three concern for executives, and more of a challenge to firms than insufficient subject matter knowledge
AI talent is a key challenge for businesses. What are the skills that the AI-first business will need?
At H3, prompt engineers will be in demand, and they are already commanding salaries of more than $300,000 a year.
For systems not yet in H3, (most advanced AI systems), data engineers with cross-domain knowledge will be hugely attractive, as will a new breed of AI architects that have both depth and breadth of software engineering knowledge
The AI-first operating model will have to be product-centric. This means building the organization around dedicated customer journeys and value streams rather than around traditional functions. But making these changes isn't going to be easy. We recommend microchange management to achieve this.
74% of C-suite and IT executives invest their money in product management, underlining product-centricity as a key business priority.