Artificial intelligence (AI) has come a long way, transforming from basic programmable functions to advanced learning systems. Join us as we explore the exciting world of generative AI and its potential to revolutionise all industries.
With the emergence of machine learning, algorithms gained the ability to improve through experience. Deep learning took it a step further, enabling algorithms to process data with greater abstraction. Now, we have entered the newest stage of AI evolution: generative AI. This cutting-edge technology allows machines to create new content by building on patterns learned from vast amounts of data.
Here are our 6 key lessons on what is required to succeed with AI:
Artificial intelligence is the technology enabling machines to mimic human cognitive functions and behaviours.
It has evolved from basic programmable functions to advanced learning systems. Machine learning emerged, enabling algorithms to improve through experience. Deep learning further refined this by structuring algorithms in layers, processing data with greater abstraction. The latest stage is generative AI, which synthesises new content by drawing on patterns learned from vast amounts of data.
28%
faster
with
38%
better quality
Generative AI learns to recognise patterns from vast amounts of datasets, for example text and images. It then uses this knowledge to generate new content based on probability.
When creating prompts for generative AI, it's crucial to be clear and specific. The quality of the output is directly influenced by how well the input is structured. A well-crafted prompt guides the AI towards generating content that aligns with your intended context and goals.
AI has been around for a long time. Why did generative AI suddenly change "everything"?
AI's transition from tools to teammates
Generative AI is not just a technology or a business trend. It is a profound shift in how humans and machines interact.
Another way of saying that is that machines are evolving from being our tools to becoming our teammates.
As generative AI has made machines conversational, they have also become more like humans.
Until now, we have had to learn the machine’s language. Now it's learning ours.
While the potential is evident, generative AI introduces several concerns that stall adoption
In our experience, security and costs remain common barriers toentry. This is also evident in a recent MIT survey of over 300 executives, which we have summarised here:
Regulatory, compliance and mitigating risks related to data breaches, misuse of AI systems, and ensuring robust security protocols.
Regulatory landscape is unclear.
Navigating the moral implications of AI-generated content, including biases, fairness, and the potential for misuse.
Managing the financial implications of adopting generative AI, including initial investment, ongoing maintenance, and scaling can be complex and uncertain.
Can current hardware and infrastructure provide the backbone for generative AI platforms and tools? The choices are complex and require planning.
Generative AI requires high quality, accessible data. Firms that have not thoroughly set up their data platform to scale will have issues ensuring the reliability, accuracy, and consistency of outputs generated by AI systems to meet business standards.
The skills to run generative AI projects are in short supply. Talent acquisitions and development will require investment.
The AI platform "Sapience" is ready for you
Sapience, Itera's AI-platform, was built to accelerate our own adoption of generative AI. We now make this solution available to our customers.
When Open AI released ChatGPT 3.0 in 2022, Itera quickly scrambled to examine how we should start adopting the technology, since generative AI can fundamentally impact how we work. Providing access to everyone is a key to success. But as tools develop so fast and introduce new risks, it is challenging to decide where and how to start.
To overcome this, Itera built the Sapience AI platform to
give every employee safe and cost-efficient access to generative AI to drive learning and adoption
allow us to rapidly create value through simple use-cases while also enabling advanced use
allow both personal and enterprise exploration
Get in touch
We help enterprises create, customise, deploy and manage generative AI copilots including Copilot for Microsoft 365.
What are the industry perspectives on the Digital Factory and digital product strategies?
McKinsey & Company refers to a digital service-focused, strategic approach as building a Digital Factory.
As a business and technology consulting firm, McKinsey states that they consider the Digital Factory approach to be the answer to scaling digital transformation in large organizations. They describe the impact on executives from traditional organizations when they observe the creative energy inherent within cross-functional Digital Factory teams.
Forrester Research presents a similar approach that they call the product-centric IT operating model.
As a technology research and consulting firm, Forrester equates digital services to products and asserts that product management is a core capability for most organizations. They describe the product-centric IT operating model as a complementary approach for technology teams that can better support the business shift from traditional marketing-led product development to agile, iterative digital innovation creation.
Gartner defines their recommended approach as Fusion Teams.
As a research and advisory firm, Gartner advocates for the adoption of fusion teams – multidisciplinary digital business teams – as critical to finding success with digital transformation. In their view, technology leaders can foster distributed digital service delivery in their organizations by forming fusion teams to maximize business value while addressing the human aspects of digital business and innovation.
