Is your AI strategy tool or value focused?
According to Gartner, “By 2028, more than 50% of enterprises that have built their own large language models (LLMs) from scratch will abandon their efforts due to costs, complexity, and technical debt.”?This is an alarming prediction, especially given the immense attention that Generative AI is receiving.
Last month, we shared key takeaways from the Gartner Data & AI Summit in?Ignition. Kris and Nischay highlighted three critical focus areas for AI success:
Catch up on their insights from our recent webinar.
As you shape your AI strategy, where are you focusing your efforts? If your focus is on the tool, how are you ensuring it creates value for both your customers and employees.
Harj Chand - [email protected]
Gartner Decoded: Translating AI trends to realise practical business value.
Recorded: 5th September, 2024 | Runtime: 26min
A discussion with Ignite’s Consulting Services Manager Kris and Solution Architect Nischay on how to translate Gartner’s trends into practical, business value.
AI | The Gradient | 9 Sept
What's Missing From LLM Chatbots: A Sense of Purpose
Ignite: This article offers an interesting critique: LLM chatbots lack a genuine sense of purpose and if we want these tools to be genuinely useful, maybe it's time to rethink how they grasp the deeper intentions behind human communication. So, I guess the question is: Do we want chatbots that just talk the talk, or ones that truly understand what we're saying?
AI | The Conversation | 5 Sept
If robots could lie, would we be okay with it? A new study throws up intriguing results.
Ignite: Intriguing and unsettling. While we may accept certain lies from humans – like white lies to protect feelings – extending this same ‘flexibility’ to robots feels inherently different. This notion feels like opening Pandora's box – once we allow some forms of dishonesty, where do we draw the line?
Business | Towards Data Science | 30 Aug
Stop Being Data-Driven
Ignite: For all that we harp on about the positives of data-driven solutions this article provides an interesting take on how it actually might be holding us back. While data is crucial, relying solely on it can lead to narrow thinking, ignoring context, intuition, and human experience. Instead, we should aim to be “data-informed” – using data as one of MANY tools to make better decisions.
DA | Diving into Data | 28 Aug
Analytics Frameworks Every Data Scientist Should Know
Ignite: This article is a great reminder that being a successful data scientist isn’t just about algorithms and models – it’s about having a strategic approach to problem-solving and communicating it effectively. Filled with practical advice, it’s essential reading for anyone aiming to turn data insights into impactful decisions.
DA | iTNews | 19 Aug
Flight Centre taps more data in customer sentiment analysis
Ignite: At the core of every business is the customer and Flight Centre have seemed to clue in on the power of truly understanding its customers by capturing a broader spectrum of feedback from social media comments to review site comments. A good read into how disparate department feeds were centralised using natural language processing to gather actionable insights to enhance customer experiences and drive growth.
Data | Orchestra | 8 Aug
Snowflake vs. Databricks 2024 (actually useful)
Ignite: A fair bit more than a feature comparison between the two technologies. If you’re on the fence about this or that between Databricks and Snowflake, hopefully this provides some assistance. A better option is to thoroughly scope requirements across users and departments and formulate a tech stack that delivers the value your business seeks. Otherwise, you’re looking at an expensive cost exercise in deploying the latest and greatest. Reach out to us if you need some help with an options analysis.
AI | Planet Earth Beyond | 6 Aug
The UK Government Has Pulled the Plug On AI
Ignite: The UK government’s decision to cut AI funding could signal a major shift in the AI landscape, marking the first instance of a major institution – be it a government or investment bank – stepping back from AI.?What caused the withdrawal? The lack of value. As the author notes, AI is still far from profitable. Even Open AI, the largest AI company by popularity, revenue and investment, continues to spend billions of dollars on training and staffing, with an operational cost of US$700,000 per day, and a projected operational loss of US$5. Money talks…
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Effort, usage and health. The three key criteria to determine if your analytics investment is worth its spend.
Through Ignite’s?DataPulse?reporting tool, plan your analytics investment?based on value, follow through to adoption/consumption?and see which investments are creating a return.
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