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Imagine if you could clone yourself to be in multiple places at once, handling all your responsibilities effortlessly. Remember the sci-fi comedy film Multiplicity (circa 1996), where Doug Kinney (played by Michael Keaton), clones himself to manage his work and personal life.
For those who haven’t been paying attention, generative AI has taken the world by storm. One of the more popular services is ChatGPT, an AI-powered chatbot created by OpenAI, which was released in November 2022.
In Part 1 of this series, we discussed the parallels between the current hype surrounding artificial intelligence (AI)–especially with ChatGPT and Bard with three other transformative technological changes that molded human history.
In Part 1 of this series, we discussed the parallels between the current hype surrounding artificial intelligence (AI)–especially with ChatGPT and Bard with three other transformative technological changes that molded human history. These included:
We’ve all seen the headlines, artificial intelligence (AI) is either a) going to mark the end of humanity, or b) make the world a better place. With all the hype surrounding generative AI and ChatGPT,
The digital economy is now more customer-centric than ever. In business-to-business (B2B) marketing, we have moved from advertising, press releases, inbound sales, and promotions to more customer-centric and experiential marketing. Organizations now need to deeply understand what B2B buying committees want across an ever increasing number of channels.
With the current economic uncertainty that surrounds us, Chief Data and Analytics Officers are under increasing pressure to track the business impact of data, analytics, and AI initiatives on business outcomes. In most organizations, that leads to talk of key performance indicators (KPIs).
Have you started using artificial intelligence (AI) and machine learning (ML) in your analytics initiatives? What kind of results are you seeing?
The analytics chasm fundamentally boils down to one thing: a lack of analytics capacity within your organization.
When you think about decision making in the context of those steps, you realize that the information value of data is perishable, and it decays with time. If you remove the analytics waste from your decision-making process, you can become much more competitive.
Dynamic and uncertain marketing conditions have created new opportunities, but organizations will need to prioritize their analytics investments to seize them.
Given the historical context and a high-level definition of how AI is applied, how can we create a practical definition of AI? In my mind, the definition of AI is quite simple. Artificial Intelligence = Data + Analytics + Automation
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