Artificial Intelligence (AI) at GigE - Three Takeaways

When it comes to Artificial Intelligence is your organization “All In” (AI), dip a toe in, or watch from the sidelines?. That was the question posed this week at Collaboration in the Gig Economy in Dallas, TX. 

All In

In the key note by Tom Davenport, President’s Distinguished Prof. of Information Tech & Mgmt., Babson College, he opened by noting that “there’s a lot of babel about AI” but that “60-70% of companies are doing something”.

And while a lot are “all in”, most are just experimenting. He also reminded us that "there was AI before ChatGPT” but it's the generative chatbot that has reignited the excitement, and some concern.

In Tom’s view some of the main applications of AI are to Summarize, Personalize, Tailor and Document with some of the main beneficiaries being the in Marketing and Communications functions. Although in a more people based scenario it also has the capability to deal with straightforward business questions and queries. He shared one case study where a 100+ page manual had been converted into a simple conversational query tool.

As well as these “beneficiaries” there are potential “losers” whose roles will be impacted, although it should be noted that many past technology advances have, in fact, created as many new job opportunities as they have impacted albeit requiring a different, more knowledge based skill set. He opined that “many knowledge work job tasks can be automated but not many entire jobs” and that many of us will be “working with AI as a colleague (Co-pilot)” and the likely outcome was “Augmentation (by AI) not automation”.

While ChatGPT and generative AI are making the headlines, there are more game changers here, or coming, in Automated Machine Learning (ML), “Intelligent Automation” (combining Generative AI and ML), transparent deep learning, and smart data discovery.

But for the organizations embarking on an AI journey – what is his advice?

• Think big – how can we transform?
• Start small – focus on pilots and low hanging fruit.
• Scale up – develop a pipeline.
• Skill out - emphasize augmentation not replacement. Offer skills training and give employees options.

Workforce use cases

The session on “ChatGPT & Generative AI Unleashed: Practical Applications” looked at use cases for where the technology could streamline or enhance the recruitment process, identifying eight opportunities:

1. Learning journeys – “teach me about…” self-managed learning of technical, soft and insights-based skills.
2. Mastering brand consistency – unifying your voice and message.
3. Payrate benchmarking – identifying rates by industry title, location, experience and availability through simple queries.
4. CV and profile summaries – on brand and consistent (although it had to be noted that the challenge of personal data wasn’t full recognized or explored.
5. Covering rules and processes in learning – easy query base learning.
6. Smart sales email automation – creating individualized sales communication.
7. Job descriptions
8. General interview questions – converting a “job spec” into a question prompt.

It's not you it's me

As with any change, or opportunity, there are also challenges. Tom noted that when asked about the challenges to become a data driven company, 20% of company executives referred to the tech while 80% said it was people/process/culture.

While for one leader “All (his) problems are people problems” this can be overcome. An earlier session on “Reskilling and Upskilling Your Business and Organization for the Age of AI” recommended “ensure you're solving the right problems. then you don’t have as much resistance to change” and showing the benefits that “it’s a productivity tool, it’s a multiplier” with an emphasis on augmentation not replacement.

But we also might have to accept the fact that, to a degree, “We have to be comfortable with the uncomfortable”.

In summary

AI offers an opportunity to streamline and improve many services and processes, including within the workforce, but the right applications have to be identified, the right process or pilots used to achieve them. But we need to remember that for the “responsible use of generative AI we need a human at the beginning and the end“ and we need to bring those people on the journey as well.