From above photo of an anonymous African-American woman analyzing business graph on a laptop … More computer while sitting at restaurant desk with notebook, pen and eyeglasses. getty

When considering any implementation of AI in a business, leadership teams have a weighty responsibility.

This is an approach that people want to get right.

They face a few challenges – that the technology is so nascent, that there doesn’t seem to be a lot of road maps available for companies, and that many people instinctively distrust large language models to automate processes.

So what’s to be done?

A Leader’s Perspective

Here’s where I recently got some insights from a paper written by Lidiane Jones, who was previously head of Slack, and CEO of Bumble, a major dating platform.

Jones breaks down some of the aspects of AI implementation that C-suite people are looking at.

Data Transfers and Governance

Jones points out that transformations like ETL (extract, transform, load) and ELT (extract, load, transform) predated AI, but data is still siloed in many cases.

One solution Jones touts is an “omnichannel data strategy” – this, she writes, “will ensure privacy and security of your data, ease of access for business applications, offer real time capabilities and can integrate with your everyday tools.”

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Compliance with Financial Data Rules

For example, Jones speaks about the need to focus on compliance in some areas.

“Every company has critical financial data, subject to audit, regulation and compliance that must be carefully protected,” she writes. “Normally, for more scaled companies, this data sits on an ERP system. Every CEO, CFO, COO and CRO needs critical real-time insight from these systems, to determine how the business is performing against plans, how expenses are tracking against the budget or how a change in employee investment … will affect the overall cost structure and velocity of the business, among numerous other capital allocation considerations.”

Business Intelligence for the Win

In terms of general business intelligence, Jones spins a story to illustrate:

“Imagine a Sales Executive who develops a multi-year high trust relationship with one of a company’s most important large customer, and she decides to leave the company for a better career opportunity,” she writes. “Historically, though there will be good information about that customer and notes from this leader, much of her institutional knowledge leaves with her. Corporate human knowledge exists within organizations, and is shaped by the culture, people and business processes.”

She then addresses the role of workflow tools and other platform resources.

“Collaboration software of all kinds like Slack, Google Workspace and Teams … have a lot of people’s knowledge embedded in them that is hardly ever nurtured,” she adds. “Unstructured data like this is highly effective in training LLMs, and can provide opportunities that haven’t existed before – like capturing the sentiment of what this large customer loved the most about their relationship with this Sales Executive.”

She also gave a nod to the potential difficulties, conceding that “ it might feel daunting to expand data strategy planning to be as broad as this,” but notes that partnering with vendors and other firms can help.

“Phasing and prioritizing how you bring more of your data into a single system is key to making progress and capturing business value along the way,” she writes.

Agents do the Agenting

Jones also makes an important point about the use of AI agents. It goes sort of like this: we’re used to computers doing calculations, and digesting and presenting information, but these new systems can actually brainstorm on their own to change processes.

“In many instances, agents can optimize workflows themselves as they determine more effective ways to get the work done,” she writes.

A Story of Implementation

Citing ChatGPT’s meteoric rise, Jones talked about using these technologies in the context of her work at Slack, which is, after all, a business communication tool. She chronicled the firm’s connection with companies like OpenAI circa 2017.

“At the time, when I was leading Slack, it was exciting to collaborate with OpenAI, Cohere and Anthropic to use their LLMs to help our customers with some of the most challenging productivity challenges at Slack,” she writes.

The challenges she enumerates: “finding a conversation they knew they had but couldn’t remember in what channel, or help customers manage the large amount of messages they received with summaries and prioritization, optimize search for information discovery and so much more.”

Then, too, the company created tools.

“We introduced Slack Canvas based templates to help our customers quickly create content based on their corporate information, and captured Huddles’ meeting notes and action items, and that was just the beginning,” she explains. “The capabilities of LLMs gave us the opportunity to solve real-world customer challenges in a pleasant and insightful way, while maintaining the experience of the Slack brand.”

Calling this kind of thing the “table stakes” of the new corporate world, Jones walks us through a lot of the way stations on the path to what she calls “co-intelligence.” That includes workflow automation, agentic AI, multi-agent systems, and new interfaces.

Our AI Brethren

Here’s one way that Jones characterizes managing an AI:

“Considering autonomous agents as truly ‘digital workers’ can be a helpful framing for questions we already think of today with “human workers” like: how does the employer track the quality of the work done? What systems does the digital worker have access to? If the company is audited, how do we track what steps and actions were taken by the digital worker? If the digital worker’s actions turn malicious, how do we terminate the agent?”

As for the extent of agent autonomy, Jones suggests that fully autonomous agents will be able to handle a complex or “scoped” job on their own, conceding, though, that “even an autonomous agent, like a human, needs a job scope and definition – or a set of instructions – on the job at hand.” This new world is one we will have to reckon with soon.

Four Principles of Leadership

Jones finished with a set of ideas for those who are considering these kinds of deployments.

1. Be hands-on: as a leader, stay close to what’s happening

2. This one goes back to prior points: working with vendors and partners is a plus

3. Build an AI-first culture with AI-native projects

4. Find the value for your company

I found this to be pretty useful for someone who is contemplating a big move in the age of AI. Some of the best ideas for leadership can be gleaned from TED talks, conferences, and these kinds of personal papers on experience with the industry.