How AI Powers Actionable Business Insights (Forbes Interview)

Omega Venture Partners’ Thomas Malone recently sat down for an interview with Forbes.  The following are excerpts from Tom’s interview, with examples and insights from his recent book, Superminds: The Surprising Power of People and Computers Thinking Together.

“The narrative is shifting from competing against machines to collaborating with them.”

Malone defines a Supermind as a group of individuals acting collectively in ways that seem intelligent. Superminds have operated since the time of hunter-gatherer tribes. So, what is collective intelligence? Think of it as the attribute that Superminds possess. It is the collective intelligence of people that helped forge civilizations, form governments, and build successful organizations.

In recent years, machines have started doing tasks that many consider intelligent. Thanks to advances in Artificial Intelligence and Machine Learning (AI/ML), computers now bring their exceptional abilities to the Supermind.

But, even if computers could do almost everything humans do today, Malone explains that there will always be more things for people to do. These could, for instance, be tasks that involve providing the much-needed human touch, conversation, or connection.

For example, while AI is getting better than human doctors at making disease diagnoses, can it advise patients on their prognosis with the same care and empathy as humans? Clearly, both humans and machines serve vital yet distinct functions.

How Enterprises can use AI for Actionable Business Insights

Let’s take the case of strategic planning. A Harvard Business School report outlines that 85% of executive leadership teams spend under one hour per month discussing strategy, and 50% spend no time at all. Strategy planning is primarily restricted to an annual exercise with participation from a few senior leaders. It’s no wonder that 95% of a company’s employees don’t understand its strategy.

Superminds can transform this dated process of corporate strategic planning, says Malone. Today, machines’ involvement in this process is restricted to automating a few computations or tracking metrics. He envisions a new approach that leverages greater human-machine collaboration.

Malone outlines three significant steps to rethink strategic planning:

1. Democratize Human Involvement

Almost everyone within a company can have a say in the strategic planning process. Each employee can be empowered to share inputs on corporate plans, recommend strategic offerings, or suggest features in the product roadmap.

While this sounds great in principle, how do you get people to participate? Gamification can turn the planning process into a live contest. In fact, several enterprises have used gamification to improve employee engagement radically across their business.

For example, Spotify gamified the dreaded annual appraisal process through live scorecards and badges shared on an internal social network. Consequently, over 90% of employees participated voluntarily in reporting live performance scores throughout the year.

2. Augment with Artificial Intelligence

At this point, you can bring in machines to augment the decisions. Machine learning models can observe this process of manual decision-making and learn from the outcomes. In case you’re wondering whether machines can find patterns in such decisions, it has been done before.

Bridgewater Associates, the famed investment management firm founded by Ray Dalio, showed that algorithmic decision-making could be applied to run a large business. The firm’s management coach system collects copious amounts of data to study employee behavior. The system bases judgments on continuous decisions made by employees across the firm. Dalio said that this collective decision-making approach helped turn the firm into one of the world’s most successful hedge funds.

By studying examples of how humans evaluate strategic planning ideas, Intelligent Software can start recognizing patterns in human judgments. Given enough decision volume and variety, ML models can be trained to make recommendations to humans. When machines reach an acceptable threshold of decision quality, you can delegate certain types of decisions entirely to them.

3. Scale Across the Organization

A gamified, machine-enabled strategic planning process can be replicated across all organizational levels, from individual product teams to the corporate level. Instead of creating strategic plans annually or quarterly, organizations can now make thousands of plans every single day.

“Most people think of strategy as an event, but that’s not the way the world works,” said Clayton Christensen. “More often than not, the strategy that leads to success emerges through a process that’s at work 24/7 in almost every industry.”

Whether it’s because of a competitor launching a new offensive or the entire industry going into a decline during the pandemic, the strategy needs continual reassessment. This blueprint for a AI-augmented strategy machine could enable organizations to respond continuously and rapidly to ongoing changes in their environment.

However, the approach does pose some inherent challenges. Culturally, it calls for organizations to switch from a hierarchical, closed planning process to one that’s more flat and inclusive. From a technical perspective, machine learning stumbles when predicting scenarios with few historical examples or those involving nuanced decision-making, as they involve fewer digital trails.

However, with the proper executive support and technology investment, these roadblocks can be overcome. The potential upside of acquiring such an enhanced strategic capability will motivate organizations to make the switch. “A company that manages to build something even close to this has the potential to run circles around its competitors,” says Malone.

“A company that manages to build something even close to this has the potential to run circles around its competitors”

Executives who aspire to apply Superminds to rethink and rewire their organizational processes can tap into the Supermind design methodology. This systematic process was developed at the MIT Center for Collective Intelligence with guidance from Thomas Malone. It helps organizations redesign their operations by leveraging novel approaches in human-machine collaboration.




The original article can be found here.