Successful AI can only be achieved with holistic collaboration

By  Aaron Harris, Chief Technology Officer at Sage

  • 1 month ago Posted in

It can be easy to forget that behind the algorithms, automation and robots, there are teams of talented humans. Harnessing AI requires collective human effort to solve a plethora of business challenges.

However, the reality is that 85% of AI projects fail, and 87% of data science projects never get to production. It is not hard to imagine the impact of this tremendous rate of failure on the data scientists, ML engineers and colleagues who pour blood, sweat and tears into projects that fade into obscurity. While much can be learned from failure - the popular ‘fail fast’ culture can provide impetus to take creative risks - no ambitious and talented engineer wants to waste their time on continuously doomed projects.

However, the truth is that most enterprises are failing at artificial intelligence, and more often than not, it is due to the organization’s failure to create the conditions and collaborative ecosystem well-designed teams need to thrive.

The promise of AI is already achieving real impact in numerous ways, targeting very specific business challenges. As Gartner reflects, AI talent is often hired directly into departments such as finance, HR, marketing and sales with clear objectives to remain close to the deployment and consumption of their work. For example, within financial and accounting functions, AI can help remove uncertainty through always-on, real-time forecasting. Within HR, time-tracking tools that dynamically learn working patterns from an employee’s calendar and activities can maximize productivity and ensure accurate project costing. Similarly, AI intelligence tools can spot accounting mistakes and irregularities

With AI working around the clock, human talent in core business functions can be freed from back-office mundane tasks to concentrate on more valuable tasks.

AI is already reducing uncertainty, improving accuracy, delivering insights and lessening the administrative burden. As a result, it has emerged from hopeful hype to essential actuality. Modern enterprises know that investing in emerging technology is the key to winning in today’s market.

So how can organizations create the conditions for humans to create the tools that realize these benefits?

Zoom out

One mistake organizations often make in their AI talent strategy is narrowing AI teams to encompass those in ‘traditional’ tech roles such as data scientist, AI and machine learning, and big data specialists. Of course, specific technical skills are vital in successful AI innovation, but colleagues must support technological know-how with complementary skillsets.

Data science is just one part of the puzzle. Your AI will fall flat without expertise in project management, engineering, and cloud infrastructure. All too often, these elements are either overlooked or disregarded entirely. When it comes to innovation, the team is greater than the sum of its parts. That means zooming out from the ‘tech skills’ pixel to see the whole picture.

Correctly implementing AI into collaborative workstreams as part of a holistic approach cannot be underestimated. For example, Kimberly Graham, Director of AI Transformation at Sage AI Labs (SAIL - the team transforming Sage to become an AI-enabled technology business), brings experience from customer success and marketing to ensure Sage's AI delivers customer-focused innovation.

Within SAIL Product Managers look for customer value, Data Scientists run experiments, ML Engineers deliver pipelines and Cloud Infrastructure specialists support platforms. As VP of Machine Learning at Sage, David Dickson says, “Enterprises often underestimate what it takes to deliver machine learning at scale and creating a highly cross-functional team has been critical to our success.”

The war for talent can be won

While appreciation of the need for comprehensive, complementary skillsets is crucial, we know that specific technical knowledge is essential - and in short supply. The AI skills shortage is well documented. According to recent research, 64% of IT executives report the technological talent shortage is the biggest adoption barrier to emerging technologies. It is also the primary adoption risk factor for most IT automation technologies (75%) and nearly half of digital workplace technologies (41%).

Firstly, it is hugely important to consider widening the focus of our hiring lens. For example, Gartner highlights by 2024, 80% of large-enterprise CIOs will have a neurodiversity talent strategy. Neurodiverse employees will comprise 3% to 5% of the IT workforces.

Diversity of thought is integral to innovation and innovation is integral to our success. We can’t have one without the other. Within my technology organization, we have an international globally distributed team with representation from a number of nationalities and cultures including the UK, Europe, North America, Canada, Australia, India, and the Middle East. We've increased our hiring rate for women in technology to 43%. Within Sage AI Labs or SAIL, our new hires are around 50/50, in regards to gender.

Similarly, upskilling current employees and looking for talent from within and further afield is an intelligent strategy. As Kevin Dewalt, of AI engineering firm Prolego, says, “the most successful companies have built effective teams by investing in their existing employees and recruiting from nontraditional regions and backgrounds.”

Similarly, Darktrace Cofounder and CPO Dave Palmer, recently attributed successful AI product rollout to, “hiring people with diverse backgrounds and interests and helping them to develop skills in areas such as cybersecurity and machine learning.”

Wherever talent comes from, the competition among organizations is undeniable. Well-funded start-ups and established enterprises (including tech giants like Google, Amazon, and Meta) are competing for the same talent pool, which can mean the salaries and benefits on offer are beyond the reach of some organizations.

However, attracting and retaining talent goes beyond pay. Increasingly, technologists put workplace culture, organizational values, impact and social cause above the figure on the paycheck. They want to work in successful teams that make a difference. That means recruitment and retention rely on creating the right environment for innovation.

For example, Sage AI Labs’ broad mission is to harness artificial intelligence and machine learning to transform Accounting, Financial and HR to speed up admin tasks, improve accuracy and enable customers to achieve their goals, all in support of our purpose to knock down barriers so that everyone can thrive. A vital part of SAIL’s culture is summed up in the mantra, ‘Production or it didn’t happen’. Until the product reaches customers, it is meaningless.

We empower our team through a delivery-focused mindset to ensure their hard work has value. There are many benefits that come with this delivery-focused mindset. Whether it’s eliminating the month-end close, automatically capturing financial documents, or providing bold financial insights, any ML practitioner worth their weight can see SAIL has a critical role to play in delivering customer value at Sage.

Having a strong, defined culture focused on impact is incredibly important in attracting the right tech talent, supported to deliver within a holistic team aligned on the same goal.

A collective future for AI

While there is fierce competition between AI-oriented companies, the future must see greater inter-organization and peer innovation to realize the true benefits of AI. Humans must increasingly come together to derive the most from the data-enabled world we have created.

With the amount and quality of data AI-enabled systems will generate, the ability to gather powerful collective intelligence capabilities is getting closer. This is going to make it significantly easier for companies to compare how they are performing against competitors. What will be powerful though, is when they can analyze all that information among peer groups and develop the relationships that drive performance.

In the end, what’s needed is the imagination, creativity, and resourcefulness of the humans on our AI teams to solve problems and to deliver on our purpose to knock down barriers so that everyone can thrive.


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