Jay Allardyce is General Manager, Data & Analytics at insightsoftware. He’s a Technology Executive with 23+ years of experience across Enterprise B2B companies such as Google, Uptake, GE, and HP. He is also the co-founder of GenAI.Works that leads the largest artificial intelligence community on LinkedIn.
insightsoftware is a global provider of financial and operational software solutions. The company offers tools that support financial planning and analysis (FP&A), accounting, and operations. Its products are designed to improve data accessibility and help organizations make timely, informed decisions.
You’ve emphasized the urgency for businesses to adopt AI in response to rising customer expectations. What are the key steps businesses should take to avoid falling into the trap of “AI FOMO” and adopting generic AI solutions?
Customers are letting businesses know loud and clear that they want increased AI capabilities in the tools they’re using. In response, businesses are rushing to meet these demands and keep pace with their competitors, which creates a hectic cycle for all parties involved. And yes, the end result is AI FOMO, which can push a business to rush their innovation in an attempt to simply say, “we have AI!”
The biggest advice I have for companies to avoid falling into this trap is to take the time to understand what pain points customers are asking the AI to solve. Is there a process issue that’s too manually-intensive? Is there a repeating task that needs to be automated? Are there calculations that could easily be computed by a machine?
Once businesses have this necessary context, they can start adopting solutions with purpose. They’ll be able to offer customers AI tools that solve an issue, instead of those that just add to the confusion of their existing problems.
Many companies rush to implement AI without fully understanding its use cases. How can businesses identify the right AI-driven solutions tailored to their specific needs rather than relying on generic implementations?
On the customer side, it’s important to maintain constant communication to better understand what use cases are the most pressing. Customer advocacy boards can provide a helpful solution. But beyond customers, it’s also important for teams to look internally and understand how adding new AI tools will impact internal functionality. For each new tool that’s introduced to a customer, internal data teams are faced with a mountain of new variables and new data that’s being created.
While we all want to add new capabilities and show them off to customers, no AI deployment will be successful without the support of internal data teams and scientists behind their development. Align internally to understand bandwidth and then look outward to decide which customer requests can be accommodated with proper support behind them.
You’ve helped Fortune 1000 companies embrace a data-first approach. What does it truly mean for a company to be “data-driven,” and what are some of the common pitfalls that businesses encounter during this transformation?
In order for a company to be “data-driven,” businesses need to learn how to effectively leverage data correctly. A truly data-driven team can execute properly on data-driven decision-making, which involves using information to inform and support business choices. Instead of relying solely on intuition or personal experience, decision-makers gather and analyze relevant data to guide their strategies. Making decisions based on data can help businesses derive more informed, objective insights, which in a rapidly changing market can mean the difference between a strategic decision and an impulsive one.
A common pitfall to achieving this is ineffective data management, which leads to a “data overload,” where teams are burdened with large amounts of data and rendered unable to do anything with it. As businesses try to focus their efforts on the most important data, having too much of it accessible can lead to delays and inefficiencies if not properly managed.
Given your background working with IoT and industrial technologies, how do you see the intersection of AI and IoT evolving in industries such as energy, transportation, and heavy construction?
When IoT came onto the scene, there was a belief that it would allow for greater connectivity to enhance decision-making. In turn, this connectivity unlocked a whole new world of economic value, and indeed this was, and continues to be, the case for the industrial sector.
The issue was, so many focused on “smart plumbing,” using IoT to connect, extract, and communicate with distributed devices, and less on the outcome. You need to determine the exact problem to be solved, now that you’re connected to say, 400 heavy construction assets or 40 owned powerplants. The outcome, or problem to solve, ultimately comes down to understanding what KPI could be improved upon that drove top line, workflow productivity, or bottom-line savings (if not a combination). Every business is governed by a set of top-level KPIs that measure operating and shareholder performance. Once these are determined, the problem to solve (and therefore what data would be useful) becomes clear.
With that foundation in place, AI – whether predictive or generative – can have a 10-50x more impact on helping a business be more productive in what they do. Optimized supply, truck-rolls, and service cycles for repairs are all based on a clear demand signal pattern that are matched with the input variables needed. To illustrate, the notion of having the ‘right part, at the right time, at the right location’ can mean millions to a construction company – for they have less stocking level requirements for inventory and optimized service techs based on an AI model that knows or predicts when a machine might fail or when a service event might occur. In turn, this model, combined with structured operating data and IoT data (for distributed assets), can help a company be more dynamic and marginally optimized while not sacrificing customer satisfaction.
You’ve spoken about the importance of leveraging data effectively. What are some of the most common ways companies misuse data, and how can they turn it into a true competitive advantage?
The term “artificial intelligence,” when taken at face value, can be a bit misleading. Inputting any and all data into an AI engine does not mean that it will produce helpful, relevant, or accurate results. As teams try to keep up with the rate of AI innovation in today’s world, occasionally we forget the importance of complete data preparation and control, which are critical to ensuring that the data that feeds AI is entirely accurate. Just like the human body relies on high-quality fuel to power itself, AI depends on clean, consistent data that ensures the accuracy of its forecasts. Especially in the world of finance teams, this is of the utmost importance so teams can produce accurate reports.
What are some of the best practices for empowering non-technical teams within an organization to use data and AI effectively, without overwhelming them with complex tools or processes?
My advice is for leaders to focus on empowering non-technical teams to generate their own analyses. To be truly agile as a business, technical teams need to focus their efforts on making the process more intuitive for employees across the organization, as opposed to focusing on the ever-growing backlog of requests from finance and operations. Removing manual processes is really the first important step in this process, as it allows operating leaders to spend less time on collecting data, and more time analyzing it.
insightsoftware focuses on bringing AI into financial operations. How is AI changing the way CFOs and finance teams operate, and what are the top benefits that AI can bring to financial decision-making?
AI has had a profound impact on financial decision-making and finance teams. In fact, 87% of teams are already using it at a moderate to high rate, which is a fantastic measure of its success and impact. Specifically, AI can help finance teams produce vital forecasts faster and therefore more frequently – significantly improving on current forecast cadences, which estimate that 58% of budgeting cycles are longer than five days.
By adding AI into this decision-making process, teams can leverage it to automate tedious tasks, such as report generation, data validation, and source system updates, freeing up valuable time for strategic analysis. This is particularly important in a volatile market where finance teams need the agility and flexibility to drive resilience. Take, for example, the case of a financial team in the midst of budgeting and planning cycles. AI-powered solutions can deliver more accurate forecasts, helping financial professionals make better decisions through more in-depth planning and analysis.
How do you see the needs for data evolving in the next five years, particularly in relation to AI integration and the shift to cloud resources?
I think the next five years will demonstrate a need for enhanced data agility. With how quickly the market changes, data must be agile enough to allow businesses to stay competitive. We saw this in the transition from on-prem to off-prem to cloud, where businesses had data, but none of it was useful or agile enough to aid them in the shift. Enhanced flexibility means enhanced data decision-making, collaboration, risk management, and a wealth of other capabilities. But at the end of the day, it equips teams with the tools they need to address challenges effectively and adapt as needed to changing trends or market demands.
How do you ensure that AI technologies are used responsibly, and what ethical considerations should businesses prioritize when deploying AI solutions?
Drawing a parallel between the rise and adoption of the cloud, organizations were fearful of giving their data to some unknown entity, to run, maintain, manage, and safeguard. It took a number of years for that trust to be built. Now, with AI adoption, a similar pattern is emerging.
Organizations must again trust a system to safeguard their information and, in this case, produce viable information that is factual, referenceable and also, in turn, trusted. With cloud, it was about ‘who owned or managed’ your data. With AI, it centers around the trust and use of that data, as well as the derivation of information created as a result. With that said, I would suggest organizations focus on the following three things when deploying AI technologies:
- Lean in – Don’t be afraid to use this technology, but adopt and learn.
- Grounding – Enterprise data you own and manage is the ground truth when it comes to information accuracy, provided that information is truthful, factual, and referenceable. Ensure when it comes to building off of your data that you understand the origin of how the AI model is trained and what information it is using. Like all applications or data, context matters. Non-AI-powered applications produce false or inaccurate results. Just because AI produces an inaccurate result, does not mean we should blame the model, but rather understand what’s feeding the model.
- Value – Understand the use case whereby AI can significantly improve impact.
Thank you for the great interview, readers who wish to learn more should visit insightsoftware.