Decision making in a time of technology change

Steve Ponting at Software AG argues that organisations must adapt their culture of decision making for the age of techceleration

 

Making decisions has never been easy. Perhaps surprisingly more data and technology to contend with hasn’t made it easier. While new technologies have opened up opportunities to gain fresh insights and extract more from our businesses’ information, the decision making process has changed faster than we can. It’s rapid techceleration.

 

Wading through data to find valuable insights was previously like searching for a needle in a haystack. Analytics tools and, more recently, AI are changing this situation at breakneck speed, reshaping decision making across businesses. For instance, Gartner predicts that by 2025, 95% of decisions that use data will be at least partially automated. There is a real risk of losing control for those who aren’t ready. 

 

In a world where tech moves faster than we can think, we run the risk of acting on impulse. Snap decisions can cause fragmented ways of working and reduce standardisation – ultimately resulting in operational chaos and further clouding the decision making process. In fact, Software AG’s recent Reality Check Survey highlights that this operational chaos of different processes and systems slows down decision making and action for 80% of businesses, with 76% saying that their growing tech infrastructure has made work more challenging.

 

Decisions are increasingly being made at a tactical level, based on specific tools, instead of at a strategic level, based on overall goals. So, what’s the secret to implementing new technologies without sacrificing operational efficiency? 

 

AI infiltrates decision making

Over time, quick technology fixes can distort the execution of the overall corporate mission. AI tools are a perfect example: as employees stare down a rabbit hole of different tools that they can use, they get further away from any clear goal that they might have started out with. A great example of how new technologies can impact processes and decision making is the implementation of AI.

 

While AI in its current form helps businesses to alleviate mundane and manual tasks so that teams can focus on revenue generation, value-add activities, or innovation, it has also fast become an integral part of the decision making process. Able to sift through data rapidly and offer up insights at the click of a button, LLMs and generative AI are taking a lot of the grunt work out of decisions, allowing leaders to focus on the strategic side of their roles. But fragmented and uncontrolled use, can cause issues with inefficiency, security, and an imbalance in skills.

 

Bringing balance to chaos

Moving too quickly can result in technology that’s not tailored to its purpose, which can result in greater inefficiency and non-compliance than before. If that chaos slows down decision making as well, it compounds inefficiencies and employees feel forced to find their own [unregulated] resources. Leaders hit the right balance when they take both the interests of the board and shareholders, and the reality of the those on the ground into account. 

 

While a top-down approach can ensure accountability, strategic alignment, and standardisation, sometimes it doesn’t match employee realities. For instance, an estimated 80% of AI projects fail due to misunderstanding or miscommunication from stakeholders on which problems need to be solved using AI. Making decisions from the bottom up, balanced with standardisation and training from central management, creates a more accurate picture of an organisation’s reality. Turning staff who are executing processes into decision makers gives businesses and employees the best of both worlds.

 

Creating a clear view for decision makers

Looking to the future capabilities of AI, we need to see business leaders grasping the fundamentals now to avoid chaos, confusion, and falling behind competitors. Decision-makers need to grasp not just technical skills like how to train a model, but also discern how models operate at their cores, anticipate the ripple effects of integration across functions, and tailor workflows accordingly to maximize the tools at their disposal. 

 

As we can see, a certain level of chaos is acceptable, even key, but only if it is managed. All this starts with a clear view of operations and transparency in decision making, fuelling process intelligence. 

 

Remember, increasing productivity within just one area of an end-to-end process can cause disruption and  increases the risk of organisational chaos. Especially where end-to-end processes traverse departments and functional areas -think new employee onboarding, product launches and customer onboarding to name a few.  To mitigate the risk of increased chaos end-to-end process intelligence and visibility is an imperative. 

 

The ability to assess which processes are highest risk or least efficient means that decision makers prioritise where to focus their efforts, and where there are acceptable levels of deviation (or chaos). Holistic insight into performance, process, and goals can allow the organisation to “think” as a single organism, instead of an amalgam of siloed groups and processes.

 

This transparency also powers evidence-based decision making. Being able to explain a decision is important, but that doesn’t always mean it’s the right one. Different perspectives and expertise are essential in decision making, like AI strategies needing AI expertise, but also the opinions of those using the technology.

 

It’s time for patience and process

Whether the goal is to create the best product, the best services, or simply to make core functions more efficient, the processes that organisations put in place have a fundamental influence on success. Retrofitting AI into these processes wil require great time and patience.

 

Those who rush into AI adoption, only to realise that their existing business infrastructure and long-standing processes are not built with automation in mind, will see a disruptive impact. AI cannot be considered a magic wand that will instantly solve all decision making problems.

 

The transition requires incremental changes and ongoing optimisation of systems to ensure success. A constant feedback loop of analysis approaches can help hone in on long-term goals, and acknowledge the complexities at play. It also gives leaders more trust in AI-powered decision making, safe in the knowledge that this new tech is a positive force.

 

Investing time and resources to navigate techceleration helps leaders create an environment where AI can flourish, innovation is nurtured and efficiency reigns. Short-term focus and a rush to jump on the latest tech trends are disruptive in a negative sense. Get the fundamentals in place and make new tech an enabler to efficient operations and good decisions. 

 

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Steve Ponting, Director at Software AG

 

Main image courtesy of iStockPhoto.com and Aree Sarak